The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at , so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

Traditional anatomical studies of the parahippocampal region (PHR) defined the lateral portion into two subregions, the perirhinal (PRC) and parahippocampal (PHC) cortices. Based on this organization, several models suggested that the PRC and the PHC play different roles in memory through connections with different memory-related brain networks. To identify the key components of the human PHR, we used a well accepted connection-based parcellation method on two independent datasets. Our parcellation divided the PRC and PHC into three subregions, specifically, the rostral PRC, caudal PRC (PRCc), and PHC. The connectivity profile for each subregion showed that the rostral PRC was connected to the anterior temporal (AT) system and the PHC was connected to the posterior medial (PM) system. The transition area (PRCc) integrated the AT-PM systems. These results suggest that the lateral PHR not only contains functionally segregated subregions, but also contains a functionally integrated subregion.

These results indicate that disease severity is associated with altered PHG connectivity, contributing to knowledge about the reduction in cognitive ability and impaired brain activity that occur in AD/MCI. These early changes in the functional connectivity of the PHG might provide some potential clues for identification of imaging markers for the early detection of MCI and AD.

There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation. In this paper, we present an integrated open source pipeline (https://www.nitrc.org/projects/atpp), named Automatic Tractography-based Parcellation Pipeline (ATPP) to realize the framework of parcellation with automatic processing and massive parallel computing. ATPP is developed to have a powerful and flexible command line version, taking multiple regions of interest as input, as well as a user-friendly graphical user interface version for parcellating single region of interest. We demonstrate the two versions by parcellating two brain regions, left precentral gyrus and middle frontal gyrus, on two independent datasets. In addition, ATPP has been successfully utilized and fully validated in a variety of brain regions and the human Brainnetome Atlas, showing the capacity to greatly facilitate brain parcellation.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder which causes dementia, especially in the elderly. The posteromedial cortex (PMC), which consists of several subregions involved in distinct functions, is one of the critical regions associated with the progression and severity of AD. However, previous studies always ignored the heterogeneity of the PMC and focused on one stage of AD. Using resting-state functional magnetic resonance imaging, we studied the respective alterations of each subregion within the PMC along the progression of AD. Our data set consisted of 21 healthy controls, 18 patients with mild cognitive impairment (MCI), 17 patients with mild AD (mAD), and 18 patients with severe AD (sAD). We investigated the functional alterations of each subregion within the PMC in different stages of AD. We found that subregions within the PMC have differential vulnerability in AD. Disruptions in functional connectivity began in the transition area between the precuneus and the posterior cingulate cortex (PCC) and then extended to other subregions of the PMC. In addition, each of these subregions was associated with distinct alterations in the functional networks that we were able to relate to AD. Our research demonstrated functional changes within the PMC in the progression of AD and may elucidate potential biomarkers for clinical applications.

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.

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Chromatin accessibility and post-transcriptional histone modifications play important roles in gene expression regulation. However, little is known about the joint effect of multiple chromatin modifications on the gene expression level in plants, despite that the regulatory roles of individual histone marks such as H3K4me3 in gene expression have been well-documented. By using machine-learning methods, we systematically performed gene expression level prediction based on multiple chromatin modifications data in Arabidopsis and rice. We found that as few as four histone modifications were sufficient to yield good prediction performance, and H3K4me3 and H3K36me3 being the top two predictors with known functions related to transcriptional initiation and elongation, respectively. We demonstrated that the predictive powers differed between protein-coding and non-coding genes as well as between CpG-enriched and CpG-depleted genes. We also showed that the predictive model trained in one tissue or species could be applied to another tissue or species, suggesting shared underlying mechanisms. More interestingly, the gene expression levels of conserved orthologs are easier to predict than the species-specific genes. In addition, chromatin state of distal enhancers was moderately correlated to gene expression but was dispensable if given the chromatin features of the proximal regions of genes. We further extended the analysis to transcription factor (TF) binding data. Strikingly, the combinatorial effects of only a few TFs were roughly fit to gene expression levels in Arabidopsis. Overall, by using quantitative modeling, we provide a comprehensive and unbiased perspective on the epigenetic and TF-mediated regulation of gene expression in plants.