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| TEAM HOME |
| Thematic : Neurosciences |
| Team name: : | |
| Institute for Memory and Alzheimer’s Disease | |
| Team Home Manager | Supervisor |
| Harald Hampel | HAMPEL Harald(PU) |
| [log in to unmask] | |
| Title of the research unit: : |
| Institut du Cerveau et de la Moelle Épinière (ICM), INSERM |
| Name of Director : Alexis Brice |
| Other financing is envisaged? If so, to which organization : AXA Research Fu |
| Number of PhD (s) currently directed by the supervisor : 0 |
| Number of PhD (s) currently on the team : 0 |
| PROPOSED TOPIC | |
| Title : | |
| Structural, Functional and Effective Connectivity of AD Related Neural Networks | |
| Project : | |
| Applications are invited for a fully funded PhD position (3 years) at the Pierre and Marie Curie University (Université Pierre et Marie Curie (Paris 6), UPMC, Paris, France), at the Doctoral School of Brain, Cognition, Behavior (Ecole Doctorale Cerveau-Cognition-Comportement, “ED3C”). The UPMC, part of the Sorbonne Universities, is the leading University in France in the area of science, technology, and medicine and among the leading universities in the world. The scientific policy of the “ED3C” is strongly characterized by its multidisciplinary nature and its commitment towards both human sciences and mathematical disciplines. Project: Objectives The PhD will be involved in the investigation of structural, functional and effective connectivity of neural network models related to Alzheimer’s disease (AD), such as the limbic system (especially the hippocampal formation, the amygdala, and the entorhinal cortex) and the basal forebrain cholinergic system using both Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI) including advanced tractography methods. The associations between brain pathology and indices of functional and structural connectivity are expected to help our understanding of the role of specific neural networks and their connectivity in brain function in healthy aging and neurodegenerative disease. The PhD student will be involved in the study of the multi-modal nature of specific neural networks – both in the structural and the functional domains and how these two components interact with each other – along with the staging spectrum of AD (from preclinical to prodromal to dementia). To this aim, he/she will have access to different landmark clinical cohorts and datasets of patients including the INSIGHT, SOCRATES, and EDSD cohorts. The PhD student will be involved in the exploration of the various uses that structural and functional neuroimaging biomarkers can play in detecting, diagnosing, assessing treatment response and in investigating neurodegenerative diseases with a special emphasis on AD. The successful applicant will work under the supervision of the AXA Research Fund and UPMC Chair, Prof. Harald Hampel, located at the Institute for Memory and Alzheimer’s Disease (IM2A) and the Brain & Spine Institute (Institut du Cerveau et de la Moelle Épinière, ICM), Paris, the leading French Institute on brain research, centrally located within the Pitié-Salpêtrière University Hospital – Charles Foix. The Brain & Spine Institute (http://icm-institute.org/menu/actualites) is a widely renowned research centre of excellence of international dimensions. It brings together motivated scientists from various horizons and countries in order to develop innovative and cutting-edge research in the area of Neuroscience. Research teams work at the Brain & Spine Institute independently but are strictly interconnected through cross-disciplinary research programs (both basic and clinical), thus encouraging the amalgamation of different skills. The multidisciplinary approach to Neuroscience (Neurobiology, Neurochemistry, Neurogenetics, Neuropsychology as well as structural / functional / diffusion / molecular Neuroimaging) taken by the Brain & Spine Institute represents a vital and dynamic advance in research. Background There is growing evidence that brain activity supports complex cognitive function that occurs within large- scale brain networks rather than within single isolated brain regions. For the definition of connectivity of brain activity between brain regions, two major concepts have been applied (Horwitz, 2003). The first concept refers to functional connectivity, i.e., the correlation between neuronal changes within one brain region related to another (Friston, 1998). Functional connectivity has been applied to explore the correlative pattern of brain activity (Bokde et al., 2006; 2001). In contrast, effective connectivity refers to the causal influence of one brain region’s activity on another where that direction of influence can be explicitly modelled (Ramnani et al., 2004). Furthermore, global [rather than local] network properties may be characterized, using graph theory to describe the properties of a network’s architecture in terms of efficiency or connectedness (Bullmore & Sporns, 2009). In recent years, more and more centers have successfully begun employing formal network analyses as biomarkers of neurodegenerative diseases (Hampel et al., 2014; 2012; Horwitz & Rowe, 2011). Actually, current understanding of the effects of focal damage on neural networks is rudimentary, even though such understanding could provide greater insight into important neurological and neurodegenerative diseases (Bokde et al., 2008; 2006). AD is characterized by early, non-linear dynamic, chronically progressive cellular and molecular2 mechanisms (protein