Biology subjects

Arnatkeviciute, A.

Publications and source records attributed to Arnatkeviciute, A..

18 recordsLinked to original sources

bioRxiv2025

Benchmarking overlapping community detection methods for applications in human connectomics

Brain networks exhibit non-trivial modular organization, with groups of densely connected areas participating in specialized functions. Traditional community detection algorithms assign each node to one module, but this representation cannot capture integrative, multi-functional nodes that span multiple communities. Despite the increasing availability of overlapping community detection algorithms (OCDAs) to capture such integrative nodes, there is no objective procedure for selecting the most appropriate method and its parameters for a given problem. Here we overcome this limitation by introducing a datadriven method for selecting an OCDA and its parameters from performance on a tailored ensemble of generated benchmark networks, assessing 22 unique algorithms and parameter settings. Applied to the human structural connectome, we find that the Order Statistics Local Optimization Method (OSLOM) best identifies ground-truth overlapping structure in the benchmark ensemble and yields a seven-network decomposition of the human cortex. These modules are bridged by fifteen overlapping regions that generally sit at the apex of the putative cortical hierarchy--suggesting integrative, higher-order function-- with network participation increasing along the cortical hierarchy, a finding not supported using a non-overlapping modular decomposition. This data-driven approach to selecting OCDAs is applicable across domains, opening new avenues to detecting and quantifying informative structures in complex real-world networks.

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bioRxiv2024

Mapping the impact of DUSP6 in an ADHD iPSC derived-dopaminergic neuronal model

Although the gene DUSP6 has been implicated as a risk gene for attention deficit hyperactivity disorder (ADHD) in recent GWAS studies, its functional role in the aetiology of the condition remains poorly understood. DUSP6 is reported to regulate dopaminergic neurotransmission by decreasing available synaptic dopamine, suggesting a potential mechanism by which DUSP6 may confer risk to ADHD. In this study, we employed CRISPR-Cas9 to generate isogenic iPSC lines from an individual with ADHD, including heterozygous and homozygous DUSP6 knockouts alongside the parental control. These iPSC lines were differentiated into dopaminergic neurons and assessed at both cellular, and transcriptomic levels. We observed significant increase in extracellular dopamine in proportion to DUSP6 gene dosage, with greater elevation in homozygous compared to heterozygous knockouts. RNA sequencing further supported this finding, showing that DUSP6-knockout increased expression of genes regulating dopamine secretion and synaptic processes critical for dopaminergic system. Beyond dopamine signalling, transcriptomic changes implicated DUSP6 in neurodevelopmental biological mechanisms, essential to the development of ADHD. Furthermore, we identified significant overlaps between DUSP6-regulated genes and those associated with major depression, bipolar disorder, and schizophrenia, suggesting shared genetic pathways potentially influenced by DUSP6. This study provides mechanistic insights into the role that DUSP6 could potentially play in the aetiology of ADHD and its broader implications across related neuropsychiatric conditions.

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bioRxiv2024

Transcriptome profiling of dopaminergic neurons derived from an ADHD induced pluripotent stem cell (iPSC) model

The most recent ADHD-GWAS meta-analysis highlighted the potential role of 76 genes enriched among genes expressed in early brain development and associated with midbrain dopaminergic neurons. However, the precise functional importance of the GWAS-identified single nucleotide polymorphisms (SNPs) remain unknown. In contrast to GWAS, transcriptome analysis directly investigates gene products by assessing the transcribed RNA. This allows one to gain functional insights into gene expression paving the way for a better understanding of the molecular risk mechanisms of conditions, such as ADHD. In this study, we performed transcriptome profiling of highly homogeneous dopamine neurons developed from induced pluripotent cells (iPSCs) that were derived from an individual with ADHD and a neurotypical comparison individual. Comparative gene expression analysis between the examined lines revealed that the top differentially expressed genes (DEGs) were predominantly associated with nervous system functions related to neuronal development and dopaminergic regulation. Notably, 29 of the DEGs overlapped with those identified by ADHD-GWAS meta- analysis. These genes are overrepresented in biological processes including developmental growth regulation, axonogenesis, and nervous system development. In addition, gene set analysis revealed significant enrichment for meta categories such as ion channel activity, synaptic function and assembly, neuronal development and cell differentiation. Further, we observed significantly reduced projections in the ADHD dopamine neurons at the mid- differentiation stage (day 14 in vitro), providing preliminary support for the delayed neuronal maturation hypotheses of ADHD. This study underscores the potential of using iPSC-derived cell type-specific models that integrate genome and transcriptome analyses for biological discovery in ADHD.

