Biology subjects

McIntosh, A. R.

Publications and source records attributed to McIntosh, A. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Chemoarchitectural influences on the cortical connectome confer resilience in aging

The macroscale signaling of the brain emerges from the integration of connected areas, orchestrated by microscale, regional molecular processes. Despite growing multimodal neuroimaging data resources, the role of cortical chemoarchitecture in shaping inter-regional functional connectivity remains poorly understood. Here, we examine whether brain regions that share a chemoarchitectural signature exhibit strong frequency-resolved functional connectivity, and whether aging moderates this neurochemical-functional alignment. Using magnetoencephalography data from across the healthy adult lifespan (n = 569), we identify a frequency-dependent organization of functional connectivity by slow neuromodulator systems, with low-frequency bands ({theta}-) shaped most strongly by noradrenergic systems, and faster ({beta}) alignment dependent on serotonergic chemoarchitecture. Aging strengthens the influence of neurochemistry on inter-regional connectivity, with frequency-specific implications for age-related cognitive performance. Neurochemical influences on{theta} -band connectivity were associated with worse cognition in older adults, while the opposite was true for the low-{gamma} ({gamma}{downarrow}) band. This suggests that neuromodulatory preservation of high-frequency dynamics in older adults may reflect neural resilience. Together, our findings indicate that neurochemical-functional alignment is frequency-dependent, distinguishing between maladaptive and resilient modes of brain organization.

neuroscience↗

bioRxiv2025

Contribution of Mesenchymal-like and Epithelial Cellular Subsets to Chemotherapy Resistance in Triple-Negative Breast Cancer

Background/ObjectivesTriple-negative breast cancer (TNBC) tumors are typically heterogeneous, predominantly epithelial tissues with discrete patches of mesenchymal-like TNBC cells, that differ in their invasiveness, proliferation potential and response to treatment. However, the contribution of mesenchymal-like and epithelial TNBC cells in the persistence of chemotherapy resistant disease remains poorly understood. MethodsMesenchymal-like and epithelial TNBC cell types were detected by multiplex fluorescent immunohistochemistry using antibodies against vimentin, Ki67, and Annexin A6 (AnxA6). Chemotherapy drug resistant mesenchymal-like and epithelial TNBC cell populations were established by pulse exposure and stepwise dose escalation and validated by 3D cultures and unbiased antibody arrays. ResultsAnalysis of the response of stage IV TNBC tumors treated with six common chemotherapy regimens resulted in 36% complete response and 64% partial response with residual tumor sizes ranging from 0.5 to 37.0 mm. Treatment of TNBC cells with chemotherapy agents led to distinct resistance signatures including downregulation of survivin and upregulation of M-CSF and CXCL8/IL-8 in model mesenchymal-like, and upregulation of CCL2/MCP-1, CTSS and DKK-1 in model epithelial TNBC cells. The inhibitory phosphorylation of GSK-3{beta} (p-S9) increased in paclitaxel resistant epithelial cells but decreased in resistant mesenchymal-like TNBC cells. Finally, chemotherapy resistance also activated p90 ribosomal S6 kinases (RSK1/2) in both cell types, while activation of mitogen- and stress-activated kinases (MSK1/2) was only observed in chemotherapy resistant epithelial TNBC cells. ConclusionsThese data reveal that chemotherapy resistance of epithelial and mesenchymal-like TNBC cellular subsets upregulated distinct profiles of proinflammatory and immune cell chemotactic cytokines and modulated the activities of GSK-3{beta}, p90 RSK1/2 and the related MSK1/2. Targeting these factors and/or the associated signaling pathways may help overcome chemotherapy resistance in TNBC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/685128v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@1c5481dorg.highwire.dtl.DTLVardef@101a766org.highwire.dtl.DTLVardef@1954523org.highwire.dtl.DTLVardef@ee2ddb_HPS_FORMAT_FIGEXP M_FIG C_FIG TNBC tumors are typically heterogeneous, predominantly epithelial tissues with discrete patches of mesenchymal-like TNBC cells, that differ in their invasiveness, proliferation potential and response to treatment. This study reveals that Chemotherapy resistance in epithelial and mesenchymal-like TNBC cells is maintained by distinct signatures of proinflammatory and immune cell chemotactic cytokines, and activities of GSK-3{beta} and p90 ribosomal S6 kinases.

