Biology subjects

Patow, G. A.

Publications and source records attributed to Patow, G. A..

7 recordsLinked to original sources

bioRxiv2026

Preserved but not functional: growth biology shapes connectivity resilience in meningioma and glioma

Brain tumors disrupt neural connectivity, but the nature of this disruption depends on tumor growth biology. Here, we analyze pre-operative structural connectivity (SC), functional connectivity (FC), and generalized effective connectivity (GEC) in 14 meningioma patients, 10 glioma patients, and 10 matched controls to characterize how extra-axial and intra-axial tumors differentially affect brain networks. We introduce FC resilience, the relative preservation of functional connectivity in structurally damaged regions, and find that meningioma patients exhibit significantly higher FC resilience than glioma patients, with SC-dominant damage and preserved neural activity in damaged regions. Glioma patients show balanced SC-FC damage and degraded neural activity, consistent with infiltrative destruction of both white matter and neural substrate. Connectivity damage is not localized to the tumor vicinity and is non-randomly distributed across functional networks, with distinct propagation patterns: glioma SC damage clusters along white matter pathways, while meningioma SC damage preferentially targets Limbic and Default networks. Network topology analysis reveals that more segregated functional and effective connectivity, particularly higher modularity, predicts FC resilience in meningioma patients but not in glioma patients, while structural connectivity topology shows no predictive value. Non-equilibrium dynamics, quantified via the Fluctuation-Dissipation Theorem, are elevated in damaged regions of meningioma patients, serving as a dynamical marker of structural damage rather than an independent compensatory mechanism. Clinically, higher FC resilience in glioma patients is associated with worse cognitive outcomes, suggesting that preserved FC without an intact neural substrate does not reflect genuine functional preservation. These findings demonstrate that the interpretation of functional connectivity resilience depends fundamentally on tumor type and its underlying growth biology.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

Impact of meningioma and glioma on whole-brain dynamics

Brain tumors, particularly meningiomas and gliomas, can profoundly affect neural function, yet their impact on brain dynamics remains incompletely understood. This study investigates alterations in normal brain function among meningioma and glioma patients by assessing dynamical complexity through the Intrinsic Ignition Framework. We analyzed resting-state fMRI data from 34 participants to quantify brain dynamics using intrinsic ignition and metastability metrics. Our results revealed distinct patterns of disruption: glioma patients showed significant reductions in both metrics compared to controls, indicating widespread network disturbances. In contrast, meningioma patients exhibited significant changes predominantly in regions with substantial tumor involvement. Resting-state network analysis demonstrated strong metastability and metastability/ignition correlations between regions in controls, which were slightly weakened in meningioma patients and severely disrupted in glioma patients. These findings highlight the differential impacts of gliomas and meningiomas on brain function, offering insights into their distinct pathophysiological mechanisms. Furthermore, these results show that brain dynamics metrics can be effective biomarkers for identifying disruptions in brain information transmission caused by tumors.

neuroscience↗

bioRxiv2024

Whole-brain dynamics and hormonal shifts throughout women's lifespan: From reproductive stages to menopausal transition and beyond

Neuroimaging studies have identified significant age-related disruptions in whole-brain dynamics, yet the influence of womens reproductive stages and associated hormonal shifts remains underexplored. This study leverages resting-state fMRI data from the Human Connectome Project in Aging to examine brain dynamics through five reproductive stages: reproductive, late reproductive, perimenopause, early postmenopause, and late postmenopause. Our results indicate that the late reproductive stage is characterized by the highest dynamical complexity across whole-brain and resting-state networks, while brain dynamics significantly decline at menopause onset. Additionally, we employ machine learning classifiers using two approaches: (1) brain dynamics alone and (2) brain dynamics combined with follicle-stimulating hormone (FSH) and estradiol (brain-hormone model). Both models accurately distinguished reproductive stages, but the brain-hormone model outperformed the brain dynamics model. Key predictors included decreased estradiol, increased FSH, and altered brain dynamics in later life stages. These results offer a framework for assessing brain health across womens reproductive lifespan.

neuroscience↗

bioRxiv2024

Off-Equilibrium Fluctuation-Dissipation Theorem Paves the Way in Alzheimer's Disease Research

