192 research outputs found
Clinical correlates of mathematical modeling of cortical spreading depression: Single‐cases study
Introduction: Considerable connections between migraine with aura and cortical spreading depression (CSD), a depolarization wave originating in the visual cortex and traveling toward the frontal lobe, lead to the hypothesis that CSD is underlying migraine aura. The highly individual and complex characteristics of the brain cor‐ tex suggest that the geometry might impact the propagation of cortical spreading depression.
Methods: In a single‐case study, we simulated the CSD propagation for five migraine with aura patients, matching their symptoms during a migraine attack to the CSD wavefront propagation. This CSD wavefront was simulated on a patient‐specific tri‐ angulated cortical mesh obtained from individual MRI imaging and personalized dif‐ fusivity tensors derived locally from diffusion tensor imaging data.
Results: The CSD wave propagation was simulated on both hemispheres, despite in all but one patient the symptoms were attributable to one hemisphere. The CSD wave diffused with a large wavefront toward somatosensory and prefrontal regions, devoted to pain processing.
Discussion: This case‐control study suggests that the cortical geometry may con‐ tribute to the modality of CSD evolution and partly to clinical expression of aura symptoms. The simulated CSD is a large and diffuse phenomenon, possibly capa‐ ble to activate trigeminal nociceptors and to involve cortical areas devoted to pain processing
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have
paved the way for unprecedented opportunities for understanding the complexity
of the brain and its emulation by computational systems. Cutting-edge
advancements in neuroscience research have revealed the intricate relationship
between brain structure and function, while the success of artificial neural
networks highlights the importance of network architecture. Now is the time to
bring them together to better unravel how intelligence emerges from the brain's
multiscale repositories. In this review, we propose the Digital Twin Brain
(DTB) as a transformative platform that bridges the gap between biological and
artificial intelligence. It consists of three core elements: the brain
structure that is fundamental to the twinning process, bottom-layer models to
generate brain functions, and its wide spectrum of applications. Crucially,
brain atlases provide a vital constraint, preserving the brain's network
organization within the DTB. Furthermore, we highlight open questions that
invite joint efforts from interdisciplinary fields and emphasize the
far-reaching implications of the DTB. The DTB can offer unprecedented insights
into the emergence of intelligence and neurological disorders, which holds
tremendous promise for advancing our understanding of both biological and
artificial intelligence, and ultimately propelling the development of
artificial general intelligence and facilitating precision mental healthcare
Computational predictive modeling of integrated cerebral metabolism, electrophysiology and hemodynamics
Understanding the energetic requirement of brain cells during resting state and during high neuronal activity is a very active research area where mathematical models have contributed significantly by providing a context for the interpretation of the experimental results. In this thesis, we present three new computational predictive mathematical models to elucidate several dynamics in the brain, comprising electrophysiological activity, cellular metabolism and hemodynamic response. Many computational challenges had to be addressed, mostly due to the very different characteristic times at which the electrical, metabolic and hemodynamic events occur. The first part of the thesis proposes a novel predictive mathematical electro-metabolic model connecting the electrophysiological activity and the metabolism through a double feedback mechanism based on energy demand and production. This model sheds light on the role of the glial potassium cleaning in brain energy metabolism by integrating a four compartment metabolic model with one describing in details the electrical activity. The results of computed experiments performed with this model for different protocols, namely awake resting state, transitions between resting state and neuronal activation and ischemic episodes are in agreement with experimental observations.
In the second part of the thesis, the electro-metabolic model is expanded to comprise the brain hemodynamic response. This is attained through a triple feedback mechanism between the electrophysiology, metabolism and a three compartment hemodynamic model tracking the changes of cerebral blood flow and cerebral blood volume through arteries, capillaries and veins. During neuronal activation, the increase in extracellular potassium concentration triggers an increase in the cerebral blood flow and concurrently vasodilation, ensuring the supply of nutrients necessary for the metabolic response to sustain the increased energy demand. The ensuing hemo-electro-metabolic model provides a better insight on the transitions between resting state and neuronal activation.
In the third and last part of the thesis, we propose a variant of the electro-metabolic model that adequately describes the changes in the brain in connection with cortical spreading depression (CSD) waves. In addition the dynamics of sodium and potassium, the new model accounts for chloride dynamics, the glutamate-glutamine cycle, as well as neuronal swelling accompanied by shrinkage of extracellular space. As illustrated with computed experiments, with this model it is possible to follow simultaneously the changes in ionic homeostasis, the alterations in the volumes of the cellular compartments and of the extracellular space, and large modifications in brain metabolism during cortical spreading depression waves. The model predictions, in agreement with findings reported in the experimental literature, show a large decrease in glucose and oxygen concentration and a significant increase in lactate concentration during the passing of cortical spreading depression waves.SVP-2014-06872
Hemodynamic Traveling Waves in Human Visual Cortex
Functional MRI (fMRI) experiments rely on precise characterization of the blood oxygen level dependent (BOLD) signal. As the spatial resolution of fMRI reaches the sub-millimeter range, the need for quantitative modelling of spatiotemporal properties of this hemodynamic signal has become pressing. Here, we find that a detailed physiologically-based model of spatiotemporal BOLD responses predicts traveling waves with velocities and spatial ranges in empirically observable ranges. Two measurable parameters, related to physiology, characterize these waves: wave velocity and damping rate. To test these predictions, high-resolution fMRI data are acquired from subjects viewing discrete visual stimuli. Predictions and experiment show strong agreement, in particular confirming BOLD waves propagating for at least 5–10 mm across the cortical surface at speeds of 2–12 mm s-1. These observations enable fundamentally new approaches to fMRI analysis, crucial for fMRI data acquired at high spatial resolution
Predicting the spatiotemporal diversity of seizure propagation and termination in human focal epilepsy
Recent studies have shown that seizures can spread and terminate across brain
areas via a rich diversity of spatiotemporal patterns. In particular, while the
location of the seizure onset area is usually in-variant across seizures in a
same patient, the source of traveling (2-3 Hz) spike-and-wave discharges (SWDs)
during seizures can either move with the slower propagating ictal wavefront or
remain stationary at the seizure onset area. In addition, although most focal
seizures terminate quasi-synchronously across brain areas, some evolve into
distinct ictal clusters and terminate asynchronously. To provide a unifying
perspective on the observed diversity of spatiotemporal dynamics for seizure
spread and termination, we introduce here the Epileptor neural field model. Two
mechanisms play an essential role. First, while the slow ictal wavefront
propagates as a front in excitable neural media, the faster SWDs propagation
results from coupled-oscillator dynamics. Second, multiple time scales interact
during seizure spread, allowing for low-voltage fast-activity (>10 Hz) to
hamper seizure spread and for SWD propagation to affect the way a seizure
terminates. These dynamics, together with variations in short and long-range
connectivity strength, play a central role on seizure spread, maintenance and
termination. We demonstrate how Epileptor field models incorporating the above
mechanisms predict the previously reported diversity in seizure spread
patterns. Furthermore, we confirm the predictions for synchronous or
asynchronous (clustered) seizure termination in human seizures recorded via
stereotactic EEG. Our new insights into seizure spatiotemporal dynamics may
also contribute to the development of new closed-loop neuromodulation therapies
for focal epilepsy.Comment: 10 pages + 9 pages Supporting Information (SI), 7 figures, 1 SI
table, 7 SI figure
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