Biology subjects

Holmes, A.

Publications and source records attributed to Holmes, A..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Genome scale metabolic modelling of human gut microbes to inform rational community design

The human gut microbiome impacts host health through metabolite production, notably short-chain fatty acids (SCFAs) derived from digestion-resistant carbohydrates (DRCs). While DRC supplementation offers a means to modulate the microbiome therapeutically, its effectiveness is often limited by the microbial communitys complexity and individual variability in microbiome functionality. We utilized genome-scale metabolic models (GEMs) from the AGORA collection to provide a system-level overview of the metabolic capabilities of human gut microbes in terms of carbohydrate trophic networks and propose improved therapeutic interventions, based on microbial community design. Our study inferred the capability of AGORA strains to consume carbohydrates of varying structural complexities--including DRCs--and to produce metabolites amenable to cross-feeding, such as SCFAs. The resulting functional database indicated that DRC-degrading abilities are rare among gut microbes, suggesting that the presence or absence of specific taxa can determine the success of DRC-based interventions. Additionally, we found that metabolite production profiles exceed family-level variation, highlighting the limitations in predicting intervention outcomes based on gut microbial composition assessed at higher taxonomic levels. In response to these findings, we integrate reverse ecology principles, network analysis and GEM community modelling to guide the design of minimal yet resilient microbial communities to better guarantee intervention response (purpose-based communities). As a proof of principle, we predicted a purpose-based community designed to enhance butyrate production when used in conjunction with DRC supplementation, that displays resilience under nutritional stress, such as amino acid restriction. We further seeded the identified purpose-based community into modelled human microbiomes previously demonstrated to accurately predict SCFA production profiles. The analysis confirmed that such intervention significantly promotes butyrate production across samples, with those that presented a comparatively lower butyrate production pre-intervention displaying the largest increase in butyrate production after seeding. Our work highlights the potential of combining GEMs with community design to infer effective microbiome interventions, ultimately leading to improved health outcomes.

ecology↗

bioRxiv2024

Volumetric mesoscopic electrophysiology: a new imaging modality for the non-human primate

The primate brain is a densely interconnected organ whose function is best understood by recording from the entire structure in parallel, rather than parts of it in sequence. However, available methods either have limited temporal resolution (functional magnetic resonance imaging), limited spatial resolution (macroscopic electroencephalography), or a limited field of view (microscopic electrophysiology). To address this need, we developed a volumetric, mesoscopic recording approach (MePhys) by tessellating the volume of a monkey hemisphere with 992 electrode contacts that were distributed across 62 chronically implanted multi-electrode shafts. We showcase the scientific promise of MePhys by describing the functional interactions of local field potentials between the more than 300,000 simultaneously recorded pairs of electrodes. We find that a subanesthetic dose of ketamine -believed to mimic certain aspects of psychosis- can create a pronounced state of functional disconnection and prevent the formation of stable large-scale intrinsic states. We conclude that MePhys provides a new and fundamentally distinct window into brain function whose unique profile of strengths and weaknesses complements existing approaches in synergistic ways.

neuroscience↗

bioRxiv2024

Genomic epidemiology of Candida auris introduction and outbreaks in the United Kingdom

