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Recent studies have shown that quantitative description of gyral shape patterns offers a novel window to examine the relationship between brain structure and function. Along this research line, this paper examines a unique and interesting type of cortical gyral region where 3 different gyral crests meet, termed 3-hinge gyral region. We extracted 3-hinge gyral regions in macaque/chimpanzee/human brains, quantified and compared the relevant DTI-derived fiber densities in 3-hinge and 2-hinge gyral regions. Our observations consistently showed that DTI-derived fiber densities in 3-hinge regions are much higher than those in 2-hinge regions. Therefore, we hypothesize that besides the cortical expansion, denser fiber connections can induce the formation of 3-hinge gyri. To examine the biomechanical basis of this hypothesis, we constructed a series of 3-dimensional finite element soft tissue models based on continuum growth theory to investigate fundamental biomechanical mechanisms of consistent 3-hinge gyri formation. Our computational simulation results consistently showed that during gyrification gyral regions with higher concentrations of growing axonal fibers tend to form 3-hinge gyri. Our integrative approach combining neuroimaging data analysis and computational modeling appears effective in probing a plausible theory of 3-hinge gyri formation and providing new insights into structural and functional cortical architectures and their relationship., (© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science.

Quantitative modeling and analysis of structural and functional brain networks based on diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data have received extensive interest recently. However, the regularity of these structural and functional brain networks across multiple neuroimaging modalities and also across different individuals is largely unknown. This paper presents a novel approach to inferring group-wise consistent brain subnetworks from multimodal DTI/resting-state fMRI datasets via multi-view spectral clustering of cortical networks, which were constructed upon our recently developed and validated large-scale cortical landmarks-DICCCOL (dense individualized and common connectivity-based cortical landmarks). We applied the algorithms on DTI data of 100 healthy young females and 50 healthy young males, obtained consistent multimodal brain networks within and across multiple groups, and further examined the functional roles of these networks. Our experimental results demonstrated that the derived brain networks have substantially improved inter-modality and inter-subject consistency.

Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and statistical power to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the spatial alignment established by coregistration of individual brains' fMRI images into the same template space, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignment among multiple brains could substantially degrade the accuracy and specificity of group-wise fMRI activation detection. To address these challenges, this paper presents a novel methodology to detect group-wise fMRI activation based on a publicly released dense map of DTI-derived structural cortical landmarks, which possess intrinsic correspondences across individuals and populations. The basic idea here is that a first-level general linear model (GLM) analysis is performed on fMRI signals of each corresponding cortical landmark in each individual brain's own space, and then the single-subject effect size of the same landmark from a group of subjects are statistically integrated and assessed at the group level using the mixed-effects model. As a result, the consistently activated cortical landmarks are determined and declared group-wisely in response to external block-based stimuli. Our experimental results demonstrated that the proposed approach can map meaningful group-wise activation patterns on the atlas of cortical landmarks without image registration between subjects and spatial smoothing.

The human brain function involves complex processes with population codes of neuronal activities. Neuroscience research has demonstrated that when representing neuronal activities, sparsity is an important characterizing property. Inspired by this finding, significant amount of efforts from the scientific communities have been recently devoted to sparse representations of signals and patterns, and promising achievements have been made. However, sparse representation of fMRI signals, particularly at the population level of a group of different brains, has been rarely explored yet. In this paper, we present a novel group-wise sparse representation of task-based fMRI signals from multiple subjects via dictionary learning methods. Specifically, we extract and pool task-based fMRI signals for a set of cortical landmarks, each of which possesses intrinsic anatomical correspondence, from a group of subjects. Then an effective online dictionary learning algorithm is employed to learn an over-complete dictionary from the pooled population of fMRI signals based on optimally determined dictionary size. Our experiments have identified meaningful Atoms of Interests (AOI) in the learned dictionary, which correspond to consistent and meaningful functional responses of the brain to external stimulus. Our work demonstrated that sparse representation of group-wise fMRI signals is naturally suitable and effective in recovering population codes of neuronal signals conveyed in fMRI data.

Traditional task-based fMRI activation detection methods, e.g., the widely used general linear model (GLM), assume that the brain's hemodynamic responses follow the block-based or event-related stimulus paradigm. Typically, these activation detections are performed voxel-wise independently, and then are usually followed by statistical corrections. Despite remarkable successes and wide adoption of these methods, it remains largely unknown how functional brain regions interact with each other within specific networks during task performance blocks and in the baseline. In this paper, we present a novel algorithmic pipeline to statistically infer and sparsely represent higher-order functional interaction patterns within the working memory network during task performance and in the baseline. Specifically, a collection of higher-order interactions are inferred via the greedy equivalence search (GES) algorithm for both task and baseline blocks. In the next stage, an effective online dictionary learning algorithm is utilized for sparse representation of the inferred higher-order interaction patterns. Application of this framework on a working memory task-based fMRI data reveals interesting and meaningful distributions of the learned sparse dictionary atoms in task and baseline blocks. In comparison with traditional voxel-wise activation detection and recent pair-wise functional connectivity analysis, our framework offers a new methodology for representation and exploration of higher-order functional activities in the brain.

Due to the difficulties in establishing accurate correspondences of brain network nodes across individual subjects, systematic elucidation of possible functional connectivity (FC) alterations in mild cognitive impairment (MCI) compared with normal controls (NC) is a challenging problem. To address this challenge, in this paper, we develop and apply novel predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and DTI data to assess large-scale FC alterations in MCI. Our rationale is that some RSNs in MCI are substantially altered and can hardly be directly compared with those in NC. Instead, structural landmarks derived from DTI data are much more consistent and correspondent across MCI/NC brains, and therefore can be employed to encode RSNs in NC and serve as the predictive models of RSNs for MCI. To derive these predictive models, RSNs in NC are constructed by group-wise ICA clustering and employed to functionally annotate corresponding structural landmarks. Afterwards, these functionally-annotated structural landmarks are predicted in MCI based on DTI data and used to assess FC alterations in MCI. Experimental results demonstrated that the predictive models of RSNs are effective and can comprehensively reveal widespread FC alterations in MCI.

Study of structural and functional connectivities of the human brain has received significant interest and effort recently. A fundamental question arises when attempting to measure the structural and/or functional connectivities of specific brain networks: how to best identify possible Regions of Interests (ROIs)? In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on learned fiber shape models from multimodal task-based fMRI and diffusion tensor imaging (DTI) data. In the training stage, ROIs are identified as activation peaks in task-based fMRI data. Then, shape models of white matter fibers emanating from these functional ROIs are learned. In addition, ROIs' location distribution model is learned to be used as an anatomical constraint. In the prediction stage, functional ROIs are predicted in individual brains based on DTI data. The ROI prediction is formulated and solved as an energy minimization problem, in which the two learned models are used as energy terms. Our experiment results show that the average ROI prediction error is 3.45 mm, in comparison with the benchmark data provided by working memory task-based fMRI. Promising results were also obtained on the ADNI-2 longitudinal DTI dataset.