Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics DOI Creative Commons
Janine Thome, Robert Steinbach, Julian Großkreutz

и другие.

Human Brain Mapping, Год журнала: 2021, Номер 43(2), С. 681 - 699

Опубликована: Окт. 16, 2021

Abstract Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification amyotrophic lateral sclerosis (ALS). While most focus on structural data, recent assessing functional connectivity between brain regions by linear methods highlight role function. These have yet be combined with structure nonlinear features. We investigate features, benefit combining function for ALS classification. patients ( N = 97) healthy controls 59) underwent resting state magnetic resonance imaging. Based key hubs networks, we defined three feature sets comprising volume, (rsFC), as well (nonlinear) dynamics assessed via recurrent neural networks. Unimodal multimodal random forest classifiers were built classify ALS. Out‐of‐sample prediction errors five‐fold cross‐validation. achieved a accuracy 56.35–61.66%. Multimodal outperformed unimodal achieving accuracies 62.85–66.82%. Evaluating ranking individual features' scores across all revealed that rsFC features dominant in univariate analyses reduced patients, more generally indicated deficits information integration networks The present work undermines provides an additional classification, classifiers, while emphasizing capturing both properties identify discriminative

Язык: Английский

Modulation of neural co-firing to enhance network transmission and improve motor function after stroke DOI Creative Commons
Karunesh Ganguly, Preeya Khanna, Robert J. Morecraft

и другие.

Neuron, Год журнала: 2022, Номер 110(15), С. 2363 - 2385

Опубликована: Авг. 1, 2022

Язык: Английский

Процитировано

25
Dynamical flexible inference of nonlinear latent factors and structures in neural population activity DOI Creative Commons

Hamidreza Abbaspourazad,

Eray Erturk, Bijan Pesaran

и другие.

Nature Biomedical Engineering, Год журнала: 2023, Номер 8(1), С. 85 - 108

Опубликована: Дек. 11, 2023

Язык: Английский

Процитировано

14
Small, correlated changes in synaptic connectivity may facilitate rapid motor learning DOI Creative Commons
Barbara Feulner, Matthew G. Perich, Raeed H. Chowdhury

и другие.

Nature Communications, Год журнала: 2022, Номер 13(1)

Опубликована: Сен. 2, 2022

Abstract Animals rapidly adapt their movements to external perturbations, a process paralleled by changes in neural activity the motor cortex. Experimental studies suggest that these originate from altered inputs (H input ) rather than local connectivity ), as covariance is largely preserved during adaptation. Since measuring synaptic vivo remains very challenging, we used modular recurrent network qualitatively test this interpretation. As expected, H resulted small and covariance. Surprisingly given presumed dependence of stable on circuit connectivity, led only slightly larger covariance, still within range experimental recordings. This similarity due requiring small, correlated for successful Simulations tasks impose increasingly behavioural revealed growing difference between , which could be exploited when designing future experiments.

Язык: Английский

Процитировано

22
Distributed functions of prefrontal and parietal cortices during sequential categorical decisions DOI Creative Commons
Yang Zhou, Matthew C. Rosen,

Sruthi K. Swaminathan

и другие.

eLife, Год журнала: 2021, Номер 10

Опубликована: Авг. 24, 2021

Comparing sequential stimuli is crucial for guiding complex behaviors. To understand mechanisms underlying decisions, we compared neuronal responses in the prefrontal cortex (PFC), lateral intraparietal (LIP), and medial (MIP) areas monkeys trained to decide whether sequentially presented were from matching (M) or nonmatching (NM) categories. We found that PFC leads M/NM whereas LIP MIP appear more involved stimulus evaluation motor planning, respectively. Compared LIP, showed greater nonlinear integration of currently visible remembered stimuli, which correlated with monkeys’ decisions. Furthermore, multi-module recurrent networks on same task exhibited key features encoding, including PFC-like module, was causally networks’ Network analysis units have stronger widespread connections input, output, within-area units, indicating putative circuit-level

Язык: Английский

Процитировано

27
Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics DOI Creative Commons
Janine Thome, Robert Steinbach, Julian Großkreutz

и другие.

Human Brain Mapping, Год журнала: 2021, Номер 43(2), С. 681 - 699

Опубликована: Окт. 16, 2021

Abstract Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification amyotrophic lateral sclerosis (ALS). While most focus on structural data, recent assessing functional connectivity between brain regions by linear methods highlight role function. These have yet be combined with structure nonlinear features. We investigate features, benefit combining function for ALS classification. patients ( N = 97) healthy controls 59) underwent resting state magnetic resonance imaging. Based key hubs networks, we defined three feature sets comprising volume, (rsFC), as well (nonlinear) dynamics assessed via recurrent neural networks. Unimodal multimodal random forest classifiers were built classify ALS. Out‐of‐sample prediction errors five‐fold cross‐validation. achieved a accuracy 56.35–61.66%. Multimodal outperformed unimodal achieving accuracies 62.85–66.82%. Evaluating ranking individual features' scores across all revealed that rsFC features dominant in univariate analyses reduced patients, more generally indicated deficits information integration networks The present work undermines provides an additional classification, classifiers, while emphasizing capturing both properties identify discriminative

Язык: Английский

Процитировано

26