[HTML][HTML] Network classification with applications to brain connectomics
… Network methods are a popular tool in the neuroscience literature (Bullmore and Sporns, …
A brain network represents connectivity between different locations of an individual’s brain. …
A brain network represents connectivity between different locations of an individual’s brain. …
Functional brain network classification with compact representation of SICE matrices
… as a compact representation of brain network. Moreover, to cater … insights into SICE-based
brain networks, we conduct extensive … and improves the classification accuracy significantly. …
brain networks, we conduct extensive … and improves the classification accuracy significantly. …
A toolbox for brain network construction and classification (BrainNetClass)
… novel toolbox, namely Brain Network Construction and Classification toolbox (BrainNetClass…
Therefore, we performed a brain network-based individualized classification between MDD …
Therefore, we performed a brain network-based individualized classification between MDD …
Functional brain network classification for Alzheimer's disease detection with deep features and extreme learning machine
… mechanisms of human brain, and to aid brain disease detection, such … brain network
classification for AD detection. Two deep learning methods of functional brain network classification …
classification for AD detection. Two deep learning methods of functional brain network classification …
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Machine learning methods for brain network classification: Application to autism diagnosis using cortical morphological networks
… We provide an open-source Python toolbox for brain network classification. … for ASD
diagnosis using CMN datasets, they can be utilized for brain network classification in general. …
diagnosis using CMN datasets, they can be utilized for brain network classification in general. …
BrainNPT: Pre-training Transformer Networks for Brain Network Classification
… brain network data by sliding windows over time on rs-fMRI data, and used a large amount
of these brain snapshot network … -based model, even unlabeled brain network data is scarce. …
of these brain snapshot network … -based model, even unlabeled brain network data is scarce. …
Classification of early and late mild cognitive impairment using functional brain network of resting-state fMRI
T Zhang, Z Zhao, C Zhang, J Zhang, Z Jin… - Frontiers in Psychiatry, 2019 - frontiersin.org
… In the current study, we aim to evaluate the efficacy of a classification framework to … functional
brain network of three frequency bands during Rest States. On the basis of classification …
brain network of three frequency bands during Rest States. On the basis of classification …
Brain network similarity: methods and applications
… We can classify metrics measured for comparing brain networks into four categories:
global-level, node-wise, edge-wise, and spectral graph analysis (for more details about the metrics, …
global-level, node-wise, edge-wise, and spectral graph analysis (for more details about the metrics, …
Support vector machine classification of brain states exposed to social stress test using EEG-based brain network measures
S Lotfan, S Shahyad, R Khosrowabadi… - Biocybernetics and …, 2019 - Elsevier
… In this study, the brain network states exposed to stress were monitored based on
electroencephalography (EEG) measures extracted by complex network analysis. To this regard, 23 …
electroencephalography (EEG) measures extracted by complex network analysis. To this regard, 23 …
Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier
… individual brain network as feature representation, and use a deep neural network (DNN)
classifier to perform ASD/TC classification. Firstly, we construct an individual brain network for …
classifier to perform ASD/TC classification. Firstly, we construct an individual brain network for …