Biology subjects

Bernhardt, B. C.

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

At least 19 recordsLinked to original sources

bioRxiv2026

Differential subcortical-cortical circuit reorganization in autism and ADHD

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) often co-occur and share symptoms as well as genetic risk factors. While subcortical-cortical networks have been implicated in both conditions, particularly involving thalamic and striatal circuits, patterns of shared and diverging disruptions remain unclear. Here we integrated resting-state functional connectivity (FC) and regression dynamic causal modeling (rDCM) across a transdiagnostic multi-site cohort. We used statistical group comparisons and individualized normative deviation mapping to map network deviations in ASD and ADHD relative to neurotypical controls and against one another. Multivariate associative techniques examined associations to clinical and cognitive phenotypes. Our transdiagnostic analyses revealed significant differentiation of ASD and ADHD across subcortical-cortical circuits. While ASD was characterized by widespread thalamocortical hyperconnectivity and bidirectional elevated coupling across thalamic subdivisions, ADHD exhibited a focal pattern of striatocortical disruption, anchored predominantly within the caudate. Multivariate analyses identified shared and disorder-specific brain-behavior associations. Across both conditions, aberrant striatocortical signaling was associated with reduced overall intelligence measures. Clinically, ASD-related thalamocortical connectivity disruptions mapped onto generalized cognitive impairment, whereas ADHD-related subcortical alterations tracked hyperactive and inattentive symptom dimensions. Our findings differentiate ASD and ADHD through distinct patterns of functional and directional subcortical-cortical dysregulations.

neuroscience↗

bioRxiv2026

RADIAL AND TANGENTIAL FIBER COMPONENTS IN HUMAN SUPERFICIAL WHITE MATTER AND THEIR ASSOCIATIONS WITH MACROSCALE FUNCTION

The superficial white matter (SWM), a thin layer of white matter beneath the cortex, contains a complex arrangement of short- and long-range fiber systems that support communication between cortical regions. These fiber populations include subcortical U-shaped fibers (U-fibers) that predominantly follow a tangential trajectory along the gray matter-white matter interface and long-range fibers that extend more radially through the deeper white matter to connect distant cortical regions. Here, we developed a surface-based framework to dissociate radial and tangential fiber components within the human SWM and investigated their microstructural properties and functional relevance using in-vivo high-field MRI. These components were quantified using apparent fiber density (AFD), fractional anisotropy (FA), and mean diffusivity (MD). We further contextualized SWM fiber architecture by examining depth-dependent microstructural profiles and their relationship with normative patterns of cortical organization and hierarchical specialization. The anatomical assessment of the proposed fiber orientation separation was supported by tractography-based bundle analyses, while the anatomical accuracy of tractography reconstructions was confirmed through comparison with post-mortem white matter dissection data. To assess their functional relevance, the fiber predominance map derived from radial and tangential AFD was related to spatial patterns of functional variability across the cortex. Regions with greater radially oriented fiber contribution exhibited more gradual changes in functional similarity across geodesic distance, whereas tangentially oriented fiber-enriched regions showed more rapid local functional differentiation. These findings reveal distinct structural and functional signatures of radial and tangential SWM fiber components and provide a framework for investigating their contributions to cortical organization and functional specialization. SIGNIFICANCE STATEMENTThe human cerebral cortex is organized into specialized regions that support diverse functions, but the structural principles underlying this organization remain incompletely understood. The superficial white matter (SWM), a thin layer of white matter beneath the cortex, contains intermingled short- and long-range fiber systems that support communication between cortical regions. However, these fiber populations have remained difficult to characterize because they are closely intertwined within the SWM. By developing a reproducible framework for mapping and quantifying distinct SWM fiber systems, this work provides a foundation for systematic investigation of this complex compartment. Integration of multimodal datasets, together with open sharing of analytical tools and standardized features, promotes transparency, reproducibility, enabling future studies of how superficial connectivity supports brain organization.