misfolding) leading to neurodegeneration that translates clinically into multi-domain cognitive and behavioral decline, psychopathological disturbances with subsequent loss of function to perform day-to-day tasks and ultimately total loss of independence. Findings derived from neuroimaging studies of both the structural and functional organization of the human brain have led to the widely supported hypothesis that neural networks of temporally coordinated brain activity across different regional brain structures underpin cognitive function. Thus, a failure of the regions of a network to interact at a high level of coordination may underpin progressive cognitive decline which is present in AD (Bokde et al., 2009). The breakdown of network function may be due to interaction failure among the regions of a network, which is denoted the disconnection hypothesis (Friston, 1998). In other words, a disruption in the temporal- spatially coordinated activity among different regions in the brain rather than isolated changes in specific brain regions may underlie cognitive impairment in AD. The breakdown is thought to be due to progressive AD pathophysiology with underlying molecular mechanisms leading downstream to neuronal and synaptic dysfunction and ultimately to neuronal loss. Such AD-characteristic structural and functional alterations are hypothesized to reflect at least partially the progressive impairment of fiber tract connectivity and integrity (Stoub et al., 2006; Morrison & Hof, 2002), suggesting that the disconnection in AD is evident at both the functional and structural level. Notably, the multi-modal nature of networks should be examined, i.e., both the structural and functional components that define a network. Given the substantial changes that the brain undergoes with the presence of AD-related pathophysiology, these alterations will manifest themselves not only in the functional and structural modules but also in how the changes in the two domains interact with one another (Teipel et al., 2007a). Neuroimaging biomarkers will need to be developed and analyzed crossectionally and longitudinally in terms of underlying brain networks rather than in terms of individual regions (Horwitz & Rowe, 2011). Overall, the current discussion on AD argues that it presents in part a dynamically progressive structural, functional and metabolic disconnection syndrome that may undergo distinct stages from potentially reversible adaptation to functional compensation to irreversible decompensation. Studies using fMRI (Bokde et al., 2008; 2006) and electroencephalography (Jelles et al., 2008; Babiloni et al., 2006) demonstrate that synchronicity of brain activity is altered in AD and correlates with cognitive deficits. Moreover, recent advances in diffusion tensor imaging (DTI) to examine white matter microstructural changes have made it possible to track axonal projections across the brain, revealing substantial regional impairment in fiber-tract integrity in AD (Teipel et al., 2011; Teipel et al. 2007b). This work will substantially help develop biomarkers for early detection, prediction and progression of AD and will support the discovery and validation of markers that map the effects of disease modifying therapies on the brain, ultimately providing much needed surrogate biological markers. Key references Horwitz B. (2003). Neuroimage 19:466–470. Friston KJ. (1998). Schizophr Res 30:115–125. Bokde ALW et al. (2006). Brain 129:1113–1124. Bokde ALW et al. (2001). Neuron 30:609–617. Ramnani N. et al. (2004). Biol Psychiatry 56:613–619. Bullmore E & Sporns O (2009). Nat Rev Neurosci 10:186–198. Hampel et al. (2014) Biochem Pharmacol 88:426-449. Hampel et al. (2012) Alzheimers Dement 8:312-336. Horwitz B & Rowe JB (2011). Prog Neurobiol 95:505-509. Bokde ALW. et al. (2008). Psychiatr Res Neuroimaging 163:248 259. Bokde ALW et al. (2009). Prog Neurobiol 89:125–133 Stoub TR et al. (2006). Proc Natl Acad Sci USA 103:10041–10045. Morrison JH & Hof PR (2002). Prog Brain Res 136:467–486. Teipel SJ et al. (2007a). Brain 130:1745–1758. Jelles B et al. (2008). Clin Neurophysiol 119:837–841. Babiloni et al. (2006). Brain Res Bull 69:63–73. Teipel SJ et al. (2011) Hum Brain Mapp 32:1349-1362. Teipel SJ et al. (2007b). Neuroimage 34:985–995. Requirements The ideal candidate is expected to have a robust academic and science background. A preference will be given to students with profound knowledge in neuroscience, neuroimaging data analysis, applied mathematics, biostatistics, or computer science at the master’s level. Candidates demonstrating competencies on structural and functional MRI methods, knowledge and experience with MRI-related data analysis packages (SPM, Matlab, Freesurfer, AFNI), statistical softwares (e.g., SPSS or R), and programming skills (e.g., MATLAB, Python, C++) will have a strong advantage. The candidate has to be fluent both in written and spoken English. The position is expected to begin in October 2015. Applications should include a full Curriculum Vitae and a Cover Letter detailing the applicant’s interest and motivation for this position. Two letters of academic reference, assessing the applicant’s skills, research and learning potential, ability to team work and personality, should be sent independently by the referees. Applications together with all documents, including reference letters, should be submitted electronically to: [log in to unmask] with reference to “PhD position" in the E-mail header. Applications must be received within the 30th of June 2015. | |