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http://www.lsoft.com/images/listserv_small.gifRe: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;51ffff8a.2403
On Mon, 18 Mar 2024 at 14:56, Vladimir Litvak <litvak.vladimir@gmail.com><br>wrote:<br><br>> Dear Julia,<br>><br>> On Mon, Mar 18, 2024 at 2:45 PM Júlia Soares <julii.f.soares@gmail.com><br>> wrote:<br>><br>>><br>>> What do you mean with "native images coregistered with MNI" and " write<br>>> out the results of your source analysis in the native space on a template<br>>> MNI mesh"?<br>>><br>><br>> I mean coregistering your images to the MNI template (Coregister/Estimate)<br>> so that their coordinate system doesn't have a big offset or some rotation<br>> with respect to MNI coordinates. Then in my opinion locations specified in<br>> this MNI-aligned native space will be similar enough to MNI coordinates for<br>> the purposes of EEG analysis. Also by default your results image for source<br>> reconstruction is generated in MNI template space no matter how your priors<br>> are specified.<br>><br>><br>><br>>> Does this mean to transform my structural images to MNI using the<br>>> "normalise write" function from SPM,<br>>><br>> to build the head models and then do source reconstruction in this space?<br>>><br>><br>> No, that's not what I mean.<br>><br>><br>>><br>>> About the epoching step, just to make sure: do you suggest separating my<br>>> continuous signal into the periods of time of my conditions and then<br>>> separating each condition into epochs of 1-2s like a sub epoching step ?<br>>><br>>><br>> I'm not sure how your conditions are recorded. If they are in separate<br>> files then you could just epoch each one into 1 sec epochs and then merge<br>> the resulting files. Otherwise you could convert each epoch separately (by<br>> specifying a time window) and then epoch it and merge. The most<br>> straightforward way to do this kind of custom epoching is write your own<br>> function for specifying the trl and conditionlabels variables that the<br>> epoching function takes as the input. But if you want to only use the GUI,<br>> you could do as suggested above.<br>><br>> Best,<br>><br>> Vladimir<br>><br>><br>><br>><br>><br>><br>>><br>>> Em sex., 15 de mar. de 2024 às 16:28, Vladimir Litvak <<br>>> litvak.vladimir@gmail.com> escreveu:<br>>><br>>>> Dear Julia,<br>>>><br>>>> On Fri, Mar 15, 2024 at 4:20 PM Júlia Soares <julii.f.soares@gmail.com><br>>>> wrote:<br>>>><br>>>>> 1) Regarding the source locations in MNI I didn't quite understand why<br>>>>> this doesn't matter. The DCM model requires an EEG signal after source<br>>>>> reconstruction, right? So the space will be the source space instead of the<br>>>>> sensor space (the actual electrodes), right? If so, how come the resolution<br>>>>> of the coordinates doesn't make a difference? Isn't it possible that<br>>>>> sources are several mm misaligned with corresponding locations in MNI ?<br>>>>><br>>>><br>>>> The kind of differences in source locations that make a difference in<br>>>> EEG are on the order of cm so if your native images are coregistered to MNI<br>>>> and the head sizes are not unusually large or small I wouldn't expect the<br>>>> mm differences to matter. But you can always write out the results of your<br>>>> source analysis in the native space on a template MNI mesh and then you<br>>>> won't have that problem at all.<br>>>><br>>>><br>>>><br>>>><br>>>>> 2) About data epoching: I have a continuous signal acquired during<br>>>>> performance of a task constituted by 4 conditions: 8 periods of "baseline"<br>>>>> (22 seconds), 5 periods of "condition A" (18 seconds), 4 periods of<br>>>>> "condition B" (18 seconds) and 3 periods of "condition C" (18 seconds). I<br>>>>> was thinking about separating my continuous signal into epochs of equal<br>>>>> length to the periods of each condition. So, for example for "condition A"<br>>>>> I would have 5 epochs of 18 seconds each corresponding to "condition A "<br>>>>> which would then be averaged into one single epoch. Does this make sense?<br>>>>><br>>>>><br>>>> The implementation assumes short epochs 1-2 sec at most so I'd suggest<br>>>> you epoch your conditions into epochs of that length and then the<br>>>> differences in duration won't matter.<br>>>><br>>>> Best,<br>>>><br>>>> Vladimir<br>>>><br>>>><br>>>>> Regards,<br>>>>> Júlia Soares<br>>>>><br>>>>><br>>>>><br>>>>> Em ter., 12 de mar. de 2024 às 15:24, Vladimir Litvak <<br>>>>> litvak.vladimir@gmail.com> escreveu:<br>>>>><br>>>>>> Dear Julia,<br>>>>>><br>>>>>> On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>>>>>> wrote:<br>>>>>><br>>>>>>> 1) Is it only possible to do DCM in MNI space since the prior source<br>>>>>>> locations should be given in MNI coordinates or is it possible to conduct<br>>>>>>> DCM analysis in native space for each specific subject ?<br>>>>>>><br>>>>>>><br>>>>>> I think this distinction is too fine to matter for DCM if you are<br>>>>>> doing it at the sensor level. So I'd just define source locations in MNI<br>>>>>> space and not worry too much about it.<br>>>>>><br>>>>>><br>>>>>><br>>>>>>> 2) In source reconstruction I inverted a continuous signal, i.e., I<br>>>>>>> did not separate the signal into epochs (trials). However I have a task<br>>>>>>> which has 3 conditions in which I intend to study connectivity in each of<br>>>>>>> them. Is there a way to separate my signal after source reconstruction so I<br>>>>>>> can include them in the DCM model?<br>>>>>>><br>>>>>><br>>>>>> Both source analysis and DCM were not intended to work on long<br>>>>>> continuous data segments. I'd suggest you epoch your data into arbitrary<br>>>>>> 1-2 sec epochs. There is a way to do it in the epoching tool. Then I would<br>>>>>> do both steps on these epoched data.<br>>>>>><br>>>>>> Best,<br>>>>>><br>>>>>> Vladimir<br>>>>>><br>>>>>><br>>>>>><br>>>>>>><br>>>>>>> Thank you in advance.<br>>>>>>> Regards,<br>>>>>>> Júlia Soares<br>>>>>>><br>>>>>>
2024-03-18T17:53:12+00:00Vladimir Litvakhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;51ffff8a.2403Research Assistant Position in the Neuroscience of Memory
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;7de24a69.2403
Research Assistant Position in the Neuroscience of Memory<br><br>A research assistant position is available in Dr. Noa Herz’s laboratory in the Department of Neurology at Thomas Jefferson University. Research in the lab focuses on the neural substrates underlying episodic memory. We use direct brain recording and stimulation collected from neurosurgical patients who have implanted electrodes for seizure mapping. Our research focuses on characterizing memory deficits in psychopathological (depression, anxiety, post-traumatic stress disorder) and neurological (epilepsy) disorders and on developing direct stimulation approaches to address them. <br><br>We are closely collaborating and holding routine meetings with Prof. Michael Kahana's research group at the University of Pennsylvania. Duties will include assisting with all aspects of data collection, experiment preparation, data postprocessing and report generation. Data analyses and manuscript writing are offered to interested individuals.<br><br>Review of applications will start immediately and will continue until the position is filled. <br><br> <br><br>Requirements:<br><br>- BA/BS in cognitive science, neuroscience, biology, psychology, computer science, engineering, or other related scientific fields.<br>- Strong computing skills (knowledge of python/R/Matlab is a plus)<br>- An ability to solve technical problems independently<br>- Strong organization skills and high attention to detail<br>- High motivation and work commitment<br>- Ability to work well with patients in a hospital environment<br>- At least one, but preferably a 2-year commitment<br><br>The Department of Neurology, located in the city center of Philadelphia, is among the ten best neuroscience departments in the country. The work includes collaboration with top neurologists, neurosurgeons, and neuropsychologists and is, therefore, ideal for students thinking about an MD. The Herz lab is currently under development, allowing the selected applicant to shape future work in the lab and assist in forming new research collaborations.<br><br>For inquiries, please email: noa.herz@jefferson.edu<br><br>To apply, please submit a resume (including a description of computer skills, relevant coursework, grades, previous research, and contact information for at least two references) and a cover letter describing academic and research interests on:<br><br>https://recruit.jefferson.edu/psc/hcmp/EMPLOYEE/HRMS/c/HRS_HRAM_FL.HRS_CG_SEARCH_FL.GBL?Page=HRS_APP_JBPST_FL&Action=U&FOCUS=Applicant&SiteId=1&JobOpeningId=9298395&PostingSeq=1
2024-03-18T15:22:06+00:00Sam Javidihttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;7de24a69.2403Problem with A matrix's PEB-model for DCM
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;44214972.2403
Hi everyone,<br><br>I am reaching out due to a curious problem with DCM to seek any insights you might offer. Our analysis involves a comprehensive first-level full model that includes 10 regions of interest (ROIs). To streamline the model's complexity, we strategically reduced the connections from an the initial pool of 10x10 = 100 down to 57. (This reduction was based on the posterior probability (>0.95) obtained by inverting the A matrix in a preparatory step for the actual analysis.)<br><br>Consequently, in the single-subject full models, we set these 57 connections as active (value of 1), while the remaining connections were inactivated / pruned (value of 0) in our A and B matrices.<br><br>Following Zeidman et al. 2019, we then ran BMA+PEB on the inverted single subject model for both, the A and the B matrix. The results from the B matrix's PEB model appeared sensible and only included those 57 connections. However, the A matrix presented an unexpected outcome: 21 connections, which were previously pruned and set to 0 in the original single-subject DCMs, were part of the A matrix PEB model. I'm confused why PEB would have different parameters for A and B matrices because it is done on the same single-subject models. If I understand correctly, it should only calculate the Bayesian averages of those parameters.<br><br>I double checked the specifications etc. and I am not sure whether something has gone wrong. I would greatly appreciate any input, advice, or suggestions. Thank you very much in advance for your time.<br><br>Best,<br>Sabrina
2024-03-18T15:21:17+00:00Sabrina Goldehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;44214972.2403Postdoctoral Fellowship in Cognitive Electrophysiology of Human Memory
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;cb3bc02c.2403
Postdoctoral Fellowship in Cognitive Electrophysiology of Human Memory<br><br>We are looking for a postdoctoral scientist to join our emerging team and carry out a research program in electrophysiology. The successful applicant will collaborate with the research group of Dr. Noa Herz and Dr. Michael Sperling at the Comprehensive Epilepsy Center of Thomas Jefferson University.<br><br>The purpose of the post is to conduct cutting-edge research on the neural basis of episodic memory in humans using direct brain recordings and stimulation. We are seeking to develop a memory mapping procedure to reduce the risk of memory loss following neurosurgery, as well as a direct stimulation intervention to treat memory-related disorders such as post-traumatic stress disorder. We use data collected from epilepsy patients undergoing seizure monitoring at the hospital as well as patients implanted with a responsive neurostimulation system (RNS).<br><br>The successful candidate will undertake project management, analyze data and will be expected to submit publications to top journals, assist in applying for external research funding, and promote collaborations with research groups outside of Jefferson.<br><br>This post offers an excellent opportunity for those interested in state-of-the-art translational research, bridging cognitive and clinical neuroscience. The selected researcher will become an early member of an inclusive and interdisciplinary team of neurologists, neurosurgeons, and neuroscientists working to study and develop treatment interventions for memory problems in psychopathological and neurological disorders. We are closely collaborating and holding routine meetings with Prof. Michael Kahana's research group at the University of Pennsylvania.<br><br>The ideal candidate will have expertise in analyzing electrophysiological data (either in humans or animals) and strong computing skills, including coding experience. Knowledge of machine learning methods will be an advantage. <br><br>We are located at the Center City Campus of Thomas Jefferson University (The Vickie & Jack Farber Institute for Neuroscience). Applications will be reviewed on a rolling basis and salary will be based on the NIH postdoctoral scale.<br><br>Applicant must have a Ph.D. in neuroscience/biology/psychology/computer science or a related field. Applicants close to completion of their PhDs will also be considered where experience is directly relevant.<br><br>Queries relating to the position should be directed to Dr. Noa Herz: noa.herz@jefferson.edu.<br><br> <br><br>Apply here:<br>https://recruit.jefferson.edu/psc/hcmp/EMPLOYEE/HRMS/c/HRS_HRAM_FL.HRS_CG_SEARCH_FL.GBL?Page=HRS_APP_SCHJOB_FL&Action=U<br><br> <br><br>Key Responsibilities:<br><br>- Analyses of behavioral and intracranial data<br>- Take initiative in the planning and conduct of research<br>- Acquire and interpret research data and results<br>- Ensure the validity and reliability of collected data<br>- Prepare publications for submission in top refereed journals<br>- Assist in the preparation of research grant proposals<br>- Assist with designing and building experimental tasks, as well as in data collection<br><br>Required Experience:<br><br>- Proven track record of electrophysiology research.<br>- Experience in data analyses and coding (e.g., using Python/Matlab/R/C++).<br>- Experience in delivering research project results, as exemplified by a track record of peer-reviewed publications in a relevant area.<br><br>Skills and Abilities<br><br>- Capable of working collaboratively with neurologists and neurosurgeons in a hospital environment.<br>- Capable of working independently, exercising a high degree of initiative, and demonstrating a proactive approach to work.<br>- Strong quantitative background (multivariate methods such as machine learning are a plus)<br>- Ability to conduct a detailed review of recent literature.<br>- Demonstrated ability to conduct independent research.<br>- Creative approach to problem-solving.<br>- Excellent written communication skills in scientific English and the ability to write clearly and succinctly at a level suitable for international conferences and peer-reviewed journal publications.<br>- Self-motivation and ability to exercise initiative and judgment in carrying out research tasks.<br><br> <br><br>Interested applicants should upload a cover letter including a statement of research interests (describing how past experience and future plans fit the advertised position), a CV, and the details of at least two referees.<br><br> <br><br>We are committed to equality of opportunity, eliminating discrimination, and creating an inclusive working environment for all. We therefore encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender reassignment, sex, or sexual orientation.
