A multi-modal parcellation of human cerebral cortex

Abstract

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Cortical pathology in multiple sclerosis detected by the T1/T2‐weighted ratio from routine magnetic resonance imaging

“…Furthermore, MRI‐based T1w/T2w ratio maps resembled early histology‐based myelin maps by Hopf so convincingly that MRI‐based T1w/T2w ratio maps have also been termed "myelin maps. "12, 13, 14, 15, 18, 25 This evidence, however, is exclusively based on visual comparisons and, hence, indirect. Unfortunately, analogous histology‐based maps of dendrite density do not exist, to the best of our knowledge.…”

“…This method is based on the ratio of T1‐ and T2‐weighted (w) image signal intensities 12. Heretofore, the histological substrate has been unclear, although myelin content has been suggested based on indirect evidence 12, 13, 14. Yet 3 features of this method are particularly attractive for MS research: (1) with regard to precision, it seems to enable mapping of human cortical areas across the whole brain; (2) with regard to feasibility, it can be applied to large cohorts of patients and it is based on the ratio of intensity values from 2 conventional sequences, namely the T1w and T2w sequences, which are part of many clinical MRI protocols; and (3) with regard to functional relevance, its measures have been described to be related with performance variability in normal subjects 15…”

Striatal subdivisions that coherently interact with multiple cerebrocortical networks

“…Although the alignment of the cerebral cortex was successful, even fine normalization using DARTEL was not successful in the striatal parcellation results (Supporting Information Figure S2). Thus, normalization using multimodal features such as structure and functional connectivity, as implemented in MSMAll (Glasser et al, 2016; Robinson et al, 2014) for the cerebral cortex, seems to be required for the striatum.…”

“…Parcellation analyses based on boundary mapping (Cohen et al, 2008; Glasser et al, 2016; Gordon et al, 2016; Gordon, Laumann, Gilmore, et al, 2017; Hirose et al, 2012, 2013, 2016; Laumann et al, 2015; Osada et al, 2017) were applied to the striatum (Figure 1). Each voxel in the striatum of each participant was used as a seed to calculate its correlations with the voxels in the gray matter of the cerebral cortex.…”

The impact of hemodynamic variability and signal mixing on the identifiability of effective connectivity structures in BOLD fMRI

“…Most of the recent open‐source fMRI datasets such as Human Connectome Project, ADHD‐200 or ABIDE, already support the functional parcellation of the data (Craddock, James, Holtzheimer, Hu, & Mayberg, 2012; Glasser et al., 2016; Rosenberg et al., 2016). Multiple functional parcellations are available in the field of fMRI (Bellec et al., 2006; Bellec, Rosa‐Neto, Lyttelton, Benali, & Evans, 2010; Blumensath et al., 2013; Chen et al., 2013; Craddock et al., 2012; Eickhoff et al., 2011; Flandin et al., 2002; Glasser et al., 2016; Golland, Golland, & Malach, 2007; Janssen, Jylänki, Kessels, & van Gerven, 2015; Janssen, Jylänki, & van Gerven, 2016; Kahnt, Chang, Park, Heinzle, & Haynes, 2012; Lashkari et al., 2012; Lashkari, Vul, Kanwisher, & Golland, 2010; Michel et al., 2012; Orban et al., 2014; Thirion et al., 2006; Tucholka et al., 2008; van den Heuvel, Mandl, & Pol, 2008; Yeo et al., 2011), and the issue of optimal functional parcellation is broadly discussed in the field (Stanley et al., 2013). In particular, in cognitive paradigms, the ROIs can be built in a data‐driven way and on the basis of the patterns of activation only (task localizers, Fedorenko, Hsieh, Nieto‐Castañón, Whitfield‐Gabrieli, & Kanwisher, 2010; Heinzle, Wenzel, & Haynes, 2012).…”