cancer biology↗

bioRxiv2025

Next generation neural mass model with dopamine modulation mediated by D1-type receptors

Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing its rich repertoire of behaviors. Among these substances, dopamine has a preponderant role, being involved in several mechanisms. Moreover, dysfunctions in the dopamine connections has been observed in pathology, such as Parkinsons disease and schizophrenia. To investigate the mechanism of neuromodulation, we expand a previously proposed mean-field formalism, that describes the average activity of a neural population, by adding the effect of dopamine modulation. This mean-field reduction allows for a direct comparison with the underlying neural network to test its ability to qualitatively reproduce population behavior. The resulting mathematical framework is able to capture network activity in distinct dynamical regimes and transitions between them. Thus, this approach provides a reliable foundation for the development of personalized medicine tools to study how the effect of dopamine modulation on single brain region affects whole brain behavior.

neuroscience↗

bioRxiv2025

Disturbance provides limited respite for native fish against invaders

Understanding how flow-related disturbance regimes influence species interactions is critical for conserving threatened species in freshwater ecosystems, where both the alteration of these regimes and the invasion of non-native species pose major threats. Using 30 years of sampling data from a well-studied river system in Aotearoa New Zealand, we applied empirically- parameterized Lotka-Volterra competition models across a gradient of flood disturbance regimes to assess the potential for coexistence between threatened native galaxiids and invasive trout. Our models suggest trout dominate under low disturbance regimes, leading to galaxiid extinction. Coexistence peaks at intermediate disturbance regimes, though native fish densities are highly suppressed. Only at high disturbance regimes do native fish prevail, but priority effects are likely. Thus, restoration and conservation efforts focused solely on high disturbance habitats without accompanying biological interventions may not ensure native fish persistence. Further, prioritizing such habitats at the expense of others risks misdirecting resources towards sub-optimal conditions, particularly under climate change.

ecology↗

bioRxiv2025

Young and old adult brains experience opposite effects of acute sleep restriction on the functional connectivity network

Chronic, long-term sleep loss is detrimental to brain health and cognitive ability. However, older adults are affected differently by acute, short-term loss of sleep than young and middle-aged adults. Older adults are more resilient to the effects of acute sleep loss and, depending on the cognitive domain, may be completely unaffected while younger adults suffer. To elucidate the brain network responses to sleep loss underlying these cognitive differences between age groups, we investigated the static and dynamic functional connectivity effects of sleep restriction (sleep limited to 3 hours) and how these effects differ between younger adults (20-30 years) and older adults (65-75 years). We found a functional connectivity subnetwork that was primarily strengthened in younger adults after sleep restriction but weakened in older adults after sleep restriction. Similar crossover interactions were consistently observed in further analyses of functional connectivity degree, modularity, and dynamic functional connectivity state fractional occupancy. Our findings demonstrate that the effect of sleep restriction on older adults is fundamentally different from younger adults. These results most strongly support the compensation theory of aging, which predicts a fundamental shift in the effects of sleep loss, rather than a mere dampening of the sleep benefits experienced by younger adults.

neuroscience↗

bioRxiv2024

Introduced trout hinder the recovery of native fish following an extreme flood disturbance