neuroscience↗

bioRxiv2026

Hippocampal subfields support human novelty detection via distinct signals

Detecting novelty is fundamental to adaptive memory, yet the neural comparisons that allow new events to be distinguished from prior experience are not fully understood. Hippocampal novelty signals are often framed as associative comparator responses that register violations of learned relationships. However, recognition memory models also predict a broader form of comparison: a global, item-based mismatch between an incoming stimulus and aggregate memory traces. Whether such global matching is implemented within human hippocampal circuitry, and whether it contributes to memory decisions, remains unknown. Using ultra-high-field 7T fMRI during dense sampling of a continuous object recognition task (76,800 trials across five sessions), we estimated both univariate novelty responses and multivariate global mismatch signals across hippocampal subfields. CA1 carried the novelty response that most reliably predicted accurate novelty detection, whereas the subiculum expressed a global mismatch signal that predicted detection independently of CA1. This subicular signal also shaped subsequent recognition behaviour, in which low global mismatch was associated with false alarms to novel objects that propagated into later hits. Finally, subicular global mismatch scaled with the experienced similarity of past events, consistent with an item-based comparison against accumulated memory. These findings identify distinct hippocampal mismatch signals for novelty detection and extend the human hippocampal comparator role beyond local associative mismatch to include global, item-based matching.

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bioRxiv2026

HippoGenes: A robust workflow for subregional transcriptomic profiling of the human hippocampus

The human hippocampus is a unique cortical structure central to brain function, plasticity, and disease. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to mesoscale anatomy and macroscale functional patterns. Gene expression is a fundamental microscale phenotype, and its profiling can provide a reference description of how molecular features are distributed across the brain. Capitalizing on recent imaging-transcriptomic analyses, we introduce HippoGenes, a repository of fine-grained gene expression patterns across human hippocampal subregions. We leveraged spatial statistical models and hippocampal surface mapping to reconstruct dense transcriptomic maps from sparse post-mortem tissue samples of the Allen Human Brain Atlas, generating continuous expression estimates for thousands of genes aligned to a common surface-based coordinate system. We illustrate the utility of HippoGenes to (i) map medial-lateral and anterior-posterior transcriptomic gradients that align with subfield and tripartite subdivisions of the hippocampal formation, (ii) examine associations between gene expression and canonical microstructural and functional features of the hippocampus, and (iii) perform a molecular decoding of subregional alterations in neurological patients with hippocampal pathology. HippoGenes provides a framework for exploring the molecular organization of the hippocampus, opening avenues for multiscale integration in health and disease, and is openly available on https://hippogenes.readthedocs.io/.

neuroscience↗

bioRxiv2026

A surface-intrinsic framework for topology-preserving hippocampal alignment and precision morphometry

Accurate alignment of hippocampal anatomy across individuals remains challenging due to complex and highly variable folding patterns that are not well captured by conventional volumetric approaches. HippUnfold introduced a surface-based representation of the hippocampus, but key components--including coordinate estimation and inter-subject correspondence--were defined in the volumetric domain, making them susceptible to topological errors and interpolation artifacts. Here, we introduce a surface-intrinsic formulation of hippocampal unfolding in which geometry, intrinsic coordinates, and correspondence are defined directly on subject-specific surface manifolds. Intrinsic anterior-posterior and proximal-distal coordinates are computed by solving Laplace equations on the surface, and correspondence is established through surface-based resampling in unfolded space, replacing inverse volumetric warping. Relative to the original HippUnfold approach, this formulation improves test-retest consistency, subject identifiability, and mesh quality, while better preserving subject-specific gyral and sulcal morphology. Surface representations show reduced distortion between folded and unfolded spaces and eliminate misplaced or outlier vertices associated with volumetric warping. These improvements translate to enhanced sensitivity in a clinical application, improving lateralization of temporal lobe epilepsy. These results demonstrate that a surface-intrinsic formulation provides a principled and robust foundation for hippocampal unfolding, enabling topology-preserving alignment and more accurate characterization of inter-individual variability in health and disease.

