Hierarchical Bayesian pharmacometrics analysis of Baclofen for alcohol use disorder DOI Creative Commons
Nina Baldy, Nicolas Simon, Viktor Jirsa

и другие.

Machine Learning Science and Technology, Год журнала: 2023, Номер 4(3), С. 035048 - 035048

Опубликована: Сен. 1, 2023

Abstract Alcohol use disorder (AUD), also called alcohol dependence, is a major public health problem, affecting almost 10 % of the world’s population. Baclofen, as selective $\mathrm{GABA}_\mathrm{B}$?> mathvariant="normal">G mathvariant="normal">A mathvariant="normal">B receptor agonist, has emerged promising drug for treatment AUD. However, inter-trial, inter-individual and residual variability in concentration over time population patients with AUD unknown. In this study, we hierarchical Bayesian workflow to estimate parameters pharmacokinetic (PK) model from Baclofen administration By monitoring various convergence diagnostics, probabilistic methodology first validated on synthetic longitudinal datasets then applied infer PK based clinical data that were retrospectively collected outpatients treated oral Baclofen. We show state-of-the-art advances automatic inference using self-tuning Hamiltonian Monte Carlo (HMC) algorithms provide accurate decisive predictions plasma at both individual group levels. Importantly, leveraging information prior provides faster computation, better substantially higher out-of-sample prediction accuracy. Moreover, root mean squared error measure within-sample predictive accuracy can be misleading evaluation, whereas fully criteria correctly select true generating parameters. This study points out capability non-parametric estimation adaptive HMC sampling methods easy reliable settings optimize dosing regimens efficiently treat

Язык: Английский

Personalised virtual brain models in epilepsy DOI Creative Commons
Viktor Jirsa, Huifang Wang, Paul Triebkorn

и другие.

The Lancet Neurology, Год журнала: 2023, Номер 22(5), С. 443 - 454

Опубликована: Март 24, 2023

Язык: Английский

Процитировано

92
Delineating epileptogenic networks using brain imaging data and personalized modeling in drug-resistant epilepsy DOI
Huifang Wang, Marmaduke Woodman, Paul Triebkorn

и другие.

Science Translational Medicine, Год журнала: 2023, Номер 15(680)

Опубликована: Янв. 25, 2023

Precise estimates of epileptogenic zone networks (EZNs) are crucial for planning intervention strategies to treat drug-resistant focal epilepsy. Here, we present the virtual epileptic patient (VEP), a workflow that uses personalized brain models and machine learning methods estimate EZNs aid surgical strategies. The structural scaffold patient-specific whole-brain network model is constructed from anatomical T1 diffusion-weighted magnetic resonance imaging. Each node equipped with mathematical dynamical simulate seizure activity. Bayesian inference sample optimize key parameters using functional stereoelectroencephalography recordings patients’ seizures. These together their determine given patient’s EZN. Personalized were further used predict outcome surgeries. We evaluated VEP retrospectively 53 patients VEPs reproduced clinically defined precision 0.6, where physical distance between regions identified by was small. Compared resected 25 who underwent surgery, showed lower false discovery rates in seizure-free (mean, 0.028) than non–seizure-free 0.407). now being an ongoing clinical trial (EPINOV) expected 356 prospective

Язык: Английский

Процитировано

67
Virtual brain twins: from basic neuroscience to clinical use DOI Creative Commons
Huifang Wang, Paul Triebkorn, Martin Breyton

и другие.

National Science Review, Год журнала: 2024, Номер 11(5)

Опубликована: Фев. 27, 2024

ABSTRACT Virtual brain twins are personalized, generative and adaptive models based on data from an individual’s for scientific clinical use. After a description of the key elements virtual twins, we present standard model personalized whole-brain network models. The personalization is accomplished using subject’s imaging by three means: (1) assemble cortical subcortical areas in subject-specific space; (2) directly map connectivity into models, which can be generalized to other parameters; (3) estimate relevant parameters through inversion, typically probabilistic machine learning. We use healthy ageing five diseases: epilepsy, Alzheimer’s disease, multiple sclerosis, Parkinson’s disease psychiatric disorders. Specifically, introduce spatial masks demonstrate their physiological pathophysiological hypotheses. Finally, pinpoint challenges future directions.

Язык: Английский

Процитировано

37
Linking Brain Structure, Activity, and Cognitive Function through Computation DOI Creative Commons
Katrin Amunts, Javier DeFelipe, Cyriel M. A. Pennartz

и другие.

eNeuro, Год журнала: 2022, Номер 9(2), С. ENEURO.0316 - 21.2022

Опубликована: Фев. 25, 2022

Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling simulation. Dynamic generative multiscale models, which investigation of causation across scales are guided principles theories function, instrumental linking structure function. An example resource enabling such an integrated approach neuroscientific discovery BigBrain, spatially anchors tissue models data different ensures that data, making bridge both basic neuroscience medicine. Research at intersection neuroscience, computing robotics has potential advance neuro-inspired technologies taking advantage growing body insights into perception, plasticity learning. To render tools methods, theories, concepts interoperable, Human Brain Project (HBP) launched EBRAINS, digital research infrastructure, brings together transdisciplinary community researchers united quest understand brain, with fascinating perspectives societal benefits.

Язык: Английский

Процитировано

42
Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators DOI Creative Commons
Meysam Hashemi, Anirudh Nihalani Vattikonda, Jayant Jha

и другие.