O_LIIn rivers, we are seeing a shift away from natural flow regimes towards larger and more frequent extreme drought and flood events. However, it is unclear how increasing intensity and frequency of extreme flow disturbances will play out alongside existing biotic pressures, such as biological invasions, to impact aquatic biodiversity. In New Zealand, vulnerable native non-diadromous galaxiid fishes face pressure from introduced trout through interspecific competition and predation, which may influence the recovery of native galaxiids after flood disturbances. C_LIO_LIHere, we employed a capture-mark-recapture study across 12 sites, along a gradient of disturbance following a major flood event, to examine the impact of extreme flooding on the population structure of non-diadromous galaxiids (Galaxias vulgaris and G. paucispondylus), and the effect of trout presence on individual galaxiid growth rates recovering from this event. C_LIO_LIWe found a lower abundance of all non-diadromous galaxiid size classes under higher flood magnitudes, but smaller size classes (i.e., young-of-year and 1-2 year cohorts) were more impacted. C_LIO_LIFurthermore, the presence of trout, whether at low or high abundances, reduced the individual growth of native non-diadromous galaxiids, despite interspecific effects being a weaker regulator of individual growth compared to conspecific effects. Moreover, trout effects on galaxiids varied by both galaxiid size and density, such that growth of smaller individual galaxiids in low densities were most affected by the presence of trout regardless of trout density. C_LIO_LIIn summary, our results demonstrate that non-diadromous galaxiid population dynamics in future are likely to be affected by flood disturbance regimes and introduced trout presence, the outcome of which involves a complex balance between reduced population persistence and increased individual resistance of larger individuals. C_LIO_LIConservation efforts that focus on maintaining strategically placed trout-free source populations of adult galaxiids could therefore be an important tool to enable native dispersal into trout-affected habitat and maintain population resilience in the face of increasingly larger and more frequent extreme events, given that recruitment of non-diadromous galaxiids is higher in the absence of trout. C_LI

ecology↗

bioRxiv2024

Dynamic network features of functional and structural brain networks support visual working memory in aging adults

In this work, we investigated the relationship between structural connectivity and the dynamics of functional connectivity and how this relationship changes with age to benefit cognitive functions. Visual working memory (VWM) is an important brain function that allows us to maintain a mental representation of the world around us, but its capacity and precision peaks by around 20 years old and decreases steadily throughout the rest of our lives. This research examined the functional brain network dynamics associated with VWM throughout the lifespan and found that Default Mode Network and Fronto-Parietal Network states were more well represented in individuals with better VWM. Furthermore, transitions between the Visual/Somatomotor Network state and the Attention Network state were more well-represented in older adults, and a network control theory simulation demonstrated that structural connectivity differences supporting this transition were associated with better VWM, especially in middle-aged individuals. The structural connectivity of regions from all states was important for supporting this transition in younger adults, while regions within the Visual/Somatomotor and Attention Network states were more important in older adults. These findings demonstrate that structural connectivity supports flexible, functional dynamics that allow for better VWM with age and may lead to important interventions to uphold healthy VWM throughout the lifespan.

neuroscience↗

bioRxiv2023

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine if age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals. Author SummaryWe utilized network neuroscience methods to investigate why some older adults suffer tremendous cognitive decline while others are mostly spared. Past functional research found that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether structural connectivity reorganizes to preserve cognitive ability. We analyzed age and fluid intelligence as a function of structural connectivity and regional graph theory measures using partial least squares. Some differences with age were associated with poorer cognitive outcomes, but other reorganizations spared cognitive ability. Beneficial reorganizations included strengthened local intrahemispheric connectivity and increased nodal efficiency of focal regions for older adults, as well as widespread increased local efficiency for middle-aged individuals.

neuroscience↗

bioRxiv2023

Network connectivity differences between perceptual and emotional music listening

The human brain is a complex, adaptive system capable of parsing complex stimuli and generating complex behaviour. Understanding how to model and interpret the dynamic relationship between brain, behaviour, and the environment will provide vital information on how the brain responds to real-world stimuli, develops and ages, and adapts to pathology. Modelling together numerous streams of dynamic data, however, presents sizable methodological challenges. In this paper, we present a novel workflow and sample interpretation of a data set incorporating brain, behavioural, and stimulus data from a music listening study. We use hidden Markov modelling (HMM) to extract state timeseries from continuous high-dimensional EEG and stimulus data, estimate timeseries variables consistent with HMM from continuous low-dimensional behavioural data, and model the multi-modal data together using partial least squares (PLS). We offer a sample interpretation of the results, including a discussion on the limitations of the currently available tools, and discuss future directions for dynamic multi-modal analysis focusing on naturalistic behaviours.