2024-03-18T15:19:03+00:00Sam Javidihttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;cb3bc02c.2403Re: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8ff560ae.2403
Dear Julia,<br><br>On Mon, Mar 18, 2024 at 2:45 PM Júlia Soares <julii.f.soares@gmail.com><br>wrote:<br><br>><br>> What do you mean with "native images coregistered with MNI" and " write<br>> out the results of your source analysis in the native space on a template<br>> MNI mesh"?<br>><br><br>I mean coregistering your images to the MNI template (Coregister/Estimate)<br>so that their coordinate system doesn't have a big offset or some rotation<br>with respect to MNI coordinates. Then in my opinion locations specified in<br>this MNI-aligned native space will be similar enough to MNI coordinates for<br>the purposes of EEG analysis. Also by default your results image for source<br>reconstruction is generated in MNI template space no matter how your priors<br>are specified.<br><br>> Does this mean to transform my structural images to MNI using the<br>> "normalise write" function from SPM,<br>><br>to build the head models and then do source reconstruction in this space?<br>><br><br>No, that's not what I mean.<br><br>><br>> About the epoching step, just to make sure: do you suggest separating my<br>> continuous signal into the periods of time of my conditions and then<br>> separating each condition into epochs of 1-2s like a sub epoching step ?<br>><br>><br>I'm not sure how your conditions are recorded. If they are in separate<br>files then you could just epoch each one into 1 sec epochs and then merge<br>the resulting files. Otherwise you could convert each epoch separately (by<br>specifying a time window) and then epoch it and merge. The most<br>straightforward way to do this kind of custom epoching is write your own<br>function for specifying the trl and conditionlabels variables that the<br>epoching function takes as the input. But if you want to only use the GUI,<br>you could do as suggested above.<br><br>Best,<br><br>Vladimir<br><br>><br>> Em sex., 15 de mar. de 2024 às 16:28, Vladimir Litvak <<br>> litvak.vladimir@gmail.com> escreveu:<br>><br>>> Dear Julia,<br>>><br>>> On Fri, Mar 15, 2024 at 4:20 PM Júlia Soares <julii.f.soares@gmail.com><br>>> wrote:<br>>><br>>>> 1) Regarding the source locations in MNI I didn't quite understand why<br>>>> this doesn't matter. The DCM model requires an EEG signal after source<br>>>> reconstruction, right? So the space will be the source space instead of the<br>>>> sensor space (the actual electrodes), right? If so, how come the resolution<br>>>> of the coordinates doesn't make a difference? Isn't it possible that<br>>>> sources are several mm misaligned with corresponding locations in MNI ?<br>>>><br>>><br>>> The kind of differences in source locations that make a difference in EEG<br>>> are on the order of cm so if your native images are coregistered to MNI and<br>>> the head sizes are not unusually large or small I wouldn't expect the mm<br>>> differences to matter. But you can always write out the results of your<br>>> source analysis in the native space on a template MNI mesh and then you<br>>> won't have that problem at all.<br>>><br>>><br>>><br>>><br>>>> 2) About data epoching: I have a continuous signal acquired during<br>>>> performance of a task constituted by 4 conditions: 8 periods of "baseline"<br>>>> (22 seconds), 5 periods of "condition A" (18 seconds), 4 periods of<br>>>> "condition B" (18 seconds) and 3 periods of "condition C" (18 seconds). I<br>>>> was thinking about separating my continuous signal into epochs of equal<br>>>> length to the periods of each condition. So, for example for "condition A"<br>>>> I would have 5 epochs of 18 seconds each corresponding to "condition A "<br>>>> which would then be averaged into one single epoch. Does this make sense?<br>>>><br>>>><br>>> The implementation assumes short epochs 1-2 sec at most so I'd suggest<br>>> you epoch your conditions into epochs of that length and then the<br>>> differences in duration won't matter.<br>>><br>>> Best,<br>>><br>>> Vladimir<br>>><br>>><br>>>> Regards,<br>>>> Júlia Soares<br>>>><br>>>><br>>>><br>>>> Em ter., 12 de mar. de 2024 às 15:24, Vladimir Litvak <<br>>>> litvak.vladimir@gmail.com> escreveu:<br>>>><br>>>>> Dear Julia,<br>>>>><br>>>>> On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>>>>> wrote:<br>>>>><br>>>>>> 1) Is it only possible to do DCM in MNI space since the prior source<br>>>>>> locations should be given in MNI coordinates or is it possible to conduct<br>>>>>> DCM analysis in native space for each specific subject ?<br>>>>>><br>>>>>><br>>>>> I think this distinction is too fine to matter for DCM if you are doing<br>>>>> it at the sensor level. So I'd just define source locations in MNI space<br>>>>> and not worry too much about it.<br>>>>><br>>>>><br>>>>><br>>>>>> 2) In source reconstruction I inverted a continuous signal, i.e., I<br>>>>>> did not separate the signal into epochs (trials). However I have a task<br>>>>>> which has 3 conditions in which I intend to study connectivity in each of<br>>>>>> them. Is there a way to separate my signal after source reconstruction so I<br>>>>>> can include them in the DCM model?<br>>>>>><br>>>>><br>>>>> Both source analysis and DCM were not intended to work on long<br>>>>> continuous data segments. I'd suggest you epoch your data into arbitrary<br>>>>> 1-2 sec epochs. There is a way to do it in the epoching tool. Then I would<br>>>>> do both steps on these epoched data.<br>>>>><br>>>>> Best,<br>>>>><br>>>>> Vladimir<br>>>>><br>>>>><br>>>>><br>>>>>><br>>>>>> Thank you in advance.<br>>>>>> Regards,<br>>>>>> Júlia Soares<br>>>>>><br>>>>>
2024-03-18T14:56:21+00:00Vladimir Litvakhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8ff560ae.2403Re: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4e198a66.2403
Dear Vladimir,<br><br>What do you mean with "native images coregistered with MNI" and " write out<br>the results of your source analysis in the native space on a template MNI<br>mesh"? Does this mean to transform my structural images to MNI using the<br>"normalise write" function from SPM, to build the head models and then do<br>source reconstruction in this space?<br><br>About the epoching step, just to make sure: do you suggest separating my<br>continuous signal into the periods of time of my conditions and then<br>separating each condition into epochs of 1-2s like a sub epoching step ?<br><br>I apologize for my insistence and appreciate your help,<br>Júlia Soares<br><br>Em sex., 15 de mar. de 2024 às 16:28, Vladimir Litvak <<br>litvak.vladimir@gmail.com> escreveu:<br><br>> Dear Julia,<br>><br>> On Fri, Mar 15, 2024 at 4:20 PM Júlia Soares <julii.f.soares@gmail.com><br>> wrote:<br>><br>>> 1) Regarding the source locations in MNI I didn't quite understand why<br>>> this doesn't matter. The DCM model requires an EEG signal after source<br>>> reconstruction, right? So the space will be the source space instead of the<br>>> sensor space (the actual electrodes), right? If so, how come the resolution<br>>> of the coordinates doesn't make a difference? Isn't it possible that<br>>> sources are several mm misaligned with corresponding locations in MNI ?<br>>><br>><br>> The kind of differences in source locations that make a difference in EEG<br>> are on the order of cm so if your native images are coregistered to MNI and<br>> the head sizes are not unusually large or small I wouldn't expect the mm<br>> differences to matter. But you can always write out the results of your<br>> source analysis in the native space on a template MNI mesh and then you<br>> won't have that problem at all.<br>><br>><br>><br>><br>>> 2) About data epoching: I have a continuous signal acquired during<br>>> performance of a task constituted by 4 conditions: 8 periods of "baseline"<br>>> (22 seconds), 5 periods of "condition A" (18 seconds), 4 periods of<br>>> "condition B" (18 seconds) and 3 periods of "condition C" (18 seconds). I<br>>> was thinking about separating my continuous signal into epochs of equal<br>>> length to the periods of each condition. So, for example for "condition A"<br>>> I would have 5 epochs of 18 seconds each corresponding to "condition A "<br>>> which would then be averaged into one single epoch. Does this make sense?<br>>><br>>><br>> The implementation assumes short epochs 1-2 sec at most so I'd suggest you<br>> epoch your conditions into epochs of that length and then the differences<br>> in duration won't matter.<br>><br>> Best,<br>><br>> Vladimir<br>><br>><br>>> Regards,<br>>> Júlia Soares<br>>><br>>><br>>><br>>> Em ter., 12 de mar. de 2024 às 15:24, Vladimir Litvak <<br>>> litvak.vladimir@gmail.com> escreveu:<br>>><br>>>> Dear Julia,<br>>>><br>>>> On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>>>> wrote:<br>>>><br>>>>> 1) Is it only possible to do DCM in MNI space since the prior source<br>>>>> locations should be given in MNI coordinates or is it possible to conduct<br>>>>> DCM analysis in native space for each specific subject ?<br>>>>><br>>>>><br>>>> I think this distinction is too fine to matter for DCM if you are doing<br>>>> it at the sensor level. So I'd just define source locations in MNI space<br>>>> and not worry too much about it.<br>>>><br>>>><br>>>><br>>>>> 2) In source reconstruction I inverted a continuous signal, i.e., I did<br>>>>> not separate the signal into epochs (trials). However I have a task which<br>>>>> has 3 conditions in which I intend to study connectivity in each of them.<br>>>>> Is there a way to separate my signal after source reconstruction so I can<br>>>>> include them in the DCM model?<br>>>>><br>>>><br>>>> Both source analysis and DCM were not intended to work on long<br>>>> continuous data segments. I'd suggest you epoch your data into arbitrary<br>>>> 1-2 sec epochs. There is a way to do it in the epoching tool. Then I would<br>>>> do both steps on these epoched data.<br>>>><br>>>> Best,<br>>>><br>>>> Vladimir<br>>>><br>>>><br>>>><br>>>>><br>>>>> Thank you in advance.<br>>>>> Regards,<br>>>>> Júlia Soares<br>>>>><br>>>>
2024-03-18T14:44:45+00:00Júlia Soareshttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4e198a66.2403Re: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ba42100a.2403
Are you all having fun?<br><br>On Wed, 13 Mar 2024, 4:25 am Vladimir Litvak, <litvak.vladimir@gmail.com><br>wrote:<br><br>> Dear Julia,<br>><br>> On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>> wrote:<br>><br>>> 1) Is it only possible to do DCM in MNI space since the prior source<br>>> locations should be given in MNI coordinates or is it possible to conduct<br>>> DCM analysis in native space for each specific subject ?<br>>><br>>><br>> I think this distinction is too fine to matter for DCM if you are doing it<br>> at the sensor level. So I'd just define source locations in MNI space and<br>> not worry too much about it.<br>><br>><br>><br>>> 2) In source reconstruction I inverted a continuous signal, i.e., I did<br>>> not separate the signal into epochs (trials). However I have a task which<br>>> has 3 conditions in which I intend to study connectivity in each of them.<br>>> Is there a way to separate my signal after source reconstruction so I can<br>>> include them in the DCM model?<br>>><br>><br>> Both source analysis and DCM were not intended to work on long continuous<br>> data segments. I'd suggest you epoch your data into arbitrary 1-2 sec<br>> epochs. There is a way to do it in the epoching tool. Then I would do both<br>> steps on these epoched data.<br>><br>> Best,<br>><br>> Vladimir<br>><br>><br>><br>>><br>>> Thank you in advance.<br>>> Regards,<br>>> Júlia Soares<br>>><br>>
2024-03-18T11:52:22+13:00Fani Golemihttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ba42100a.2403Job Ad: Lecturer in Psychology @ UEA, Norwich, UK
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4da81854.2403
*Lecturer in Psychology (2 posts) REF: ATR1682*<br><br>*Salary on appointment will be £46,974 per annum, dependent on skills and<br>experience, with an annual increment up to £54,395 per annum. *<br><br>An exciting opportunity has arisen for two new Lecturers to join the School<br>of Psychology. The posts will augment our current excellence in research<br>and take an active role in the delivery of research-led teaching.<br><br>In REF 2021, 96% of our research in Psychology, Psychiatry and Neuroscience<br>was recognised as world-leading or internationally excellent, with a 100%<br>4* world leading research environment. The School has outstanding<br>laboratory equipment and facilities including a Siemens Prisma 3T MRI<br>scanner in the UEA Wellcome-Wolfson Brain Imaging Centre (UWWBIC:<br>https://uwwbic.uea.ac.uk/).<br><br>You will join a network of researchers within the School of Psychology,<br>with clinical colleagues in the School of Medicine and others across the<br>University and the wider Norwich Research Park, consistent with the<br>multidisciplinary ethos at UEA. You will have the opportunity to develop<br>your research profile which should compliment the existing research<br>strengths, producing high quality proposals to secure external research<br>funding, disseminate finding through academic publications and external<br>impact.<br><br>Teaching is a key part of this role and as such you will be expected to<br>plan, teach and assess at undergraduate and postgraduate levels and<br>supervise PhD students.<br><br>We are seeking candidates specialising in any area of Psychology who will<br>expand and enhance our existing research strengths. Those working in Social<br>Psychology, Cognitive and/or Social Development, Cognitive Neuroscience,<br>Social Neuroscience, or Developmental Neuroscience would complement the<br>School’s approach to psychological science and would be especially welcome.<br><br>You must have a PhD (or equivalent) in a relevant subject area or<br>equivalent experience, with experience of undergraduate teaching and<br>student assessment. A strong publication record is also essential, and the<br>ability to supervise PhD students would be advantageous.<br><br>These full-time posts are available from 1 August 2024 on an indefinite<br>basis.<br><br>UEA offers a variety of flexible working options and although these roles<br>are advertised on a full-time basis, we encourage applications from<br>individuals who would prefer a flexible working pattern including part<br>time, or job share arrangements. Details of any preferred flexible hours<br>should be stated in the personal statement and will be discussed further at<br>interview.<br><br>Benefits include:<br><br>- *44 days annual leave* inclusive of Bank Holidays and University<br>Customary days (pro rata for part-time).<br>- *Family and Work-life balance policies *including hybrid working and<br>considerable maternity, paternity, shared parental leave and adoption<br>leave.<br>- *Generous pension scheme* with life cover for dependants, plus<br>incapacity cover.<br>- *Health and Wellbeing: *discounted access to Sportspark facilities,<br>relaxation rooms, 320 acres of rolling parkland, wellbeing walks, Wellbeing<br>Ambassador network, medical centre, Occupational Health and a 24/7 Employee<br>Assistance Programme.<br>- *Campus Facilities:* Sportspark, library, nursery, supermarket, post<br>office, bars and catering outlets.<br>- Exclusive shopping *discounts* to help cut the cost of household<br>bills, childcare salary sacrifice scheme, Cycle to Work scheme and public<br>transport discounts.<br>- *Personal Development: *unlimited access to LinkedIn Learning courses,<br>specialist advice and training from our Organisational Development and<br>Professional Learning Team.<br><br>*Closing date: 12 April 2024*<br><br>*We strongly encourage applicants from Black, Asian or other minority<br>ethnic backgrounds and welcome applications from all protected groups as<br>defined by the Equality Act 2010. Appointment will be made on merit.*<br><br>*The University holds an Athena Swan Silver Institutional Award in<br>recognition of our advancement towards gender equality.*<br>Further Information<br><br>For further information, including the Job Description and Person<br>Specification, please see the attached Candidate Brochure.<br><br>For an informal discussion about the post please contact the Head of<br>School, Professor Neil Cooper via neil.cooper@uea.ac.uk.
2024-03-18T10:31:34+00:00William Pennyhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4da81854.2403parametric modulations
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8fc3bed5.2403
Dear all,<br>I was wondering if there is a state of the art procedure for subjects with no parametric modulation.<br>One subject has same ratings for one contrast (therefore no modulation).<br>Should the contrast be excluded, or can we add some slight modulation..<br>Or are there some polynomial expansion that can deal with such cases?<br>Thanks<br>J.A.