BackgroundCandida auris is a globally emerging fungal pathogen that causes nosocomial invasive infections, particularly in intensive care units (ICU). Four prominent genetic clades originating from distinct geographic regions have been identified: South Asia (Clade I), East Asia (Clade II), Africa (Clade III) and South America (Clade IV) with each clade presenting differing antifungal resistance profiles. We aimed to elucidate the epidemiology of this infection in the United Kingdom (UK) 2014 - 2019 using genomic approaches. MethodsGenome sequences from 24 isolates from six patients in four London hospitals were combined with genome sequences from 183 additional outbreak isolates from nine UK hospitals. These data were used to assess the numbers and timings of C. auris lineage introductions into the UK and to infer subsequent patterns of inter- and intra-hospital transmission. FindingsWe found evidence for at least three introductions of C. auris into the UK, one from Clade I and one from Clade III. The evolutionary rate of C. auris Clade I and Clade III were estimated at 2.764 x 10-4 and 3.186 x 10-4 substitutions per polymorphic site per year, respectively, with entry into the UK as 2013 and 2016 for Clades I and III respectively. We observed temporal and geographical evidence for multiple transmission events between hospitals and identified local within-hospital patient-to-patient transmission events. InterpretationThese data confirm that C. auris is a newly emerged infection in the UK with at least three original introductions into this country. Our analysis shows that hospital outbreaks are linked and confirms that transmission amongst patients has occurred, explaining local hospital outbreaks. Our findings argue that enhanced surveillance of C. auris infection within the UK is necessary in order to protect healthcare and to curtail expansion of this emerging infection. FundingThis work was supported by a Wellcome Trust Institutional Strategic Support Fund Springboard Fellowship, and by the Medical Research Council (MR/R015600/1), the Natural Environment Research Council (NE/P001165/1) and the Wellcome Trust (219551/Z/19/Z). Research in contextSince its first description in 2009, Candida auris has spread across the globe. For this study, authors considered all publications describing whole genome sequences of isolates within the UK up until 2020 to assess the genomic epidemiology of this fungal pathogen. This study adds value to the current understanding of C. auris epidemiology by undertaking the first analysis to assess transmission between and within hospitals using genomic data. We also provide the first in-depth temporal analysis to estimate likely times of introduction into the UK. These results have clinical implications, encouraging hospitals to identify yeast upon admission and to assess multiple isolates from single patients, which may harbour much diversity in terms of genetics and drug resistance.

genomics↗

bioRxiv2023

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

neuroscience↗

bioRxiv2023

Functional brain networks are associated with both sex and gender in children

Sex and gender are associated with human behavior throughout the lifespan and across health and disease, but whether they are associated with similar or distinct neural phenotypes is unknown. Here, we demonstrate that, in children, sex and gender are uniquely reflected in the intrinsic functional connectivity of the brain. Unimodal networks are more strongly associated with sex while heteromodal networks are more strongly associated with gender. These results suggest sex and gender are irreducible to one another not only in society but also in biology.

neuroscience↗

bioRxiv2023

ATR kinase supports normal proliferation in the early S phase by preventing replication resource exhaustion.

The ATR kinase, which coordinates cellular responses to DNA replication stress, is also essential for the proliferation of normal unstressed cells. Although its role in the replication stress response is well defined, the mechanisms by which ATR supports normal cell proliferation remain elusive. Here, we show that ATR is dispensable for the viability of G0-arrested naive B cells. However, upon cytokine-induced proliferation, Atr-deficient B cells initiate DNA replication efficiently in early S phase, but by mid-S phase they display dNTP depletion, fork stalling, and replication failure. Nonetheless, productive DNA replication can be restored in Atr-deficient cells by pathways that suppress origin firing, such as downregulation of CDC7 and CDK1 kinase activities. Together, these findings indicate that ATR supports the proliferation of normal unstressed cells by tempering the pace of origin firing during the early S phase to avoid exhaustion of dNTPs and other replication factors.

genetics↗

bioRxiv2023

Distinct brain network features predict internalizing and externalizing traits in children and adults

Internalizing and externalizing traits are two distinct classes of behaviors in psychiatry. However, whether shared or unique brain network features predict internalizing and externalizing behaviors in children and adults remain poorly understood. Using a sample of 2262 children from the Adolescent Brain Cognitive Development (ABCD) study and 752 adults from the Human Connectome Project (HCP), we show that network features predicting internalizing and externalizing behavior are, at least in part, dissociable in children, but not in adults. In ABCD children, traits within internalizing and externalizing behavioral categories are predicted by more similar network features concatenated across task and resting states than those between different categories. We did not observe this pattern in HCP adults. Distinct network features predict internalizing and externalizing behaviors in ABCD children and HCP adults. These data reveal shared and unique brain network features accounting for individual variation within broad internalizing and externalizing categories across developmental stages.

