Biology subjects

Kember, J.

Publications and source records attributed to Kember, J..

4 recordsLinked to original sources

bioRxiv2024

Three Subtypes Of Autism Spectrum Disorder With Transcriptomic Signatures Derived From Morphometric Similarity Networks

Autism spectrum disorder (ASD) is a prevalent and highly heterogeneous neurodevelopmental disorder. Previous studies have attempted to identify ASD subgroups by analyzing isolated cortical structural features. However, these studies have not considered the relationship between multiple structural features, which provide information on the structural organization of the brain. Morphometric similarity network (MSN), a structural brain network contributed by multiple anatomical features (gray matter volume, mean cortical thickness, surface area, mean curvature, Gaussian curvature, curvature index, and fold index), strongly relates to cytoarchitectonic and genomic measures of histological similarity between cortical areas. We applied K-means clustering on MSN from 236 individuals with ASD and identified three subtypes. Subtype-1 showed relatively similar MSN values with typically developmental individuals (TD). Subtype-2 showed higher morphometric similarities in the lateral frontal and temporal cortical regions and lower in anterior prefrontal and occipital regions compared to TD. These patterns were the opposite in subtype-3. Behaviorally, subtype-3 had more severe verbal and social deficits compared to subtype-2. The weaker resting-state functional connectivity (rs-FC) between the language and salience networks was observed between subtype-2 and TD. Subtype-3 had stronger rs-FC between salience and default mode networks (DMN), between frontoparietal and visual networks, and between language and dorsal attention networks, while weaker rs-FC within DMN, within sensorimotor, and within salience networks. In addition, genes with expression patterns associated with regional MS changes in ASD subtypes were functionally enriched in neuron-specific biological processes related to nervous system development, synaptic signaling and chromatin organization. These genes were particularly enriched in GABAergic neurons, glutamatergic neurons, astrocytes and microglia. Taken together, our findings suggest the existence of different neuroanatomical subtypes based on multiple anatomical features in ASD with distinct transcriptomic signatures and functional connectome patterns.

neuroscience↗

bioRxiv2024

Developmental changes in hippocampal neurite colocalize with the expression of genes involved in modulating low-theta oscillations

The hippocampus is a critical brain structure supporting memory encoding and retrieval, yet the development of its microstructure in humans remains unknown. Understanding this development may provide insight into the mechanisms underlying memory and their disruption in disease. To address this, we non-invasively estimated the density and branching complexity of neurite (dendrites, axons, glial processes) using diffusion-weighted MRI in 364 participants aged 8-21. With development, we observed large increases in neurite density and branching complexity that persisted until approximately 15 years of age before stabilizing at adult-like values. Increases in neurite density were relatively homogenous across hippocampal axes, whereas increases in branching complexity were heterogeneous; increasing primarily in CA1, SRLM, subiculum, and anterior hippocampus. To assess whether this development may be attributable to specific cell-types, we tested for spatial overlap between age-related change in neurite and the cell-type composition of hippocampal tissue via cross-reference with an out-of-sample gene-expression atlas. We found age-related changes in neurite density spatially overlapped with a granule cell component; whereas age-related changes in neurite branching complexity overlapped with a pyramidal neuron component. These results provide the first glimpse at the nonlinear maturation of hippocampal microstructure and the cell-type composition of hippocampal tissue underlying these changes.

neuroscience↗

bioRxiv2022

Resting-state EEG dynamics help explain differences in response control in ADHD: Insight into electrophysiological mechanisms and sex differences

Reductions in response control (greater reaction time variability and commission error rate) are consistently observed in those diagnosed with attention-deficit/hyperactivity disorder (ADHD). Previous research suggests these reductions arise from a dysregulation of large-scale cortical networks. Here, we extended our understanding of this cortical-network/response-control pathway important to the neurobiology of ADHD. First, we assessed how dynamic changes in three resting-state EEG network properties thought to be relevant to ADHD (phase-synchronization, modularity, oscillatory power) covaried with response control during a simple perceptual decision-making task in 112 children/adolescents (aged 8-16) with and without formal ADHD diagnoses. Second, we tested whether these associations differed in males and females who were matched in age, ADHD-status and ADHD-subtype. We found that changes in oscillatory power (as opposed to phase-synchrony and modularity) are most related with response control, and that this relationship is stronger in ADHD compared to controls. Specifically, a tendency to dwell in an electrophysiological state characterized by high alpha/beta power (8-12/13-30Hz) and low delta/theta power (1-3/4-7Hz) supported response control, particularly in those with ADHD. Time in this state might reflect an increased initiation of alpha-suppression mechanisms, recruited by those with ADHD to suppress processing unfavourable to response control. We also found marginally significant evidence that this relationship is stronger in males compared to females, suggesting a distinct etiology for response control in the female presentation of ADHD. Highlights- Hidden Markov modeling of EEG functional network properties in ADHD and controls. - EEG power dynamics, not phase-synchrony or modularity, covary with response control. - Association between EEG dynamics and response control is more pronounced in ADHD. - ADHD cortical-network/response-control pathway is more prominent in males vs. females.

neuroscience↗

bioRxiv2021

Dynamic configuration of large-scale cortical networks during an inhibitory task accounts for heterogeneity in attention-deficit/hyperactivity disorder traits

The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) traits (inattention vs. hyperactivity/impulsivity) complicates diagnosis and intervention. Identifying how the configuration of large-scale functional brain networks during cognitive processing correlate with this heterogeneity could help us understand the neural mechanisms altered across ADHD presentations. Here, we recorded high-density EEG while 62 non-clinical participants (ages 18-24; 32 male) underwent an inhibitory control task (Go/No-Go). Functional EEG networks were created using sensors as nodes and across-trial phase-lag index values as edges. Using cross-validated LASSO regression, we examined whether graph-theory metrics applied to both static networks (averaged across time-windows: -500-0ms, 0-500ms) and dynamic networks (temporally layered with 2ms intervals), were associated with hyperactive/impulsive and inattentive traits. Network configuration during response execution/inhibition was associated with hyperactive/impulsive (mean R2 across test sets = .20, SE = .02), but not inattentive traits. Post-stimulus results at higher frequencies (Beta, 14-29Hz; Gamma, 30-90Hz) showed the strongest association with hyperactive/impulsive traits, and predominantly reflected less burst-like integration between modules in oscillatory beta networks during execution, and increased integration/small-worldness in oscillatory gamma networks during inhibition. We interpret the beta network results as reflecting weaker integration between specialized pre-frontal and motor systems during motor response preparation, and the gamma results as reflecting a compensatory mechanism used to integrate processing between less functionally specialized networks. This research demonstrates that the neural network mechanisms underlying response execution/inhibition might be associated with hyperactive/impulsive traits, and that dynamic, task-related changes in EEG functional networks may be useful in disentangling ADHD heterogeneity.

neuroscience↗