Thought Chart: tracking the thought with manifold learning during emotion regulation.

The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks.

The significance of negative correlations in brain connectivity.

Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures.

The intrinsic geometry of the human brain connectome.

This paper describes novel methods for constructing the intrinsic geometry of the human brain connectome using dimensionality-reduction techniques. We posit that the high-dimensional, complex geometry that represents this intrinsic topology can be mathematically embedded into lower dimensions using coupling patterns encoded in the corresponding brain connectivity graphs. We tested both linear and nonlinear dimensionality-reduction techniques using the diffusion-weighted structural connectome data acquired from a sample of healthy subjects.

Cognitive and connectome properties detectable through individual differences in graphomotor organization.

We investigated whether graphomotor organization during a digitized Clock Drawing Test (dCDT) would be associated with cognitive and/or brain structural differences detected with a tractography-derived structural connectome of the brain. 72 non-demented/non-depressed adults were categorized based on whether or not they used 'anchor' digits (i.e.

Measuring embeddedness: Hierarchical scale-dependent information exchange efficiency of the human brain connectome.

This article presents a novel approach for understanding information exchange efficiency and its decay across hierarchies of modularity, from local to global, of the structural human brain connectome. Magnetic resonance imaging techniques have allowed us to study the human brain connectivity as a graph, which can then be analyzed using a graph-theoretical approach. Collectively termed brain connectomics, these sophisticated mathematical techniques have revealed that the brain connectome, like many networks, is highly modular and brain regions can thus be organized into communities or modules.

Brain connectivity in late-life depression and aging revealed by network analysis.

Objective: To use novel methods to examine age associations across an integrated brain network in healthy older adults (HOA) and individuals with late-life depression (LLD). Graph theory metrics describe the organizational configuration of both the global network and specified brain regions.

Methods: Cross-sectional data were acquired.

Altered structural brain connectome in young adult fragile X premutation carriers.

Fragile X premutation carriers (fXPC) are characterized by 55-200 CGG trinucleotide repeats in the 5' untranslated region on the Xq27.3 site of the X chromosome. Clinically, they are associated with the fragile X-Associated Tremor/Ataxia Syndrome, a late-onset neurodegenerative disorder with diffuse white matter neuropathology.

Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space.

Advances in resting state fMRI and diffusion weighted imaging (DWI) have led to much interest in studies that evaluate hypotheses focused on how brain connectivity networks show variations across clinically disparate groups. However, various sources of error (e.g.

Constructing the resting state structural connectome.

Background: Many recent studies have separately investigated functional and white matter (WM) based structural connectivity, yet their relationship remains less understood. In this paper, we proposed the functional-by-structural hierarchical (FSH) mapping to integrate multimodal connectome data from resting state fMRI (rsfMRI) and the whole brain tractography-derived connectome.

Methods: FSH first observes that the level of resting-state functional correlation between any two regions in general decreases as the graph distance of the corresponding structural connectivity matrix between them increases.

Investigating brain community structure abnormalities in bipolar disorder using path length associated community estimation.

In this article, we present path length associated community estimation (PLACE), a comprehensive framework for studying node-level community structure. Instead of the well-known Q modularity metric, PLACE utilizes a novel metric, Ψ(PL), which measures the difference between intercommunity versus intracommunity path lengths. We compared community structures in human healthy brain networks generated using these two metrics and argued that Ψ(PL) may have theoretical advantages.

White matter tract integrity of anterior limb of internal capsule in major depression and type 2 diabetes.

A number of studies have shown an association between diabetes and depression. However, the underlying mechanisms are still unclear. Previous findings indicate a role for the prefrontal cortex and subcortical gray matter regions in type 2 diabetes and major depressive disorder (MDD).

White matter microstructure in body dysmorphic disorder and its clinical correlates.

Body dysmorphic disorder (BDD) is characterized by an often-delusional preoccupation with misperceived defects of appearance, causing significant distress and disability. Although previous studies have found functional abnormalities in visual processing, frontostriatal, and limbic systems, no study to date has investigated the microstructure of white matter connecting these systems in BDD. Participants comprised 14 medication-free individuals with BDD and 16 healthy controls who were scanned using diffusion-weighted magnetic resonance imaging (MRI).

Abnormal brain network organization in body dysmorphic disorder.

Body dysmorphic disorder (BDD) is characterized by preoccupation with misperceived defects of appearance, causing significant distress and disability. Previous studies suggest abnormalities in information processing characterized by greater local relative to global processing. The purpose of this study was to probe whole-brain and regional white matter network organization in BDD, and to relate this to specific metrics of symptomatology.

Hierarchical structural mapping for globally optimized estimation of functional networks.

In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct "N-step reachable structural maps". Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals.

A framework for quantifying node-level community structure group differences in brain connectivity networks.

We propose a framework for quantifying node-level community structures between groups using anatomical brain networks derived from DTI-tractography. To construct communities, we computed hierarchical binary trees by maximizing two metrics: the well-known modularity metric (Q), and a novel metric that measures the difference between inter-community and intra-community path lengths. Changes in community structures on the nodal level were assessed between generated trees and a statistical framework was developed to detect local differences between two groups of community structures.

Impaired inter-hemispheric integration in bipolar disorder revealed with brain network analyses.

Background: This represents the first graph theory-based brain network analysis study in bipolar disorder, a chronic and disabling psychiatric disorder characterized by severe mood swings. Many imaging studies have investigated white matter in bipolar disorder, with results suggesting abnormal white matter structural integrity, particularly in the fronto-limbic and callosal systems. However, many inconsistencies remain in the literature, and no study to date has conducted brain network analyses with a graph-theoretic approach.