Shadia Mikhael

A critical analysis of neuroanatomical software protocols reveals clinically relevant differences in parcellation schemes

ABSTRACT A high replicability in region‐of‐interest (ROI) morphometric or ROI‐based connectivity analyses is essential for such methods to provide biomarkers of good health or disease. In this article, we focus on package design, and more specifically on cortical parcellation protocols, for novel insight into their contribution to inter‐package differences. A critical analysis of cortical parcellation protocols from FreeSurfer, BrainSuite, BrainVISA and BrainGyrusMapping revealed major limitations. Details of reference populations are generally missing, cortical variability is not always explicitly accounted for and, more importantly, definition of gyral borders can be inconsistent. We recommend that in the package selection process end users incorporate protocol suitability for the ROIs under investigation, with these particular points in mind, as inter‐package differences are likely to be significant and the source of incompatibility between studies’ results. HIGHLIGHTSInvestigation of 4 packages’ parcellation protocols and their implications.Variablity and lack of protocol explicitness regarding population atlas details.Variablity and lack of protocol explicitness regarding landmarks and gyral borders.Inconsistency in handling cortical variability.Large variations between software protocols underpinning lack of reproducibility.

Masks2Metrics (M2M): A Matlab Toolbox for Gold Standard Morphometrics

Human brains undergo morphometric changes over a lifetime, from conception through to birth, infancy, adolescence, adulthood, and old age (Thambisetty et al. (2010); Madan and Kensinger (2016)). This is further compounded by the changes associated with various brain pathologies such as tumours (e.g. Bauer et al. (2013)) and dementia (e.g., B. C. Dickerson et al. (2011)). It is therefore essential to accurately and scientifically characterise such changes by using an array of morphologic measurements, for a better understanding of the natural progression of ageing and disease (Mills et al. (2016); Madan (2017)). While many existing brain image analysis tools (e.g., FreeSurfer (Fischl et al. (2004); Desikan et al. (2006)), BrainSuite (Shattuck and Leahy (2002)), and BrainVISA (Kochunov et al. (2012))) automatically compute such data from a 3-dimensional (3D) brain image, they lack the ability to do so for the equivalent manually-traced regions of interest (ROIs). This is all the more significant as such ROIs are considered as the gold standard, thus making knowledge of their metrics essential.

Do 2-year changes in superior frontal gyrus and global brain atrophy affect cognition?

Metabolic alterations to the superior frontal gyrus (SFG) have been linked to cognitive decline. Whether these indicate structural atrophy, which could be screened for at a larger scale using noninvasive structural imaging, is unknown.