Biology subjects

Corbetta, M.

Publications and source records attributed to Corbetta, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Whole-brain receptor markers and hub excitabilities determine acute and long-term outcomes in stroke

Following a stroke, changes in receptor systems have been observed that may influence the excitability of affected brain regions and drive recovery trajectories. Translating results from animal studies to patients remains a major challenge, and while imaging modalities have provided a wealth of findings, a mechanistic link between local receptor systems and observed imaging data is still missing. To bridge this gap, we built patient-specific digital twins with their own neuromodulatory profiles: we integrated 19 receptor and transporter density maps into a mechanistic whole-brain model, and estimated their influence on cortical excitability for each stroke patient and how these systems subsequently explain recovery trajectories. We found that distinct receptor profiles modulated excitability in perilesional versus non-perilesional tissue. Favourable acute motor outcomes were most strongly explained by increased inhibition mediated by non-perilesional GABA-A receptors, together with enhanced excitability driven by cannabinoid, histaminergic, and noradrenergic systems. For acute language outcomes, we identified receptor signatures that, together with motor findings, formed two cross-domain clusters linking excitability to either better or poorer performance. Remarkably, structurally highly connected hubs played a key role: their acute excitability explained cognitive and language deficits and predicted language recovery at one year. Together, our results establish a mechanistic link between receptor-specific modulation of cortical excitability and behavioural recovery, highlighting potential targets for pharmacological and non-invasive therapeutic interventions.

neuroscience↗

bioRxiv2026

White-matter disconnection shapes distributed cortical spectral dynamics after stroke

Focal brain lesions are thought to induce sleep-like slow-wave activity in perilesional cortex through altered excitation-inhibition balance and structural disconnection, but whether these dynamics extend to remote yet structurally intact regions remain unclear. Here we combined source-reconstructed high-density EEG (128 channels) with structural disconnection mapping in 49 acute stroke patients and 20 age-matched controls. Cortical regions were classified as perilesional, structurally disconnected, or non-disconnected using individual lesion masks registered to normative white-matter atlases. Perilesional cortex showed increased delta and theta power and reduced beta power relative to controls. Critically, structurally disconnected regions exhibited electrophysiological changes comparable to perilesional cortex, including enhanced low-frequency activity and steeper aperiodic spectral slopes. These alterations correlated with neurological severity and multidomain behavioral impairment. Our findings demonstrate that post-stroke slow-wave activity propagates along structural disconnection pathways, providing direct electrophysiological evidence for connectional diaschisis and identifying distributed network targets for physiology-guided neuromodulation.

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bioRxiv2026

A Whole-Brain Dynamical Framework Linking Resting-State Activity to TMS-Evoked Responses

A major challenge in systems neuroscience is understanding how external perturbations interact with ongoing brain activity. Transcranial magnetic stimulation (TMS), increasingly used in both basic and clinical neuroscience and often combined with electroencephalography (EEG), provides a unique opportunity to probe this interaction. However, how intrinsic dynamics constrain the propagation of TMS-evoked activity remains poorly understood. In particular, effective connectivity (EC)--capturing directed, state-dependent interactions between brain regions--is thought to critically shape perturbational spread, yet remains difficult to estimate at the whole-brain EEG level. Here we introduce an analytically tractable, generative whole-brain model that links spontaneous EEG activity to cortical responses under perturbation. By deriving a closed-form expression for the models cross-spectral density, we directly fit empirical resting-state EEG spectra and infer biophysically interpretable local dynamical parameters without time-domain simulations. We then estimate stimulation-site-specific EC using only a small fraction of the TMS-EEG trials. The resulting model accurately predicts the spatiotemporal structure of TMS-evoked potentials (TEPs) in unseen trials. Moreover, even without subject-specific refitting, group-level EC templates capture canonical site-specific propagation motifs underlying single-subject early TMS responses. Together, our results establish an analytical framework for individualized whole-brain modeling of TMS-EEG with potential applicability to model-based neuromodulation.