2024-03-18T10:29:32+00:00Jamy Andyhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8fc3bed5.2403Re: SPM EEG - Displaying contrast and T maps in SPM ?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;f2da4e85.2403
Dear Benedetta,<br><br>I think the issue there was not that it was a t-map but that it was<br>thresholded so there was a sharp transition that the plotting tool didn't<br>handle well. With a continuous t-map there should be no problem. Thanks for<br>posting your tools.<br><br>Best,<br><br>Vladimir<br><br>On Mon, Mar 18, 2024 at 9:22 AM BENEDETTA CECCONI <bcecconi@wisc.edu> wrote:<br><br>> Dear Vladmir,<br>><br>> thank you for your answer.<br>><br>> I managed to plot multiple topographies (for con images) modifying the SPM<br>> function "spm12/toolbox/MEEGtools/spm_eeg_img2maps.m". In case it might be<br>> useful to others, I attach the modified function here, and an example<br>> script for plotting 16 topographies at specified time points, customizing<br>> colormap, style and scale.<br>><br>> Regarding the t-maps, I saw that in other thread you had recommended not<br>> to use the function spm_eeg_img2maps.m because of biased outputs:<br>> https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=spm;b735448.1202.. Is this<br>> still the case? If yes, I found this very nice and easy to use toolbox to<br>> plot t-maps. Again, I attach the links in case they may be useful to<br>> others: (<br>> https://link.springer.com/article/10.1007/s12021-019-09447-6#Sec24) and<br>> scripts:https://github.com/JeremyATaylor/Porthole<br>> <https://github.com/JeremyATaylor/Porthole><br>> GitHub - JeremyATaylor/Porthole<br>> <https://github.com/JeremyATaylor/Porthole><br>> Contribute to JeremyATaylor/Porthole development by creating an account on<br>> GitHub.<br>> github.com<br>><br>> <https://link.springer.com/article/10.1007/s12021-019-09447-6#Sec24><br>> Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG<br>> Statistics - Neuroinformatics<br>> <https://link.springer.com/article/10.1007/s12021-019-09447-6#Sec24><br>> Electro- and magneto-encephalography are functional neuroimaging<br>> modalities characterised by their ability to quantify dynamic<br>> spatiotemporal activity within the brain. However, the visualisation<br>> techniques used to illustrate these effects are currently limited to<br>> single- or multi-channel time series plots, topographic scalp maps and<br>> orthographic cross-sections of the spatiotemporal data structure. Whilst<br>> these methods each have their own strength and weaknesses, they are only<br>> able to show a subset of the data and are suboptimal at articulating one or<br>> both of the space-time components.Here, we propose Porthole and Stormcloud,<br>> a set of data visualisation tools which can automatically generate context<br>> appropriate graphics for both print and screen with the following graphical<br>> capabilities: Animated two-dimensional scalp maps with dynamic timeline<br>> annotation and optional user interaction; Three-dimensional construction of<br>> discrete clusters within sparse spatiotemporal volumes, rendered with<br>> ‘cloud-like’ appe<br>> link.springer.com<br>><br>> Best,<br>> Benedetta<br>><br>> ------------------------------<br>> *From:* Vladimir Litvak <litvak.vladimir@gmail.com><br>> *Sent:* Friday, March 15, 2024 5:19 AM<br>> *To:* BENEDETTA CECCONI <bcecconi@wisc.edu><br>> *Cc:* SPM@jiscmail.ac.uk <SPM@jiscmail.ac.uk><br>> *Subject:* Re: [SPM] SPM EEG - Displaying contrast and T maps in SPM ?<br>><br>> Dear Benedetta,<br>><br>><br>><br>> On Thu, Mar 14, 2024 at 3:42 PM BENEDETTA CECCONI <<br>> 00006e6f00386e18-dmarc-request@jiscmail.ac.uk> wrote:<br>><br>><br>><br>> 1. MEEG Tools > `Plot Scalp maps from M/EEG image` and input my<br>> contrast image* but I can't adjust the scale *(nor select<br>> multiple time points, but this is less of a problem)<br>><br>><br>> The function has an option S.clim that you can use to adjust the scale.<br>> You would need to run it from a script to specify that.<br>><br>><br>><br>> 1. for T maps, I opened the SPM.mat in Results and saved the<br>> thresholded image. I tried to open it with MEEG Tools > `Plot Scalp maps<br>> from M/EEG image` again but it didn't work. I then tried to collapse the<br>> thresholded t image in time using `Images > Collapse time` (input: original<br>> spmT-map) and then tried to input the output image in MEEG Tools > `Plot<br>> Scalp maps from M/EEG image` (selecting time 1 1) but it doesn't work...<br>><br>><br>><br>><br>> I'm not sure what you mean by 'didn't work'. If you send your error<br>> message I might be able to say more.<br>><br>> Best,<br>><br>> Vladimir<br>><br>><br>>
2024-03-18T09:30:58+00:00Vladimir Litvakhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;f2da4e85.2403CMM_NMM
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;916b65a9.2403
Dear SPM experts,<br><br>We used the default CMM_NMDA model in SPM12 to model the first 50ms of a TMS-evoked response registered with EEG. With PEB we then tested which (if any) parameter of the model was related to an external variable.<br>I have issues interpreting the effect we see on the GABAa parameter as I do not fully understand what information it is conveying in the model:<br><br>1.<br>Is GABAa mediating all the inhibitory connections allowed in the model (thus both the inhibitory feedback loops and the projections from the inhibitory interneuron to the other subpopulations)?<br>2.<br>Is it mediating just the inhibitory connections from the inhibitory interneuron to the other subpopulations?<br>3.<br>Or is it conveying another type of information?<br><br>Thank you a lot in advance for your time and help.<br><br>Best,<br>Ilenia
2024-03-18T09:29:59+00:00Paparella Ileniahttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;916b65a9.2403Re: SPM EEG - Displaying contrast and T maps in SPM ?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;63f1c83f.2403
Dear Vladmir,<br><br>thank you for your answer.<br><br>I managed to plot multiple topographies (for con images) modifying the SPM function "spm12/toolbox/MEEGtools/spm_eeg_img2maps.m". In case it might be useful to others, I attach the modified function here, and an example script for plotting 16 topographies at specified time points, customizing colormap, style and scale.<br><br>Regarding the t-maps, I saw that in other thread you had recommended not to use the function spm_eeg_img2maps.m because of biased outputs: https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=spm;b735448.1202.. Is this still the case? If yes, I found this very nice and easy to use toolbox to plot t-maps. Again, I attach the links in case they may be useful to others: (https://link.springer.com/article/10.1007/s12021-019-09447-6#Sec24) and scripts:https://github.com/JeremyATaylor/Porthole<br>[https://opengraph.githubassets.com/d44765b8123e0c83a8ae181f9f3735dda58dc72f409d278ad696fe609a3fa306/JeremyATaylor/Porthole]<https://github.com/JeremyATaylor/Porthole><br>GitHub - JeremyATaylor/Porthole<https://github.com/JeremyATaylor/Porthole><br>Contribute to JeremyATaylor/Porthole development by creating an account on GitHub.<br>github.com<br><br>[https://static-content.springer.com/image/art%3A10.1007%2Fs12021-019-09447-6/MediaObjects/12021_2019_9447_Fig1_HTML.png]<https://link.springer.com/article/10.1007/s12021-019-09447-6#Sec24><br>Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics - Neuroinformatics<https://link.springer.com/article/10.1007/s12021-019-09447-6#Sec24><br>Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are currently limited to single- or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities: Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction; Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with ‘cloud-like’ appe<br>link.springer.com<br><br>Best,<br>Benedetta
2024-03-18T09:22:38+00:00BENEDETTA CECCONIhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;63f1c83f.2403Using ChatGPT and Copilot for Efficient Data Analysis in R - livestream seminar
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;11ec8e46.2403
Hi everyone<br><br>Instats is incredibly pleased to be offering a 2-day workshop on Using<br>ChatGPT and Copilot for Efficient Data Analysis in R<br><https://instats.org/seminar/using-chatgpt-and-github-copilot-for-eff2>,<br>running March 20 - 21, by professor Peter Gruber (who holds dual PhDs in<br>physics and economics). This workshop provides a 21st-century introduction<br>to Statistical Analysis with R, focusing on the efficient use of AI<br>assistants including ChatGPT and Github Copilot to automate R coding with<br>plain language requests. Because R is free, this revolution will help<br>democratize access to basic and advanced analysis tools without having to<br>suffer the steep learning curve of coding in R. Participants will learn<br>step by step how to install AI tools and how to harness their power for<br>efficient data analysis in R, making them many times more efficient. They<br>will be able to create R code in the blink of an eye and with unprecedented<br>ease of use, while learning some of the underlying principles of the R<br>language as this relates to competently assessing and using AI-generated<br>code.<br><br>Register now<br><https://instats.org/seminar/using-chatgpt-and-github-copilot-for-eff2> and<br>don't miss out on this unique opportunity to learn how to easily and<br>rapidly code all of your analyses in R, and please feel free to tell your<br>friends and colleagues!<br><br>Best wishes<br><br>Michael Zyphur<br>Director<br>Institute for Statistical and Data Science<br>*instats.org* <http://instats.org><br><http://instats.org>
2024-03-18T19:05:00+11:00Michael Zyphurhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;11ec8e46.2403MRI volume "origin": how it is defined during scanning ?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;24825219.2403
Dear all,<br><br>Clicking on the "Origin" button in the display window brings the crosshair<br>to a coordinate which is a so-called origin. What is this "origin"<br>coordinate from the point of view of the scanning operator? What's the name<br>of this original coordinate? Maybe someone knows how to set its location<br>in 7T SIEMENS MAGNETOM Terra. I am asking because for different subjects I<br>get different origin locations. While the volumes can obviously be<br>reoriented in SPM, a more straightforward way would be to fix in the<br>location at the scanner side.<br><br>Thank you for the help.<br>Vadim
2024-03-17T17:32:56+02:00Vadim Axelrodhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;24825219.2403Re: What is the DCM.U.idx parameter for?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;2394d8bd.2403
Hi Peter,<br><br>Thank you!<br><br>Initially, I thought I understood the logic behind the numbers in the DCM.U.idx generated by the DCM GUI. The matrix looked like this:<br><br>DCM.U.idx=[1 1 2 1<br> 3 1<br> 4 1]<br><br>I interpreted it as follows: the first column ranging from 1 to 4 corresponds to the four experimental conditions, and the second column consists entirely of ones.<br><br>Based on this understanding, I defined all DCMs in my code using the same idx matrix. Namely, I applied this matrix to DCM.U.idx in all subjects' DCM files.<br><br>However, I later discovered that the DCM GUI generates the DCM.U.idx for another DCM (including parametric modulation regressors) in this format:<br><br>DCM.U.idx=[1 1<br> 1 2<br> 1 3<br> 1 4]<br><br>This discrepancy has left me uncertain whether my approach of defining the same matrix for all subjects' DCM.U.idx was incorrect.<br><br>Would defining the DCM.U.idx matrix incorrectly in the code have a significant impact on the results?<br><br>Cheers,Luna<br><br>12 Mar 2024, 18:07 by peter.zeidman@ucl.ac.uk:<br><br>><br>> Hi Luna<br>><br>><br>> This is nothing to worry about. If I remember correctly, I added that field to help with specifying DCMs using the batch editor. Its omission won’t alter your results.<br>><br>><br>> <br>><br>><br>> Best<br>><br>><br>> Peter<br>><br>><br>> <br>><br>><br>> From:> SPM (Statistical Parametric Mapping) <SPM@JISCMAIL.AC.UK> > On Behalf Of > Luna Sato<br>> > Sent:> 06 March 2024 03:23<br>> > To:> SPM@JISCMAIL.AC.UK<br>> > Subject:> [SPM] What is the DCM.U.idx parameter for?<br>><br>> <br>><br>><br>> ⚠> Caution: External sender<br>><br>><br>> <br>><br>><br>> Hi experts,<br>><br>><br>> <br>><br>><br>> When checking DCM results, I found certain subjects' DCM fields include the parameter DCM.U.idx, while others don't. I suspect this variation might be due to different SPM versions used.<br>><br>><br>> <br>><br>><br>> I'm wondering about the significance of DCM.U.idx. Can I combine subjects with and without this parameter in group analysis? Or should I consider redoing some DCM analyses?<br>><br>><br>> <br>><br>><br>> Best regards,<br>><br>><br>> Luna<br>><br>><br>> <br>><br>>
2024-03-17T09:50:28+01:00Luna Satohttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;2394d8bd.2403Clinical Research Coordinator Position at Duke University - Adcock Lab
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ec1935e5.2403
The *Motivated Memory Laboratory* <https://www.adcocklab.org/> in the<br>Center for Cognitive Neuroscience and Department of Psychiatry at Duke<br>University is seeking a full-time *Clinical Research Coordinator* with<br>strong interest in understanding how motivation shapes human experience to<br>work on an NIH-funded project (*PI: R. Alison Adcock*). This project aims<br>to uncover the neural mechanisms of motivation and its regulation using<br>fMRI neurofeedback and advanced statistical modeling.<br><br>Ongoing projects include: i) using real-time fMRI neurofeedback for<br>endogenous regulation of dopaminergic midbrain activity, ii) understanding<br>how midbrain neuromodulation contributes to downstream behavioral and<br>cognitive outcomes (e.g. memory and decision-making), iii) identifying how<br>different motivational states influence learning and memory formation.<br><br>Review of applications will start immediately and will continue until the<br>position is filled.<br><br>*Required qualifications*<br>Education: Completion of an Associate's degree<br>Experience: Work requires a minimum of two years of relevant research<br>experience. A Bachelor's degree may substitute for 2 years required<br>experience.<br><br>For inquiries please email: alison.adcock@duke.edu.<br><br>Please see full postings and apply using the following link:<br>Clinical Research Coordinator - Psychiatry - Beh Med Div - Adcock Team<br><https://careers.duke.edu/job-invite/242300/>
2024-03-15T17:42:11-04:00Jia-Hou, Pohhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ec1935e5.2403Postdoctoral Position at Duke University - Adcock Lab
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;72f9dec.2403
*Postdoctoral Researcher in the Cognitive Neuroscience of Motivation and<br>Memory at Duke University*<br>The *Motivated Memory Laboratory* <https://www.adcocklab.org/> in the<br>Center for Cognitive Neuroscience and Department of Psychiatry at Duke<br>University is seeking a full-time Cognitive Neuroscience *Postdoctoral<br>Researcher* with strong interest in understanding how motivation shapes<br>human experience to join the team and lead an NIH-funded project (*PI: R.<br>Alison Adcock*). This project aims to uncover the neural mechanisms of<br>motivation and its regulation using fMRI neurofeedback and advanced<br>statistical modeling.<br><br>Ongoing projects include: i) using real-time fMRI neurofeedback for<br>endogenous regulation of dopaminergic midbrain activity, ii) understanding<br>how midbrain neuromodulation contributes to downstream behavioral and<br>cognitive outcomes (e.g. memory and decision-making), iii) identifying how<br>different motivational states influence learning and memory formation.<br><br>Review of applications will start immediately and will continue until the<br>position is filled. Salary is commensurate with NIH guidelines and<br>applicant experience.<br><br>*Required qualifications*<br>Candidates must have a PhD in Psychology, Cognitive Science, Neurobiology,<br>Neuroscience, or a related field by the position start date.<br><br>*Required skills and experience*<br><br>- Experience collecting and analyzing behavioral and fMRI data<br>(preferably in humans)<br>- Ability to work with and provide guidance to junior lab members and<br>trainees<br>- Ability to work effectively with mentor(s) to conduct and implement<br>research projects<br>- Demonstrated ability to conduct independent research - including<br>formulating hypotheses, designing experiments, collecting data, analyzing<br>data, and communication of results<br>- Demonstrated ability to independently write manuscripts and grant<br>application with guidance from mentor(s)<br>- Experience with programming and troubleshooting experimental tools -<br>preferably in Matlab and/or Python (e.g. for stimulus presentation, data<br>cleaning, computational modeling)<br><br>*Strongly preferred but not required qualifications*<br><br>- Experience working with real-time fMRI and/or neuro/biofeedback in<br>other modalities (e.g. EEG, Pupillometry, HRV)<br>- Experience using advanced quantitative method for analysis of<br>behavioral and imaging data (e.g. computational modeling, machine learning,<br>mixed-models)<br>- Strong background in neuroanatomy and neurobiology<br>- Interest in translating basic science towards clinical applications<br><br>For inquiries please email: alison.adcock@duke.edu.<br><br>Please see full postings and apply using the following link:<br>Postdoctoral Associate - Psychiatry -<br><http://careers.duke.edu/job/Durham-Postdoctoral-Associate-Psychiatry-Behavioral-Adcock-Team-NC-27710/1135094700/?from=email&refid=22020353400&utm_source=J2WEmail&source=2&eid=148200-202424230624-28752723000&locale=en_US>Behavioral<br>- Adcock Team<br><http://careers.duke.edu/job/Durham-Postdoctoral-Associate-Psychiatry-Behavioral-Adcock-Team-NC-27710/1135094700/?from=email&refid=22020353400&utm_source=J2WEmail&source=2&eid=148200-202424230624-28752723000&locale=en_US>
2024-03-15T17:42:03-04:00Jia-Hou, Pohhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;72f9dec.2403Re: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;1922268e.2403
Dear Julia,<br><br>On Fri, Mar 15, 2024 at 4:20 PM Júlia Soares <julii.f.soares@gmail.com><br>wrote:<br><br>> 1) Regarding the source locations in MNI I didn't quite understand why<br>> this doesn't matter. The DCM model requires an EEG signal after source<br>> reconstruction, right? So the space will be the source space instead of the<br>> sensor space (the actual electrodes), right? If so, how come the resolution<br>> of the coordinates doesn't make a difference? Isn't it possible that<br>> sources are several mm misaligned with corresponding locations in MNI ?<br>><br><br>The kind of differences in source locations that make a difference in EEG<br>are on the order of cm so if your native images are coregistered to MNI and<br>the head sizes are not unusually large or small I wouldn't expect the mm<br>differences to matter. But you can always write out the results of your<br>source analysis in the native space on a template MNI mesh and then you<br>won't have that problem at all.<br><br>> 2) About data epoching: I have a continuous signal acquired during<br>> performance of a task constituted by 4 conditions: 8 periods of "baseline"<br>> (22 seconds), 5 periods of "condition A" (18 seconds), 4 periods of<br>> "condition B" (18 seconds) and 3 periods of "condition C" (18 seconds). I<br>> was thinking about separating my continuous signal into epochs of equal<br>> length to the periods of each condition. So, for example for "condition A"<br>> I would have 5 epochs of 18 seconds each corresponding to "condition A "<br>> which would then be averaged into one single epoch. Does this make sense?<br>><br>><br>The implementation assumes short epochs 1-2 sec at most so I'd suggest you<br>epoch your conditions into epochs of that length and then the differences<br>in duration won't matter.<br><br>Best,<br><br>Vladimir<br><br>> Regards,<br>> Júlia Soares<br>><br>><br>><br>> Em ter., 12 de mar. de 2024 às 15:24, Vladimir Litvak <<br>> litvak.vladimir@gmail.com> escreveu:<br>><br>>> Dear Julia,<br>>><br>>> On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>>> wrote:<br>>><br>>>> 1) Is it only possible to do DCM in MNI space since the prior source<br>>>> locations should be given in MNI coordinates or is it possible to conduct<br>>>> DCM analysis in native space for each specific subject ?<br>>>><br>>>><br>>> I think this distinction is too fine to matter for DCM if you are doing<br>>> it at the sensor level. So I'd just define source locations in MNI space<br>>> and not worry too much about it.<br>>><br>>><br>>><br>>>> 2) In source reconstruction I inverted a continuous signal, i.e., I did<br>>>> not separate the signal into epochs (trials). However I have a task which<br>>>> has 3 conditions in which I intend to study connectivity in each of them.<br>>>> Is there a way to separate my signal after source reconstruction so I can<br>>>> include them in the DCM model?<br>>>><br>>><br>>> Both source analysis and DCM were not intended to work on long continuous<br>>> data segments. I'd suggest you epoch your data into arbitrary 1-2 sec<br>>> epochs. There is a way to do it in the epoching tool. Then I would do both<br>>> steps on these epoched data.<br>>><br>>> Best,<br>>><br>>> Vladimir<br>>><br>>><br>>><br>>>><br>>>> Thank you in advance.<br>>>> Regards,<br>>>> Júlia Soares<br>>>><br>>>
2024-03-15T16:28:08+00:00Vladimir Litvakhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;1922268e.2403Re: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;9dcdb947.2403
Dear Vladimir,<br><br>First of all, thank you for your prompt response and valuable suggestions.<br><br>1) Regarding the source locations in MNI I didn't quite understand why this<br>doesn't matter. The DCM model requires an EEG signal after source<br>reconstruction, right? So the space will be the source space instead of the<br>sensor space (the actual electrodes), right? If so, how come the resolution<br>of the coordinates doesn't make a difference? Isn't it possible that<br>sources are several mm misaligned with corresponding locations in MNI ?<br><br>2) About data epoching: I have a continuous signal acquired during<br>performance of a task constituted by 4 conditions: 8 periods of "baseline"<br>(22 seconds), 5 periods of "condition A" (18 seconds), 4 periods of<br>"condition B" (18 seconds) and 3 periods of "condition C" (18 seconds). I<br>was thinking about separating my continuous signal into epochs of equal<br>length to the periods of each condition. So, for example for "condition A"<br>I would have 5 epochs of 18 seconds each corresponding to "condition A "<br>which would then be averaged into one single epoch. Does this make sense?<br><br>Regards,<br>Júlia Soares<br><br>Em ter., 12 de mar. de 2024 às 15:24, Vladimir Litvak <<br>litvak.vladimir@gmail.com> escreveu:<br><br>> Dear Julia,<br>><br>> On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>> wrote:<br>><br>>> 1) Is it only possible to do DCM in MNI space since the prior source<br>>> locations should be given in MNI coordinates or is it possible to conduct<br>>> DCM analysis in native space for each specific subject ?<br>>><br>>><br>> I think this distinction is too fine to matter for DCM if you are doing it<br>> at the sensor level. So I'd just define source locations in MNI space and<br>> not worry too much about it.<br>><br>><br>><br>>> 2) In source reconstruction I inverted a continuous signal, i.e., I did<br>>> not separate the signal into epochs (trials). However I have a task which<br>>> has 3 conditions in which I intend to study connectivity in each of them.<br>>> Is there a way to separate my signal after source reconstruction so I can<br>>> include them in the DCM model?<br>>><br>><br>> Both source analysis and DCM were not intended to work on long continuous<br>> data segments. I'd suggest you epoch your data into arbitrary 1-2 sec<br>> epochs. There is a way to do it in the epoching tool. Then I would do both<br>> steps on these epoched data.<br>><br>> Best,<br>><br>> Vladimir<br>><br>><br>><br>>><br>>> Thank you in advance.<br>>> Regards,<br>>> Júlia Soares<br>>><br>>
