Average quality scores of the models of the analysis set as a function of the <i>w</i>_<i>SP</i> with which the DOPE potential has been included in the objective function... by Giacomo Janson (8138517)

Acurracy comparison of CCA and FBCCA algorithms. by Hossein Hamidi Shishavan (19688384)

Reward function related parameters. by Honglei Pang (22693724)

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Image_2_Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.JPEG by Yao Zhang (134381)

“…In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. …”

Image_3_Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.JPEG by Yao Zhang (134381)

“…In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. …”

Image_1_Distinct Brain Dynamic Functional Connectivity Patterns in Schizophrenia Patients With and Without Auditory Verbal Hallucinations.JPEG by Yao Zhang (134381)

“…In this study, 25 Schizophrenia patients with AVHs (AVHs group, 23.2 ± 5.35 years), 52 Schizophrenia patients without AVHs (non-AVHs group, 25.79 ± 5.63 years) and 28 healthy subjects (NC group, 26.14 ± 5.45 years) were enrolled. Dynamic functional connectivity was studied with a sliding-window method and functional connectivity states were then obtained with the k-means clustering algorithm in the three groups. …”

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Resting state functional MRI brain signatures of fast disease progression in amyotrophic lateral sclerosis: a retrospective study by Francesca Trojsi (707124)

“…We aim to investigate brain functional and structural magnetic resonance imaging (MRI) changes in a cohort of ALS patients, examined at diagnosis and clinically monitored over 18 months, in order to early discriminate fast progressors (FPs) from slow progressors (SPs). …”

PyNoetic’s online mode in action. Data is streamed from an Emotiv EPOC headset. by Gursimran Singh (575288)

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The multi-session PS-VAE (MSPS-VAE) accounts for session-level differences between videos in the head-fixed mouse dataset [46]. by Matthew R. Whiteway (11467528)

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Frame reconstruction video still. by Matthew R. Whiteway (11467528)

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The PS-VAE enables targeted downstream neural analyses of the mouse face video. by Matthew R. Whiteway (11467528)

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The PS-VAE successfully partitions the latent representation of a freely moving mouse video. by Matthew R. Whiteway (11467528)

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Neural decoding video still. by Matthew R. Whiteway (11467528)

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The PS-VAE successfully partitions the latent representation of a head-fixed mouse video [46]. by Matthew R. Whiteway (11467528)

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The PS-VAE enables targeted downstream behavioral analyses of the mouse face video. by Matthew R. Whiteway (11467528)

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MSPS-VAE session swap video still. by Matthew R. Whiteway (11467528)

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The PS-VAE successfully partitions the latent representation of a mouse face video. by Matthew R. Whiteway (11467528)

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Results of hyperparameter searches for neural decoding models, which varied the number of hidden layers and number of hidden units per layer in an MLP neural network. by Matthew R. Whiteway (11467528)

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ARHMM movement detector video still. by Matthew R. Whiteway (11467528)

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