Fangfei Ge

Connectome-scale group-wise consistent resting-state network analysis in autism spectrum disorder

•A new effective volumetric network descriptor, named connectivity map.•A novel Spark-enabled functional connectomics framework•Identified 144 group-wisely common intrinsic connectivity networks (ICNs).•Revealed connectomics signatures characterizing and differentiating ASD patients and controls.

Gyral net: A new representation of cortical folding organization

HighlightsA new representation of cortical gyri/sulci organization pattern.A novel framework to efficiently and automatically construct gyral net from mesh surface.A new tool for a comprehensive and reliable characterization of the cortical folding organization.Enable large‐scale cortical folding pattern analyses in the future. ABSTRACT One distinct feature of the cerebral cortex is its convex (gyri) and concave (sulci) folding patterns. Due to the remarkable complexity and variability of gyral/sulcal shapes, it has been challenging to quantitatively model their organization patterns. Inspired by the observation that the lines of gyral crests can form a connected graph on each brain hemisphere, we propose a new representation of cortical gyri/sulci organization pattern – gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the reconstructed cortical surfaces. A novel computational framework is developed to efficiently and automatically construct gyral nets from surface meshes, and four measurements are devised to quantify the folding patterns. Using an MRI dataset for autism study as a test bed, we identified reduced local connectivity cost and increased curviness of gyral net bilaterally on the parietal lobe, occipital lobe, and temporal lobe in autistic patients. Additionally, we found that the cortical thickness and the gyral straightness of gyral joints are higher than the rest of gyral crest regions. The proposed representation offers a new tool for a comprehensive and reliable characterization of the cortical folding organization. This novel computational framework will enable large‐scale analyses of cortical folding patterns in the future. Graphical abstract Illustration of the concept of gyral net and gyral joint. (a) Reconstructed cortical surface color‐coded by gyral altitude. (b) Extracted gyral net. (c) Zoom in view of the circled area in (b). In this paper, we propose a new representation of cortical gyri/sulci organization pattern – gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the surface reconstructions. Figure. No caption available.

Temporal Concatenated Sparse Coding of Resting State fMRI Data Reveal Network Interaction Changes in mTBI

Resting state fMRI (rsfMRI) has been a useful imaging modality for network level understanding and diagnosis of brain diseases, such as mild traumatic brain injury (mTBI). However, there call for effective methodologies which can detect group-wise and longitudinal changes of network interactions in mTBI. The major challenges are two folds: (1) There lacks an individualized and common network system that can serve as a reference platform for statistical analysis; (2) Networks and their interactions are usually not modeled in the same algorithmic structure, which results in bias and uncertainty. In this paper, we propose a novel temporal concatenated sparse coding (TCSC) method to address these challenges. Based on the sparse graph theory the proposed method can model the commonly shared spatial maps of networks and the local dynamics of the networks in each subject in one algorithmic structure. Obviously, the local dynamics are not comparable across subjects in rsfMRI or across groups; however, based on the correspondence established by the common spatial profiles, the interactions of these networks can be modeled individually and statistically assessed in a group-wise fashion. The proposed method has been applied on an mTBI dataset with acute and sub-acute stages, and experimental results have revealed meaningful network interaction changes in mTBI.

3D Deep Convolutional Neural Network Revealed the Value of Brain Network Overlap in Differentiating Autism Spectrum Disorder from Healthy Controls

Spatial distribution patterns of functional brain networks derived from resting state fMRI data have been widely examined in the literature. However, the spatial overlap patterns among those brain networks have been rarely investigated, though spatial overlap is a fundamental principle of functional brain network organization. To bridge this gap, this paper presents an effective 3D convolutional neural network (CNN) framework to derive discriminative and meaningful spatial brain network overlap patterns that can characterize and differentiate Autism Spectrum Disorder (ASD) from healthy controls. Our experimental results demonstrated that the spatial distribution patterns of connectome-scale functional network maps per se have little discrimination power in differentiating ASD from controls via the CNN framework. In contrast, the spatial overlap patterns instead of spatial patterns per se among these connectome-scale networks, learned via the same CNN framework, have remarkable differentiation power in separating ASD from controls. Our work suggested the promise of using CNN deep learning methodologies to discover discriminative and meaningful spatial network overlap patterns and their applications in functional connectomics of brain disorders such as ASD.

Exploring 3-hinge gyral folding patterns among HCP Q3 868 human subjects

Comparison and integration of neuroimaging data from different brains and populations are fundamental in neuroscience. Over the past decades, the neuroimaging field has largely depended on image registration to compare and integrate neuroimaging data from individuals in a common reference space, with a basic assumption that the brains are similar. However, the intrinsic neuroanatomical complexity and huge interindividual cortical folding variation remain underexplored. Here we focus on a specific cortical convolution pattern, termed 3‐hinge gyral folding, which is the conjunction of gyri from multiple orientations and has unique and consistent anatomically, structurally, and functionally connective patterns across subjects. By developing a novel shape descriptor and a two‐stage clustering pipeline, we devise an automatic method to identify 3‐hinges in the Human Connectome Project Q3 868 human brains, and further parameterize the complexity of such a pattern and quantify its regularity and variation in terms of 3‐hinge number, position, and morphology. Our results not only exhibit the huge interindividual variations, but also reveal regular relationship between gyral hinges and other factors, such as their locations and cortical morphologies. It is found that “line‐shape” cortices have relatively more consistent 3‐hinge shape pattern distributions, and certain types of 3‐hinge patterns favor particular cortical morphologies. In addition, more 3‐hinges are found on “line‐shape” cortices while their numbers vary more across subjects than those on “non‐line‐shape” cortices. This study adds new insights into a better understanding of the regularity and variability of human brain anatomy, and their functional aspects.

Denser Growing Fiber Connections Induce 3-hinge Gyral Folding

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.

Group-wise sparse representation of brain states reveal network abnormalities in mild traumatic brain injury

Mild traumatic brain injury (mTBI) is a leading public health care burden. Recent research has shown that the functional impairment in mTBI patients could be captured by resting state fMRI (rsfMRI) at network level. Moreover exploring brain response to mTBI over time at large scale network level can help physicians better diagnose brain injury and order appropriate rehabilitation plan. Therefore, there is a need for methodological innovation that could assess brain impairment in rsfMRI data and further define biomarkers for network changes. In this paper, we propose a novel group-wise sparse representation of brain states (GSRBS) approach, based on rsfMRI data, to explore the effect of mTBI on functional networks across different groups and longitudinal stages. Specifically, a dictionary of brain networks is learned from the volumes of rsfMRI data, and at each time point these networks are linearly and sparsely combined to realize a brain state. Our results showed that group-wise statistical difference on the network composition of brain states could be found between healthy controls and mTBI patients at two different temporal stages.

Deriving ADHD biomarkers with sparse coding based network analysis

Sparse coding has been increasingly used to explore brain networks using functional magnetic resonance imaging (fMRI). However, modeling and comparing brain network based on sparse coding is still challenging, especially in clinical applications. In this study, we propose a novel temporal sparse coding method to identify functional connectivity biomarkers in patients with Attention-Deficit/Hyperactivity Disorder (ADHD). Specifically, a group-wise temporal sparse coding method was proposed to localize corresponding brain regions of interest (ROIs) in rsfMRI data. The localized common ROIs were then used as brain network nodes for further functional connectivity analysis. By using a publicly available ADHD-200 dataset, we demonstrated that our method can identify functional connectivity biomarkers with improved performance in patient-healthy controls classification compared with the widely used independent component analysis (ICA).