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bioRxiv2026

Multiscale alterations and cortex-wide organizational trends in focal cortical dysplasia

BACKGROUND AND OBJECTIVESFocal cortical dysplasia (FCD) is a leading cause of surgically remediable pharmaco-resistant epilepsy. Previous research has used various MRI sequences to profile FCD lesions, but multi-site studies using personalized features derived from sequences routinely used in the clinic are sparse. This study aimed to quantify changes in cortical morphology, myeloarchitecture, and function within FCD lesions, compare MRI signatures between lesion subtypes and outcomes, and investigate links to macroscale brain organization. METHODSThis cross-sectional study included patients with FCD-related epilepsy aggregated across 3 datasets from Canada and Germany. The 3T MRI acquisitions included T1-weighted and fluid-attenuated inversion recovery (FLAIR) sequences, with the addition of a resting-state functional sequence for two datasets. We derived cortex-wide maps of patient-specific variations in morphology, myeloarchitecture, and function. Variations were quantified in lesions, ipsilateral cortex, and homotopic regions using age- and sex-adjusted normative models. Subgroup analyses explored the role of histological subtype and surgical outcome. Variations were also related to anterior-posterior and sensory-association organizational gradients. RESULTSWe included 159 patients with epilepsy and FCD (47 % female) and 183 healthy control participants (50 % female). Lesions exhibited changes in morphology (cortical thickness; FLAIR blurring) and in specific measures of depth-dependent intracortical myelin (variance and kurtosis of intracortical myelin profiles across depths), but not in local function (regional homogeneity; node strength). Spatial contextualization indicated more pronounced thickness changes in transmodal association cortices, while increased blurring of the gray-white matter interface co-localized with posterior cortical regions. FCD Type IIb lesions contained more marked changes in blurring and depth-dependent intracortical myelin (kurtosis of intracortical myelin profiles across depths) than Type IIa lesions. DISCUSSIONOur findings provide robust evidence that multiscale MRI profiling can identify FCD signatures and contribute to in-vivo subtyping. Furthermore, the novel use of myeloarchitecture profiling and contextualization with macro-scale brain gradients provides new avenues to understand intracortical alterations and the embedding of FCD lesions into broader organizational patterns.

neuroscience↗

bioRxiv2026

Nucleus-level thalamic organization anchors multimodal signatures of thalamocortical maturation

The human thalamus is composed of multiple nuclei that differ in structure and function. From early development onwards, these nuclei form reciprocal, nucleus-specific connections with the cerebral cortex, contributing to sensory and cognitive processing. In childhood and adolescence, a key period of neurocognitive development, these connections undergo widespread refinement, yet how developmental trajectories of thalamocortical connections vary across nuclei remains unknown. Here, we leveraged the Human Connectome Project in Development dataset (HCP-D, N = 604, age range 8-21) and segmented 10 thalamic nuclei using a segmentation approach optimized for intrathalamic contrast. Applying probabilistic tractography, we reconstructed nucleus-specific thalamocortical connections and charted their maturational profiles based on changes in fractional anisotropy (FA) using generalized additive models. We found FA to increase in thalamocortical connections, with nucleus-specific variation in temporal profiles and magnitude of age effects. Connections of core-cell-rich, sensory-projecting nuclei, such as the lateral geniculate nucleus, showed earlier maturational plateaus, whereas matrix-cell-rich, association-projecting nuclei, such as ventral anterior nucleus, showed more sustained maturation. This links maturational heterochronicity to thalamic organization of cell distribution and connectivity embedding. In parallel, functional thalamocortical connectivity decreased with age, with FA and functional connectivity age effects coupled in nucleus-connections showing prolonged maturation. Finally, concordant age effects in connectivity and nucleus volumes suggest that intra-nucleus remodeling may support refinement of structural connections while reducing thalamocortical functional synchrony. Together, our work reveals that thalamocortical maturation is anchored in the developmental and organizational heterogeneity of thalamic nuclei, offering a framework for understanding how diverse thalamic nuclei contribute to neurocognitive development.

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bioRxiv2026

The electro-MICA toolbox for integrating electrophysiology within multimodal imaging and connectomics workflows