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bioRxiv2026

Structural brain networks shape individual-level progression of brain atrophy after stroke

Stroke starts as a focal vascular lesion, but its structural consequences often extend beyond the lesion site, resulting in distributed brain atrophy whose organizing principles remain unclear. Here, using longitudinal MRI data from two stroke cohorts spanning the hyperacute to chronic stages, we tested whether and how post-stroke grey matter volume (GMV) change over time is constrained by large-scale brain networks across individuals. Coordinated deformation modelling showed that longitudinal GMV atrophy patterns were more strongly shaped by structural than by functional connectivity, with structural constraints already detectable in the acute phase. Network diffusion modelling further showed that atrophy patterns at 3 and 12 months were consistent with lesion-initiated propagation on the structural connectome, whereas model performance was weak in the early acute stage. The model-derived propagation stage did not increase monotonically with chronological time, indicating that it reflects the degree to which observed atrophy conforms to a network-consistent diffusion-like pattern rather than elapsed biological time. Inter-individual variation in propagation stage was associated with lesion size and with the topological and molecular properties of lesioned regions. Finally, lesion information combined with network diffusion modelling supported individualized prediction of later GMV atrophy patterns. These findings provide a network-based account of secondary degeneration after stroke and may inform individualized characterization of post-stroke structural reorganization.

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bioRxiv2026

Non-invasive Neuromodulation Targeting Approach by Mapping Stimulations and Lesions That Modify Visual Memory

Therapeutic brain stimulation is believed to target specific networks, but targeting approaches for memory remain debated. For other symptoms, neuromodulation targets have been localized by mapping connectivity of lesions and stimulation sites to specific symptoms. This approach has yielded networks for global memory, but it remains unclear whether it applies to specific types of memory. Here, we mapped connectivity of stimulation sites, lesions, and atrophy patterns associated with different memory types. We included 544 individuals across three datasets: transcranial magnetic stimulation (N=262), penetrating head trauma (N=169), and ischemic stroke (N=113). We identified a network preferentially connected to lesions and stimulation sites specifically associated with changes in visual memory. Of note, the direction of this effect was inverted depending on whether lesions or stimulation occurred at younger age or an older age, consistent with prior results. This age effect was replicated in an independent dataset of patients with preclinical Alzheimers disease (N=1240). To examine neuromodulation targets, we computed electrical field models for potential TMS sites that overlap with the networks derived from each stimulation or lesion dataset; the resulting targets intersected with established targets that demonstrated efficacy for treating memory impairment - precuneus, cortical-hippocampal network, and dorsolateral prefrontal cortex - with peak intersection at medial posterior parietal lobe, angular gyrus, and left anterior middle frontal gyrus, respectively. Future head-to-head clinical trials are needed to systematically compare these proposed neuromodulation targets against each other. One Sentence SummaryNeuromodulation targets for visual memory diverge by age at the time of injury or stimulation.

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bioRxiv2026

Individual-level metabolic connectivity captures cortical morphology and their coupling strengthens with age

PurposeCerebral glucose metabolism and cortical morphology are known to undergo significant changes across the lifespan, yet their network-level coordination remains poorly understood. This study aimed to investigate whether individual-level metabolic connectivity (MC) reflects underlying inter-areal morphometric similarity, and to determine how this metabolic-morphometric coupling evolves across the adult lifespan. MethodsDynamic [18F]FDG-PET and structural MRI data were acquired from 67 healthy adults (age range: 38-86 years). Individual MC networks were estimated based on the similarity between regional time-activity curves. Corresponding structural similarity networks were generated using the morphometric inverse divergence (MIND) framework, which integrates multiple vertex-wise features of cortical morphology. The correspondence between metabolic and structural networks was quantified at both global and local scales using Spearman correlations. General linear models were employed to assess age-related effects on MC-MIND similarity. ResultsMC demonstrated a robust positive association with cortical morphometric similarity ({rho} = 0.32, p < 0.0001), an association that persisted after distance correction and was replicated at the individual level. Regional coupling followed a topographic gradient, peaking in heteromodal association cortices and reaching its minimum in paralimbic areas. Crucially, morphology-metabolism alignment systematically strengthened with age at the global level ({beta} = 0.59, p < 0.001). Local age-related increases were spatially heterogeneous, predominantly affecting visual, dorsal parietal, and premotor cortices alongside adjacent multimodal regions. ConclusionIndividual-level MC captures the morphometric organisation of the brain. The age-related increase in morphology-metabolism coupling indicates that metabolic coordination becomes progressively more aligned with cortical architecture, consistent with reduced neuroenergetic flexibility in the ageing brain.

