[1] Assuming this data to be normative without Lesions (or plaques, infarcts, space-occupying abnormal tissue). My understanding or assumption without knwoing the age of this "healthy subject" is that the tissue was segmented into normal-appearing GM, WM and CSF. The whole brain CSF (wbCSF) volume seems to be stable and the sum of wbGM and wbWM is clearly different indicating ICV = wbCSF +wbWM +wbGM (ignore vascular and non-brain extra tissue) is not the same and the methods being used in the second pass are "less" sensitive to parencyhma. This may be related most likely a threshold that deemed the values smaller as woul happen in thresholding as segmentations are usually probabilistic (0-1 values).
[2] I would also comapre the values in the first method "if normative" to published works as far as total CSF in the brain to TICV which may range 2%-20% depending on age and gender. Some published works using VBM methods and customized templates still overestimated CSF absolute volume and wbCSF percentage (compare results on healthy children from Europe/USA: Mengotti et al. 2011 vs. Franke et al 2012 & Hasan et al. 2007a) as comapred to known postmortem ventricular space and vault size.
ICV can still be estimated outside SPM calling for example BET at least to give you a reference value to optimize the threshold. Below are some works from a plethora that have extensive references and have tabulated the expected ranges and age trend across in children (Hasan et al. 2007a (meta analysis); Franke et al 2012), adults (Good et al. 2001) and the human Lifespan (Hasan et al, 2007b; Courchesene et al. 2000; Walhovd et al. 2011). Note that these works also used different MRI contrast (T1w, T2w or DTI-based)
1. Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, Harwood M, Hinds S, Press GA.
Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers.
Radiology. 2000;216(3):672-82.
2. Franke K, Luders E, May A, Wilke M, Gaser C. Brain maturation: predicting individual BrainAGE in children and adolescents using structural MRI. Neuroimage. 2012;63(3):1305-12.
3. Good CD, Johnsrude IS, Ashburner J, Henson RN, Friston KJ, Frackowiak RS. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage. 2001;14(1 Pt 1):21-36.
4. Hasan KM, Halphen C, Sankar A, Eluvathingal TJ, Kramer L, Stuebing KK, Ewing-Cobbs L, Fletcher JM. Diffusion tensor imaging-based tissue segmentation: validation and application to the developing child and adolescent brain. Neuroimage. 2007;34(4):1497-505. http://www.ncbi.nlm.nih.gov/pubmed/17166746
5. Hasan KM, Sankar A, Halphen C, Kramer LA, Brandt ME, Juranek J, Cirino PT, Fletcher JM, Papanicolaou AC, Ewing-Cobbs L. Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging. Neuroreport. 2007;18(16):1735-9.
6. Mengotti P, D'Agostini S, Terlevic R, De Colle C, Biasizzo E, Londero D, Ferro A, Rambaldelli G, Balestrieri M, Zanini S, Fabbro F, Molteni M, Brambilla P. Altered white matter integrity and development in children with autism: a combined voxel-based morphometry and diffusion imaging study. Brain Res Bull. 2011;84(2):189-95.
7. Walhovd KB, Westlye LT, Amlien I, Espeseth T, Reinvang I, Raz N, Agartz I, Salat DH, Greve DN, Fischl B, Dale AM, Fjell AM. Consistent neuroanatomical age-related volume differences across multiple samples. Neurobiol Aging. 2011;32(5):916-32.
Khader M Hasan, PhD
Associate Professor of Radiology
MSE 168, Tel 713 500 7690 (FAX 713 500 7684)
University of Texas Health Science Center at Houston
Medical School
Diagnostic and Interventional Imaging
Magnetic Resonance Imaging Research Division
Diffusion Tensor Imaging Lab, Tel 713 500 7683
http://www.uth.tmc.edu/radiology/faculty/khader-m-hasan/index.html<http://www.uth.tmc.edu/radiology/faculty/hasan.html>
________________________________
From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Kirsch, Martina [[log in to unmask]]
Sent: Friday, March 08, 2013 4:44 AM
To: [log in to unmask]
Subject: [SPM] VBM 8 TIV results
Hello,
I preceded the VBM8 analysis in two different ways – the pipeline for single measurements (separated for two time points of measurement) and the one for longitudinal analysis for the same subjects. Comparing the results of TIVs I found some differences in the results, which I don’t understand.
For example:
pXXXXX_mpr-001_seg8.txt 676.527 535.627 253.85
pmrXXXXX_mpr-001_seg8.txt 680.453 513.023 253.541
Or
pXXXXX_mpr-001_seg8.txt 529.612 431.370 249.543
pmrXXXXX_mpr-001_seg8.txt 544.029 409.007 239.779
Does anyone have an explanation for this?
Thanks
Martina
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