Biology subjects

McIntosh, R.

Publications and source records attributed to McIntosh, R..

14 recordsLinked to original sources

bioRxiv2026

Interactions between age and sex in multiscale entropy and spectral power changes across the lifespan

Multiscale entropy (MSE) changes in relation to age, whereby aging is associated with an increasing bias towards fine scale entropy. This change is thought to represent a shift toward localized information processing in the brain as we age. However, this relationship has not been tested in large sample sizes alongside other demographic factors and cognitive behaviours. This study aimed to validate previously reported effects of aging on MSE in a large open access database (Cambridge Centre for Ageing and Neuroscience, N=587) and expand the findings to include an investigation of the effects of sex and a variety of cognitive behaviours. MSE curves and power spectrum densities (PSD) were calculated for each region of interest from the magnetoencephalography data. Multivariate partial least squares analyses were used to assess the relationship between MSE or PSD and 5 behavioural / demographic factors including: age, sex, fluid intelligence, visual short-term memory and a generalized measure of cognitive function. Age was associated with increased fine scale and decreased coarse scale entropy, as well as complementary spectral changes, including slowing of peak alpha rhythms, increased beta-band activity, and reduced gamma-band activity, which replicates prior MSE and PSD findings. In both domains, these age-related patterns differentiated based on sex with advancing age. Importantly, the unique effects of sex diverged between MSE and PSD. This result indicates that entropy-based measures can isolate aspects of temporal organization that are not clearly summarized by spectral structure alone.

neuroscience↗

bioRxiv2024

Examining Relationships between Functional and Structural Brain Network Architecture, Age, and Attention Skills in Early Childhood

Early childhood is a critical period showing experience-dependent changes in brain structure and function. The complex link between the structural connectivity (SC) and functional connectivity (FC) of the brain is of particular interest, however, its relationship with both age and attention in early childhood is not well understood. In this study, children between the ages of 4 and 7, and at a one-year follow-up visit, underwent neuroimaging (diffusion-weighted and passive-viewing functional magnetic resonance imaging) and assessments for selective, sustained, and executive attention. We examined regional graph theory metrics and SC-FC coupling of the structural and functional networks. Partial least squares (PLS) was used to investigate longitudinal brain measure changes and cross-sectional associations with age and attention. We observed longitudinal changes in functional graph theory metrics and age-related decreases in SC modularity. Region-wise graph theory analyses revealed variable brain-behaviour relationships across the brain, highlighting regions where structural topology is linked to age and attentional performance. Furthermore, we identified SC as a dominant predictor of age when compared to FC and SC-FC coupling. The findings emphasise how early childhood is a dynamic period where cognitive functioning is intricately and predominantly linked to structural network features. Significance StatementThis study investigates early childhood brain development, particularly the changes in structural and functional connectivity of the brain and the relationship between them. We examined children aged 4 to 7 over a year, and used graph theory analyses to characterise variable developmental changes and brain-behaviour relationships across the brain. Our findings emphasise the nuanced and regionally specific relationship between brain network structure and behaviour in early childhood, highlighting regions where structural topology correlates with attentional performance. This research underscores the dynamic nature of childhood brain development and the predominant role of SC in cognitive maturation, providing valuable insights into typical developmental trajectories and potential targets for early intervention strategies.

neuroscience↗

bioRxiv2024

Network connectivity differences in music listening among older adults following a music-based intervention

Music-based interventions are a common feature in long-term care with clinical reports highlighting musics ability to engage individuals with complex diagnoses. While these findings are promising, normative findings from healthy controls are needed to disambiguate treatment effects unique to pathology and those seen in healthy aging. The present study examines brain network dynamics during music listening in a sample of healthy older adults before and after a music-based intervention. We found intervention effects from hidden Markov model-estimated fMRI network data. Following the intervention, participants demonstrated greater occupancy (the amount of time a network was occupied) in a temporal-mesolimbic network. We conclude that network dynamics in healthy older adults are sensitive to music-based interventions. We discuss these findings implications for future studies with individuals with neurodegeneration.

neuroscience↗

bioRxiv2023

Distinct regions of the kinesin-5 C-terminal tail are essential for mitotic spindle midzone localization and sliding force