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bioRxiv2026

Acute perilesional excitability explains long-term motor recovery after stroke

Stroke is a leading cause of disability, and changes in neuronal excitability in perilesional regions may critically influence patients recovery. While reduced excitability is often associated with impaired recovery, increased excitability following injury may support reparative mechanisms. Although animal studies have shown how synaptic transmission adapts after stroke, a mechanistic understanding of how these excitability changes in the perilesional area relate to recovery in acute patients remains unclear. Here, we apply patient-specific (N = 96) computational whole-brain models to infer regional excitability, focusing on both perilesional and non-perilesional sites. Specifically, we estimate the firing sensitivity of local excitatory neuronal populations. Our findings indicate large inter-subject variability, with patients displaying both relative perilesional hypo- and hyper-excitability. Notably, perilesional excitability emerges as a robust predictor (P = 0.002) of motor recovery one year after the stroke, but not of acute post-stroke motor impairment, emphasizing its significance in specifically shaping long-term recovery. This synaptic modulation of excitability exhibits a strong correlation with gamma-aminobutyric acid A (GABA-A) receptor density distributions before the stroke, providing a potential biological substrate. These findings highlight the subject-specific nature of perilesional excitability, positioning it as a compelling target for personalized interventions to optimize post-stroke motor recovery.

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bioRxiv2026

Neural integrator and orchestrator communities shape spontaneous signaling in the human brain

Understanding how intrinsic brain dynamics are organized is critical for explaining human cognition and sensory processing. Theoretical frameworks propose a hierarchical architecture in which some neural systems act as orchestrators, broadcasting information across the brain, whereas others serve as integrators, transforming incoming signals. Here, we quantitatively test whether this orchestration-integration framework is embedded in intrinsic brain activity, that is, in the absence of explicit cognitive/sensorial tasks. We adopt a multivariate fractional modeling framework originally developed in financial mathematics to characterize how volatility propagates across interacting markets, thereby identifying systems that act as orchestrators or integrators. We then test whether this integrator-orchestrator axis is related to human intelligence. To this end, using 7T resting-state functional magnetic resonance imaging data from 173 healthy young adults, we model spontaneous brain fluctuations with a multivariate fractional Ornstein-Uhlenbeck process to derive directional influence indices. Consistent with our predictions, we identified a bipartite organization, stable across modeling choices. At the subcortical level, the anterior thalamus, putamen, and caudate emerged as orchestrating transmitters, whereas the posterior thalamus, globus pallidus, hippocampus, amygdala, and nucleus accumbens acted as integrating receivers. At the cortical level, attentional and sensory networks functioned as orchestrating transmitters, while higher-order cognitive networks served as integrating receivers. These findings provide support for a theoretically grounded integration-orchestration framework, demonstrating that brain signaling is organized along this axis even at rest, relevant for intelligence scores. The proposed fractional framework offers a principled tool to investigate how disruptions of this balance may contribute to brain disorders. Competing Interest StatementNone.

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bioRxiv2025

Thermodynamics of consciousness: A non-invasive perturbational framework

The quest for reliable and objective measures of consciousness is critical in basic and clinical neuroscience. Across species, the Perturbational Complexity Index (PCI) has emerged as a robust empirical marker by directly perturbing the brain, yet its underlying principles of physics are not fully understood. Here, we bridge this gap by introducing a non-invasive framework based on generative whole-brain models of non-equilibrium brain dynamics. Using these models, we identified violations of the Fluctuation-Dissipation Theorem (FDT) in humans and rodents across wakefulness, anesthesia, and disorders of consciousness. Mirroring the patterns observed with PCI, we found decreased FDT violations in unresponsive disorders of consciousness and anesthesia compared to conscious conditions. This reveals a close link between PCI and non-equilibrium dynamics in spontaneous brain signals, grounding PCI in fundamental principles of physics. Overall, this framework offers new complementary, non-invasive, model-based avenues for understanding the nature of consciousness and for developing objective tools to assess its loss and recovery in health and disease. It also provides a principled foundation for discovering novel strategies to restore consciousness.