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bioRxiv2023

Molecular Signatures of Attention Networks

Attention network theory proposes three distinct types of attention - alerting, orienting, and control - that are supported by separate brain networks and modulated by different neurotransmitters, i.e., noradrenaline, acetylcholine, and dopamine. Here, we explore the extent of cortical, genetic, and molecular dissociation of these three attention systems using multimodal neuroimaging. We evaluated the spatial overlap between fMRI activation maps from the attention network test (ANT) and cortex-wide gene expression data from the Allen Human Brain Atlas. The goal was to identify genes associated with each of the attention networks in order to determine whether specific groups of genes were co-expressed with the corresponding attention networks. Furthermore, we analysed publicly available PET-maps of neurotransmitter receptors and transporters to investigate their spatial overlap with the attention networks. Our analyses revealed a substantial number of genes (3871 for alerting, 6905 for orienting, 2556 for control) whose cortex-wide expression co-varied with the activation maps, prioritizing several molecular functions such as the regulation of protein biosynthesis, phosphorylation, and receptor binding. Contrary to the hypothesized associations, the ANT activation maps neither aligned with the distribution of noradrenaline, acetylcholine, and dopamine receptor and transporter molecules, nor with transcriptomic profiles that would suggest clearly separable networks. Independence of the attention networks appeared additionally constrained by a high level of spatial dependency between the network maps. Future work may need to re-conceptualize the attention networks in terms of their segregation and re-evaluate the presumed independence at the neural and neurochemical level.

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bioRxiv2022

Can hubs of the human connectome be identified consistently with diffusion MRI?

Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n=294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome; its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines ({rho}>0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization. Author SummaryThe increasing use of diffusion MRI for mapping white matter connectivity has been matched by a similar increase in the number of ways to process the diffusion data. Here, we assess how diffusion processing affects hubs across 1760 pipeline variations. Many processing pipelines do not show a high concentration of connectivity within hubs. When present, hub location and distribution vary based on processing choices. The choice of probabilistic or deterministic tractography has a major impact on hub location and strength. Finally, node strength in weighted networks can correlate highly with node size. Overall, our results illustrate the need for prudent decision-making when processing and interpreting diffusion MRI data. Code and data availabilityAll the data used in this study is openly available on Figshare at https://doi.org/10.26180/c.6352886.v1. Scripts to analyze these data are available on GitHub at https://github.com/BMHLab/DegreeVariability. Competing InterestsThe authors declare that they have no competing interests.

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bioRxiv2022

Three transcriptional axes underpin anatomy, development, and disorders of the human cortex

Human brain organisation involves the coordinated expression of thousands of genes. For example, the first principal component (C1) of cortical transcription identifies a hierarchy from sensorimotor to association regions. Here, optimised processing of the Allen Human Brain Atlas revealed two new components of cortical gene expression architecture, C2 and C3, which are distinctively enriched for neuronal, metabolic and immune processes, specific cell-types and cytoarchitecture, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas, and BrainSpan), we found that C1-C3 represent generalisable transcriptional programmes that are coordinated within cells, and differentially phased during foetal and postnatal development. Autism spectrum disorder and schizophrenia were specifically associated with C1/C2 and C3, respectively, across neuroimaging, differential expression, and genome-wide association studies. Evidence converged especially in support of C3 as a normative transcriptional programme for adolescent brain development, which can lead to atypical supra-granular cortical connectivity in people at high genetic risk for schizophrenia.

