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Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.

TLDR
The results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.
Abstract
Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients = 86.4%, controls = 96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease.

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BrainNet Viewer: a network visualization tool for human brain connectomics.

TL;DR: This work has developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models, and helps researchers to visualize brain networks in an easy, flexible and quick manner.
Journal ArticleDOI

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.
Journal ArticleDOI

Resting-state functional connectivity in major depressive disorder: A review

TL;DR: A model that incorporates changes in functional connectivity within current hypotheses of network-dysfunction in MDD is proposed and consistent findings correspond to the current understanding of depression as a network-based disorder.
Journal ArticleDOI

Abnormal structural networks characterize major depressive disorder: a connectome analysis.

TL;DR: This is the first report to use DTI to show the structural connectomic alterations present in MDD, and highlights that altered structural connectivity between nodes of the default mode network and the frontal-thalamo-caudate regions are core neurobiological features associated with MDD.
Journal ArticleDOI

From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics

TL;DR: This review surveys the literature on pattern recognition for making diagnostic predictions in psychiatric disorders, and evaluates progress made in translating such findings towards clinical application, and identifies specific clinical contexts in which pattern recognition can add value in the short to medium term.
References
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Journal ArticleDOI

Diagnostic and Statistical Manual of Mental Disorders

TL;DR: An issue concerning the criteria for tic disorders is highlighted, and how this might affect classification of dyskinesias in psychotic spectrum disorders.
Journal ArticleDOI

A rating scale for depression

TL;DR: The present scale has been devised for use only on patients already diagnosed as suffering from affective disorder of depressive type, used for quantifying the results of an interview, and its value depends entirely on the skill of the interviewer in eliciting the necessary information.
Journal ArticleDOI

Nonlinear dimensionality reduction by locally linear embedding.

TL;DR: Locally linear embedding (LLE) is introduced, an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs that learns the global structure of nonlinear manifolds.
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Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain

TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.
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Advances in functional and structural MR image analysis and implementation as FSL.

TL;DR: A review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB) on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data.
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