Journal: Frontiers in Neurology

Article Title: A Thalamic-Fronto-Parietal Structural Covariance Network Emerging in the Course of Recovery from Hand Paresis after Ischemic Stroke

doi: 10.3389/fneur.2015.00211

Figure Lengend Snippet: Spatial topography of longitudinal structural covariance network correlating with hand function recovery . (A) shows the six largest clusters of supra-threshold voxels for the second principal component (PC2 TBM ) projected onto a standard three dimensional brain and onto a cytoarchitectonic atlas (cluster 3+) in MNI space. Clusters are labeled according to their (positive or negative) correlation with gray matter volume expansion in the medio-dorsal thalamus. The threshold for positive clusters corresponds to the first percentile of voxel values (absolute value 0.0064), the threshold for the negative cluster to the ninety-ninth percentile (absolute value 0.0095). (B) shows the spatial relationship between the covariance network clusters and lesion maps of patient subgroups. Color-coded contours define areas with ≥20% lesion probability in each subgroup. Size, localization, cytoarchitectonic assignment, and functional correlates of the individual clusters are summarized in Table .

Article Snippet: These clusters were localized using the Jülich cytoarchitectonic probabilistic atlas (SPM Anatomy toolbox, Version 1.8, made available through the Human Brain Mapping division at the Forschungszentrum Jülich at http://www.fz-juelich.de/inm/inm-1/DE/Forschung/_docs/SPMAnatomyToolbox/SPMAnatomyToolbox_node.html ).

Techniques: Labeling, Functional Assay

Journal: Frontiers in Neurology

Article Title: A Thalamic-Fronto-Parietal Structural Covariance Network Emerging in the Course of Recovery from Hand Paresis after Ischemic Stroke

doi: 10.3389/fneur.2015.00211

Figure Lengend Snippet: Clusters of the longitudinal structural covariance network (PC2 TBM ) related to hand function recovery: size, localization, cytoarchitectonic assignment, and functional correlates .

Article Snippet: These clusters were localized using the Jülich cytoarchitectonic probabilistic atlas (SPM Anatomy toolbox, Version 1.8, made available through the Human Brain Mapping division at the Forschungszentrum Jülich at http://www.fz-juelich.de/inm/inm-1/DE/Forschung/_docs/SPMAnatomyToolbox/SPMAnatomyToolbox_node.html ).

Techniques: Functional Assay, Transformation Assay

Journal: Scientific Reports

Article Title: Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts

doi: 10.1038/s41598-023-28479-2

Figure Lengend Snippet: CNN architecture for Raman spectroscopy analysis.

Article Snippet: A one-dimensional CNN for Raman spectra classification was developed in MATLAB (version R2021a) using the Deep Learning Toolbox.

Techniques: Raman Spectroscopy

Journal: Scientific Reports

Article Title: Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts

doi: 10.1038/s41598-023-28479-2

Figure Lengend Snippet: Variable importance plots of the classification of irradiated versus nonirradiated breast tissue Raman spectra based on GBR-NMF scores with Random Forest.

Article Snippet: A one-dimensional CNN for Raman spectra classification was developed in MATLAB (version R2021a) using the Deep Learning Toolbox.

Techniques: Irradiation

Journal: Scientific Reports

Article Title: Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts

doi: 10.1038/s41598-023-28479-2

Figure Lengend Snippet: Percentage of correctly classified spectra (test accuracy) corresponding to each Raman map of mouse 1 being removed from the training set (leave-one-map-out validation). CNN (violet) and GBR-NMF-RF (blue). Map labels given as mouse number_(section #)_(map #). *Represent significant difference between CNN and GBR-NMF-RF (p < 0.05), ns = not significant.

Article Snippet: A one-dimensional CNN for Raman spectra classification was developed in MATLAB (version R2021a) using the Deep Learning Toolbox.

Techniques: Biomarker Discovery