2024-03-15T16:19:58+00:00Júlia Soareshttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;9dcdb947.2403Re: SPM EEG - Displaying contrast and T maps in SPM ?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;68a4e896.2403
Dear Benedetta,<br><br>On Thu, Mar 14, 2024 at 3:42 PM BENEDETTA CECCONI <<br>00006e6f00386e18-dmarc-request@jiscmail.ac.uk> wrote:<br><br>><br>><br>> 1. MEEG Tools > `Plot Scalp maps from M/EEG image` and input my<br>> contrast image* but I can't adjust the scale *(nor select<br>> multiple time points, but this is less of a problem)<br>><br>><br>The function has an option S.clim that you can use to adjust the scale. You<br>would need to run it from a script to specify that.<br><br>><br>> 1. for T maps, I opened the SPM.mat in Results and saved the<br>> thresholded image. I tried to open it with MEEG Tools > `Plot Scalp maps<br>> from M/EEG image` again but it didn't work. I then tried to collapse the<br>> thresholded t image in time using `Images > Collapse time` (input: original<br>> spmT-map) and then tried to input the output image in MEEG Tools > `Plot<br>> Scalp maps from M/EEG image` (selecting time 1 1) but it doesn't work...<br>><br>><br>><br><br>I'm not sure what you mean by 'didn't work'. If you send your error message<br>I might be able to say more.<br><br>Best,<br><br>Vladimir<br><br>>
2024-03-15T10:19:04+00:00Vladimir Litvakhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;68a4e896.2403Open Science Room at OHBM 2024
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3bafa25b.2403
Full message available at: <a href="https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3bafa25b.2403">Open Science Room at OHBM 2024</a>2024-03-14T16:26:45+00:00Zeidman, Peterhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3bafa25b.2403SPM EEG - Displaying contrast and T maps in SPM ?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;94e35393.2403
Hello everyone,<br><br>I would like to display the results of my group analysis by showing contrast maps and thresholded t maps, something like this:<br><br>[cid:04ca0dfa-84fa-450b-bc8a-bfcce5029ce0]<br><br>I tried<br><br>1.<br>MEEG Tools > `Plot Scalp maps from M/EEG image` and input my contrast image but I can't adjust the scale (nor select multiple time points, but this is less of a problem)<br><br>2.<br>for T maps, I opened the SPM.mat in Results and saved the thresholded image. I tried to open it with MEEG Tools > `Plot Scalp maps from M/EEG image` again but it didn't work. I then tried to collapse the thresholded t image in time using `Images > Collapse time` (input: original spmT-map) and then tried to input the output image in MEEG Tools > `Plot Scalp maps from M/EEG image` (selecting time 1 1) but it doesn't work...<br><br>Can someone please help me?<br><br>Any suggestions are greatly appreciated.<br><br>Thanks a lot in advance,<br><br>Benedetta
2024-03-14T15:42:47+00:00BENEDETTA CECCONIhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;94e35393.2403Body-Brain Waves: Neuroscience events in southern Italy
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;cd3aad4c.2403
Dear colleagues,<br><br>With this email, I'd like to bring to your attention Waves’24<http://waves-conference.com>.<br><br>Are you interested in brain, respiratory, cardiovascular, oculomotor and/or gastrointestinal signals?<br><br>After the success of the first Body-Brain Waves<https://waves-conference.com/373-2/waves-23/> conference, this year we are planning a 4-day Summer School, followed by a 2-day Symposium.<br><br>Waves’24<https://waves-conference.com/> takes place in Salerno<https://www.google.com/search?sca_esv=23e333b9b80ac6d8&rlz=1C1PRFI_enNL888NL888&sxsrf=ACQVn09G6TvG74jIEW4fwftrawAyqAes4w:1707127739095&q=salerno&uds=AMwkrPuANOudBFD0X4ERrATo2Mm2JH8T11cwFXV0RzxWGAKrtKKJ25Jk4baHHXzQCHa8RWWUYlbN3cTieUIda9QMyvGI_t9_nU8cp2Tnf0Etmj0DGaj3gZU&udm=2&sa=X&ved=2ahUKEwiuzOSz-pOEAxVCxQIHHSZ0CAMQtKgLegQICRAB&biw=1920&bih=929&dpr=1> <https://www.google.com/search?sca_esv=23e333b9b80ac6d8&rlz=1C1PRFI_enNL888NL888&sxsrf=ACQVn09G6TvG74jIEW4fwftrawAyqAes4w:1707127739095&q=salerno&uds=AMwkrPuANOudBFD0X4ERrATo2Mm2JH8T11cwFXV0RzxWGAKrtKKJ25Jk4baHHXzQCHa8RWWUYlbN3cTieUIda9QMyvGI_t9_nU8cp2Tnf0Etmj0DGaj3gZU&udm=2&sa=X&ved=2ahUKEwiuzOSz-pOEAxVCxQIHHSZ0CAMQtKgLegQICRAB&biw=1920&bih=929&dpr=1> (my hometown in southern Italy) from the 23rd to 28th September 2024.<br><br>1. Summer School: 23-26 September 2024;<br>2. Symposium: 27-28 September 2024.<br><br>At the core of both events, the following questions:<br><br>1. How to best measure Body-Brain physiological signals?<br>2. How to best investigate Body-Brain interactions and their influence on cognition?<br><br>Preliminary program<br><br>* During the Summer School, young and emerging early career researchers will offer several courses and workshops on how to best acquire and analyze bodily physiological signals next to brain activity and/or behavior.<br><br>Participants will learn best practices in acquiring respiratory, cardiovascular, oculomotor and gastrointestinal signals, and how to holistically examine dynamic Body-Brain interactions and their modulatory role in cognition. Real hands-on data!<br><br>* The Summer School will be followed by a 2-day Symposium featuring talks and workshops on Body-Brain Waves and mobile body-brain imaging applications.<br><br>At the end of each day, social activities such as aperitivo on the seaside, cruise to Amalfi<https://www.google.com/search?q=amalfi&rlz=1C1CHBF_itNL979NL979&sxsrf=ALiCzsbUyyoZfyx72u2PsKC13Cm8CEd0cA:1663767583484&source=lnms&tbm=isch&sa=X&ved=2ahUKEwjrn7Wegab6AhXPzqQKHZJ6CMkQ_AUoAnoECAIQBA&biw=1666&bih=943&dpr=1.8> and visit of the Greek temples in Paestum<https://www.google.com/search?q=paestum&tbm=isch&ved=2ahUKEwj9ks-ngab6AhUt57sIHX6IC1oQ2-cCegQIABAA&oq=paestum&gs_lcp=CgNpbWcQAzIECAAQQzIFCAAQgAQyBAgAEEMyBAgAEEMyBAgAEEMyBQgAEIAEMgUIABCABDIFCAAQgAQyBAgAEEMyBQgAEIAEOgQIIxAnUPYEWJAMYKUNaABwAHgAgAFqiAH7BJIBAzUuM5gBAKABAaoBC2d3cy13aXotaW1nwAEB&sclient=img&ei=MhQrY_2xL63O7_UP_pCu0AU&bih=943&biw=1666&rlz=1C1CHBF_itNL979NL979>.<br><br>If this initiative is of interest to you and colleagues, please spread the info and apply following these<https://waves-conference.com/#applications> instructions.<br>Please, note that there are separate applications for the Summer School and Symposium.<br><br>Don't hesitate to get in touch for any doubts and questions.<br><br>Looking forward to welcoming you at Waves'24.<br><br>Thank you in advance for attention and for helping us spreading the email and info around.<br><br>Have a nice day,<br><br>Antonio Criscuolo<br><br>Faculty of Psychology and Neuroscience<br><br>Dept NP&PP, Maastricht University<br><br>Office: UNS40 2.749<br><br>Web: band-lab.com<http://www.band-lab.com/><br><br>[cid:ef3fc0eb-175f-4582-9209-c675a16df60b]<http://waves-conference.com>
2024-03-14T15:17:40+00:00Criscuolo, Antonio (PSYCHOLOGY)https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;cd3aad4c.2403Exciting Postdoctoral Position Available at UCSF's Tee Lab - Neuroimaging Research
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c0ea8175.2403
We are thrilled to announce an opening for a Postdoctoral Position at the Tee Lab, Department of Neurology, University of California, San Francisco (UCSF). This is a unique opportunity for a passionate researcher to contribute to groundbreaking work in the field of neuroimaging and neurodegenerative diseases.<br><br>Job Summary: The selected candidate will participate in projects that focus on establishing MRI protocols with cutting-edge methodologies and applying neuroimaging techniques, such as tractography, graph theory analysis, and machine learning algorithms. Our focus spans a range of neurodegenerative diseases, including Alzheimer’s disease, primary progressive aphasia, and frontotemporal dementia, particularly in the context of diverse populations and bilingualism.<br><br>Laboratory Mission: The Tee Lab is dedicated to promoting equal representation in cognitive and dementia research, enhancing our understanding of brain aging and neurodegenerative diseases. We collaborate internationally, aiming to understand bilingualism and dementia syndromes and promote language diversity in cognitive research.<br><br>Required Qualifications:<br><br>* Ph.D. in neurology, radiology, neuropsychology, cognitive neuroscience, biomedical engineering, or related fields.<br>* Experience with MRI/PET data analysis.<br>* Proficiency in neuroimaging tools and programming/scripting languages.<br><br>Preferred Qualifications:<br><br>* Experience in connectomics, tractography, functional MRI, bilingualism, and cross-linguistic studies.<br>* Proficiency in Mandarin and/or Cantonese is a plus.<br><br>Application Process: Interested candidates should send a cover letter, CV, and contact information for three references to BoonLead.Tee@ucsf.edu<mailto:BoonLead.Tee@ucsf.edu> and Stephanie.Kwan3@ucsf.edu<mailto:Stephanie.Kwan3@ucsf.edu>.<br><br>For more information about the Tee Lab and our projects, please visit our website: Tee Lab - UCSF<https://teelab.ucsf.edu/><br><br>To view the detailed job posting, please visit: Tee Lab Open Positions - UCSF<https://teelab.ucsf.edu/open-positions><br><br>We look forward to welcoming a new member to our dynamic team!
2024-03-13T23:38:04+00:00Chen Yuhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c0ea8175.2403UCSF Postdoctoral Fellowship Position
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;20fa36d4.2403
Dr. David Perry (https://perrylab.ucsf.edu/) is now inviting applications for a NIH-funded postdoctoral fellowship position in his lab at the University of California, San Francisco (UCSF) Memory and Aging Center. The goal of our lab’s research is to elucidate brain-behavior relationships in neurodegenerative disease in order to improve diagnostic certainty and identify therapeutic targets.<br><br>The UCSF Memory and Aging Center (memory.ucsf.edu<https://memory.ucsf.edu/>) is part of the Department of Neurology and Weill Institute for Neurosciences. It has an extensive research infrastructure, with over 250 full-time research faculty and staff. The postdoctoral fellow will have the opportunity to participate in our innovative, interdisciplinary research environment. We are looking for candidates who have a background in neuroimaging, strong statistical training, and programming experience. The start date is flexible; review of applications is ongoing. Applicants should send a brief cover letter describing interests and relevant prior experience, CV, and contact information for three references to (david.perry@ucsf.edu).<br><br>The postdoctoral fellow will work on our lab's study investigating abnormalities in reward processing in neurodegenerative diseases and mood disorders. Reward processing involves a determination of what an individual will work for or pursue, such as food, money, or social approval. Patients with neurodegenerative and mood disorders have profound changes in their reward valuation. We propose that a greater understanding of reward-seeking behavior in these illnesses and their underlying neural mechanisms will improve diagnostic accuracy and lead to therapeutic targets for behavioral symptoms that currently have no adequate treatment. Our studies of reward processing use behavioral paradigms with tools such as psychophysiology, as well as structural and functional neuroimaging.<br><br>Noah Cryns | Assistant Clinical Research Coordinator<br><br>Memory and Aging Center<br><br>University of California – San Francisco<br><br>Phone: (415) 514-7580<br><br>https://perrylab.ucsf.edu/<br><br>https://decisionlab.ucsf.edu/<br><br>[cid:3f0db6cf-1e1f-4894-8797-f6d565b85484]
2024-03-13T15:51:56+00:00Cryns, Noahhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;20fa36d4.2403Postdoctoral Position on PET and structural MRI/DTI at Brown University
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3c533532.2403
Position: Postdoctoral Position in Neuroimaging Research on Brain<br>Aging and Alzheimer’s disease<br><br>Area: Brain Aging and Alzheimer’s disease<br><br>Department: Department of Psychiatry and Human Behavior<br><br>Institution: Brown University<br><br>We are seeking a highly motivated postdoctoral researcher to lead an<br>NIH-funded project in the Laboratory for Cognitive and Translational<br>Neuroscience (Director: Dr. Hwamee Oh) at Brown University. The<br>project is to use amyloid and tau PET and structural MRI/DTI to study<br>the impact of Alzheimer’s disease pathologies on structural and<br>functional networks among cognitively normal older adults and patients<br>with cognitive impairment.<br><br>Applicants with a Ph.D. degree in cognitive neuroscience,<br>computational neuroscience, biomedical engineering, or a relevant field<br>are encouraged to apply. Prior experience with human neuroimaging in<br>PET and/or structural MRI, and familiarity with programming in relevant<br>languages (e.g., Python, MATLAB) are required. Applicants with<br>experience with amyloid and tau PET or diffusion weighted imaging are<br>especially encouraged to apply. The successful candidate is expected<br>to demonstrate communication skills, motivation and interest in the<br>area of neuroscience of cognitive and brain aging and Alzheimer’s<br>disease, and the ability to independently develop research questions<br>and work in collaboration with other team members.<br><br>A preferred start date is Summer 2024, although it can be flexible. The<br>position is primarily affiliated with the Department of Psychiatry and<br>Human Behavior at Brown University.<br><br>Interested applicants should email a brief description of research<br>background and career goals, and CV with a list of 3 references to Dr.<br>Hwamee Oh at hwamee_oh@brown.edu.<br><br>Contact Website:<br>https://sites.brown.edu/oh-ctnlab/available-positions/<br><br>--<br>Mackenzie Topper<br>Research Assistant & Lab Manager<br>Oh Lab<br>Brown University
2024-03-12T16:13:37-04:00Topper, Mackenziehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3c533532.2403Re: DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;b473c382.2403
Dear Julia,<br><br>On Tue, Mar 12, 2024 at 2:55 PM Júlia Soares <julii.f.soares@gmail.com><br>wrote:<br><br>> 1) Is it only possible to do DCM in MNI space since the prior source<br>> locations should be given in MNI coordinates or is it possible to conduct<br>> DCM analysis in native space for each specific subject ?<br>><br>><br>I think this distinction is too fine to matter for DCM if you are doing it<br>at the sensor level. So I'd just define source locations in MNI space and<br>not worry too much about it.<br><br>> 2) In source reconstruction I inverted a continuous signal, i.e., I did<br>> not separate the signal into epochs (trials). However I have a task which<br>> has 3 conditions in which I intend to study connectivity in each of them.<br>> Is there a way to separate my signal after source reconstruction so I can<br>> include them in the DCM model?<br>><br><br>Both source analysis and DCM were not intended to work on long continuous<br>data segments. I'd suggest you epoch your data into arbitrary 1-2 sec<br>epochs. There is a way to do it in the epoching tool. Then I would do both<br>steps on these epoched data.<br><br>Best,<br><br>Vladimir<br><br>><br>> Thank you in advance.<br>> Regards,<br>> Júlia Soares<br>>
2024-03-12T15:24:47+00:00Vladimir Litvakhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;b473c382.2403DCM after EEG source reconstruction informed by fMRI priors
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8c87c916.2403
Dear SPM experts,<br><br>I performed EEG source reconstruction informed by fMRI priors. I intend to<br>use this signal to do DCM analysis however, after reading the section<br>"Dynamic Causal Modelling for M/EEG" in SPM manual I have a few questions I<br>hope you can clarify:<br><br>1) Is it only possible to do DCM in MNI space since the prior source<br>locations should be given in MNI coordinates or is it possible to conduct<br>DCM analysis in native space for each specific subject ?<br><br>2) In source reconstruction I inverted a continuous signal, i.e., I did not<br>separate the signal into epochs (trials). However I have a task which has 3<br>conditions in which I intend to study connectivity in each of them. Is<br>there a way to separate my signal after source reconstruction so I can<br>include them in the DCM model?<br><br>Thank you in advance.<br>Regards,<br>Júlia Soares
2024-03-12T14:54:31+00:00Júlia Soareshttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8c87c916.2403Re: What is the DCM.U.idx parameter for?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c3fd8a86.2403
Hi Luna<br>This is nothing to worry about. If I remember correctly, I added that field to help with specifying DCMs using the batch editor. Its omission won’t alter your results.<br><br>Best<br>Peter<br><br>From: SPM (Statistical Parametric Mapping) <SPM@JISCMAIL.AC.UK> On Behalf Of Luna Sato<br>Sent: 06 March 2024 03:23<br>To: SPM@JISCMAIL.AC.UK<br>Subject: [SPM] What is the DCM.U.idx parameter for?<br><br>⚠ Caution: External sender<br><br>Hi experts,<br><br>When checking DCM results, I found certain subjects' DCM fields include the parameter DCM.U.idx, while others don't. I suspect this variation might be due to different SPM versions used.<br><br>I'm wondering about the significance of DCM.U.idx. Can I combine subjects with and without this parameter in group analysis? Or should I consider redoing some DCM analyses?<br><br>Best regards,<br>Luna
2024-03-12T08:07:21+00:00Zeidman, Peterhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c3fd8a86.2403Re: Searching over PEB models in DCM
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;41c7e002.2403
Dear Lukas<br>Yes you’ve done this correctly. The A-matrix represents the average connectivity over conditions if you mean-centre your design matrix (in DCM for fMRI, DCM.options.centre=true), otherwise it represents the baseline or intercept. The B-matrix add to this for each experimental condition. So a value of zero in the A-matrix, and a non-zero value in the B-matrix, means that the connectivity was zero on average (or at baseline), but there was a difference between experimental conditions.<br><br>All the best<br>Peter<br><br>From: SPM (Statistical Parametric Mapping) <SPM@JISCMAIL.AC.UK> On Behalf Of Lorentz, Lukas Kay<br>Sent: 06 March 2024 10:06<br>To: SPM@JISCMAIL.AC.UK<br>Subject: [SPM] Searching over PEB models in DCM<br><br>⚠ Caution: External sender<br><br>Dear experts,<br><br>I am currently employing DCM on a task-based dataset and ran into a problem when interpreting the results. As a framework, we are following the two Zeidman et al. papers from 2019 on Parametric Empirical Bayes (https://doi.org/10.1016/j.neuroimage.2019.06.031 and https://doi.org/10.1016/j.neuroimage.2019.06.032).<br><br>For inference regarding our modulatory inputs (B matrix), we conducted an automatic "search over reduced PEB models" as described in section 4.7 in the 2nd Zeidman paper. This yielded significant results for several connections that were very much in line with our hypotheses.<br>However, when we then conducted another automatic search on our A matrix to derive average connectivity parameters (described in section 4.8 in 2nd Zeidman paper), we found that two connections were pruned away, even though our first analysis suggests that these connections would be significantly modulated by specific conditions.<br><br>Can anyone explain to me how to interpret this? Is searching over reduced A matrix models the correct way to estimate average effective connectivity through Bayesian Model Averaging?<br><br>Best regards,<br>Lukas
2024-03-12T08:03:19+00:00Zeidman, Peterhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;41c7e002.2403Postdoc on multimodal integration (TEP-IRM-EEG) and computational modelling : • INSERM U1077 – Brain imaging center, Cyceron, Caen, France
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Duration: Two years (renewable)<br><br>The INSERM-UNICAEN-EPHE U1077 research unit based in Caen (Normandy, France, https://nimh.unicaen.fr) invites applications for a position as a Postdoc in the field of brain aging and time processing.<br><br>The recruited research will join the TIMES research program “Time processing changes with aging”, led by Dr. Thomas Hinault (Ph.D). TIMES aims to understand the cognitive and neural mechanisms of temporal cognition and their evolution with aging.<br><br>The Unit 1077 and Cyceron Neuroimaging Platform offer an exciting and friendly multi-disciplinary research environment, with ample opportunities for training and collaboration, and excellent technical facilities. Cyceron is a structure devoted to multimodal imaging (pre- clinical and clinical) and provides a stimulating work environment as it groups several research units and several research instruments, such as a cyclotron for molecular marking, 2 PET-CT, 2 MRI (including a brand-new GE 3T), and a molecular and cellular imaging department. Caen is a friendly environment with an excellent work-life balance. We are located 12 km away from the Normandy coast and beaches. Caen is a young and vibrant city with many venues for music and culture.<br><br>We are looking for a postdoc with a strong expertise in neuroimaging (EEG, PET, MRI data, at least two of these methods), multiscale brain modelling (knowledge about The Virtual Brain is recommended), and excellent programming skills.<br><br>Review of applications will continue until the position is filled.<br><br>Salary: approximately 2900 euros (gross salary i.e. salary before taxes) per month<br><br>Application (motivation letter + CV + at least one recommendation letter) should be sent to Dr. Thomas Hinault (thomas.hinault@inserm.fr<mailto:thomas.hinault@inserm.fr>).
