Multi-Site Mild Traumatic Brain Injury Classification with Machine Learning and Harmonization.

Traumatic brain injury (TBI) can drastically affect an individual's cognition, physical, emotional wellbeing, and behavior. Even patients with mild TBI (mTBI) may suffer from a variety of long-lasting symptoms, which motivates researchers to find better biomarkers. Machine learning algorithms have shown promising results in detecting mTBI from resting-state functional network connectivity (rsFNC) data.

Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers.

Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies.

Dynamic connectivity predicts acute motor impairment and recovery post-stroke.

Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset.

Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states.

Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke.

Determining the number of states in dynamic functional connectivity using cluster validity indexes.

Background: Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.

Selection of Efficient Clustering Index to Estimate the Number of Dynamic Brain States from Functional Network Connectivity.

Clustering analysis is employed in brain dynamic functional connectivity to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined.

Functional outcome is tied to dynamic brain states after mild to moderate traumatic brain injury.

The current study set out to investigate the dynamic functional connectome in relation to long-term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting-state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 healthy subjects. Patients underwent a neuropsychological assessment at 3 months postinjury.

Resting-state fMRI dynamic functional network connectivity and associations with psychopathy traits.

Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample.

Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations.

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments.

Concurrent Cross-Sectional and Longitudinal Analyses of Multivariate White Matter Profiles and Clinical Functioning in Pre-Diagnosis Huntington Disease.

Background: Gray matter (GM) atrophy in the striatum and across the brain is a consistently reported feature of the Huntington Disease (HD) prodrome. More recently, widespread prodromal white matter (WM) degradation has also been detected. However, longitudinal WM studies are limited and conflicting, and most analyses comparing WM and clinical functioning have also been cross-sectional.

Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures.

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements.

High and Low Levels of an NTRK2-Driven Genetic Profile Affect Motor- and Cognition-Associated Frontal Gray Matter in Prodromal Huntington's Disease.

This study assessed how (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington's disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort ( = 715). pICA identified a genetic component highlighting , which encodes BDNF's TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas ( < 0.

Genetics Modulate Gray Matter Variation Beyond Disease Burden in Prodromal Huntington's Disease.

Huntington's disease (HD) is a neurodegenerative disorder caused by an expansion mutation of the cytosine-adenine-guanine (CAG) trinucleotide in the gene. Decline in cognitive and motor functioning during the prodromal phase has been reported, and understanding genetic influences on prodromal disease progression beyond CAG will benefit intervention therapies. From a prodromal HD cohort ( = 715), we extracted gray matter (GM) components through independent component analysis and tested them for associations with cognitive and motor functioning that cannot be accounted for by CAG-induced disease burden (cumulative effects of CAG expansion and age).

Aberrant functional network connectivity in psychopathy from a large (N = 985) forensic sample.

Psychopathy is a personality disorder characterized by antisocial behavior, lack of remorse and empathy, and impaired decision making. The disproportionate amount of crime committed by psychopaths has severe emotional and economic impacts on society. Here we examine the neural correlates associated with psychopathy to improve early assessment and perhaps inform treatments for this condition.

Whole-Brain Connectivity in a Large Study of Huntington's Disease Gene Mutation Carriers and Healthy Controls.

Huntington's disease (HD) is an inherited brain disorder characterized by progressive motor, cognitive, and behavioral dysfunctions. It is caused by abnormally large trinucleotide cytosine-adenine-guanine (CAG) repeat expansions on exon 1 of the Huntingtin gene. CAG repeat length (CAG-RL) inversely correlates with an earlier age of onset.

Optimal aggregation of FcεRI with a structurally defined trivalent ligand overrides negative regulation driven by phosphatases.

To investigate why responses of mast cells to antigen-induced IgE receptor (FcεRI) aggregation depend nonlinearly on antigen dose, we characterized a new artificial ligand, DF3, through complementary modeling and experimentation. This ligand is a stable trimer of peptides derived from bacteriophage T4 fibritin, each conjugated to a hapten (DNP). We found low and high doses of DF3 at which degranulation of mast cells sensitized with DNP-specific IgE is minimal, but ligand-induced receptor aggregation is comparable to aggregation at an intermediate dose, optimal for degranulation.

Phospholipid scramblase-1-induced lipid reorganization regulates compensatory endocytosis in neuroendocrine cells.

Calcium-regulated exocytosis in neuroendocrine cells and neurons is accompanied by the redistribution of phosphatidylserine (PS) to the extracellular space, leading to a disruption of plasma membrane asymmetry. How and why outward translocation of PS occurs during secretion are currently unknown. Immunogold labeling on plasma membrane sheets coupled with hierarchical clustering analysis demonstrate that PS translocation occurs at the vicinity of the secretory granule fusion sites.

Insights into cell membrane microdomain organization from live cell single particle tracking of the IgE high affinity receptor FcϵRI of mast cells.

Current models propose that the plasma membrane of animal cells is composed of heterogeneous and dynamic microdomains known variously as cytoskeletal corrals, lipid rafts and protein islands. Much of the experimental evidence for these membrane compartments is indirect. Recently, live cell single particle tracking studies using quantum dot-labeled IgE bound to its high affinity receptor FcϵRI, provided direct evidence for the confinement of receptors within micrometer-scale cytoskeletal corrals.

Using hierarchical clustering and dendrograms to quantify the clustering of membrane proteins.

Cell biologists have developed methods to label membrane proteins with gold nanoparticles and then extract spatial point patterns of the gold particles from transmission electron microscopy images using image processing software. Previously, the resulting patterns were analyzed using the Hopkins statistic, which distinguishes nonclustered from modestly and highly clustered distributions, but is not designed to quantify the number or sizes of the clusters. Clusters were defined by the partitional clustering approach which required the choice of a distance.