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bioRxiv2022

Working memory and reaction time variability mediate the relationship between polygenic risk and ADHD traits in a general population sample

Endophenotypes are heritable and quantifiable traits indexing genetic liability for a disorder. Here, we examined three potential endophenotypes, working memory function, response inhibition and reaction time variability, for attention-deficit hyperactivity disorder (ADHD) measured as a dimensional latent trait in a large general population sample derived from the Adolescent Brain and Cognition Developmental study. The genetic risk for ADHD was estimated using polygenic risk scores (PRS) whereas ADHD traits were quantified as a dimensional continuum using Bartlett factor score estimates, derived from Attention Problems items from the Child Behaviour Checklist and Effortful Control items from the Early Adolescent Temperament Questionnaire-Revised. The three candidate cognitive endophenotypes were quantified using task-based performance measures. Higher ADHD PRSs were associated with higher ADHD traits, as well as poorer working memory performance and increased reaction time variability. Lower working memory performance, poorer response inhibition, and increased reaction time variability were associated with more pronounced ADHD traits. Working memory and reaction time variability partially statistically mediated the relationship between ADHD PRS and ADHD traits, explaining 14% and 16% of the association, respectively. The mediation effect was specific to the genetic risk for ADHD and did not generalise to genetic risk for four other major psychiatric disorders. Together, these findings provide robust evidence from a large general population sample that working memory and reaction time variability can be considered endophenotypes for ADHD that mediate the relationship between ADHD PRS and ADHD traits.

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bioRxiv2021

The individuality of shape asymmetries of the human cerebral cortex

Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales ([~]37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales ([~]65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.

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bioRxiv2021

Standardizing workflows in imaging transcriptomics with the abagen toolbox

Gene expression fundamentally shapes the structural and functional architecture of the human brain. Open-access transcriptomic datasets like the Allen Human Brain Atlas provide an unprecedented ability to examine these mechanisms in vivo; however, a lack of standardization across research groups has given rise to myriad processing pipelines for using these data. Here, we develop the abagen toolbox, an open-access software package for working with transcriptomic data, and use it to examine how methodological variability influences the outcomes of research using the Allen Human Brain Atlas. Applying three prototypical analyses to the outputs of 750,000 unique processing pipelines, we find that choice of pipeline has a large impact on research findings, with parameters commonly varied in the literature influencing correlations between derived gene expression and other imaging phenotypes by as much as{rho} [≥] 1.0. Our results further reveal an ordering of parameter importance, with processing steps that influence gene normalization yielding the greatest impact on downstream statistical inferences and conclusions. The presented work and the development of the abagen toolbox lay the foundation for more standardized and systematic research in imaging transcriptomics, and will help to advance future understanding of the influence of gene expression in the human brain.

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bioRxiv2020

Dynamical consequences of regional heterogeneity in the brains transcriptional landscape

Brain regions vary in their molecular and cellular composition, but how this heterogeneity shapes neuronal dynamics is unclear. Here, we investigate the dynamical consequences of regional heterogeneity using a biophysical model of whole-brain functional magnetic resonance imaging (MRI) dynamics in humans. We show that models in which transcriptional variations in excitatory and inhibitory receptor (E:I) gene expression constrain regional heterogeneity more accurately reproduce the spatiotemporal structure of empirical functional connectivity estimates than do models constrained by global gene expression profiles and MRI-derived estimates of myeloarchitecture. We further show that regional heterogeneity is essential for yielding both ignition-like dynamics, which are thought to support conscious processing, and a wide variance of regional activity timescales, which supports a broad dynamical range. We thus identify a key role for E:I heterogeneity in generating complex neuronal dynamics and demonstrate the viability of using transcriptional data to constrain models of large-scale brain function.

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bioRxiv2020

Genetic influences on hub connectivity of the human connectome

Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.