AO_SCPLOWBSTRACTC_SCPLOWThe integration of electrophysiological recordings with multimodal neuroimaging data holds great promise for advancing our understanding of brain function and neurological disorders. To facilitate this endeavour, we present electro-MICA, an open-access Python toolbox designed to project electrophysiological features from scalp and intracranial electroencephalography (EEG) onto cortical and hippocampal surfaces generated by validated multimodal imaging ecosystems. The toolbox comprises two pipelines: one for intracranial EEG (iEEG) recorded with stereo-EEG depth electrodes, and one for scalp EEG source localization. Both pipelines are grounded in numerical solutions to the electromagnetic equations governing electric activity in the brain, solved using the Boundary Element Method. A key methodological contribution is the use of a current density double layer model for neural generators, which avoids the mathematical singularities introduced by conventional dipole-based models when electrodes are near the cortical surface, a situation that can arise in iEEG. Electrode contacts are additionally modeled with non-zero length, improving physical realism. Scalp EEG source localization is performed using eLORETA on a subject-specific three-layer head model derived from the anatomical input. Validation against empirical gamma-band iEEG data from 32 subjects demonstrates that the distributed generator model outperforms both distance-based and dipole-based alternatives. An illustrative clinical example demonstrates the toolboxs capacity to reveal associations between intracranial spike rates, cortical thickness, and anatomical connectivity in an epilepsy patient. Electro-MICA requires no parameter selection from the user, facilitating straightforward multimodal analyses in both research and clinical settings. The toolbox is available at github.com/MICA-MNI/electromica with extensive online documentation at electromica.readthedocs.io.

neuroscience↗

bioRxiv2026

MICAFlow: Fast and Robust MRI Preprocessing Bridging Research Neuroimaging and Clinical Practice

MICAFlow is a fully automated MRI preprocessing pipeline designed to translate advanced neuroimaging workflows from research into routine clinical practice. The pipeline emphasizes speed, robustness, and ease of use, focusing on structural and diffusion MRI. Key innovations include a Label-Augmented Modality-Agnostic Registration (LAMAReg) technique driven by deep learning segmentations for reliable cross-modal alignment, integration of state-of-the-art distortion corrections, and adherence to reproducible standards (Snakemake workflow, BIDSApp specifications). We describe the design of MICAFlow and evaluate its performance across heterogeneous datasets. First, accessibility: MICAFlow processes a multimodal MRI exam in minutes with clinically accessible hardware and without requiring GPU access, making it feasible for same-day clinical use. Second, registration accuracy: LAMAReg achieves cutting-edge multi-modal registration accuracy, yielding accurate alignment of diffusion MRI, FLAIR, and intra-subject T1-weighted images while remaining generally robust to common artifacts. Third, data reliability: Using identifiability, we show MICAFlow maintains consistent performance across diverse datasets, including subjects with pathology, and is closely comparable to contemporary pipelines. In sum, MICAFlows combination of machine learning and efficient workflows produces research-grade data quality with clinical-grade speed. This work demonstrates that advanced MRI preprocessing can be done fast and robustly, helping close the gap between research neuroimaging and broad clinical application of quantitative MRI techniques. The source code for MICAFlow is available here: https://github.com/MICA-MNI/micaflow, and for LAMAReg here: https://github.com/MICA-MNI/LAMAReg.

bioinformatics↗

bioRxiv2026

Connectome-wide mega-analysis identifies a reproducible functional network signature of temporal lobe epilepsy

Resting-state functional magnetic resonance imaging (MRI) studies have reported abnormal intrinsic functional connectivity (FC) across distributed circuits in patients with temporal lobe epilepsy (TLE), indicating a system-level impact of the disorder. However, findings remain inconsistent due to limited sample sizes and methodological heterogeneity, leaving the structural determinants and clinical relevance of FC alterations unresolved. To identify a robust and reproducible FC signature, we conducted a data-driven, connectome-wide mega-analysis in a large multicentre cohort of 652 participants (297 TLE, 73 disease controls, and 282 healthy controls) with multimodal 3T MRI and deep clinical phenotyping. We identified convergent FC reconfigurations at both group and individual levels that preferentially involved densely connected hubs, manifesting as hyperconnectivity in frontoparietal association systems and hypoconnectivity in temporal and paralimbic systems. Integrating structural cortical wiring features further revealed that these extensive alterations were constrained by corticocortical proximity, microstructural similarity, and white matter connectivity. Clinically, the FC phenotype tracked symptom burden and disease progression, informed postsurgical seizure outcome, and distinguished TLE from other focal epilepsies. Collectively, these findings systematically delineate a neurobiologically grounded, hub-centric pattern of intrinsic network disruption in TLE, anchored in temporolimbic and adjacent transmodal systems, with potential utility for individualized phenotypic stratification and outcome prognostication.

