Biology subjects

Vespa, P. M.

Publications and source records attributed to Vespa, P. M..

5 recordsLinked to original sources

bioRxiv2025

GFAP Proteolysis in TBI: Linking Novel Modified Cleavage Products to Astrocyte Pathology and Patient Outcomes

Glial fibrillary acidic protein (GFAP) is a significant clinical biomarker of traumatic brain injury (TBI), yet understanding the nature, timing, and impact of its degraded and modified products would inform clinical utility. We report novel GFAP breakdown products (BDPs) and post-translational modifications (PTMs) that are unique to TBI including fragment- and patient-specific citrullination signatures that destabilize GFAP filaments. GFAP and its fragments were sequenced by mass spectrometry (MS) from severe TBI patients cerebrospinal fluid (CSF) and sera, identifying two distinct TBI-specific jointly generated product sets within the rod-domain covering coil1 (20-26kDa) and coil2 (15-19kDa). Their endings differed from GFAP fragments described in Alzheimers and Alexander disease. Label-free quantification showed biofluid co-product abundance differences with coil1-BDPs enriched over coil2-BDPs. Coil imbalance was independently confirmed by immunoblot densitometry in twenty-three TBI patients. Measurements over ten days showed injury day peaks of full-length and end-clipped GFAP fragments, while proteolytic 37/39kDa and small fragments remained elevated. Six-month outcome (Extended Glasgow Outcome Scale) correlated with GFAP fragments, whereas levels of uncleaved and end-clipped GFAP did not. A human astrocyte culture trauma model provided mechanistic insights linking fluid GFAP levels, subcellular localization, and injury-induced astrocytopathy. A new cleavage site between the two coils was identified through selective epitope loss. Coil1-BDPs were fluid-released post-injury, while coil2-BDPs remained intracellular. Their cellular retention was explained by non-filamentous aggregation of citrulline-modified coil2-BDPs in pathological astrocytes. Trauma-triggered proteolysis involved calpains and caspases in specific astrocyte injury states using protease inhibitors and live-dye-reporter imaging. These novel data link TBI biofluid GFAP fragments with assembly-defective GFAP aggregation in pathological astrocytes. Clinical TBI outcome correlated with GFAP degradation rather than with overall GFAP release. These translational findings indicate that TBI biomarker GFAP undergoes degradation and modification alongside astrocyte pathology, providing a new conceptual framework for investigating biomarker-associated astrocyte proteinopathy potentially linked to post-traumatic neurodegeneration.

neuroscience↗

bioRxiv2024

An AI-Driven Model of Consciousness, Its Disorders, and Their Treatment

Understanding disorders of consciousness (DOC) remains one of the most challenging problems in neuroscience, hindered by the lack of experimental models for probing mechanisms or testing interventions [1]. To address this, we introduce a generative adversarial AI framework [2] that pits deep neural networks--trained to detect consciousness across over 680,000 neuroelectro-physiology samples and validated on 565 patients, healthy volunteers, and animals--against interpretable, machine learning-driven neural field models [3]. This adversarial architecture produces biologically realistic simulations of both conscious and comatose brains that recapitulate empirical neurophysiological features across humans, monkeys, rats, and bats. Without explicit programming, the AI model retrodicts known DOC responses to brain stimulation and generates testable predictions about unconsciousness mechanisms. Two such predictions are validated here: selective disruption of the basal ganglia indirect pathway, supported by diffusion MRI in 51 DOC patients; and increased cortical inhibitory-to-inhibitory synaptic coupling, supported by RNA sequencing from resected brain tissue in six human coma patients and a rat stroke model. The model also identifies high-frequency subthalamic nucleus stimulation as a promising DOC intervention, supported here using electrophysiology data from human patients. This work introduces an AI framework for causal inference and therapeutic discovery in consciousness research and complex systems more broadly.

neuroscience↗

bioRxiv2021

Evaluating denoising strategies in resting-state fMRI in traumatic brain injury (EpiBioS4Rx)

ObjectiveResting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity of traumatic brain injury patients. MethodsWe applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 traumatic brain injury patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial (EpiBioS4Rx). Pipelines were evaluated by three quality control metrics across three exclusion regimes based on the participants head movement profile. ResultsWhile no pipeline eliminated noise effects on functional connectivity, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data driven methods performed comparatively worse. ConclusionIn this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related functional connectivity in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as traumatic brain injury datasets.

neuroscience↗

bioRxiv2021

Consciousness is supported by near-critical cortical electrodynamics

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition, and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, for the first time, we empirically identify the specific critical point near which conscious cortical dynamics operate as the edge-of-chaos critical point, or the boundary between periodicity/stability and chaos/instability. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magne-toencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, GABAergic anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of cortical dynamics away from this critical point; conversely, we show that psychedelics may increase the information-richness of cortical activity by tuning cortical electrodynamics closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC), and show that assessing the proximity of cortical electrodynamics to the edge-of-chaos critical point may be clinically useful as a new biomarker of consciousness. Significance StatementWhat changes in the brain when we lose consciousness? One possibility is that the loss of consciousness corresponds to a transition of the brains electric activity away from edge-of-chaos criticality, or the knifes edge in between stability and chaos. Recent mathematical developments have produced novel tools for testing this hypothesis, which we apply for the first time to cortical recordings from diverse brain states. We show that the electric activity of the cortex is indeed poised near the boundary between stability and chaos during conscious states and transitions away from this boundary during unconsciousness, and that this transition disrupts cortical information processing.

neuroscience↗

bioRxiv2019

The thalamic basis of outcome and cognitive impairment in traumatic brain injury

ObjectiveTo understand how, biologically, the acute event of traumatic brain injury gives rise to a long-term disease, we address the relationship between evolving cortical and subcortical brain damage and measures of functional outcome and cognitive functioning at six months post-injury.\n\nMethodsLongitudinal analysis of clinical and MRI data collected, in a tertiary neurointensive care setting, in a continuous sample of 157 patients surviving moderate to severe traumatic brain injury between 2000 and 2018. For each patient we collected T1- and T2-weighted MRI data, acutely and at a six-months follow-up, as well as acute measures of injury severity (Glasgow Coma Scale) and follow-up measures of functional impairment (Glasgow Outcome Scale extended), and, in a subset of patients, neuropsychological measures of attention, executive functions, and episodic memory.\n\nResultsIn the final cohort of 113 subcortical and 92 cortical datasets that survived (blind) quality control, extensive atrophy was observed over the first six months post-injury across the brain. Nonetheless, only atrophy within subcortical regions, particularly in left thalamus, were associated with functional outcome and neuropsychological measures of attention, executive functions, and episodic memory. Furthermore, when brought together in an analytical model, longitudinal brain measurements could distinguish good versus bad outcome with 90% accuracy, whereas acute brain and clinical measurements alone could only achieve 20% accuracy.\n\nInterpretationDespite great injury heterogeneity, secondary thalamic pathology is a measurable minimum common denominator mechanism directly relating biology to clinical measures of outcome and cognitive functioning, potentially linking the acute \"event\" and the long(er)-term \"disease\" of TBI.

neuroscience↗