1
|
Hong R, Zheng T, Marra V, Yang D, Liu JK. Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration. J Neural Eng 2024; 21:021002. [PMID: 38621378 DOI: 10.1088/1741-2552/ad3eb4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.
Collapse
Affiliation(s)
- Rongqi Hong
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Tingting Zheng
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | | | - Dongping Yang
- Research Centre for Frontier Fundamental Studies, Zhejiang Lab, Hangzhou, People's Republic of China
| | - Jian K Liu
- School of Computer Science, Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| |
Collapse
|
2
|
Zhang JM, Masvidal-Codina E, Nguyen D, Illa X, Dégardin J, Goulet R, Prats-Alfonso E, Matsoukis S, Guger C, Garrido JA, Picaud S, Guimerà-Brunet A, Wykes RC. Concurrent functional ultrasound imaging with graphene-based DC-coupled electrophysiology as a platform to study slow brain signals and cerebral blood flow under control and pathophysiological brain states. NANOSCALE HORIZONS 2024; 9:544-554. [PMID: 38323517 DOI: 10.1039/d3nh00521f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Current methodology used to investigate how shifts in brain states associated with regional cerebral blood volume (CBV) change in deep brain areas, are limited by either the spatiotemporal resolution of the CBV techniques, and/or compatibility with electrophysiological recordings; particularly in relation to spontaneous brain activity and the study of individual events. Additionally, infraslow brain signals (<0.1 Hz), including spreading depolarisations, DC-shifts and infraslow oscillations (ISO), are poorly captured by traditional AC-coupled electrographic recordings; yet these very slow brain signals can profoundly change CBV. To gain an improved understanding of how infraslow brain signals couple to CBV we present a new method for concurrent CBV with wide bandwidth electrophysiological mapping using simultaneous functional ultrasound imaging (fUS) and graphene-based field effect transistor (gFET) DC-coupled electrophysiological acquisitions. To validate the feasibility of this methodology visually-evoked neurovascular coupling (NVC) responses were examined. gFET recordings are not affected by concurrent fUS imaging, and epidural placement of gFET arrays within the imaging window did not deteriorate fUS signal quality. To examine directly the impact of infra-slow potential shifts on CBV, cortical spreading depolarisations (CSDs) were induced. A biphasic pattern of decreased, followed by increased CBV, propagating throughout the ipsilateral cortex, and a delayed decrease in deeper subcortical brain regions was observed. In a model of acute seizures, CBV oscillations were observed prior to seizure initiation. Individual seizures occurred on the rising phase of both infraslow brain signal and CBV oscillations. When seizures co-occurred with CSDs, CBV responses were larger in amplitude, with delayed CBV decreases in subcortical structures. Overall, our data demonstrate that gFETs are highly compatible with fUS and allow concurrent examination of wide bandwidth electrophysiology and CBV. This graphene-enabled technological advance has the potential to improve our understanding of how infraslow brain signals relate to CBV changes in control and pathological brain states.
Collapse
Affiliation(s)
- Julie Meng Zhang
- Sorbonne Université, INSERM, CNRS, Institute de la Vision, Paris F75012, France
| | - Eduard Masvidal-Codina
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, Barcelona, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
| | - Diep Nguyen
- Sorbonne Université, INSERM, CNRS, Institute de la Vision, Paris F75012, France
| | - Xavi Illa
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
- Institute of Microelectronics of Barcelona, (IMB-CNM), CSIC, Spain
| | - Julie Dégardin
- Sorbonne Université, INSERM, CNRS, Institute de la Vision, Paris F75012, France
| | - Ruben Goulet
- Sorbonne Université, INSERM, CNRS, Institute de la Vision, Paris F75012, France
| | - Elisabet Prats-Alfonso
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
- Institute of Microelectronics of Barcelona, (IMB-CNM), CSIC, Spain
| | - Stratis Matsoukis
- G-Tec Medical Engineering GmbH, Austria
- Institute for Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Jose Antonio Garrido
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Serge Picaud
- Sorbonne Université, INSERM, CNRS, Institute de la Vision, Paris F75012, France
| | - Anton Guimerà-Brunet
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain.
- Institute of Microelectronics of Barcelona, (IMB-CNM), CSIC, Spain
| | - Rob C Wykes
- University College London Queen Square Institute of Neurology, London, UK.
- Nanomedicine Lab, Division of Neuroscience, University of Manchester, UK
| |
Collapse
|