2024-03-11T19:44:32+00:00Thomas HINAULThttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;e94749f0.2403Re: Normalization quality assessment
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;9aec1fc4.2403
Unfortunately, there is no automated way that I can think of for doing this. Approaches for validating spatial normalisation methods normally involve manually defining regions on images and then assessing how closely these regions can be made to overlap using the estimated warps.<br><br>Perhaps you could identify an appropriate method from the literature. The Klein et al paper might be a good place to start, but remember that the technology has progressed in the last 15 years:<br>Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.C., Christensen, G.E., Collins, D.L., Gee, J., Hellier, P. and Song, J.H., 2009. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage, 46(3), pp.786-802.<br><br>This paper includes some more recent comparisons among methods:<br>Brudfors, Mikael, Yaël Balbastre, Guillaume Flandin, Parashkev Nachev, and John Ashburner. "Flexible Bayesian modelling for nonlinear image registration." In Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III 23, pp. 253-263. Springer International Publishing, 2020.<br><br>Some people use the mean squares difference between aligned images to assess performance. By itself, this is not a good way to do things.<br>Rohlfing, T., 2011. Image similarity and tissue overlaps as surrogates for image registration accuracy: widely used but unreliable. IEEE transactions on medical imaging, 31(2), pp.153-163.<br><br>Best regards,<br>-John
2024-03-11T13:58:46+00:00Ashburner, Johnhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;9aec1fc4.2403Group contrast distorted after mni resampling
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;a33f4dc5.2403
Dear experts,<br><br>I currently have an issue with my 2nd level analyses of fmri data of a brain slab.<br><br>I have run my single-subject/1st level analysis all in native space, which worked fine. We then used ANTs to morph all the contrast files into mni space, and those resampled contrast files were then used to calculate our group contrast.<br><br>Transforming the native contrast files into mni-space worked just fine and the native-space as well as mni-space contrasts all look correct. We did not observe any resampling issues with ANTs there. However, when we the use SPM to calculate the 2nd level/group analyses with the mni-contrasts, the result looks very noisy and not identifiable.<br><br>For demonstration purposes, I have attached an image of a dummy-like group contrast calculated of three contrast files of the same subject in native space (A) and then the group contrast with the same files that were transformed into mni space (B). As you can see, the voxels look much smaller and frizzy.<br><br>It would be great if anyone could give me some advice on how to deal with this issue - or ideally check two example files to identify what changed in those aside from morphing the blobs into mni space.<br><br>Thank you very much for your help and advice in advance!<br><br>Best,<br><br>Lisa<br><br>Lisa Jeschke<br>PhD student<br>lisa.jeschke@tu-dresden.de<br>+49 351 463-43897<br><br>Cognitive and Clinical Neuroscience / Professur für Kognitive und Klinische Neurowissenschaft<br>TUD Dresden University of Technology<br>https://tu-dresden.de/mn/psychologie/ifap/kknw
2024-03-11T13:15:28+00:00Lisa Jeschkehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;a33f4dc5.2403[reminder] Birmingham-Leiden Summer School in Computational Social Cognition 2024
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;679ee9f9.2403
(Apologies for cross-posting)<br><br>FINAL REMINDER:<br><br>We are delighted to welcome applications for the first edition of the<br>Birmingham-Leiden<br>summer school in Computational Social Cognition (CSC).<br><https://www.compsoccog.com/> The summer school is hosted at the University<br>of Birmingham (UK), in collaboration with Leiden University (NL), and will<br>take place from 15th-17th July 2024. Apply by *14th March 2024* (see<br>information below)!<br><br>Attending the Birmingham-Leiden CSC Summer School will equip a diverse<br>cohort of early career researchers (trainees through to junior faculty<br>members) with the ability to understand, program and interpret the output<br>of a range of computational models of social cognition. Attendees will<br>receive different types of training aimed at understanding modelling as<br>well as the theoretical and practical inferences that can be drawn from<br>computational models. For more information about this year’s training<br>program, including criteria and application instructions, please visit our<br>website <https://www.compsoccog.com/>. The deadline is March 14, 2024. We<br>hope to see you this summer in Birmingham!<br><br>Keynote speakers:<br><br>- Cecilia Heyes, University of Oxford (UK)<br><br>- Christian Ruff, University of Zurich (CH)<br><br>- Wolfram Schultz, University of Cambridge (UK)<br><br>Instructors (alphabetical):<br><br>- Matt Apps, University of Birmingham (UK)<br><br>- Jo Cutler, University of Birmingham (UK)<br><br>- Anna Van Duijvenvoorde, Leiden University (NL)<br><br>- Romy Froemer, University of Birmingham (UK)<br><br>- Arkady Konovalov, University of Birmingham (UK)<br><br>- Patricia Lockwood, University of Birmingham (UK)<br><br>- Ili Ma, Leiden University (NL)<br><br>- Lei Zhang, University of Birmingham (UK)<br><br>Anna & Lei, on behalf of the CSC 2024 organization team<br><br>---<br>Dr. Lei Zhang<br>Associate Professor<br>Centre for Human Brain Health, University of Birmingham<br>w: lei-zhang.net<br>t: @lei_zhang_lz <https://twitter.com/lei_zhang_lz>
2024-03-11T12:29:52+00:00Lei Zhanghttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;679ee9f9.2403AW: [EXT] [SPM] Finite Impulse Response and event durations in SPM
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;547a5c14.2403
Dear Batiah, you have to be careful here. FIR is an onset-related model, where I guess only duration=0 makes sense. The length of the modelled response and the granularity is determined by "order" and "length", where the granularity/resolution is length ./ order. Note that the resolution cannot be infinitely high, because you then might end up with empty regressors due to sampling. Usually people define the granularity as a TR i.e. order=10, then length = 10*TR gives you 10 bins after stimulus onset with a resolution of one TR per bin. PS. The units depend on whether you specify everything in seconds or scans (...fmri_spec.timing.units). I hope this helps, Christian -- Prof. Dr. Christian Büchel Institut für Systemische Neurowissenschaften Haus W34, Universitätsklinikum Hamburg-Eppendorf Martinistr. 52, D-20246 Hamburg, Germany Tel.: +49-40-7410-54726 Fax.: +49-40-7410-59955 buechel@uke.de http://www.uke.uni-hamburg.de/institute/systemische-neurowissenschaften/ > -----Ursprüngliche Nachricht----- > Von: SPM (Statistical Parametric Mapping) [mailto:SPM@JISCMAIL.AC.UK] Im > Auftrag von Batiah Keissar > Gesendet: Montag, 11. März 2024 09:12 > An: SPM@JISCMAIL.AC.UK > Betreff: [EXT] [SPM] Finite Impulse Response and event durations in SPM > > Hello SPM experts, > > I am using Finite Impulse Response in an SPM fMRI analysis, and I wanted to > ask if it is possible to do this with a window containing events with durations of > 0 seconds? What would be the implications for my analysis in comparison to > longer durations? > Also, If anyone has recommendations for further learning materials on this > topic I would greatly appreciate it. > > Thank you all kindly, > > Batiah --
2024-03-11T09:36:32+01:00Christian Büchelhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;547a5c14.2403Finite Impulse Response and event durations in SPM
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8bd69116.2403
Hello SPM experts,<br><br>I am using Finite Impulse Response in an SPM fMRI analysis, and I wanted to ask if it is possible to do this with a window containing events with durations of 0 seconds? What would be the implications for my analysis in comparison to longer durations?<br>Also, If anyone has recommendations for further learning materials on this topic I would greatly appreciate it.<br><br>Thank you all kindly,<br><br>Batiah
2024-03-11T08:12:00+00:00Batiah Keissarhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8bd69116.2403Re: ROI analysis: VBM/DBM error
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8e672951.2403
Dear Pavlina,<br><br>the ROI tool checks the filenames of the original (VBM) analysis and should contain one of the following patterns<br>mwp<br>m0wp<br>wp<br><br>These are the patterns usually found in the filenames after segmentation. In your case, I assume that you have renamed or moved the files.<br><br>Best,<br><br>Christian<br><br>On Thu, 7 Mar 2024 14:18:24 +0100, Pavlina Lieskovsky <pavlina.lieskovsky@GMAIL.COM> wrote:<br><br>>Dear all,<br>><br>>I am currently facing an issue while attempting ROI analysis in CAT12.<br>>Specifically, I keep encountering the following error message: "ROI<br>>analysis is only supported for VBM of GM/WM/CSF. No ROI values for DBM will<br>>be estimated." I followed the VBM longitudinal data CAT12 manual during<br>>preprocessing.<br>><br>>I have attached my script and screenshots of my batch. I would greatly<br>>appreciate it if someone could review these and offer insights into why<br>>this error is occurring and if there is some reason this data could be DBM.<br>><br>>Thank you very much for your attention and assistance.<br>><br>>Warm regards,<br>><br>>Pavlina<br>>%-----------------------------------------------------------------------<br>>%%<br>>matlabbatch{1}.spm.tools.cat.long.datalong.timepoints = {<br>>{<br>>..............baseline files<br>>}<br>>{<br>>..............follow up files'<br>>}<br>>}';<br>>%%<br>>matlabbatch{1}.spm.tools.cat.long.longmodel = 2;<br>>matlabbatch{1}.spm.tools.cat.long.enablepriors = 1;<br>>matlabbatch{1}.spm.tools.cat.long.prepavg = 2;<br>>matlabbatch{1}.spm.tools.cat.long.bstr = 0;<br>>matlabbatch{1}.spm.tools.cat.long.avgLASWMHC = 0;<br>>matlabbatch{1}.spm.tools.cat.long.nproc = 4;<br>>matlabbatch{1}.spm.tools.cat.long.opts.tpm = {<br>>'/Users/Downloads/spm12/tpm/TPM.nii'};<br>>matlabbatch{1}.spm.tools.cat.long.opts.affreg = 'mni';<br>>matlabbatch{1}.spm.tools.cat.long.opts.biasacc = 0.5;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.restypes.optimal = [1 0.3];<br>>matlabbatch{1}.spm.tools.cat.long.extopts.setCOM = 1;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.APP<br>><http://spm.tools.cat.long.extopts.app/> = 1070;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.affmod = 0;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.spm_kamap = 0;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.LASstr = 0.5;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.LASmyostr = 0;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.gcutstr = 2;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.WMHC = 2;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.registration.shooting.shootingtpm<br>>= {<br>>'/Users/Downloads/spm12/toolbox/cat12/templates_MNI152NLin2009cAsym/Template_0_GS.nii'<br>>};<br>>matlabbatch{1}.spm.tools.cat.long.extopts.registration.shooting.regstr =<br>>0.5;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.vox = 1.5;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.bb = 12;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.SRP = 22;<br>>matlabbatch{1}.spm.tools.cat.long.extopts.ignoreErrors = 1;<br>>matlabbatch{1}.spm.tools.cat.long.output.BIDS.BIDSno = 1;<br>>matlabbatch{1}.spm.tools.cat.long.output.surface = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.neuromorphometrics = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.lpba40 = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.cobra = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.hammers = 0;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.thalamus = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.thalamic_nuclei = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.suit = 1;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.ibsr = 0;<br>>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.ownatlas = {''};<br>>matlabbatch{1}.spm.tools.cat.long.longTPM = 1;<br>>matlabbatch{1}.spm.tools.cat.long.modulate = 1;<br>>matlabbatch{1}.spm.tools.cat.long.dartel = 0;<br>>matlabbatch{1}.spm.tools.cat.long.printlong = 2;<br>>matlabbatch{1}.spm.tools.cat.long.delete_temp = 1;<br>><br>><br>><br>>[image: image.png]<br>><br>>[image: image.png]<br>>
2024-03-10T19:40:00+00:00Christian Gaserhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;8e672951.2403How to Remove Motor Response Artifacts in fMRI Experiments
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ada07fe6.2403
Hi SPM experts,<br><br>In my fMRI experiment, participants were instructed to perform motor responses during blocks. These responses, however, were not of interest as the task solely aimed at improving concentration levels. So no timestamps were recorded for these motor responses.<br><br>However, in our group analysis, we observed significant effects of these motor responses: all experimental conditions exhibited negative activations in regions of interest. This could potentially be attributed to motor responses between blocks, leading to a higher baseline in the GLM.<br><br>Hence, I’m wondering if there are any noise-reduction techniques available to eliminate such noise from the data?<br><br>Best,
2024-03-10T02:28:47+01:00Luna Satohttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ada07fe6.2403Normalization quality assessment
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;5acb78e3.2403
Hi all,<br><br>For my master's thesis I need to normalize several PDw-MRIs from different subjects to a MNI template. For this I would like to compare the quality between first creating a DARTEL template or normalizing the MRIs directly to the MNI template.<br>Is there an automatic way to assess and quantify the quality of the normalization? Or is there a standardized procedure?<br><br>Thank you very much for your support.<br><br>Stephan
2024-03-09T21:50:28+00:00Stephan Klaus DEHENhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;5acb78e3.2403Re: [EXT] [SPM] Normalization shifting image upwards (axially)
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c51d1ead.2403
Dear Christian,<br>Thank you very much for taking the time to answer my question.<br>After tinkering a bit more I found that running 'normalization (estimate<br>and write)' as opposed to just the write option took care of the problem.<br>Best,<br>Gaël<br><br>On Thu, 7 Mar 2024 at 13:46, Christian Büchel <buechel@uke.de> wrote:<br><br>> Dear Gael,<br>><br>> if the template is "higher" than your images this would be the expected<br>> behavior of spatial normalization. Open a template in the same "checkreg"<br>> to see whether this is the case. In general I would point you to the<br>> excellent PDF in the SPM distribution under /man which explains all these<br>> concepts.<br>><br>> I hope this helps,<br>><br>> Christian<br>> --<br>> Prof. Dr. Christian Büchel<br>> Institut für Systemische Neurowissenschaften Haus W34,<br>> Universitätsklinikum Hamburg-Eppendorf Martinistr. 52, D-20246 Hamburg,<br>> Germany<br>> Tel.: +49-40-7410-54726<br>> Fax.: +49-40-7410-59955<br>> buechel@uke.de<br>> http://www.uke.uni-hamburg.de/institute/systemische-neurowissenschaften/<br>><br>><br>><br>><br>> > -----Ursprüngliche Nachricht-----<br>> > Von: SPM (Statistical Parametric Mapping) [mailto:SPM@JISCMAIL.AC.UK] Im<br>> > Auftrag von Ga ël Cordero Otero<br>> > Gesendet: Donnerstag, 7. März 2024 13:38<br>> > An: SPM@JISCMAIL.AC.UK<br>> > Betreff: [EXT] [SPM] Normalization shifting image upwards (axially)<br>> ><br>> > Dear experts,<br>> ><br>> > During preprocessing, normalization seems to be moving my images upwards<br>> > (axially speaking). To better illustrate what I mean I've attached an<br>> image of a<br>> > realigned & unwarped image (left) and the same image after normalization<br>> > (right). We acquired the volumes with a slice tilt since there is<br>> evidence that<br>> > suggests that this increases the SNR of temporal lobes, that's why there<br>> isn't<br>> > whole brian coverage. I'm using SPM12 on matlab 2021a, if that is of any<br>> help.<br>> ><br>> > Has anyone run into this issue previously? If so, how can it be solved?<br>> > Thank you very much for your time,<br>> > Gaël<br>><br>> --<br>><br>> _____________________________________________________________________<br>><br>> Universitätsklinikum Hamburg-Eppendorf; Körperschaft des öffentlichen<br>> Rechts; Gerichtsstand: Hamburg | www.uke.de<br>> Vorstandsmitglieder: Prof. Dr. Christian Gerloff (Vorsitzender), Joachim<br>> Prölß, Prof. Dr. Blanche Schwappach-Pignataro, Matthias Waldmann (komm.)<br>> _____________________________________________________________________<br>><br>> SAVE PAPER - THINK BEFORE PRINTING<br>><br><br>--<br>Gaël Cordero Otero<br>Department of Basic Sciences<br>Faculty of Medicine and Health Sciences<br>UIC Barcelona<br>Telf. 93 504 20 00 (ext. 5240)