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bioRxiv2025

Cognition Disconnected: The Influence of Domain-General Cognition (g) in Lesion-Deficit Mapping

Lesion-deficit mapping is a premier technique for dissociating neural correlates of cognitive functions. However, the contribution of domain-general cognition (g) to the performance of virtually all complex behavioral tasks may encumber such efforts. This possibility has been largely unexplored. Here, we examined the influence of g in lesion-deficit mapping using a combination of structural equation modeling, lesion-behavior mapping (LBM), and structural (sLNM) and functional (fLNM) lesion network mapping. We modeled latent variables for visuospatial ability, processing speed, language, and anterograde memory among 473 patients with focal brain damage (age range 20-93, 257 men, 216 women), both with and without g-associated variance embedded within each factor. Lesion-deficit maps for domain-specific cognitive abilities were statistically significantly less spatially inter-correlated after partitioning out g (p = 0.010 for LBM; p = 0.003 for sLNM; p = 0.003 for fLNM). Lesion-deficit maps also implicated the existence of distinct neuroanatomy for specific cognitive domains that was, in many cases, only apparent and clearly distinct from lesion-deficit maps of g after partitioning out the influence of g. Overall, our findings suggest that, if left unaccounted for, domain-general cognitive variance could tangibly affect conclusions about the extent to which different cognitive functions rely on distinct neuroanatomy.

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bioRxiv2025

Slow waves generation and propagation in a model of brain lesions

Slow waves (SWs), the hallmark of non-rapid eye movement (NREM) sleep, reflect the periodic occurrence of transient silent periods in cortical neurons (Down states). During NREM, SWs and Down states physiologically disrupt large-scale network interactions. Since early EEG studies, SWs have also been observed in awake patients after brain injury. Emerging evidence indicates that these intrusions of sleep-like activity interfere with ongoing network activity and contribute to motor and cognitive deficits; yet, the mechanisms governing the generation and spread of post-lesional SWs remain unclear. Here, we extend a neural mass model of EEG to capture transitions between wake-like and sleep-like dynamics and embed it in connectome-based networks with virtual lesions. This model supports that local disfacilitation, topology-dependent propagation, and synchrony-dependent amplification throughout the connectome are sufficient to produce post-lesional SWs. These mechanisms reproduce the spatial gradients of post-lesional SWs seen in patients and identify actionable targets for neuromodulation and rehabilitation.

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bioRxiv2025

Glucose Metabolism echoes Long-Range Temporal Correlations in the Human Brain

Intrinsic brain activity is characterized by pervasive long-range temporal correlations. While these scale-invariant dynamics are a fundamental hallmark of brain function, their implications for individual-level metabolic regulation remain poorly understood. Here, we address this gap by integrating resting-state functional Magnetic Resonance Imaging (fMRI) and dynamic [18F]FDG Positron Emission Tomography (PET) data acquired from the same cohort of participants. We uncover a systematic relationship between long-range temporal correlations, quantified via the Hurst exponent, and glucose metabolism. Our findings reveal that persistent temporal dependencies impose a measurable metabolic cost, with brains exhibiting higher long-range temporal correlations incurring greater energetic demands. Beyond glucose metabolism, we also show that these dynamics are likely supported by continuous biosynthetic processes, such as protein synthesis, which are critical for neural circuit maintenance and remodeling. Overall, our results suggest that a significant fraction of the brains so-called "Dark Energy" is actively spent to power spontaneous long-range temporal correlations.