neuroscience↗

bioRxiv2022

Brain-based predictions of psychiatric illness-linked behaviors across the sexes

BackgroundIndividual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined. MethodsIn this work, we used predictive modelling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development study (n=5260; 2571 females). ResultsWe demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females, but only predict internalizing behaviors in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks including default, limbic, control, and dorsal attention networks are associated with internalizing and externalizing behaviors as well as attentional deficits. ConclusionsTaken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. These results highlight the need to consider factors beyond just neurobiology in the diagnosis and treatment of psychiatric illnesses.

neuroscience↗

bioRxiv2022

Bringing language to dynamic brain states: the default network dominates neural responses to evolving movie stories

Naturalistic neuroscience opened the door to new insights into neural circuits that serve real-world dynamic perception. Such studies have often neglected the rich texture of the movie narrative itself, but semantic content can be used to contextualize the induced neural responses. Here, we translated natural language processing tools from machine learning to characterize brain states estimated from hidden Markov models. Our analytical strategy allowed pitting shallow unimodal against the deep associative brain network layers in explaining how semantic content of the movie links to observed neural activity. Pooling information across >53,000 brain image time points watching Forrest Gump, we could show that distinct dynamic brain states capture unique semantic facets along the unfolding movie narrative. The spatiotemporal dynamics of brain states explicitly captured subject-level responses throughout the brain network hierarchy. Across all analyses, the default network was most intimately linked to semantic information integration, and this neural system switched online for longest durations during movie watching. Further, we identified and described two mechanisms of how the default network liaises dynamically with microanatomically defined subregion partners: the amygdala and the hippocampus. Our study thus unlocks the potential of natural language processing to explore neural processes in everyday life situations that engage key aspects of conscious awareness.

neuroscience↗

bioRxiv2022

Human Brain Development: a cross-sectional and longitudinal study integrating multiple neuromorphological features

Brain maturation studies typically examine relationships linking a single morphometric feature with aspects of cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the long-running National Institute of Mental Health developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. We sought to use this technique longitudinally to examine covariance in rates of change, which highlighted a preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV; 96 % covariance explained) that recapitulated patterns of reduced CT, GI, and SA that are generally related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment. SignificanceThe complex anatomy of the cortical sheet is best characterized using multiple morphometric characteristics. We expanded on recent developments in matrix factorization to identify spatial patterns of covariance across the cortical sheet. Using a large, well-characterized dataset, we examined the differential contributions of neuroanatomical features to cortical covariation in a single analytical framework using both cross-sectional and longitudinal data. We identified dominant modes of covariance between cortical morphometric features and their coordinated pattern of change, demonstrating sexually differentiated patterns and a strong association with variability in age, socioeconomic status, and cognitive ability. This novel characterization of cortical morphometry provides an important understanding of the interdependencies between neuroanatomical measures in the brain and behavioral development context.

neuroscience↗

bioRxiv2022

Reduced ventral attention network connectivity is linked to the accelerated maturation of adult-like cortical organization in childhood

The human brain experiences functional changes through childhood and adolescence, shifting from an organizational framework anchored within sensorimotor and visual regions into one that is balanced through interactions with later-maturing aspects of association cortex. Here, we link this profile of functional reorganization to the development of ventral attention network connectivity across independent datasets. We demonstrate that maturational changes in cortical organization preferentially link to within-network connectivity and heightened degree centrality in the ventral attention network, while connectivity within network-linked vertices predicts cognitive ability. This connectivity is closely associated with maturational refinement of cortical organization. Children with low ventral attention network connectivity exhibit adolescent-like topographical profiles, suggesting that attentional systems may be relevant in understanding how brain functions are refined across development. These data suggest a role for attention networks in supporting age-dependent shifts in cortical organization and cognition across childhood and adolescence.