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bioRxiv2020

Overcoming bias in gene-set enrichment analyses of brain-wide transcriptomic data

The recent availability of whole-brain atlases of gene expression, which quantify the transcriptional activity of thousands of genes across many different brain regions, has opened new opportunities to understand how gene-expression patterns relate to spatially varying properties of brain structure and function. To aid interpretation of a given neural phenotype, gene-set enrichment analysis (GSEA) has become a standard statistical methodology to identify functionally related groups of genes, annotated using systems such as the Gene Ontology (GO), that are associated with a given phenotype. While GSEA has identified groups of genes related to diverse aspects of brain structure and function in mouse and human, here we show that these results are affected by substantial statistical biases. Quantifying the falsepositive rates of individual GO categories across an ensemble of random phenotypic maps, we found an average 875-fold inflation of significant findings relative to expectation in mouse, and a 582-fold inflation in human, with some categories being judged as significant for over 20% of random phenotypes. Concerningly, the probability of a GO category being reported as significant in the extant literature increases with its estimated false-positive rate, suggesting that published reports are strongly affected by the reporting of false-positive bias. We show that the bias is primarily driven by within-category gene-gene coexpression and spatial autocorrelation, which are not accounted for in conventional GSEA nulls, and we introduce flexible ensemble-based null models that can account for these effects. Testing a range of structural connectivity and cell density phenotypes in mouse and human, we demonstrate that many GO categories that would conventionally be judged as highly significant are in fact consistent with ensembles of random phenotypes. Our results highlight major pitfalls with applying standard GSEA to brain-wide transcriptomic data and outline solutions to this pervasive problem, which is made available as an open toolbox.

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bioRxiv2020

The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

Head motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction. HighlightsO_LIWe assess how motion affects structural connectivity in 240 preprocessing pipelines C_LIO_LIMotion contamination of structural connectivity depends on preprocessing choices C_LIO_LIAdvanced motion correction tools reduce motion confounds C_LIO_LIFA edge weighting is more susceptible to motion effects than streamline count C_LI

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bioRxiv2019

Individual differences in haemoglobin concentration influence BOLD fMRI functional connectivity and its correlation with cognition

Resting-state connectivity measures the temporal coherence of the spontaneous neural activity of spatially distinct regions, and is commonly measured using BOLD-fMRI. The BOLD response follows neuronal activity, when changes in the relative concentration of oxygenated and deoxygenated haemoglobin cause fluctuations in the MRI T2* signal. Since the BOLD signal detects changes in relative concentrations of oxy/deoxy-haemoglobin, individual differences in haemoglobin levels may influence the BOLD signal-to-noise ratio in a manner independent of the degree of neural activity. In this study, we examined whether group differences in haemoglobin may confound measures of functional connectivity. We investigated whether relationships between measures of functional connectivity and cognitive performance could be influenced by individual variability in haemoglobin. Finally, we mapped the neuroanatomical distribution of the influence of haemoglobin on functional connectivity to determine where group differences in functional connectivity are manifest. In a cohort of 518 healthy elderly subjects (259 men) each sex group was median split into two groups with high and low haemoglobin concentration. Significant differences were obtained in functional connectivity between the high and low haemoglobin groups for both men and women (Cohens d 0.17 and 0.03 for men and women respectively). The haemoglobin connectome in males showed a widespread systematic increase in functional connectivity correlational scores, whilst the female connectome showed predominantly parietal and subcortical increases and temporo-parietal decreases. Despite the haemoglobin groups having no differences in cognitive measures, significant differences in the linear relationships between cognitive performance and functional connectivity were obtained for all 5 cognitive tests in males, and 4 out of 5 tests in females. Our findings confirm that individual variability in haemoglobin levels that give rise to group differences are an important confounding variable in BOLD-fMRI-based studies of functional connectivity. Controlling for haemoglobin variability as a potentially confounding variable is crucial to ensure the reproducibility of human brain connectome studies, especially in studies that compare groups of individuals, compare sexes, or examine connectivity-cognition relationships. HighlightsO_LIIndividual differences in haemoglobin significantly impact measures of functional connectivity in the elderly. C_LIO_LISignificant differences in connectivity-cognition relationships are shown between groups separated by haemoglobin values without accompanying cognitive differences. C_LIO_LIThe influence of haemoglobin on functional connectivity differs between men and women. C_LI

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bioRxiv2019

Timescales of spontaneous activity fluctuations relate to structural connectivity in the brain

