Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation

Abstract

Parcellation of the human brain into fine-grained units by grouping voxels into distinct clusters has been an effective approach for delineating specific brain regions and their subregions. Published neuroimaging studies employing coordinate-based meta-analyses have shown that the activation foci and their corresponding behavioral categories may contain useful information about the anatomical–functional organization of brain regions. Inspired by these developments, we proposed a new parcellation scheme called meta-analytic activation modeling-based parcellation (MAMP) that uses meta-analytically obtained information. The raw meta data, including the experiments and the reported activation coordinates related to a brain region of interest, were acquired from the Brainmap database. Using this data, we first obtained the “modeled activation” pattern by modeling the voxel-wise activation probability given spatial uncertainty for each experiment that featured at least one focus within the region of interest. Then, we processed these “modeled activation” patterns across the experiments with a K-means clustering algorithm to group the voxels into different subregions. In order to verify the reliability of the method, we employed our method to parcellate the amygdala and the left Brodmann area 44 (BA44). The parcellation results were quite consistent with previous cytoarchitectonic and in vivo neuroimaging findings. Therefore, the MAMP proposed in the current study could be a useful complement to other methods for uncovering the functional organization of the human brain.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

“…Because of the variety of needs of different fields within neuroscience and the shortcomings of existing brain atlases, a new human brain atlas with a framework for integrating multimodal information is urgently needed ( Evans et al 2012 ; Amunts et al 2014 ). Consequently, many studies have used different MRI modalities to identify individual brain region or provide more comprehensive maps of the cerebral cortex ( Tzourio-Mazoyer et al 2002 ; Desikan et al 2006 ; Cohen, Fair, et al 2008 ; Cohen, Lombardo, et al 2008 ; Eickhoff et al 2011 ; Wang et al 2012 ; Fan et al 2014 ; Wig et al 2014 ; Laumann Timothy et al 2015 ; Liu et al 2015 ; Yang et al 2015 ). While acknowledging that there is no consensus on which modality or aspect of brain organization may be most reflective of the brains’ “true” organization (and in fact, there may be no single answer to this question), brain atlases are crucial to advance understanding of the human brain given that macroanatomical landmarks or coordinate systems are not valid indicators of regional specialization ( Brett et al 2002 ; Bohland et al 2009 ; Evans et al 2012 ).…”

“…Using resting-state connectivity, meta-analytic coactivation and structural covariance, but not fiber tracking, Kelly et al (2012) found a consistent pattern in the parcellations of the insula. In our recent work, we consistently identified 5 subregions in the superior parietal lobule of each hemisphere based on its anatomical connections as well as its resting-state connectivity and coactivation patterns ( Yang et al 2015 ). Further systematic comparison—across modalities, features, and methods—of the maps that may be computed using connectivity-based parcellation is still needed.…”

Structural connectivity of the amygdala in young adults with autism spectrum disorder

“…These amygdala subregions appear to differ based on their cytoarchitecture, connectivity, and function — with the centromedial nucleus associated with motor responses (Kalin, Shelton, & Davidson, ), the laterobasal nucleus with stimulus‐effect memory and fear response conditioning through connections with sensory regions of the cortex (Adhikari et al, ; Johansen et al, ; Stefanacci & Amaral, ), and the superficial nucleus posited to play a role in social cognition and responses to emotionally‐salient stimuli via its insula and basal ganglia connectivity (Bzdok, Laird, Zilles, Fox, & Eickhoff, ). Recent analysis of high‐resolution MRI acquired in vivo have subdivided the amygdala based on co‐registration of T 1 ‐ and T 2 ‐weighted structural images (Tyszka & Pauli, ), and measures of functional connectivity and co‐activation (Bzdok et al, ; Kerestes, Chase, Phillips, Ladouceur, & Eickhoff, ; Mishra, Rogers, Chen, & Gore, ; Yang et al, ). Recent tractography studies in healthy adults have shown that it is possible to subdivide the amygdala in vivo using WM connectivity‐based parcellation schemes: Abivardi and Bach () and Bach, Behrens, Garrido, Weiskopf, and Dolan () used a k ‐means algorithm to parcellate the amygdala into two clusters based on connectivity to the frontal cortex and the temporal pole; Saygin et al () used a priori assumptions of amygdala—cortical connections in order to subdivide the amygdala into clusters that were in good agreement with manual tracing on a high‐resolution T 1 ‐weighted image.…”

Prediction of the Clinical Severity of Progressive Supranuclear Palsy by Diffusion Tensor Imaging

“…An accurate prediction of LEDD may be hampered by numerous factors that can influence drug dosage-including age, sex, disease duration, genetic background, and pathological status. Here, we found that regions associated with LEDD were related to face recognition (medial superior temporal gyrus) [22], emotions of fear or disgust (amygdala) [23], and emotion processing-especially in the reward/pain domain (caudal cingulate gyrus) [24]. Difficulties in recognizing negative emotions are part of the cognitive impairment occurring in patients with PSP [25], who are characterized by an impaired metabolism in this part of caudal cingulate gyrus [26].…”