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bioRxiv2025

Structural Connectome Dimension Shapes Brain Dynamics in Health and Disease

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to high-cognitive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

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bioRxiv2025

Spontaneously emerging patterns in human motor cortex code for somatotopic specific movements

The role of spontaneous brain activity remains a key question in neuroscience. While prior work shows sensory regions preferentially replay stimulus-specific patterns (e.g., faces in fusiform face area), we investigated whether motor cortex replays reflect somatotopic organization. Using fMRI, we compared resting-state activity to task-evoked patterns during specific movements (finger, toe, tongue). Spontaneous activity in each effector-specific motor subregion (hand, foot, mouth) exhibited greater similarity to task patterns of its corresponding movement than to non-preferred ones. For instance, hand movement patterns replayed more in hand regions than elsewhere. Positive rest-task correlations emerged for preferred movements, while negative correlations characterized non-preferred ones (except toe). This structured somatotopic organization suggests spontaneous activity encodes functional specialization, not just activation strength, mirroring task-evoked representations.

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bioRxiv2024

Benchmarking Stroke Outcome Prediction through Comprehensive Data Analysis - NeuralCup 2023

Stroke remains a leading cause of mortality and long-term disability worldwide, with variable recovery trajectories posing substantial challenges in anticipating post-event care and rehabilitation planning. To address these challenges, we established the NeuralCup consortium to benchmark predictive models of stroke outcome through a collaborative, data-driven approach. This study presents findings from 15 international teams who used a comprehensive dataset including clinical and imaging data, to identify and compare predictors of motor, cognitive, and emotional outcomes one year post-stroke. Our analyses integrated traditional statistical approaches and novel machine learning algorithms to uncover optimal recipes for predicting each domain. The differences in these optimal recipes reflect distinct brain mechanisms in response to different tasks. Key predictors across all domains included infarct characteristics, T1-weighted MRI sequences, and demographic factors. Additionally, integrating FLAIR imaging and white matter tract analysis significantly improved the prediction of cognitive and motor outcomes, respectively. These findings support a multifaceted approach to stroke outcome prediction, underscoring the potential of collaborative data science to develop personalized care strategies that enhance recovery and quality of life for stroke survivors. To encourage further model development and validation, we provide access to the training dataset at http://neuralcup.bcblab.com

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bioRxiv2024

The brain's "dark energy" puzzle upgraded: FDG uptake, delivery and phosphorylation, and their coupling with resting-state brain activity

The brains resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [18F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [18F]FDG kinetic parameters according to Sokoloffs model, i.e., Ki (irreversible uptake rate), K1 (delivery), k3 (phosphorylation), in a large healthy control group. The parameters spatial distribution was described at a high spatial resolution. We showed that while K1 is the least redundant, there are relevant differences between Ki and k3 (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [18F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2), estimated from 15O PET data. We found that combining rs-fMRI and CMRO2 led to satisfactory prediction of individual Ki variance (45%). Although more difficult to describe, Ki and k3 were both most sensitive to local rs-fMRI variables, while K1 was sensitive to CMRO2. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.

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bioRxiv2024

Switching patterns of cortical-subcortical interaction in the human brain

Resting-state fMRI studies show that functional connectivity (FC) undergoes rapid fluctuations. Although the underlying neural mechanisms are poorly understood, a recent contribution analyzing stroke patients suggested that FC fluctuations involve a dynamic reconfiguration of cortico-subcortical interactions. Here, we analyze cortical-subcortical dynamic FC in a large cohort of healthy subjects (Human Connectome Project data base). Our analysis confirms that FC shifts are synchronized in cortex and subcortex. Two core subcortical clusters comprising, respectively, limbic regions (hippocampus and amygdala) and subcortical nuclei (thalamus and basal ganglia) change their connectivity pattern with cortical regions. We consistently identify two recurring FC patterns (states). In state 1, limbic regions couple with the default mode network, in state 2 with sensorimotor networks. An opposite pattern is observed for thalamus/basal ganglia. Our findings hint at a general relevance of cortico-subcortical interactions in the generation of whole-brain spontaneous FC patterns.

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bioRxiv2024

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the timeseries of activity between two brain regions, so-called resting functional connectivity. We show that adding information about the structural disconnections produced by a patients lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects predicts the resting functional connectivity of the patient about as well as fitting the model directly to the patients data. Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients as well as measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that even after fixing those parameters, the model reproduces results from a population very different than the population on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures relationships between the anatomical structure and functional activity of the human brain.

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