neuroscience↗

bioRxiv2022

Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features and populations

Individual differences in brain anatomy can be used to predict variability in cognitive function. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n=1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n=1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranium volumes from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

neuroscience↗

bioRxiv2022

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive domain. Furthermore, all modalities were able to predict cognition better than other behavioral domains. Combining all modalities improved prediction of cognition, but not other behavioral domains. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC. HighlightsO_LIFC predicts behavior better than anatomical and diffusion features C_LIO_LICognition is predicted better than other behavioral domains regardless of modality C_LIO_LICombining resting & task FC improves prediction as much as combining all modalities C_LIO_LIFindings were replicated over 3 regression models and 2 datasets C_LI

neuroscience↗

bioRxiv2021

Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population

BackgroundA strong predictor for the development of alcohol use disorders (AUDs) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool for elucidating the genetic basis of behavioral and physiological traits relevant to AUDs; but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations allow for the opportunity to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification. MethodsWe have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high density MEGAMuga and GIGAMuga arrays to obtain genotypes ranging from 77,808 - 143,259 SNPs. In addition, we performed RNA sequencing in striatum to map expression QTLs and to identify gene expression-trait correlations. ResultsWe then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exist between DNA sequence, gene expression values and ethanol-related phenotypes to prioritize our list of positional candidate genes. ConclusionsOur results can be used to identify alleles that contribute to AUDs in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.

genetics↗

bioRxiv2021

Neural correlates of individual differences in speech-in-noise performance in a large cohort of cochlear implant users

ObjectivesUnderstanding speech in noise (SiN) is a complex task that recruits multiple cortical subsystems. Individuals vary in their ability to understand SiN. This cannot be explained by simple peripheral hearing profiles, but recent work by our group (Kim et al., 2021, Neuroimage) highlighted central neural factors underlying the variance in SiN ability in normal hearing (NH) subjects. The current study examined neural predictors of speech-in-noise ability in a large cohort of cochlear-implant (CI) users, with the long-term goal of developing a simple electrophysiological correlate that could be implemented in clinics. DesignWe recorded electroencephalography (EEG) in 114 post-lingually deafened CI users while they completed the California Consonant Test (CCT): a word-in-noise task. In many subjects, data were also collected on two other commonly used clinical measures of speech perception: a word-in-quiet task (Consonant-Nucleus-Consonant [CNC]) word and a sentence-in-noise task (AzBio sentences). Neural activity was assessed at a single vertex electrode (Cz), to maximize generalizability to clinical situations. The N1-P2 complex of event-related potentials (ERPs) at this location were included in multiple linear regression analyses, along with several other demographic and hearing factors as predictors of speech in noise performance. ResultsIn general, there was a good agreement between the scores on the three speech perception tasks. ERP amplitudes did not predict AzBio performance which was predicted by the duration of device use, low-frequency hearing thresholds, and age. However, ERP amplitudes were strong predictors for performance for both word recognition tasks: the CCT (which was conducted simultaneously with EEG recording), and the CNC (conducted offline). These correlations held even after accounting for known predictors of performance including residual low-frequency hearing thresholds. In CI-users, better performance was predicted by an increased cortical response to the target word, in contrast to previous reports in normal-hearing subjects in whom speech perception ability was accounted for by the ability to suppress noise. ConclusionsThese data indicate a neurophysiological correlate of speech-in-noise performance that can be relatively easily captured within the clinic, thereby revealing a richer profile of an individuals hearing performance than shown by psychoacoustic measures alone. These results also highlight important differences between sentence and word recognition measures of performance and suggest that individual differences in these measures may be underwritten by different mechanisms. Finally, the contrast with prior reports of NH listeners in the same task suggests CI-users performance may be explained by a different weighting of neural processes than NH listeners.