INTRODUCTIONAlzheimers disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. Although traditional methods have provided insights into brain dynamics in AD, they have limitations in capturing non-equilibrium dynamics across disease stages. Recent studies suggest that dynamic functional connectivity in resting-state networks (RSNs) may serve as a biomarker for AD, but the role of deviations from dynamical equilibrium remains underexplored. OBJECTIVEThis study applies the off-equilibrium fluctuation-dissipation theorem (FDT)1 to analyze brain dynamics in AD, aiming to compare deviations from equilibrium in healthy controls, patients with mild cognitive impairment (MCI), and those with AD. The goal is to identify potential biomarkers for early AD detection and understand disease progressions mechanisms. METHODSWe employed a model-free approach based on FDT to analyze functional magnetic resonance imaging (fMRI) data, including healthy controls, MCI patients, and AD patients. Deviations from equilibrium in resting-state brain activity were quantified using fMRI scans. In addition, we performed model-based simulations incorporating Amyloid-Beta (A{beta}), tau burdens, and Generative Effective Connectivity (GEC) for each subject. RESULTSOur findings show that deviations from equilibrium increase during the MCI stage, indicating hyperexcitability, followed by a significant decline in later stages of AD, reflecting neuronal damage. Model-based simulations incorporating A{beta} and tau burdens closely replicated these dynamics, especially in AD patients, highlighting their role in disease progression. Healthy controls exhibited lower deviations, while AD patients showed the most significant disruptions in brain dynamics. DISCUSSIONThe study demonstrates that the off-equilibrium FDT framework can accurately characterize brain dynamics in AD, providing a potential biomarker for early detection. The increase in non-equilibrium deviations during the MCI stage followed by their decline in AD offers a mechanistic explanation for disease progression. Future research should explore how combining this framework with other dynamic brain measures could further refine diagnostic tools and therapeutic strategies for AD and other neurodegenerative diseases.

neuroscience↗

bioRxiv2024

Modeling disorders of consciousness at the patient level reveals the network's influence on the diagnosis vs the local node parameters role in prognosis

The study of disorders of consciousness (DoC) is very complex because patients suffer from a wide variety of lesions, affected brain mechanisms, different severity of symptoms, and are unable to communicate. Combining neuroimaging data and mathematical modeling can help us quantify and better describe some of these alterations. The goal of this study is to provide a new analysis and modeling pipeline for fMRI data leading to new diagnosis and prognosis biomarkers at the individual patient level. To do so, we project patients fMRI data into a low-dimension latent-space. We define the latent spaces dimension as the smallest dimension able to maintain the complexity, non-linearities, and information carried by the data, according to different criteria that we detail in the first part. This dimensionality reduction procedure then allows us to build biologically inspired latent whole-brain models that can be calibrated at the single-patient level. In particular, we propose a new model inspired by the regulation of neuronal activity by astrocytes in the brain. This modeling procedure leads to two types of model-based biomarkers (MBBs) that provide novel insight at different levels: (1) the connectivity matrices bring us information about the severity of the patients diagnosis, and, (2) the local node parameters correlate to the patients etiology, age and prognosis. Altogether, this study offers a new data processing framework for resting-state fMRI which provides crucial information regarding DoC patients diagnosis and prognosis. Finally, this analysis pipeline could be applied to other neurological conditions.

neuroscience↗

bioRxiv2024

Whole-Brain Dynamics Disruptions in the Progression of Alzheimer's Disease: Understanding the Influence of Amyloid-Beta and Tau

Alzheimers disease (AD) progressively alters brain structure and function, yet the associated changes in large-scale brain network dynamics remain poorly understood. We applied the intrinsic ignition framework to resting-state functional MRI (rs-fMRI) data from AD patients, individuals with mild cognitive impairment (MCI), and cognitively healthy controls (HC) to elucidate how AD shapes intrinsic brain activity. We assessed node-metastability at the whole-brain level and in 7 canonical resting-state networks (RSNs). Our results revealed a progressive decline in dynamical complexity across the disease continuum. HC exhibited the highest node-metastability, whereas it was substantially reduced in MCI and AD patients. The cortical hierarchy of information processing was also disrupted, indicating that rich-club hubs may be selectively affected in AD progression. Furthermore, we used linear mixed-effects models to evaluate the influence of Amyloid-{beta} (A{beta}) and tau pathology on brain dynamics at both regional and whole-brain levels. We found significant associations between both protein burdens and alterations in node-metastability. Lastly, a machine learning classifier trained on brain dynamics, A{beta}, and tau burden features achieved high accuracy in discriminating between disease stages. Together, our findings highlight the progressive disruption of intrinsic ignition across whole-brain and RSNs in AD and support the use of node-metastability in conjunction with proteinopathy as a novel framework for tracking disease progression.

neuroscience↗