Neural Networks, Год журнала: 2023, Номер 163, С. 178 - 194

Опубликована: Апрель 1, 2023

Whole-brain modeling of epilepsy combines personalized anatomical data with dynamical models abnormal activities to generate spatio-temporal seizure patterns as observed in brain imaging data. Such a parametric simulator is equipped stochastic generative process, which itself provides the basis for inference and prediction local global dynamics affected by disorders. However, calculation likelihood function at whole-brain scale often intractable. Thus, likelihood-free algorithms are required efficiently estimate parameters pertaining hypothetical areas, ideally including uncertainty. In this study, we introduce simulation-based virtual epileptic patient model (SBI-VEP), enabling us amortize approximate posterior process from low-dimensional representation patterns. The state-of-the-art deep learning conditional density estimation used readily retrieve statistical relationships between observations through sequence invertible transformations. We show that SBI-VEP able distribution linked extent epileptogenic propagation zones sparse intracranial electroencephalography recordings. presented Bayesian methodology can deal non-linear latent parameter degeneracy, paving way fast reliable on disorders neuroimaging modalities.

Язык: Английский

Процитировано

34
The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging DOI Creative Commons
Mario Lavanga, Johanna Stumme, Bahar Hazal Yalçınkaya

и другие.

NeuroImage, Год журнала: 2023, Номер 283, С. 120403 - 120403

Опубликована: Окт. 20, 2023

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization white matter tracts functional brain networks. Here, we built a network modeling framework to infer the causal link between structural connectivity architecture consequent in aging. By applying in-silico interhemispheric degradation connectivity, reproduced process dedifferentiation Thereby, found global modulation dynamics by increase age, which was steeper older adults poor performance. We validated our hypothesis via deep-learning Bayesian approach. Our results might be first mechanistic demonstration leading decline.

Язык: Английский

Процитировано

26
Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference DOI Creative Commons
Nicholas Tolley, Pedro Luiz Coelho Rodrigues, Alexandre Gramfort

и другие.

PLoS Computational Biology, Год журнала: 2024, Номер 20(2), С. e1011108 - e1011108

Опубликована: Фев. 26, 2024

Biophysically detailed neural models are a powerful technique to study dynamics in health and disease with growing number of established openly available models. A major challenge the use such is that parameter inference an inherently difficult unsolved problem. Identifying unique distributions can account for observed dynamics, differences across experimental conditions, essential their meaningful use. Recently, simulation based (SBI) has been proposed as approach perform Bayesian estimate parameters SBI overcomes not having access likelihood function, which severely limited methods models, by leveraging advances deep learning density estimation. While substantial methodological advancements offered promising, large scale biophysically challenging doing so have established, particularly when inferring time series waveforms. We provide guidelines considerations on how be applied waveforms starting simplified example extending specific applications common MEG/EEG using modeling framework Human Neocortical Neurosolver. Specifically, we describe compare results from oscillatory event related potential simulations. also diagnostics used assess quality uniqueness posterior estimates. The described principled foundation guide future wide variety dynamics.

Язык: Английский

Процитировано

11
The virtual multiple sclerosis patient DOI Creative Commons
Pierpaolo Sorrentino,

Anagh Pathak,

Abolfazl Ziaeemehr

и другие.

iScience, Год журнала: 2024, Номер 27(7), С. 110101 - 110101

Опубликована: Май 24, 2024

Язык: Английский

Процитировано

10
Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors DOI

Periyasamy Natarajan Shiammala,

N. Duraimutharasan,

Baskaralingam Vaseeharan

и другие.

Methods, Год журнала: 2023, Номер 219, С. 82 - 94

Опубликована: Сен. 29, 2023

Язык: Английский

Процитировано

23
The potential of in vitro neuronal networks cultured on micro electrode arrays for biomedical research DOI Creative Commons

Marta Cerina,

Maria Carla Piastra, Monica Frega

и другие.

Progress in Biomedical Engineering, Год журнала: 2023, Номер 5(3), С. 032002 - 032002

Опубликована: Апрель 18, 2023

Abstract In vitro neuronal models have become an important tool to study healthy and diseased circuits. The growing interest of neuroscientists explore the dynamics systems increasing need observe, measure manipulate not only single neurons but populations cells pushed for technological advancement. this sense, micro-electrode arrays (MEAs) emerged as a promising technique, made cell culture dishes with embedded micro-electrodes allowing non-invasive relatively simple measurement activity cultures at network level. past decade, MEAs popularity has rapidly grown. MEA devices been extensively used mainly derived from rodents. Rodent on employed investigate physiological mechanisms, effect chemicals in neurotoxicity screenings, model electrophysiological phenotype networks different pathological conditions. With advancements human induced pluripotent stem (hiPSCs) technology, differentiation adult donors became possible. hiPSCs-derived develop patient-specific platforms characterize pathophysiological test drugs, paving way towards personalized medicine. review, we first describe technology information that can be obtained recordings. Then, give overview studies which combination (i.e. rodent 2D three-dimensional (3D) cultures, organotypic brain slices, 3D organoids) biomedical research, including physiology studies, disease modeling, drug testing. We end by discussing potential, challenges future perspectives providing some guidance choice device, experimental design, data analysis reporting scientific publications.

Язык: Английский

Процитировано

21