2024-03-08T16:30:08+01:00Gaël Cordero Oterohttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c51d1ead.2403Last chance: Machine Learning and AI for Research in R - livestream seminar
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;f8ff4790.2403
Hi everyone<br><br>Instats is pleased to present an upcoming seminar introducing Machine<br>Learning and AI for Research in R<br><https://instats.org/seminar/machine-learning-and-ai-for-researchers-4>,<br>running March 12 - 13. This seminar is being led by professor Giovanni<br>Cerulli who has extensive experience teaching this material, and will<br>follow along with the core topics in his new book *Fundamentals of<br>Supervised Machine Learning: With Applications in Python, R, and Stata*<br><https://www.amazon.com/Fundamentals-Supervised-Machine-Learning-Applications/dp/3031413369>*.<br>*The seminar provides a comprehensive introduction to Machine Learning and<br>Artificial Intelligence methods for the social, economic, and health<br>sciences using R. After introducing the subject, the seminar will cover the<br>following methods: (i) model selection and regularization (Lasso, Ridge,<br>Elastic-net); (ii) discriminant analysis and nearest-neighbor<br>classification; and (iii) artificial neural networks. The course will offer<br>various instructional examples using real datasets in R and Python. An<br>Instats certificate of completion is provided at the end of the seminar, and<br>2 ECTS equivalent points are offered.<br><br>Register today<br><https://instats.org/seminar/machine-learning-and-ai-for-researchers-4> to<br>secure your spot, and please feel free to tell your colleagues and friends.<br><br>Best wishes and we hope to see you there!<br><br>Michael Zyphur<br>Director<br>Institute for Statistical and Data Science<br><http://goog_711907693>*instats.org* <http://instats.org>
2024-03-08T22:30:00+11:00Michael Zyphurhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;f8ff4790.24031st level global normalization - none vs. scaling
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4b057432.2403
Dear SPM users,<br><br>I have a question regarding the 1st level global normalization option (none<br>vs. scaling) for fMRI analysis.<br>[image: image.png]<br><br>I just have a very broad understanding that it does the mean centering each<br>volume in the time series, but wondering:<br>1) What's the implication of doing it (none vs. scaling)?<br>2) When is it preferable to use scaling, and when is it not recommended?<br>3) Is there a relationship between this choice and the heterogeneity of the<br>sample?<br><br>In my case, I am working with a lifespan sample ranging from ages 18 to 79.<br>I am wondering if it is optimal to use scaling or stick with the default<br>option (none).<br><br>I experimented this with my dataset, and it impacted my results (2nd level)<br>a lot (though the most significant regions seem consistent).<br><br>[image: image.png]<br><br>Any insight would be greatly appreciated.<br>Thank you!<br><br>Best,<br>Kyoungeun
2024-03-07T15:57:59-06:00Kyoungeun Leehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4b057432.2403ROI analysis: VBM/DBM error
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c1e0eb8f.2403
Dear all,<br><br>I am currently facing an issue while attempting ROI analysis in CAT12.<br>Specifically, I keep encountering the following error message: "ROI<br>analysis is only supported for VBM of GM/WM/CSF. No ROI values for DBM will<br>be estimated." I followed the VBM longitudinal data CAT12 manual during<br>preprocessing.<br><br>I have attached my script and screenshots of my batch. I would greatly<br>appreciate it if someone could review these and offer insights into why<br>this error is occurring and if there is some reason this data could be DBM.<br><br>Thank you very much for your attention and assistance.<br><br>Warm regards,<br><br>Pavlina<br>%-----------------------------------------------------------------------<br>%%<br>matlabbatch{1}.spm.tools.cat.long.datalong.timepoints = {<br>{<br>..............baseline files<br>}<br>{<br>..............follow up files'<br>}<br>}';<br>%%<br>matlabbatch{1}.spm.tools.cat.long.longmodel = 2;<br>matlabbatch{1}.spm.tools.cat.long.enablepriors = 1;<br>matlabbatch{1}.spm.tools.cat.long.prepavg = 2;<br>matlabbatch{1}.spm.tools.cat.long.bstr = 0;<br>matlabbatch{1}.spm.tools.cat.long.avgLASWMHC = 0;<br>matlabbatch{1}.spm.tools.cat.long.nproc = 4;<br>matlabbatch{1}.spm.tools.cat.long.opts.tpm = {<br>'/Users/Downloads/spm12/tpm/TPM.nii'};<br>matlabbatch{1}.spm.tools.cat.long.opts.affreg = 'mni';<br>matlabbatch{1}.spm.tools.cat.long.opts.biasacc = 0.5;<br>matlabbatch{1}.spm.tools.cat.long.extopts.restypes.optimal = [1 0.3];<br>matlabbatch{1}.spm.tools.cat.long.extopts.setCOM = 1;<br>matlabbatch{1}.spm.tools.cat.long.extopts.APP<br><http://spm.tools.cat.long.extopts.app/> = 1070;<br>matlabbatch{1}.spm.tools.cat.long.extopts.affmod = 0;<br>matlabbatch{1}.spm.tools.cat.long.extopts.spm_kamap = 0;<br>matlabbatch{1}.spm.tools.cat.long.extopts.LASstr = 0.5;<br>matlabbatch{1}.spm.tools.cat.long.extopts.LASmyostr = 0;<br>matlabbatch{1}.spm.tools.cat.long.extopts.gcutstr = 2;<br>matlabbatch{1}.spm.tools.cat.long.extopts.WMHC = 2;<br>matlabbatch{1}.spm.tools.cat.long.extopts.registration.shooting.shootingtpm<br>= {<br>'/Users/Downloads/spm12/toolbox/cat12/templates_MNI152NLin2009cAsym/Template_0_GS.nii'<br>};<br>matlabbatch{1}.spm.tools.cat.long.extopts.registration.shooting.regstr =<br>0.5;<br>matlabbatch{1}.spm.tools.cat.long.extopts.vox = 1.5;<br>matlabbatch{1}.spm.tools.cat.long.extopts.bb = 12;<br>matlabbatch{1}.spm.tools.cat.long.extopts.SRP = 22;<br>matlabbatch{1}.spm.tools.cat.long.extopts.ignoreErrors = 1;<br>matlabbatch{1}.spm.tools.cat.long.output.BIDS.BIDSno = 1;<br>matlabbatch{1}.spm.tools.cat.long.output.surface = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.neuromorphometrics = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.lpba40 = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.cobra = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.hammers = 0;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.thalamus = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.thalamic_nuclei = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.suit = 1;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.ibsr = 0;<br>matlabbatch{1}.spm.tools.cat.long.ROImenu.atlases.ownatlas = {''};<br>matlabbatch{1}.spm.tools.cat.long.longTPM = 1;<br>matlabbatch{1}.spm.tools.cat.long.modulate = 1;<br>matlabbatch{1}.spm.tools.cat.long.dartel = 0;<br>matlabbatch{1}.spm.tools.cat.long.printlong = 2;<br>matlabbatch{1}.spm.tools.cat.long.delete_temp = 1;<br><br>[image: image.png]<br><br>[image: image.png]
2024-03-07T14:18:24+01:00Pavlina Lieskovskyhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c1e0eb8f.2403AW: [EXT] [SPM] Normalization shifting image upwards (axially)
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;bcc669ad.2403
Dear Gael, if the template is "higher" than your images this would be the expected behavior of spatial normalization. Open a template in the same "checkreg" to see whether this is the case. In general I would point you to the excellent PDF in the SPM distribution under /man which explains all these concepts. I hope this helps, Christian -- Prof. Dr. Christian Büchel Institut für Systemische Neurowissenschaften Haus W34, Universitätsklinikum Hamburg-Eppendorf Martinistr. 52, D-20246 Hamburg, Germany Tel.: +49-40-7410-54726 Fax.: +49-40-7410-59955 buechel@uke.de http://www.uke.uni-hamburg.de/institute/systemische-neurowissenschaften/ > -----Ursprüngliche Nachricht----- > Von: SPM (Statistical Parametric Mapping) [mailto:SPM@JISCMAIL.AC.UK] Im > Auftrag von Ga ël Cordero Otero > Gesendet: Donnerstag, 7. März 2024 13:38 > An: SPM@JISCMAIL.AC.UK > Betreff: [EXT] [SPM] Normalization shifting image upwards (axially) > > Dear experts, > > During preprocessing, normalization seems to be moving my images upwards > (axially speaking). To better illustrate what I mean I've attached an image of a > realigned & unwarped image (left) and the same image after normalization > (right). We acquired the volumes with a slice tilt since there is evidence that > suggests that this increases the SNR of temporal lobes, that's why there isn't > whole brian coverage. I'm using SPM12 on matlab 2021a, if that is of any help. > > Has anyone run into this issue previously? If so, how can it be solved? > Thank you very much for your time, > Gaël --
2024-03-07T13:46:29+01:00Christian Büchelhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;bcc669ad.2403Normalization shifting image upwards (axially)
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;af481129.2403
Dear experts,<br><br>During preprocessing, normalization seems to be moving my images upwards (axially speaking). To better illustrate what I mean I've attached an image of a realigned & unwarped image (left) and the same image after normalization (right). We acquired the volumes with a slice tilt since there is evidence that suggests that this increases the SNR of temporal lobes, that's why there isn't whole brian coverage. I'm using SPM12 on matlab 2021a, if that is of any help.<br><br>Has anyone run into this issue previously? If so, how can it be solved?<br>Thank you very much for your time,<br>Gaël
2024-03-07T12:37:46+00:00Gaël Cordero Oterohttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;af481129.2403Re: SPM registration to MNI152 1 mm
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c0ad7fe8.2403
Thank you very much John!<br>It works!!!<br><br>BW<br>Elena<br><br>Il giorno gio 7 mar 2024 alle ore 09:09 Ashburner, John <<br>j.ashburner@ucl.ac.uk> ha scritto:<br><br>> You can get the voxel sizes and bounding box for an axial image by:<br>><br>> P = spm_select(1,'nifti');<br>> [bb,vx]=spm_get_bbox(P)<br>><br>> Note that for historical reasons, I think it still rounds to the origin to<br>> the closest voxel, so the dimensions may not quite be exactly the same. I<br>> think it should work for one of the MNI average images (in NIfTI format)<br>> though.<br>><br>> Also note that the vx and bb formulation only works for exactly axial<br>> images (imho it is better to specify this using image dimensions and a<br>> voxel-to-world matrix). Again, this should be fine for the MNI data.<br>><br>> Best regards,<br>> -John<br>><br>><br>> ------------------------------<br>> *From:* ELENA GROSSO <elena.grosso01@universitadipavia.it><br>> *Sent:* 06 March 2024 16:23<br>> *To:* Ashburner, John <j.ashburner@ucl.ac.uk><br>> *Cc:* SPM@JISCMAIL.AC.UK <SPM@jiscmail.ac.uk><br>> *Subject:* Re: [SPM] SPM registration to MNI152 1 mm<br>><br>> Thank you John for your fast reply.<br>><br>> Changing the voxel size I don't obtain the same dimensions as in MNI152<br>> (as you can see from the screenshots attached).<br>> In fact, my normalized spm image has dimensions 181x217x181, while MNI152<br>> has dimensions 182x218x182. How could I obtain the same dimensions?<br>> I also copy the batch if can be useful:<br>><br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.biasreg<br>> = 0.0001;<br>><br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.biasfwhm = 60;<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.tpm =<br>> {'spm12/tpm/TPM.nii'};<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.affreg<br>> = 'mni';<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.reg =<br>> [0 0.001 0.5 0.05 0.2];<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.fwhm =<br>> 0;<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.samp =<br>> 3;<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.bb =<br>> [-90 -126 -7 90 90 108];<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.vox =<br>> [1 1 1];<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.interp<br>> = 4;<br>> matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.prefix<br>> = 'bb';<br>><br>> Thanks!!<br>> Elena<br>><br>><br>> Il giorno mer 6 mar 2024 alle ore 16:50 Ashburner, John <<br>> j.ashburner@ucl.ac.uk> ha scritto:<br>><br>> If I understand your question, you want to be able to specify a bounding<br>> box for generating images spatially normalised to 1 mm isotropic resolution.<br>><br>> The bounding box is in units of mm, and specifies coordinates within MNI<br>> space that define the corners of your normalised images. I think you just<br>> need to change the voxel sizes for the normalised images to [1 1 1] instead<br>> of their current default values.<br>><br>> Best regards,<br>> -John<br>><br>> ------------------------------<br>> *From:* SPM (Statistical Parametric Mapping) <SPM@JISCMAIL.AC.UK> on<br>> behalf of ELENA GROSSO <elena.grosso01@UNIVERSITADIPAVIA.IT><br>> *Sent:* 06 March 2024 15:02<br>> *To:* SPM@JISCMAIL.AC.UK <SPM@JISCMAIL.AC.UK><br>> *Subject:* [SPM] SPM registration to MNI152 1 mm<br>><br>><br>> ⚠ Caution: External sender<br>><br>> Hi all,<br>><br>> Have you ever registered maps with 1x1x1 mm3 resolution in MNI space with<br>> SPM?<br>> I can' t find anywhere the bounding box to do it!<br>><br>> Thanks,<br>> Elena<br>><br>>
2024-03-07T10:41:08+01:00ELENA GROSSOhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;c0ad7fe8.2403PhD position available
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;d1ce5181.2403
PhD in psychology/ neuroscience/multi-modal neuroimaging<br><br>Start date: April 2024 or later<br>Duration: 3 years, optional extension by another year<br><br>We offer a PhD position in a project funded by the Swiss National Science Foundation (Schweizerischer Nationalfonds, SNF): „The extended metabolic phenotype of preclinical Huntington’s disease: Whole body PET studies of glucose metabolism“.<br><br>The group of Prof. Michael Orth, MD, PhD (University Hospital for Old Age Psychiatry and Psychotherapy) explores the relationship between brain structure, brain function, glucose metabolism and behaviour in Huntington’s disease, a hereditary neurodegenerative disease. The goal is to better understand what happens just before people carrying the HD mutation develop clinical signs of manifest HD. This can help to better predict the age at onset and improve the timing of disease modifying interventions. The current project is a collaboration with the Department of Nuclear Medicine at Inselspital Bern (head Prof. Axel Rominger), Prof. Jessica Peter at University Hospital for Old Age Psychiatry and Psychotherapy and Prof. Christian Wolf, Department of Psychiatry, Heidelberg University Hospital, Germany. We will examine carriers of the HD mutation who have no clinical signs of HD. They will undergo a whole-body glucose PET, and structural and resting state functional 3T MRI. The main question is whether HD mutation carriers differ from healthy volunteers in dynamic glucose uptake in the brain and/or peripheral tissues like skeletal muscle, and, if so, if there is a relationship between glucose metabolism and structural, or functional, changes in the brain. The project employs state-of-the-art PET and MRI methods, and multi-modal biostatistical methods for data analysis.<br><br>Tasks<br><br>*<br>Recruitment of study participants (healthy volunteers: HD participants are recruited via the HD clinic at the Swiss HD centre by the PI)<br>*<br>Generation of PET and MRI data; data analysis<br>*<br>Publication of results at conferences and in peer-reviewed journals<br><br>You have<br><br>*<br>A master degree in neurosciences, psychology, or a related field<br>*<br>Keen interest in research<br>*<br>Proficiency in German and English<br>*<br>Ability to work independently and self-driven<br>*<br>Knowledge in empirical methods and biostatistics<br>*<br>Previous experience in imaging research (PET or MRI) will be helpful<br><br>We offer<br><br>*<br>Close PhD supervision by the PI and the multidisciplinary team<br>*<br>Opportunities for training in neuroimaging and the analysis of complex multi-modal data<br>*<br>Opportunities for international networking in HD research<br>*<br>Salary according to the guidelines of the Swiss National Science Foundation (Schweizerischer Nationalfonds,SNF)<br><br>Contact and application<br><br>Please apply in writing to michael.orth@unibe.ch<mailto:michael.orth@unibe.ch> (deadline 15 March 2024) and include CV, cover letter, and references (as pdf no larger than 5MB). Inquiries can also be sent to the above email address.<br><br><br>Antworten<br><br>Weiterleiten<br>Teilnehmerbereich geschlossen<br><br>Universitäre Psychiatrische Dienste Bern (UPD) AG<br>Universitätsklinik für Alterspsychiatrie und Psychotherapie<br><br>Prof. Dr. Jessica Peter<br>Leiterin Forschung<br><br>Bolligenstrasse 111, 3000 Bern 60<br>Tel.: +41(0)31 932 89 03<br>Mail: jessica.peter@unibe.ch<mailto:jessica.peter@upd.unibe.ch><br>Webseite: http://www.upd.unibe.ch/research/research_groups/group_peter<http://www.upd.unibe.ch/research/research_groups/group_peter/index_eng.html>