Kinesin-5 motor proteins play essential roles during mitosis in most organisms. Their tetrameric structure and plus-end-directed motility allow them to bind to and move along antiparallel microtubules, thereby pushing spindle poles apart to assemble a bipolar spindle. Recent work has shown that the C-terminal tail is particularly important to kinesin-5 function: the tail affects motor domain structure, ATP hydrolysis, motility, clustering, and sliding force measured for purified motors, as well as motility, clustering, and spindle assembly in cells. Because previous work has focused on presence or absence of the entire tail, the functionally important regions of the tail remain to be identified. We have therefore characterized a series of kinesin-5/Cut7 tail truncation alleles in fission yeast. Partial truncation causes mitotic defects and temperature-sensitive growth, while further truncation that removes the conserved BimC motif is lethal. We compared the sliding force generated by cut7 mutants using a kinesin-14 mutant background in which some microtubules detach from the spindle poles and are pushed into the nuclear envelope. These Cut7-driven protrusions decreased as more of the tail was truncated, and the most severe truncations produced no observable protrusions. Our observations suggest that the C-terminal tail of Cut7p contributes to both sliding force and midzone localization. In the context of sequential tail truncation, the BimC motif and adjacent C-terminal amino acids are particularly important for sliding force. In addition, moderate tail truncation increases midzone localization, but further truncation of residues N-terminal to the BimC motif decreases midzone localization.

cell biology↗

bioRxiv2022

Kinesin-5/Cut7 moves bidirectionally on fission-yeast spindles with activity that increases in anaphase

Kinesin-5 motors are essential to separate mitotic spindle poles and assemble a bipolar spindle in many organisms. These tetrameric motors crosslink and slide apart antiparallel microtubules via microtubule plus-end-directed motility. However, kinesin-5s typically accumulate more at spindle poles than in the center of the spindle where antiparallel microtubule overlaps are most numerous. While the relevance of this localization has remained unclear, increasing evidence suggests that it occurs due to bidirectional motility or trafficking of kinesin-5 motors. The kinesin-5 motor Cut7p from fission yeast has been shown to move bidirectionally in reconstituted systems. However, bidirectional movement has not been shown in cells and the funtion of the minus-end-directed movement remains unclear. Here, we characterized the motility of kinesin-5/Cut7 on bipolar and monopolar spindles in fission yeast and observed movement both toward plus and minus ends of microtubules. Notably, we found that the activity of the motor increases at the onset on anaphase B. Perturbations to microtubule dynamics did not significantly change the observed Cut7p movement, while Cut7p mutation reduced or abolished observable movement. These results suggest that the directed movement of Cut7p was due to the motility of the motor itself. Mutations of Cut7p that decreased plus-end-directed motility enhanced its spindle-pole localization. In contrast, abolishing Cut7 motility or replacing it with plus-end-directed human Eg5 eliminates the pole localization. Our results suggest a new hypothesis for the function of minus-end-directed motility and spindle-pole localization of kinesin-5s in spindle assembly.

cell biology↗

bioRxiv2021

Multi-scale brain simulation with integrated positron emission tomography yields hidden local field potential activity that augments machine-learning classification of Alzheimer's disease

INTRODUCTIONComputational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimers disease. METHODSWe enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local Amyloid {beta} PET with altered excitability. We use PET and MRI data from 33 participants of Alzheimers Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for machine-learning classification. RESULTSThe combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the Alzheimers-typical spatial distribution. DISCUSSIONThe cause-and-effect implementation of local hyperexcitation caused by Amyloid {beta} can improve the machine-learning-driven classification of Alzheimers and demonstrates TVBs ability to decode information in empirical data employing connectivity-based brain simulation. RESEARCH IN CONTEXTO_LISYSTEMATIC REVIEW. Machine-learning has been proven to augment diagnostics of dementia in several ways. Imaging-based approaches enable early diagnostic predictions. However, individual projections of long-term outcome as well as differential diagnosis remain difficult, as the mechanisms behind the used classifying features often remain unclear. Mechanistic whole-brain models in synergy with powerful machine learning aim to close this gap. C_LIO_LIINTERPRETATION. Our work demonstrates that multi-scale brain simulations considering Amyloid {beta} distributions and cause-and-effect regulatory cascades reveal hidden electrophysiological processes that are not readily accessible through measurements in humans. We demonstrate that these simulation-inferred features hold the potential to improve diagnostic classification of Alzheimers disease. C_LIO_LIFUTURE DIRECTIONS. The simulation-based classification model needs to be tested for clinical usability in a larger cohort with an independent test set, either with another imaging database or a prospective study to assess its capability for long-term disease trajectories. C_LI

neuroscience↗

bioRxiv2019

Complexity matching: brain signals mirror environment information patterns during music listening and reward