neuroscience↗

bioRxiv2023

Age-related variability in network engagement during music listening

Listening to music is an enjoyable behaviour that engages multiple networks of brain regions. As such, the act of music listening may offer a way to interrogate network activity, and to examine the reconfigurations of brain networks that have been observed in healthy aging. The present study is an exploratory examination of brain network dynamics during music listening in healthy older and younger adults. Network measures were extracted and analyzed together with behavioural data using a combination of hidden Markov modelling and partial least squares. We found age- and preference-related differences in fMRI data collected during music listening in healthy younger and older adults. Both age groups showed higher occupancy (the proportion of time a network was active) in a temporal-mesolimbic network while listening to self-selected music. Activity in this network was strongly positively correlated with liking and familiarity ratings in younger adults, but less so in older adults. Additionally, older adults showed a higher degree of correlation between liking and familiarity ratings consistent with past behavioural work on age-related dedifferentiation. We conclude that, while older adults do show network and behaviour patterns consistent with dedifferentiation, activity in the temporal-mesolimbic network is relatively robust to dedifferentiation. These findings may help explain how music listening remains meaningful and rewarding in old age.

neuroscience↗

bioRxiv2023

Comparing the stability and reproducibility of brain-behaviour relationships found using Canonical Correlation Analysis and Partial Least Squares within the ABCD Sample

IntroductionCanonical Correlation Analysis (CCA) and Partial Least Squares Correlation (PLS) detect associations between two data matrices based on computing a linear combination between the two matrices (called latent variables; LVs). These LVs maximize correlation (CCA) and covariance (PLS). These different maximization criteria may render one approach more stable and reproducible than the other when working with brain and behavioural data at the population-level. This study compared the LVs which emerged from CCA and PLS analyses of brain-behaviour relationships from the Adolescent Brain Cognitive Development (ABCD) dataset and examined their stability and reproducibility. MethodsStructural T1-weighted imaging and behavioural data were accessed from the baseline Adolescent Brain Cognitive Development dataset (N > 9000, ages = 9-11 years). The brain matrix consisted of cortical thickness estimates in different cortical regions. The behavioural matrix consisted of 11 subscale scores from the parent-reported Child Behavioral Checklist (CBCL) or 7 cognitive performance measures from the NIH Toolbox. CCA and PLS models were separately applied to the brain-CBCL analysis and brain-cognition analysis. A permutation test was used to assess whether identified LVs were statistically significant. A series of resampling statistical methods were used to assess stability and reproducibility of the LVs. ResultsWhen examining the relationship between cortical thickness and CBCL scores, the first LV was found to be significant across both CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV1 from the CCA model found that covariation of CBCL scores was linked to covariation of cortical thickness. LV1 from the PLS model identified decreased cortical thickness linked to lower CBCL scores. There was limited evidence of stability or reproducibility of LV1 for both CCA and PLS. When examining the relationship between cortical thickness and cognitive performance, there were 6 significant LVs for both CCA and PLS (p < .01). The first LV showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). ConclusionCCA and PLS identify different brain-behaviour relationships with limited stability and reproducibility when examining the relationship between cortical thickness and parent-reported behavioural measures. However, both methods identified relatively similar brain-behaviour relationships that were stable and reproducible when examining the relationship between cortical thickness and cognitive performance. The results of the current study suggest that stability and reproducibility of brain-behaviour relationships identified by CCA and PLS are influenced by characteristics of the analyzed sample and the included behavioural measurements when applied to a large pediatric dataset.