2024-03-07T08:59:08+00:00<>https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;d1ce5181.2403Re: SPM registration to MNI152 1 mm
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;560acbe3.2403
You can get the voxel sizes and bounding box for an axial image by:<br><br>P = spm_select(1,'nifti');<br>[bb,vx]=spm_get_bbox(P)<br><br>Note that for historical reasons, I think it still rounds to the origin to the closest voxel, so the dimensions may not quite be exactly the same. I think it should work for one of the MNI average images (in NIfTI format) though.<br><br>Also note that the vx and bb formulation only works for exactly axial images (imho it is better to specify this using image dimensions and a voxel-to-world matrix). Again, this should be fine for the MNI data.<br><br>Best regards,<br>-John
2024-03-07T08:08:58+00:00Ashburner, Johnhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;560acbe3.2403Re: How to identify subjects with unacceptable motion?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;d3520415.2403
Hey Olivia,<br><br>there are multiple ways to check for movement.<br>The easiest way would be to visually inspect the movement plots that SPM<br>generates based on the text file. (If there was no visual output and SPM<br>also did not save any postscript files, please see the MATLAB Code at<br>the bottom).<br>There you would look for spikes, so movement between two images that<br>exceeds half a voxel size (at least that is the recommondation of<br>Poldrack et al. in their book on MRI data analysis) or you can look at<br>the total movment of the participant across the experiment or the runs.<br>We usually consider movement as too much, if they moved more that one<br>voxel across the entire experiment, which is a rather conservative<br>measure, since SPM's movement correction is fairly good correcting<br>slower movement drifts.<br>Of course you can also just read the text-file with a program of your<br>choice and calculate the maximal movement and spikes.<br><br>Beyond this inspection there is also the concept of Framewise<br>Displacement that was introduced in Power et al. (2012, Neuroimage,<br>https://doi.org/10.1016%2Fj.neuroimage.2011.10.018) which commbines the<br>6 columns into one measure to identify frames with too much movement.<br><br>But please consider, that throwing away an entire subject just because<br>there is a spike is usually not necessary, you can still exclude<br>individual images that might be "contaminated".<br><br>Good luck and best wishes,<br>Falko<br><br>%% MATLAB CODE FOR REPRODUCING PLOTS<br><br> rp_move = readmatrix(textFileName);<br><br> pics = 1:size(rp_move,1);<br><br> figure;<br> h(1) = subplot(2,1,1); % upper plot<br> plot(pics, rp_move(:,1), 'Color', [0,0,1], 'DisplayName', 'x');<br>hold on;<br> plot(pics, rp_move(:,2), 'Color', [0,1,0], 'DisplayName', 'y');<br> plot(pics, rp_move(:,3), 'Color', [1,0,0], 'DisplayName', 'z');<br>hold off;<br><br> xlabel('Scans');<br> ylabel('Translation in mm');<br> legend(gca,'show');<br><br> h(2) = subplot(2,1,2); % lower plot<br> plot(pics, rp_move(:,4)*(180/pi), 'Color', [0,0,1], 'DisplayName',<br>'pitch'); hold on;<br> plot(pics, rp_move(:,5)*(180/pi), 'Color', [0,1,0], 'DisplayName',<br>'roll');<br> plot(pics, rp_move(:,6)*(180/pi), 'Color', [1,0,0], 'DisplayName',<br>'yaw'); hold off;<br><br> xlabel('Scans');<br> ylabel('Rotation in deg');<br> legend('show');<br><br> linkaxes(h,'x'); % link the axes in x direction (just for convenience)<br><br> saveas(gcf, 'movement_plot.png')<br><br>%%%%%%<br><br>Am 07.03.2024 um 04:05 schrieb Olivia Yang:<br>> Hi all,<br>><br>> After realignment processing in SPM, we obtain a file named rp_*.txt<br>> containing a matrix with six columns.<br>><br>> How can we determine if a subject’s head motion is unacceptable using<br>> this matrix?<br>><br>> Thank you for your help.<br>><br>> I wish u good.<br>> Olivia
2024-03-07T08:40:33+01:00Falko Mecklenbrauckhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;d3520415.2403How to identify subjects with unacceptable motion?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;7e7f38b5.2403
Hi all, After realignment processing in SPM, we obtain a file named rp_*.txt containing a matrix with six columns. How can we determine if a subject’s head motion is unacceptable using this matrix? Thank you for your help. I wish u good. Olivia
2024-03-07T03:05:44+00:00Olivia Yanghttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;7e7f38b5.2403Re: SPM registration to MNI152 1 mm
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4c49634f.2403
Thank you John for your fast reply.<br><br>Changing the voxel size I don't obtain the same dimensions as in MNI152 (as<br>you can see from the screenshots attached).<br>In fact, my normalized spm image has dimensions 181x217x181, while MNI152<br>has dimensions 182x218x182. How could I obtain the same dimensions?<br>I also copy the batch if can be useful:<br><br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.biasreg<br>= 0.0001;<br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.biasfwhm<br>= 60;<br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.tpm =<br>{'spm12/tpm/TPM.nii'};<br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.affreg =<br>'mni';<br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.reg = [0<br>0.001 0.5 0.05 0.2];<br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.fwhm = 0;<br>matlabbatch{1}.spm.spatial.normalise.estwrite.eoptions.samp = 3;<br>matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.bb =<br>[-90 -126 -7 90 90 108];<br>matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.vox = [1<br>1 1];<br>matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.interp =<br>4;<br>matlabbatch{1}.spm.spatial.normalise.estwrite.woptions.prefix =<br>'bb';<br><br>Thanks!!<br>Elena<br><br>Il giorno mer 6 mar 2024 alle ore 16:50 Ashburner, John <<br>j.ashburner@ucl.ac.uk> ha scritto:<br><br>> If I understand your question, you want to be able to specify a bounding<br>> box for generating images spatially normalised to 1 mm isotropic resolution.<br>><br>> The bounding box is in units of mm, and specifies coordinates within MNI<br>> space that define the corners of your normalised images. I think you just<br>> need to change the voxel sizes for the normalised images to [1 1 1] instead<br>> of their current default values.<br>><br>> Best regards,<br>> -John<br>><br>> ------------------------------<br>> *From:* SPM (Statistical Parametric Mapping) <SPM@JISCMAIL.AC.UK> on<br>> behalf of ELENA GROSSO <elena.grosso01@UNIVERSITADIPAVIA.IT><br>> *Sent:* 06 March 2024 15:02<br>> *To:* SPM@JISCMAIL.AC.UK <SPM@JISCMAIL.AC.UK><br>> *Subject:* [SPM] SPM registration to MNI152 1 mm<br>><br>><br>> ⚠ Caution: External sender<br>><br>> Hi all,<br>><br>> Have you ever registered maps with 1x1x1 mm3 resolution in MNI space with<br>> SPM?<br>> I can' t find anywhere the bounding box to do it!<br>><br>> Thanks,<br>> Elena<br>>
2024-03-06T17:23:39+01:00ELENA GROSSOhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4c49634f.2403Re: SPM registration to MNI152 1 mm
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;bf750aca.2403
If I understand your question, you want to be able to specify a bounding box for generating images spatially normalised to 1 mm isotropic resolution.<br><br>The bounding box is in units of mm, and specifies coordinates within MNI space that define the corners of your normalised images. I think you just need to change the voxel sizes for the normalised images to [1 1 1] instead of their current default values.<br><br>Best regards,<br>-John
2024-03-06T15:50:17+00:00Ashburner, Johnhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;bf750aca.2403SPM registration to MNI152 1 mm
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;bc9a60c0.2403
Hi all,<br><br>Have you ever registered maps with 1x1x1 mm3 resolution in MNI space with<br>SPM?<br>I can' t find anywhere the bounding box to do it!<br><br>Thanks,<br>Elena
2024-03-06T16:02:59+01:00ELENA GROSSOhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;bc9a60c0.2403Re: A fundamental question about spm's high pass filtering
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;fcb65dfc.2403
Dear Mayank<br><br>I assume that y1 in your simulation is a BOLD signal of interest, and y2 is some form nuisance e.g. drift you want to filter away. If this is the case your simulation would apply to a situation where the SNR is around 1/50 (+white noise) which luckily is not quite the situation we typically are dealing with in fMRI. I have extended your simulation to SNR around 1 and SNR around 1/10. As you can see from the figure (run the attached code if it is removed due to its size) the filter performs pretty consistently for the SNR range from 0.1 to 10, but you do get some ringing if your SNR is around 0.02. In that scenario I am afraid you will have other problems :-) The ringing can be reduced if the length of the signal is extended with a factor of 100 - but that would be equally irrelevant in practice.<br><br>I hope this helps<br><br>Best<br>Torben<br><br>%%<br>L = 1024; %<length of signal<br>filter_100s = ... %< filter with hpf_cutoff = 100s<br>spm_filter( struct('RT', 1, 'HParam', 100, 'row', 1:L) );<br><br>y1 = sin(2*pi*[1:L]/50 )'; %< sinusoid with period = 50s, shouldn't be filtered<br>y2 = sin(2*pi*[1:L]/350)'; %< sinusoid with period = 350s, should be filtered<br><br>y_filter_test1 = spm_filter(filter_100s, y1+50*y2); %< 'a' = 50<br>y_filter_test2 = spm_filter(filter_100s, y1+.1*y2); %< 'a'=0.1<br><br>y_filter_test3 = spm_filter(filter_100s, y1+10*y2); %< 'a' = 10<br>y_filter_test4 = spm_filter(filter_100s, y1+1*y2); %< 'a'=1<br><br>figure;<br>subplot(2,1,1); plot([y1+50*y2 y1+10*y2 y1+1*y2 y1+.1*y2]),<br>l=legend({'SNR=0.02' 'SNR=0.1' 'SNR=1' 'SNR=10' }),xlim([1 L])<br>l.Location='NorthEastOutside';<br>title('Unfiltered signals')<br>xlabel('Time [s]')<br>subplot(2,1,2);<br>plot([y_filter_test1 y_filter_test3 y_filter_test4 y_filter_test2])<br>title('Highpass filtered T=100s')<br>xlabel('Time [s]')<br>ylim([-5 5]),xlim([1 L])<br>l=legend({'SNR=0.02' 'SNR=0.1' 'SNR=1' 'SNR=10' }),xlim([1 L])<br>l.Location='NorthEastOutside';<br><br>> Den 25. feb. 2024 kl. 21.49 skrev Mayank Jog <mayankjog@GMAIL.COM>:<br>><br>> Hello experts!<br>> I was trying to understand an oddity I observed with high-pass filtering in spm.<br>><br>> Basically, I constructed a signal = y1+ a*y2;<br>> y1 = sinusoid whose freq > hpf_cutoff, ie. it shouldn't be filtered out<br>> y2 = sinusoid whose freq < hpf_cutoff, ie. it should be filtered out.<br>><br>> The issue I'm having is that the filter gives different results based on "a" above (MATLAB code @ end of this email). Thinking from a brick wall** -type filtering POV, this shouldn't happen... the result of filtering "signal" above should be y1 everytime, independent of "a".<br>><br>> 1. Reading the documentation, I realized that SPM implements high-pass filtering using DCT.... why do we prefer filtering fMRI data with a DCT filter, since as the above case shows, a brick wall filter seems to be more accurate?<br>> 2. Thinking of y2 as "noise", it's almost as if the output is dependent on the scale of noise (captured by the scaling factor "a" above). Is this the right way to think about it/ Am I missing something here?<br>><br>> Thank you!<br>> Mayank<br>><br>><br>> **By brick wall, I mean doing an fft, and nulling all frequencies above hpf_cutoff, followed by an inverse fft.<br>><br>> MATLAB Code: ===================<br>> L = 1024; %<length of signal<br>> filter_100s = ... %< filter with hpf_cutoff = 100s<br>> spm_filter( struct('RT', 1, 'HParam', 100, 'row', 1:L) );<br>><br>><br>> y1 = sin(2*pi*[1:L]/50 )'; %< sinusoid with period = 50s, shouldn't be filtered<br>> y2 = sin(2*pi*[ylim1:L]/350)'; %< sinusoid with period = 350s, should be filtered<br>><br>><br>> y_filter_test1 = spm_filter(filter_100s, y1+50*y2); %< 'a' = 50<br>> y_filter_test2 = spm_filter(filter_100s, y1+.1*y2); %< 'a'=0.1<br>><br>> figure; subplot(3,1,1); plot([y1 y2]);<br>> subplot(3,1,2); plot(y_filter_test1);<br>> subplot(3,1,3); plot(y_filter_test2);<br>> %============================
2024-03-06T12:50:57+01:00Torben Lundhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;fcb65dfc.2403Searching over PEB models in DCM
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;162e1215.2403
Dear experts,<br><br>I am currently employing DCM on a task-based dataset and ran into a problem when interpreting the results. As a framework, we are following the two Zeidman et al. papers from 2019 on Parametric Empirical Bayes (https://doi.org/10.1016/j.neuroimage.2019.06.031 and https://doi.org/10.1016/j.neuroimage.2019.06.032).<br><br>For inference regarding our modulatory inputs (B matrix), we conducted an automatic "search over reduced PEB models" as described in section 4.7 in the 2nd Zeidman paper. This yielded significant results for several connections that were very much in line with our hypotheses.<br>However, when we then conducted another automatic search on our A matrix to derive average connectivity parameters (described in section 4.8 in 2nd Zeidman paper), we found that two connections were pruned away, even though our first analysis suggests that these connections would be significantly modulated by specific conditions.<br><br>Can anyone explain to me how to interpret this? Is searching over reduced A matrix models the correct way to estimate average effective connectivity through Bayesian Model Averaging?<br><br>Best regards,<br>Lukas
2024-03-06T10:05:48+00:00Lorentz, Lukas Kayhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;162e1215.2403What is the DCM.U.idx parameter for?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;44923acd.2403
Hi experts,<br><br>When checking DCM results, I found certain subjects' DCM fields include the parameter DCM.U.idx, while others don't. I suspect this variation might be due to different SPM versions used.<br><br>I'm wondering about the significance of DCM.U.idx. Can I combine subjects with and without this parameter in group analysis? Or should I consider redoing some DCM analyses?<br><br>Best regards,<br>Luna
2024-03-06T04:23:09+01:00Luna Satohttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;44923acd.2403Re: A fundamental question about spm's high pass filtering
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;b08bd69d.2403
https://en.wikipedia.org/wiki/Ringing_artifacts#Introduction<br><br>Brickwall filtering in the frequency domain can introduce ringing in<br>the time domain.<br><br>On Mon, Mar 4, 2024 at 5:47 PM Mayank Jog <mayankjog@gmail.com> wrote:<br>><br>> Dear experts,<br>> Just following up on my query regarding the spm implementation of high pass filtering, in case anyone had insights,<br>> Thank you,<br>> Mayank<br>><br>> On Sun, Feb 25, 2024 at 12:49 PM Mayank Jog <mayankjog@gmail.com> wrote:<br>>><br>>> Hello experts!<br>>> I was trying to understand an oddity I observed with high-pass filtering in spm.<br>>><br>>> Basically, I constructed a signal = y1+ a*y2;<br>>> y1 = sinusoid whose freq > hpf_cutoff, ie. it shouldn't be filtered out<br>>> y2 = sinusoid whose freq < hpf_cutoff, ie. it should be filtered out.<br>>><br>>> The issue I'm having is that the filter gives different results based on "a" above (MATLAB code @ end of this email). Thinking from a brick wall** -type filtering POV, this shouldn't happen... the result of filtering "signal" above should be y1 everytime, independent of "a".<br>>><br>>> 1. Reading the documentation, I realized that SPM implements high-pass filtering using DCT.... why do we prefer filtering fMRI data with a DCT filter, since as the above case shows, a brick wall filter seems to be more accurate?<br>>> 2. Thinking of y2 as "noise", it's almost as if the output is dependent on the scale of noise (captured by the scaling factor "a" above). Is this the right way to think about it/ Am I missing something here?<br>>><br>>> Thank you!<br>>> Mayank<br>>><br>>><br>>> **By brick wall, I mean doing an fft, and nulling all frequencies above hpf_cutoff, followed by an inverse fft.<br>>><br>>> MATLAB Code: ===================<br>>> L = 1024; %<length of signal<br>>> filter_100s = ... %< filter with hpf_cutoff = 100s<br>>> spm_filter( struct('RT', 1, 'HParam', 100, 'row', 1:L) );<br>>><br>>><br>>> y1 = sin(2*pi*[1:L]/50 )'; %< sinusoid with period = 50s, shouldn't be filtered<br>>> y2 = sin(2*pi*[ylim1:L]/350)'; %< sinusoid with period = 350s, should be filtered<br>>><br>>><br>>> y_filter_test1 = spm_filter(filter_100s, y1+50*y2); %< 'a' = 50<br>>> y_filter_test2 = spm_filter(filter_100s, y1+.1*y2); %< 'a'=0.1<br>>><br>>> figure; subplot(3,1,1); plot([y1 y2]);<br>>> subplot(3,1,2); plot(y_filter_test1);<br>>> subplot(3,1,3); plot(y_filter_test2);<br>>> %============================
