Biology subjects

McNabb, C. B.

Publications and source records attributed to McNabb, C. B..

5 recordsLinked to original sources

bioRxiv2026

Myelin-weighted Core Network Connectivity Supports Trait-Level Future Thinking

Why are some individuals able to recollect past events and imagine future events more richly than others? Here we examine how structural connectivity within a 'core network' supporting mnemonic cognition, substantially overlapping with the canonical default network, underpins inter-individual variation in autobiographical thought. We analysed state-of-the-art magnetic resonance imaging data from 109 adults collected on a high-gradient 3T Connectom scanner, in relation to their Survey of Autobiographical Memory (SAM) scores - a psychometric measure of self-reported episodic autobiographical, semantic, spatial, and future thinking abilities. We anatomically defined a core network, employed streamline tractography to derive participant-specific structural brain networks, and weighted their connections with the myelin water fraction derived from quantitative multicomponent relaxometry. We investigated associations between graph theoretical metrics of the core network and self-reported mnemonic abilities. We found a negative association between future thinking abilities and characteristic path length, and a positive association between future thinking abilities and mean clustering coefficient. Further analysis showed that the node-specific clustering coefficient of most core network nodes was positively correlated with future thinking scores, with frontotemporal nodes showing the strongest associations. Our findings indicate that both efficient integration and local segregation of the core network are important for trait-level future thinking.

neuroscience↗

bioRxiv2026

Gut Microbiota Signatures of Brain and Peripheral gamma-amino butyric acid (GABA) in Healthy Adult Males

Gamma-aminobutyric acid (GABA), the principal inhibitory neurotransmitter in the mature human nervous system, plays a central role in neurodevelopment, emotion regulation, and sensory processing. Many psychiatric drugs act on the GABAergic system, highlighting its importance for healthy brain function. Individual differences in brain and circulating GABA levels have traditionally been attributed to genetic variation. However, several gut bacteria produce neuroactive compounds, including GABA, raising the possibility that microbial variation influences peripheral and central GABA levels and related measures in humans. This relationship remains largely unexplored. In a cross-sectional study of 251 healthy adults, we investigated associations between gut microbiota composition, central and peripheral GABA, and associated features. Brain GABA was quantified using magnetic resonance spectroscopy, blood GABA using enzyme-linked immunosorbent assays, and gut microbiota profiles using absolute and relative abundance measures derived from Flow Cytometry Fluorescent In Situ Hybridisation and 16S rRNA gene sequencing. Motor cortex GABA+ levels were positively associated with greater numbers of Bacteroides/Prevotella bacteria. A similar association was observed for blood GABA, which was additionally associated with greater numbers of lactic acid bacteria, Eubacterium rectale/Clostridium coccoides, and Clostridium histolyticum groups, and with predicted microbial gene pathways involved in GABA, propionate, and butyrate biosynthesis. No association was observed between brain and blood GABA concentrations. These findings provide new evidence for relationships between gut bacterial groups and central and peripheral GABA pools, supporting future longitudinal studies of causal links between gut microbiota and host GABA levels, as well as the potential for future intervention studies to influence GABAergic processing.

neuroscience↗

bioRxiv2024

Deep-learning and analytical models give distinct results for the brain structure-function relationship in health and in psychosis

Understanding the intricate relationship between brain structure and function is a cornerstone challenge in neuroscience, critical for deciphering the mechanisms that underlie healthy and pathological brain function. In this work, we present a comprehensive framework for mapping structural connectivity measured via diffusion-MRI to resting-state functional connectivity measured via magnetoencephalography, utilizing a deep-learning model based on a Graph Multi-Head Attention AutoEncoder. We compare the results to those from an analytical model that utilizes shortest-path-length and search-information communication mechanisms. The deep-learning model outperformed the analytical model in predicting functional connectivity in healthy participants at the individual level, achieving mean correlation coefficients higher than 0.8 in the alpha and beta frequency bands. Our results imply that human brain structural connectivity and electrophysiological functional connectivity are tightly coupled. The two models suggested distinct structure-function coupling in people with psychosis compared to healthy participants (p < 2 x 10-4 for the deep-learning model, p < 3 x 10-3 in the delta band for the analytical model). Importantly, the alterations in the structure-function relationship were much more pronounced than any structure-specific or function-specific alterations observed in the psychosis participants. The findings demonstrate that analytical algorithms effectively model communication between brain areas in psychosis patients within the delta and theta bands, whereas more sophisticated models are necessary to capture the dynamics in the alpha and beta band.

neuroscience↗

bioRxiv2019

Similarity in resting-state functional brain connectivity is not influenced by social closeness in schoolchildren

Previous research suggests that the proximity of individuals in a social network predicts how similarly their brains respond to naturalistic stimuli. However, the relationship between social connectedness and brain connectivity in the absence of external stimuli has not been examined. To investigate whether neural homophily between friends exists at rest we collected resting-state functional magnetic resonance imaging (fMRI) data from 68 school-aged girls, along with social network information from all pupils in their year groups (total 5,066 social dyads). Participants were asked to rate the amount of time they voluntarily spent with each person in their year group, and directed social network matrices and community structure were then determined from these data. No statistically significant relationships between social distance, community homogeneity and similarity of global-level resting-state connectivity were observed. Nor were we able to predict social distance using a machine learning technique (i.e. elastic net regression based on the local-level similarities in resting-state whole-brain connectivity between participants). Although neural homophily between friends exists when viewing naturalistic stimuli, this finding did not extend to functional connectivity at rest in our population. Instead, resting-state connectivity may be less susceptible to the influences of a persons social environment.

animal behavior and cognition↗

bioRxiv2018

Inter-slice leakage and intra-slice aliasing in simultaneous multi-slice echo-planar images

Simultaneous multi-slice (SMS) imaging is a popular technique for increasing acquisition speed in echo-planar imaging (EPI) fMRI. However, SMS data are prone to motion sensitivity and slice leakage artefacts, which spread signal between simultaneously acquired slices. Relevant to motion sensitivity, artefacts from moving anatomic structures propagate along the phase-encoding (PE) direction. This is particularly relevant for eye movement. As signal from the eye is acquired along with signal from simultaneously excited slices during SMS, there is potential for signal to spread in-plane and between spatially remote slices. After identifying an artefact temporally coinciding with signal fluctuations in the eye and spatially distributed in correspondence with multiband slice acceleration and parallel imaging factors, we conducted a series of small experiments to investigate eye movement artefacts in SMS data and the contribution of PE direction to the invasiveness of these artefacts. Five healthy adult volunteers were scanned during a blinking task using a standard SMS-EPI protocol with posterior-to-anterior (P>>A), anterior-to-posterior (A>>P) or right-to-left (R>>L) PE direction. The intensity of signal fluctuations (artefact severity) was measured at expected artefact positions and control positions. We demonstrated a direct relationship between eye movements and artefact severity across expected artefact regions. Within-brain artefacts were apparent in P>>A- and A>>P-acquired data but not in R>>L data due to the shift in artefact positions. Further research into eye motion artefacts in SMS data is warranted but researchers should exercise caution with SMS protocols. We recommend rigorous piloting of SMS protocols and switching to R>>L/L>>R PE where feasible.

neuroscience↗