neuroscience↗

bioRxiv2022

Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data

AO_SCPLOWBSTRACTC_SCPLOWConnectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics observed in whole-brain activity measurements such as fMRI, M/EEG, and related techniques. Estimating physiological parameters by fitting these models to empirical data is challenging however, due to large network sizes, often physiologically detailed fast-timescale system equations, and the need for long (e.g. tens of minutes) simulation runs. Here we introduce a novel approach to connectome-based neural mass model parameter estimation by employing optimization tools developed for deep learning. We cast the system of differential equations representing both neural and haemodynamic activity dynamics as a deep neural network, implemented within a widely used machine learning programming environment (PyTorch). This allows us to use robust industry-standard optimization algorithms, automatic differentiation for computation of gradients, and other useful functionality. The approach is demonstrated using a connectome-based network with nodal dynamics specified by the two-state RWW mean-field neural mass model equations, which we use here as a model of fMRI-measured activity and correlation fluctuations. Additional optimization constraints are explored and prove fruitful, including restricting the model to domains of parameter space near a bifurcation point that yield metastable dynamics. Using these techniques, we first show robust recovery of physiological model parameters in synthetic data and then, as a proof-of-principle, apply the framework to modelling of empirical resting-state fMRI data from the Human Connectome Project database. For resting state activity, the system can be understood as a deep net that receives uncorrelated noise on its input layer, which is transformed into network-wide modelled functional connectivity on its output layer. This is consistent with the prevailing conception in theoretical neuroscience of resting-state functional connectivity patterns as an emergent phenomenon that is driven by (effectively) random activity fluctuations, which are then in turn spatiotemporally filtered by anatomical connectivity and local neural dynamics.

neuroscience↗

bioRxiv2022

A robust modular automated neuroimaging pipeline for model inputs to TheVirtualBrain

TheVirtualBrain, an open-source platform for large-scale network modelling, can be personalized to an individual using a wide range of neuroimaging modalities. With the growing number and scale of neuroimaging data sharing initiatives of both healthy and clinical populations comes an opportunity to create large and heterogeneous sets of dynamic network models to better understand individual differences in network dynamics and their impact on brain health. Here we present TheVirtualBrain-UK Biobank pipeline, a robust, automated and open-source brain image processing solution to address the expanding scope of TheVirtualBrain project. Our pipeline generates connectome-based modelling inputs compatible for use with TheVirtualBrain. We leverage the existing multimodal MRI processing pipeline from the UK Biobank made for use with a variety of brain imaging modalities. We add various features and changes to the original UK Biobank implementation specifically for informing large-scale network models, including user-defined parcellations for the construction of matching whole-brain functional and structural connectomes. Changes also include detailed reports for quality control of all modalities, a streamlined installation process, modular software packaging, updated software versions, and support for various publicly available datasets. The pipeline has been tested on both healthy and clinical populations and is robust to the morphological changes observed in aging and dementia. In this paper, we describe these and other pipeline additions and modifications in detail, as well as how this pipeline fits into the TheVirtualBrain ecosystem.

neuroscience↗

bioRxiv2021

Signal complexity indicators of health status in clinical-EEG

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across individuals in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individuals health status and is a promising avenue for clinical biomarker development.

neuroscience↗

bioRxiv2020

Identifying optimal working points of individual Virtual Brains: A large-scale brain network modelling study

Brain network modeling studies are often limited with respect to the number of data features fitted, although capturing multiple empirical features is important to validate the models overall biological plausibility. Here we construct personalized models from multimodal data of 50 healthy individuals (18-80 years) with The Virtual Brain and demonstrate that an individuals brain has its own converging optimal working point in the parameter space that predicts multiple empirical features in functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We further show that bimodality in the alpha band power - as an explored novel feature - arises as a function of global coupling and exhibits inter-regional differences depending on the degree. Reliable inter-individual differences with respect to these optimal working points were found that seem to be driven by the individual structural rather than by the functional connectivity. Our results provide the groundwork for future multimodal brain modeling studies.