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bioRxiv2025

Multiscale characterisation of the human claustrum from histology to MRI

Though linked to an unusually broad array of functions, the human claustrums complex morphology has hindered in vivo study, resulting in a small MRI literature marked by implausibly large discrepancies in reported characteristics. We constructed the first three-dimensional histological "gold standard" claustrum model, and systematically evaluated in vivo 7-Tesla MRI datasets against it and downsampled derivatives. MRI showed resolution-dependent differences rather than contrast limitations, transforming the claustrums intricate sheet into an artefactually thickened ribbon. However, submillimetre MRI reliably recovered the dorsal "core" that contains most claustral volume and density and houses major corticoclaustral connectivity. At 0.5mm resolution, extension into the temporal lobe, including irregular ventral "puddles", was partially recovered, with uncertainty reflecting boundary imprecision rather than anatomical loss. Our results refute the view that the claustrum is inaccessible in the living human brain, define practical measurement limits, and provide a foundation for future functional investigations.

neuroscience↗

bioRxiv2025

cuBNM: GPU-Accelerated Brain Network Modeling

Brain network modeling uses computer simulations to infer about latent neural properties at micro- and mesoscales by fitting brain dynamic models to empirical data of individual subjects or groups. However, computational costs of (individualized) model fitting is a major bottleneck, limiting the practical feasibility of this approach to larger cohorts and more complex models, and highlighting the need for scalable simulation implementations. Here, we introduce cuBNM, a Python package which leverages parallel processing of graphics processing units to massively accelerate simulations of brain network models. We show running simulations on graphics processing units is several hundred times faster compared to central processing units. We demonstrate the usage of cuBNM by running optimization of group-level and individualized low- and high-dimensional models. As examples of the utility of individualized models, we investigated test-retest reliability and heritability of simulated and empirical measures in the Human Connectome Project dataset. We found simulated features were fairly reliable and significantly heritable, suggesting their biological plausibility. Overall, cuBNM enables large-scale simulations of brain network models, opening new avenues for studying latent neural processes across diverse populations, dense networks, and high-dimensional models, which was previously impractical due to computational constraints.

neuroscience↗

bioRxiv2025

Patterns of intersubject correlations parallel organizational gradients during naturalistic viewing

Recent studies have robustly demonstrated that human cortical function can be described through sensory-transmodal gradients of cortical function, while naturalistic movie watching paradigms have been leveraged to index cortical synchronization. We leveraged two independent 7T movie fMRI datasets to assess correlations between intersubject correlation and functional gradients across movies, datasets, and spatial scales. At the whole-brain level, we observed robust relationships between intersubject correlations and a visual-transmodal connectivity gradient which was independent of movie content. Within functional networks, correlations were particularly pronounced for the visual, dorsal attention, and default mode networks. Our results demonstrate that naturalistic paradigms can provide targeted insight into multiscale processing hierarchies. Robust relationships across movies suggest that movie-watching can be viewed as a brain state that is independent of movie-content. Overall, this work suggests an important confluence of within-subject functional organizational axes and inter-subject synchronization when the brain is engaged in the processing of naturalistic stimuli.

neuroscience↗

bioRxiv2025

Intracortical microstructure profiling: a versatile method for indexing cortical lamination

Intracortical microstructure profiling represents a powerful, scalable approach for investigating the laminar organisation of the human cortex on both in vivo and post-mortem datasets. Building upon a long tradition of histological analysis, this method leverages surface-based intracortical sampling to generate profiles of tissue properties across cortical depths. The present work outlines a standardised workflow for intracortical microstructural profiling, newly packaged as an open-source toolbox "CortPro" (https://github.com/caseypaquola/cortpro). Here, we explore the utility of central moments as descriptors of profile shape. Using these measures, we quantify (i) the extent to which in vivo MRI can capture laminar differentiation, (ii) the test-retest reliability of profiles, and (iii) their replicability across sites and studies. Our results demonstrate that intracortical profiles are remarkably robust and effectively mitigate bias-field related limitations of non-quantitative MRI. As applications of microstructure-sensitive imaging expand across development, aging, and disease, microstructure profiling provides a principled means of linking microstructural neuroanatomy with systems-level brain organisation.