neuroscience↗

bioRxiv2021

The role of L-arabinose metabolism for Escherichia coli O157:H7 in edible plants

Arabinose is a major plant aldopentose in the form of arabinans complexed in cell wall polysaccharides or glycoproteins (AGP), but comparatively rare as a monosaccharide. L-arabinose is an important bacterial metabolite, accessed by pectolytic microorganisms such as Pectobacterium atrosepticum via pectin and hemicellulose degrading enzymes. However, not all plant-associated microbes encode cell wall degrading enzymes, yet can metabolise L-arabinose, raising questions about their use of and access to the glycan in plants. Therefore, we examined L-arabinose metabolism in the food-borne pathogen Escherichia coli O157:H7 (isolate Sakai) during its colonisation of plants. L-arabinose metabolism (araBA) and transport (araF) genes were activated at 18 {degrees}C in vitro by L-arabinose and expressed over prolonged periods in planta. Although deletion of araBAD did not impact the colonisation ability of E. coli O157:H7 (Sakai) on plants, araA was induced on exposure to spinach cell wall polysaccharides. Furthermore, debranched and arabinan oligosaccharides induced ara metabolism gene expression in vitro, and stimulated modest proliferation, while immobilised pectin did not. Thus, E. coli O157:H7 (Sakai) can utilise pectin/AGP- derived L-arabinose as a metabolite, but differs fundamentally in ara gene organisation, transport and regulation from the related pectinolytic species P. atrosepticum, reflective of distinct plant- associated lifestyles.

microbiology↗

bioRxiv2021

Anatomical and Functional Gradients Shape Dynamic Functional Connectivity in the Human Brain

Large-scale biophysical circuit models can provide mechanistic insights into the fundamental micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. By allowing local synaptic properties to vary across brain regions, recent large-scale circuit models have demonstrated better fit to empirical observations, such as inter-regional synchrony averaged over several minutes, i.e. static functional connectivity (FC). However, most previous models do not capture how inter-regional synchrony patterns vary over timescales of seconds, i.e., time-varying FC dynamics. Here we developed a spatially-heterogeneous large-scale dynamical circuit model that allowed for variation in local circuit properties across the human cortex. We showed that parameterizing local circuit properties with both anatomical and functional gradients was necessary for generating realistic static and dynamical properties of resting-state fMRI activity. Furthermore, empirical and simulated FC dynamics demonstrated remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. We found that time-varying regional fMRI amplitude tracked multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggested that sensory-motor regions were a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matched the principal gradient of gene expression that encompassed certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

neuroscience↗

bioRxiv2021

Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior

Resting-state functional MRI (rs-fMRI) allows estimation of individual-specific cortical parcellations. We have previously developed a multi-session hierarchical Bayesian model (MS-HBM) for estimating high-quality individual-specific network-level parcellations. Here, we extend the model to estimate individual-specific areal-level parcellations. While network-level parcellations comprise spatially distributed networks spanning the cortex, the consensus is that areal-level parcels should be spatially localized, i.e., should not span multiple lobes. There is disagreement about whether areal-level parcels should be strictly contiguous or comprise multiple non-contiguous components, therefore we considered three areal-level MS-HBM variants spanning these range of possibilities. Individual-specific MS-HBM parcellations estimated using 10min of data generalized better than other approaches using 150min of data to out-of-sample rs-fMRI and task-fMRI from the same individuals. Resting-state functional connectivity (RSFC) derived from MS-HBM parcellations also achieved the best behavioral prediction performance. Among the three MS-HBM variants, the strictly contiguous MS-HBM (cMS-HBM) exhibited the best resting-state homogeneity and most uniform within-parcel task activation. In terms of behavioral prediction, the gradient-infused MS-HBM (gMS-HBM) was numerically the best, but differences among MS-HBM variants were not statistically significant. Overall, these results suggest that areal-level MS-HBMs can capture behaviorally meaningful individual-specific parcellation features beyond group-level parcellations. Multi-resolution trained models and parcellations are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Kong2022_ArealMSHBM).

neuroscience↗