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brains underlying structural-connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural-connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale-related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6000 time-series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain regions spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

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bioRxiv2018

A practical guide to linking brain-wide gene expression and neuroimaging data

The recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

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bioRxiv2017

Hub connectivity, neuronal diversity, and gene expression in the C. elegans connectome

Studies of nervous system connectivity, in a wide variety of species and at different scales of resolution, have identified several highly conserved motifs of network organization. One such motif is a heterogeneous distribution of connectivity across neural elements, such that some elements act as highly connected and functionally important network hubs. These brain network hubs are also densely interconnected, forming a so-called rich-club. Recent work in mouse has identified a distinctive transcriptional signature of neural hubs, characterized by tightly coupled expression of oxidative metabolism genes, with similar genes characterizing macroscale inter-modular hub regions of the human cortex. Here, we sought to determine whether hubs of the neuronal C. elegans connectome also show tightly coupled gene expression. Using open data on the chemical and electrical connectivity of 279 C. elegans neurons, and binary gene expression data for each neuron across 948 genes, we computed a correlated gene expression score for each pair of neurons, providing a measure of their gene expression similarity. We demonstrate that connections between hub neurons are the most similar in their gene expression while connections between nonhubs are the least similar. Genes with the greatest contribution to this effect are involved in glutamatergic and cholinergic signalling, and other communication processes. We further show that coupled expression between hub neurons cannot be explained by their neuronal subtype (i.e., sensory, motor, or interneuron), separation distance, chemically secreted neurotransmitter, birth time, pairwise lineage distance, or their topological module affiliation. Instead, this coupling is intrinsically linked to the identity of most hubs as command interneurons, a specific class of interneurons that regulates locomotion. Our results suggest that neural hubs may possess a distinctive transcriptional signature, preserved across scales and species, that is related to the involvement of hubs in regulating the higher-order behaviors of a given organism.\n\nAuthor summarySome elements of neural systems possess many more connections than others, marking them as network hubs. These hubs are often densely interconnected with each other, forming a so-called rich-club that is thought to support integrated function. Recent work in the mouse suggests that connected pairs of hubs show higher levels of transcriptional coupling than other pairs of brain regions. Here, we show that hub neurons of the nematode C. elegans also show tightly coupled gene expression and that this effect cannot be explained by the spatial proximity or anatomical location of hub neurons, their chemical composition, birth time, neuronal lineage or topological module affiliation. Instead, we find that elevated coexpression is driven by the identity of most hubs of the C. elegans connectome as command interneurons, a specific functional class of neurons that regulate locomotion. These findings suggest that coupled gene expression is a highly conserved genomic signature of neural hubs that may be related to the specific functional role that hubs play in broader network function.

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bioRxiv2017

Wakefulness state modulates conscious access: Suppression of auditory detection in the transition to sleep

Mapping the reports of awareness and its neural underpinnings is instrumental to understand the limits of human perception. The capacity to become aware of objects in the world may be studied by suppressing faint target stimuli with strong masking stimuli, or - alternatively - by manipulating the level of wakefulness from full alertness to mild drowsiness. By combining these two approaches, we studied how perceptual awareness is modulated by decreasing wakefulness. We found dynamic changes in behavioural and neural signatures of conscious access in humans between awake and drowsy states. Behaviourally, we show a decrease in the steepness of the psychophysical function for conscious access in drowsy trials. Neural mapping showed delayed processing of target-mask interaction as the consciousness transition progressed, suggesting that the brain resolution of conscious access shifts from early sensory/perceptual to decision-making stages of processing. Once the goal to report the awareness of a target is set, the system behaviourally adapts to rapid changes in wakefulness, revealing the flexibility of the neural signatures of conscious access, and its suppression, to maintain performance. Significance statementMaintaining full alertness for long periods of time in attentionally demanding situations is challenging and may lead to a decrease in performance. We show the effect of wakefulness fluctuations on behaviour and brain dynamics that humans use to maintain performance. We reveal the neural strategies we have to cope with drowsiness by shifting the weights to more flexible brain processes and relaxing the precision of the decisions we take.

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