Understanding how the human brain integrates information from the environment with ongoing, internal brain signals in order to produce individual perspective is an essential element of understanding the human mind. Brain signal complexity, measured with multiscale entropy, has been employed as a measure of information processing in the brain (Carpentier et al., 2016), and we propose that it can also be used to measure the information available from a stimulus. We can directly assess the correspondence, or functional isomorphism, between brain signal complexity and stimulus complexity as an indication of how well the brain reflects the content of the environment in an analysis that we termed complexity matching. Music makes an ideal stimulus input because it is a multidimensional, complex signal, and because of its emotion and reward-inducing potential. We found that electroencephalography (EEG) complexity was lower and more closely resembled the musical complexity when participants performed a perceptual task that required them to closely track the acoustics, compared to an emotional task that asked them to think about how the music made them feel. Music-derived reward scores on the Barcelona Music Reward Questionnaire (Mas-Herrero et al., 2013) correlated with worse complexity matching and higher EEG complexity. Compared to perceptual-level processing, emotional and reward responses are associated with additional internal information processes above and beyond those in the external stimulus.\n\nSignificance StatementExperience of our world is combination of the input from the environment, our expectations, and individual responses. For example, the same piece of music can elict happiness in one person and sadness in another. We researched this by measuring the information in pieces of music and whether listeners brain more closely followed that, or whether additional information was added by the brain. We noted when listeners were reacting to how music made them feel, their brains added more information and the degree to which this occurred related to how much they find music rewarding. Thus, we were able to provide clues as to how the brain integrates incoming information, adding to it to provide a richer perceptual and emotional experience.

neuroscience↗

bioRxiv2019

Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer's disease

IntroductionWhile the prevalence of neurodegenerative diseases associated with dementia such as Alzheimers disease (AD) increases, our knowledge on the underlying mechanisms, outcome predictors, or therapeutic targets is limited. In this work, we demonstrate how computational multi-scale brain modelling links phenomena of different scales and therefore identifies potential disease mechanisms leading the way to improved diagnostics and treatment. MethodsThe Virtual Brain (TVB; thevirtualbrain.org) neuroinformatics platform allows standardized large-scale structural connectivity-based simulations of whole brain dynamics. We provide proof of concept for a novel approach that quantitatively links the effects of altered molecular pathways onto neuronal population dynamics. As a novelty, we connect chemical compounds measured with positron emission tomography (PET) with neural function in TVB addressing the phenomenon of hyperexcitability in AD related to the protein amyloid beta (Abeta). We construct personalized virtual brains based on individual PET derived distributions of Abeta in patients with mild cognitive impairment (MCI, N=8) and Alzheimers Disease (AD, N=10) and in age-matched healthy controls (HC, N=15) using data from ADNI-3 data base (http://adni.lni.usc.edu). In the personalized virtual brains, individual Abeta burden modulates regional inhibition, leading to disinhibition and hyperexcitation with high Abeta loads. We analyze simulated regional neural activity and electroencephalograms (EEG). ResultsKnown empirical alterations of EEG in patients with AD compared to HCs were reproduced by simulations. The virtual AD group showed slower frequencies in simulated local field potentials and EEG compared to MCI and HC groups. The heterogeneity of the Abeta load is crucial for the virtual EEG slowing which is absent for control models with homogeneous Abeta distributions. Slowing phenomena primarily affect the network hubs, independent of the spatial distribution of Abeta. Modeling the N-methyl-D-aspartate (NMDA) receptor antagonism of memantine in local population models, reveals potential functional reversibility of the observed large-scale alterations (reflected by EEG slowing) in virtual AD brains. DiscussionWe demonstrate how TVB enables the simulation of systems effects caused by pathogenetic molecular candidate mechanisms in human virtual brains.