neuroscience↗

bioRxiv2025

State dependent shifts in large scale functional topographies

Although functional networks can be consistently identified across cognitive states, they also undergo dynamic reconfigurations across different contexts. For example, naturalistic movie watching paradigms amplify activity in sensory systems compared to resting conditions. However, it remains unclear how these different states affect large-scale brain organization. The current study leveraged high-resolution in vivo 7T fMRI data from the Human Connectome Project (HCP) and the Precision NeuroImaging (PNI) datasets to examine large scale functional connectivity changes between resting and movie-watching conditions. To understand these changes within topographic and geometric principles of brain organization, connectivity shifts were stratified relative to macroscale cortical hierarchy and geodesic distance. Our results revealed that primary sensory areas showed increased local connectivity and reduced long-range interactions during movie watching relative to resting conditions, whereas the default mode network (DMN) exhibited an opposing pattern characterized by reduced within-network long-range connectivity and enhanced connectivity with distant regions outside the DMN. Together, these findings demonstrate that different cognitive states involve geometry-and hierarchy-informed reorganization of large-scale functional networks.

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bioRxiv2025

Personalized Biomarkers of Multiscale Functional Alterations in Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE) presents with substantial inter-patient variability in clinical and neuroimaging manifestations. This multicenter study examined inter-individual differences in spatial patterns of intrinsic brain function in TLE using normative modeling at multiple spatial scales and evaluated the effectiveness of individual functional deviations for clinical diagnosis and postsurgical outcome prediction. We analyzed multimodal MRI data on 298 healthy controls, 282 TLE patients, and 45 disease controls with extratemporal epilepsy. Cortical function was profiled at local, regional, and global scales using brain signal variability, regional homogeneity, and node strength. We estimated patient-specific W-score maps to index deviations from normative metrics. Compared to healthy controls, patients with TLE showed considerable variations in patterns of functional alterations across the cortex, with the highest overlap in the ipsilateral mesiotemporal regions. Connectome-based simulation revealed the paralimbic and medial default mode regions as key disease epicenters. Functional changes were primarily underpinned by superficial white matter anomalies. Supervised pattern learning achieved classification AUCs of 0.76 for TLE versus disease controls, 0.74 for left versus right TLE, and 0.63 for seizure-free versus non-seizure-free TLE, with greater contralateral temporal functional deviations correlating with unfavorable postsurgical seizure outcome. Our findings reveal the heterogeneous impact of TLE on intrinsic cortical function. These biomarkers hold promise for clinical translation, guiding precision therapeutics and enhancing presurgical decision-making in TLE.

neuroscience↗

bioRxiv2024

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

neuroscience↗

bioRxiv2024

Pharmaco-resistant temporal lobe epilepsy gradually perturbs the cortex-wide excitation-inhibition balance

AO_SCPLOWBSTRACTC_SCPLOWExcitation-inhibition (E/I) imbalance is theorized as a key mechanism in the pathophysiology of epilepsy, with a mounting body of previous research focusing on elucidating its cellular manifestations. However, there are limited studies into E/I imbalance at macroscale and its microcircuit-level mechanisms and clinical associations. In our current work, we computed the Hurst exponent--a previously validated index of the E/I ratio--from resting-state fMRI time series, and simulated microcircuit parameters using biophysical computational models. We found a broad reduction in the Hurst exponent in pharmaco-resistant temporal lobe epilepsy (TLE), indicative of a shift towards more excitable network dynamics. Connectome decoders pointed to temporolimbic and frontocentral areas as plausible network epicenters of E/I imbalance. Computational simulations further revealed that enhancing cortical excitability in patients likely reflected atypical increases in recurrent connection strength of local neuronal ensembles. Moreover, mixed cross-sectional and longitudinal analyses revealed heightened E/I elevation in patients with longer disease duration, more frequent electroclinical seizures and inter-ictal epileptic spikes, and worse cognitive functioning. Replicated in an independent dataset, our work provides compelling in-vivo evidence of a macroscale shift in E/I balance in TLE patients that undergoes progressive changes and underpins cognitive impairments, potentially informing treatment strategies targeting E/I mechanisms.

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