2024-03-05T10:07:52-08:00Dennis Thompsonhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;b08bd69d.2403Re: How can I convert the SPM12 to an exe file?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;936013a3.2403
Dear Sanaz,<br><br>the SPM batch system and the various extension mechanisms (toolboxes, spm_orthviews, fieldtrip...) require more knowledge than just compiling spm.m as a target function. SPM comes with a MATLAB .m-file spm_make_standalone.m that will take care of the necessary steps to compile SPM from within MATLAB. You should give it a try on your SPM installation. If you want to customize your standalone SPM (by e.g. adding extra toolboxes etc) you may need to make some changes to the code.<br><br>Hope this helps<br>Volkmar
2024-03-05T14:10:11+00:00Volkmar Glauchehttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;936013a3.24032 PhD positions in layer-fMRI of high-level cognition at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;556316f2.2403
The newly established Cognitive Neuroscience & Neurotechnology group led by Dr. Romy Lorenz is looking for two enthusiastic PhD students (m/f/d) to join our growing team at the Max Planck Institute for Biological Cybernetics in Tübingen, Germany.<br><br>Our lab focuses on advancing our understanding of the frontoparietal brain network mechanisms that underpin high-level cognition and adaptive behaviour. For this, we pursue an interdisciplinary research programme that allows studying this brain system at multiple levels of granularity. Our methodology involves subject-specific brain-computer interface technology, fMRI at 3T and ultrahigh (i.e., 7T and 9.4T) magnetic field strengths (for resolving cortical layers), EEG, non-invasive brain stimulation as well as machine learning. You can find out more about our work at: https://www.kyb.tuebingen.mpg.de/711763/cognitive-neuroscience-neurotechnology<br><br>We are seeking two ambitious PhD students who will work on the exciting field of ultrahigh resolution fMRI that allows to investigate the human cortex at the scale of layers and columns.<br><br>The ideal candidates should have a master’s degree in cognitive (neuro)science, psychology, computer science, biomedical or electrical engineering, physics, or related disciplines. A strong background in fMRI data analysis (e.g., FSL, Freesurfer, ANTS) and very good programming skills in Bash on Linux, Matlab and/or Python are required. Prior experience in MRI data acquisition and experience with ultrahigh resolution fMRI (e.g., at 7T) is desirable but not necessary. Equally, experience with machine learning-methods, code sharing platforms (e.g. GitHub) and high-performance computing clusters are highly desirable.<br><br>The Max Planck Institute for Biological Cybernetics offers a world-leading research environment with access to the latest cutting-edge MRI hardware (including a Siemens 9.4T and Prisma 3T for humans as well as a 14.2T small animal system) and other excellent research facilities (EEG, eye-tracking, fMRI-TMS). The PhD student will receive generous support for professional travel and research needs (~2500€/year). Additionally, the student will have the opportunity to become part of the Graduate Training Centre of Neuroscience that provides training courses, summer schools and conferences to further educate doctoral students. Further, the Institute is part of the TübingenNeuroCampus (with more than 100 active groups), offering a vibrant community of international researchers and enriching environment of collaboration.<br><br>The position is available from May 2024 on and remains open until filled. The salary is paid in accordance with the collective agreement for the public sector (65% TVL-E13, amounting to ~2000€ net per month).<br><br>For more details about the two advertised PhD positions and how to apply, please see: https://www.kyb.tuebingen.mpg.de/729399/join-the-lab<br><br>Dr. Romy Lorenz<br><br>Max Planck Research Group Leader<br><br>Research Group Cognitive Neuroscience & Neurotechnology<br><br>Max Planck Institute for Biological Cybernetics<br><br>Tübingen, Germany<br><br>romy.lorenz@tuebingen.mpg.de<br><br>www.kyb.tuebingen.mpg.de/711763/cognitive-neuroscience-neurotechnology
2024-03-05T15:07:42+01:00Romy Lorenzhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;556316f2.2403Re: How can I convert the SPM12 to an exe file?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;b615e45.2403
Dear experts,<br><br>Based on page 643 of EANM guideline for brain PET imaging (Guedj, Eur JNucl Med Mol Imaging, 2022, 49:632-651), “since version 8, SPM is available asa stand-alone tool”. However, I have not found the executable file of SPM 12but the m files. Please kindly let me know if the exe file is available and themethod to obtain it.<br><br>Best regards,<br><br>Sanaz Hariri<br><br>On Saturday, March 2, 2024 at 03:40:28 PM GMT+3:30, Sanaz Hariri <shanraiz@yahoo.com> wrote:<br><br>Dear experts,<br><br>I want to change the SPM12 to a standalone executable file. I used thedeploytool of MATLAB and added all files and directories of SPM folder to itwhile the main file was spm_Menu.m. However, running the built exe file resultedin an error message: “Can’t obtain SPM Revision information. Error in = >spm_Menu.m at line 25”.<br><br>In addition, I checked the built exe file when spm.m was selected as themain file. Again, an error message was appeared as “Can’t obtain SPM Revisioninformation. Error in = > spm.m at line 299”. In this line the spm_Welcome functionis called which had been added to the list of accompanying files, before.<br><br>Any help will be appreciated.<br><br>Best regards,<br><br>Sanaz
2024-03-05T08:29:06+00:00Sanaz Haririhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;b615e45.2403PhD position on concurrent TMS-fMRI and accelerated rTMS treatment in MDD (Mainz, Germany)
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;71d4b900.2403
Dear colleagues,<br><br>We have an open PhD position on concurrent TMS-fMRI and accelerated rTMS<br>treatment in MDD patients as a joined venture of the Neuroimaging Center<br>(Til Ole Bergmann) and the Department of Psychiatry (Florian<br>Müller-Dahlhaus) at the University Medical Center Mainz, Germany.<br><br>German language proficiency is required for work with patients.<br><br>For more details see job advertisement: https://t.co/SgmNEH746W<br><br>Please forward this email to potentially interested candidates!<br><br>Thank you and best wishes,<br>Til
2024-03-05T09:18:33+01:00Til Ole Bergmannhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;71d4b900.2403Re: A fundamental question about spm's high pass filtering
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3b795cc1.2403
Dear experts,<br>Just following up on my query regarding the spm implementation of high pass<br>filtering, in case anyone had insights,<br>Thank you,<br>Mayank<br><br>On Sun, Feb 25, 2024 at 12:49 PM Mayank Jog <mayankjog@gmail.com> wrote:<br><br>> Hello experts!<br>> I was trying to understand an oddity I observed with high-pass filtering<br>> in spm.<br>><br>> Basically, I constructed a signal = y1+ a*y2;<br>> y1 = sinusoid whose freq > hpf_cutoff, ie. it shouldn't be filtered out<br>> y2 = sinusoid whose freq < hpf_cutoff, ie. it should be filtered out.<br>><br>> The issue I'm having is that the filter gives different results based on<br>> "a" above (MATLAB code @ end of this email). Thinking from a brick wall**<br>> -type filtering POV, this shouldn't happen... the result of filtering<br>> "signal" above should be y1 everytime, independent of "a".<br>><br>> 1. Reading the documentation, I realized that SPM implements high-pass<br>> filtering using DCT.... why do we prefer filtering fMRI data with a DCT<br>> filter, since as the above case shows, a brick wall filter seems to be more<br>> accurate?<br>> 2. Thinking of y2 as "noise", it's almost as if the output is dependent on<br>> the scale of noise (captured by the scaling factor "a" above). Is this the<br>> right way to think about it/ Am I missing something here?<br>><br>> Thank you!<br>> Mayank<br>><br>><br>> **By brick wall, I mean doing an fft, and nulling all frequencies above<br>> hpf_cutoff, followed by an inverse fft.<br>><br>> MATLAB Code: ===================<br>> L = 1024; %<length of signal<br>> filter_100s = ... %< filter with hpf_cutoff = 100s<br>> spm_filter( struct('RT', 1, 'HParam', 100, 'row',<br>> 1:L) );<br>><br>><br>> y1 = sin(2*pi*[1:L]/50 )'; %< sinusoid with period = 50s,<br>> shouldn't be filtered<br>> y2 = sin(2*pi*[ylim1:L]/350)'; %< sinusoid with period = 350s, should be<br>> filtered<br>><br>><br>> y_filter_test1 = spm_filter(filter_100s, y1+50*y2); %< 'a' = 50<br>> y_filter_test2 = spm_filter(filter_100s, y1+.1*y2); %< 'a'=0.1<br>><br>> figure; subplot(3,1,1); plot([y1 y2]);<br>> subplot(3,1,2); plot(y_filter_test1);<br>> subplot(3,1,3); plot(y_filter_test2);<br>> %============================<br>>
2024-03-04T17:47:02-08:00Mayank Joghttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;3b795cc1.2403Porto EEG/ERP Summer School 2024 - 08-12 July
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4bbc483d.2403
Dear colleagues,<br><br>The Laboratory of Neuropsychophysiology (Faculty of Psychology and Education Sciences of the University of Porto) is pleased to announce the 11th Cognitive and Affective Neurophysiology Summer School from 08-12 July 2024 (35h course).<br><br>This summer school is focused on the application of Electroencephalography (EEG) and Event Related Potential (ERP) techniques to the study of cognitive and affective processes. The course is designed as an introduction to these techniques and does not require prior knowledge or experience with EEG/ERP. However, participants with some degree of experience also considered the course helpful. Basic knowledge of statistics is expected (general understanding of statistical tests, correlation, ANOVA, regression).<br><br>This year's program will include:<br>1) Introduction to the EEG/ERP techniques in Cognitive and Affective Neuroscience<br>2) Collecting EEG data and ERP Research Design<br>3) Electrophysiological signal processing<br>4) Statistical analysis of ERP data<br><br>This course has a 360€ fee. You can find more details and registration on the course website: https://sites.google.com/view/can-summerschool-porto<br><br>If you have any additional questions, please do not hesitate to contact us at labnpf@fpce.up.pt<mailto:labnpf@fpce.up.pt>.<br><br>Apologies for cross-postings.<br><br>Fernando Ferreira-Santos<br>Professor Auxiliar | Assistant Professor<br>Coordenador Lab. Neuropsicofisiologia | Coordinator Lab. Neuropsychophysiology<br>Diretor do Mestrado em Psicologia | Director of the Master’s Degree in Psychology<br><br>Laboratório de Neuropsicofisiologia | Laboratory of Neuropsychophysiology<br>Faculdade de Psicologia e de Ciências da Educação da Universidade do Porto<br>Faculty of Psychology and Education Sciences – University of Porto<br>Rua Alfredo Allen, 4200–135 Porto, Portugal<br>https://www.fpce.up.pt<https://www.fpce.up.pt/> | frsantos@fpce.up.pt<mailto:frsantos@fpce.up.pt> | (+351) 22 607 97 00 (ext. 409)<br><br>Lab: https://www.fpce.up.pt/labpsi<br>Personal: http://ferreira-santos.eu<http://ferreira-santos.eu/>
2024-03-04T15:19:25+00:00Fernando Ferreira Santoshttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;4bbc483d.2403How can I convert the SPM12 to an exe file?
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ee8eb608.2403
Dear experts,<br><br>I want to change the SPM12 to a standalone executable file. I used thedeploytool of MATLAB and added all files and directories of SPM folder to itwhile the main file was spm_Menu.m. However, running the built exe file resultedin an error message: “Can’t obtain SPM Revision information. Error in = >spm_Menu.m at line 25”.<br><br>In addition, I checked the built exe file when spm.m was selected as themain file. Again, an error message was appeared as “Can’t obtain SPM Revisioninformation. Error in = > spm.m at line 299”. In this line the spm_Welcome functionis called which had been added to the list of accompanying files, before.<br><br>Any help will be appreciated.<br><br>Best regards,<br><br>Sanaz
2024-03-02T12:10:28+00:00Sanaz Haririhttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;ee8eb608.2403Share widely
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;817452a0.2403
Dear DIPY community,<br><br>We created this video <https://www.youtube.com/watch?v=q9paLRCPgCA> to<br>showcase our upcoming workshop. 🚀 Join us for a thrilling journey at the<br>upcoming DIPY workshop in just 10 days 🧠✨!<br><br>If you like the video please like, share and subscribe.<br><br>Register today at https://workshop.dipy.org<br><br>Thank you,<br><br>Mansi Ranka<br><br>@dipymri<br><br>p.s.<br><br>https://twitter.com/dipymri/status/1763726195902869647<br><br>https://www.linkedin.com/feed/update/urn:li:activity:7169488982668525568
2024-03-01T20:01:02-05:00Mansi Rankahttps://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;817452a0.24035-year postdoc position in forensic psychiatry at University Duisburg-Essen (DE)
https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;e54c1165.2403
I am posting on behalf of Johannes Fuß, Director of the Institute of<br>Forensic Psychiatry and Sex Research at the University Duisburg-Essen.<br>Please direct all queries to him.<br><br>The Institute of Forensic Psychiatry and Sex Research at the University<br>Duisburg-Essen is looking for a dynamic individual in the fields of<br>Neuroscience, Psychology or Medicine (or a related discipline) to lead a<br>prospective study in forensic psychiatry. We want to compare how the<br>placement of offenders with mental disorders in forensic facilities<br>versus prison affects their mental health, brain and behaviour. The<br>study will involve experimental behavioural research methods, diagnostic<br>interviews, and imaging techniques, and is a follow-up to a DFG-funded<br>project.<br><br>Preferred qualifications include a PhD and publication experience in<br>international peer-reviewed journals. Working knowledge of German will<br>be advantageous as the role will involve supervising PhD students at the<br>Institute and coordinating with the Ministries of Justice as well as<br>visits to prisons and forensic clinics. The role will also involve<br>securing external funding, developing new research questions, and the<br>scientific preparation and publication of research results. Strong<br>communication and statistical skills are essential, as well as excellent<br>methodological expertise in at least one area (MRI studies and/or<br>behavioural experiments and/or statistical methods). The position offers<br>opportunities for interdisciplinary collaboration in an international<br>team characterised by flat hierarchies.<br><br>The Institute is located in the city of Essen (West Germany, near<br>Cologne). The position is to be filled immediately, for an initial<br>period of 5 years, on a full-time or part-time basis, and will be<br>remunerated according to remuneration group 14 (TvÖD).<br><br>Further information and access to the application portal can be found<br>at:<br>https://jobs.lvr.de/index.php?ac=jobad&code=%2B877KW8tyjSDGH%2FkbOdCcx27jMDTxJth5BzZKhMJoNniwUfx%2BNQh62OEgE%2FFtDYNjF8hx1ZdV484LxfTDlEqzeGZ1np3J8S3<br><br>Unfortunately the portal is in German but Johannes<br>(Johannes.Fuss@lvr.de) is more than happy to answer all your questions<br>and help with the portal.
2024-03-01T18:13:43+01:00Gina Joue (NINHH)https://www.jiscmail.ac.uk/cgi-bin/wa-jisc.exe?A2=SPM;e54c1165.2403