neuroscience↗

bioRxiv2019

THE HIDDEN REPERTOIRE OF BRAIN DYNAMICS AND DYSFUNCTION

The purpose of this paper is to describe a framework for the understanding of rules that govern how neural system dynamics are coordinated to produce behavior. The framework, structured flows on manifolds (SFM), posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Although this is a general framework, we focus on the application to brain disorders. We first explain the Epileptor, a phenomenological computational model showing fast and slow dynamics, but also a hidden repertoire whose expression is similar to refractory status epilepticus. We suggest that epilepsy represents an innate brain state whose potential may be realized only under certain circumstances. Conversely, deficits from damage or disease processes, such as stroke or dementia, may reflect both the disease process per se and the adaptation of the brain. SFM uniquely captures both scenarios.\n\nThe Hidden Repertoire of Brain Dynamics and Dysfunction

neuroscience↗

bioRxiv2019

Basal protrusions mediate spatiotemporal patterns of spinal neuron differentiation

During early spinal cord development, neurons of particular subtypes differentiate with a sparse periodic pattern while later neurons differentiate in the intervening space to eventually produce continuous columns of similar neurons. The mechanisms that regulate this spatiotemporal pattern are unknown. In vivo imaging of zebrafish reveals differentiating spinal neurons transiently extend two long protrusions along the basal surface of the spinal cord prior to axon initiation. These protrusions express Delta protein consistent with the possibility they influence Notch signalling at a distance of several cell diameters. Experimental reduction of laminin expression leads to smaller protrusions and shorter distances between differentiating neurons. The experimental data and a theoretical model support the proposal that the pattern of neuronal differentiation is regulated by transient basal protrusions that deliver temporally controlled lateral inhibition mediated at a distance. This work uncovers novel, stereotyped protrusive activity of new-born neurons that organizes long distance spatiotemporal patterning of differentiation.

developmental biology↗

bioRxiv2018

A macaque connectome for large-scale network simulations in TheVirtualBrain

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

neuroscience↗

bioRxiv2018

The functional reach of the hippocampal memory system to the oculomotor system.

Visual exploration is related to activity in the hippocampus (HC) and/or extended medial temporal lobe system (MTL), is influenced by stored memories, and is altered in amnesic cases. An extensive set of polysynaptic connections exists both within and between the HC and oculomotor systems such that investigating how HC responses ultimately influence neural activity in the oculomotor system, and the timing by which such neural modulation could occur is not trivial. We leveraged TheVirtualBrain, a software platform for large-scale network simulations, to model the functional dynamics that govern the interactions between the two systems in the macaque cortex. Evoked responses following the stimulation of the MTL and some, but not all, subfields of the HC resulted in observable responses in oculomotor regions, including the frontal eye fields (FEF), within the time of a gaze fixation. Modeled lesions to some MTL regions slowed the dissipation of HC signal to oculomotor regions, whereas HC lesions generally did not affect the rapid MTL activity propagation to oculomotor regions. These findings provide a framework for investigating how information represented by the HC/MTL may influence the oculomotor system during a fixation and predict how HC lesions may affect visual exploration. Author SummaryNo major account of oculomotor (eye movement) guidance considers the influence of the hippocampus (HC) and broader medial temporal lobe (MTL) system, yet it is clear that information is exchanged between the two systems. Prior experience influences current viewing, and cases of amnesia due to compromised HC/MTL function show specific alterations in viewing behaviour. By modeling large-scale network dynamics, we show that stimulation of subregions of the HC, and of the MTL, rapidly results in observable responses in oculomotor control regions, and that HC/MTL lesions alter signal propagation. These findings suggest that information from memory may readily guide visual exploration, and calls for a reconsideration of the neural circuitry involved in oculomotor guidance.

neuroscience↗

bioRxiv2018

BED-domain containing immune receptors confer diverse resistance spectra to yellow rust

Introductory paragraph Introductory paragraph Main Methods Author contributions Data availability Competing interests References Crop diseases reduce wheat yields by ~25% globally and thus pose a major threat to global food security1. Genetic resistance can reduce crop losses in the field and can be selected for through the use of molecular markers. However, genetic resistance often breaks down following changes in pathogen virulence, as experienced with the wheat yellow (stripe) rust fungus Puccinia striiformis f. sp. tritici (Pst)2. This highlights the need to (i) identify genes that alone or in combination provide broad-spectrum resistance and (ii) increase our understanding of the underlying molecular mod ...

plant biology↗

bioRxiv2018

Subject-specificity of the correlation between large-scale structural and functional connectivity

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal co-activations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individuals SC and FC is not subject-specific for five of the datasets. Subject-specificity of SC-FC fit is achieved only for one of the six datasets, the multi-modal Glasser HCP parcellated dataset. We show that subject-specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared to the larger variability in FC.

neuroscience↗