neuroscience↗

bioRxiv2020

Completion and augmentation of connectomic datasets in dementia and Alzheimer's Disease using Virtual Patient Cohorts

Large neuroimaging datasets, including information about structural (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features (e.g., lack of concurrent DTI SC and resting-state fMRI FC measurements for many of the subjects). We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the ADNI dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects) we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data. Significance statementPersonalized information on anatomical connectivity ("structural connectivity", SC) or coordinated resting state activation patterns ("functional connectivity, FC) is a source of powerful neuromarkers to detect and track the development of neurodegenerative diseases. However, there are often "gaps" in the available information, with only SC (or FC) being known but not FC (or SC). Exploiting whole-brain modelling, we show that gap in databases can be filled by inferring the other connectome through computational simulations. The generated virtual connectomic data carry information analogous to the one of empirical connectomes, so that machine learning algorithms can be trained on them. This opens the way to the release in the future of cohorts of "virtual patients", complementing traditional datasets in data-driven predictive medicine.

neuroscience↗

bioRxiv2019

A connectome-based, corticothalamic model of state- and stimulation-dependent modulation of rhythmic neural activity and connectivity

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including a 1/f spectral profile, spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state-(e.g. idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory or neuromodulatory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intra-columnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.\n\nAuthor SummaryOne of the most distinctive features of brain activity is that it is highly rhythmic. Developing a better understanding of how these rhythms are generated, and how they can be controlled in clinical applications, is a central goal of modern neuroscience. Here we have developed a computational model that succinctly captures several key aspects of the rhythmic brain activity most easily measurable in human subjects. In particular, it provides both a conceptual and a concrete mathematical framework for understanding the well-established experimental observation of antagonism between high- and low-frequency oscillations in human brain recordings. This dynamic has important implications for how we understand the modulation of rhythmic activity in diverse cognitive states relating to arousal, attention, and cognitive processing. As we demonstrate, our model also provides a tool for investigating and improving the use of rhythmic brain stimulation in clinical applications.

neuroscience↗

bioRxiv2018

BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder

Variability of neural signaling is an important index of healthy brain functioning, as is signal complexity, which relates to information processing capacity. It is thought that alterations in variability and complexity may underlie certain brain dysfunctions. Here, resting-state fMRI was used to examine brain signal variability and complexity in male children and adolescents with and without autism spectrum disorder (ASD), a highly heterogeneous neurodevelopmental disorder. Variability was measured using the mean square successive difference (MSSD) of the time series, and complexity of these time series was assessed using sample entropy. A categorical approach was implemented to determine if the brain measures differed between diagnostic groups (ASD and typically developing (TD) groups). A dimensional approach was used to examine the continuum of relationships between each brain measure and behavioural severity, age, IQ, and the global efficiency (GE) of each participants structural connectome, a metric that reflects the structural capacity for information processing. Using the categorical approach, no significant group differences were found for neither MSSD nor entropy. However, the dimensional approach revealed significant positive correlations between each brain measure, GE, and age. Further, negative correlations were observed between each brain measure and behavioural severity across all participants, whereby lower MSSD and entropy were associated with more severe ASD behaviours. These results reveal the nature of variability and complexity of fMRI signals in children and adolescents with and without ASD, and highlight the importance of taking a dimensional approach when analyzing brain function in ASD.\n\nSignificance statementIn this study, resting-state functional magnetic resonance imaging was used to examine the variability and complexity of brain signals in children and adolescents with and without autism spectrum disorder (ASD). Group differences were not observed in variability or complexity. However, there were significant associations between these measures, behavioural severity, age, and a measure of the brains capacity to perform information processing, called global efficiency. Variability and complexity were higher in older participants and in participants with higher global efficiency, but were lower in participants with more severe ASD behaviours. These results provide insight into two important components of brain function in children and adolescents with and without ASD.

neuroscience↗