More articles from Functional
- Deep Learning of Time–Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma
The autoencoder perfusion pattern analysis enabled tissue characterization of peritumoral areas, providing heterogeneity and dynamic information that may provide useful prognostic information in IDH wild-type glioblastoma.
- Phenotyping Superagers Using Resting-State fMRI
Functional connectivity in the default mode, salience, and language networks can provide potential imaging biomarkers for predicting superagers.
- Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?
The authors demonstrate variability in the accuracy of blinded identification of resting-state-fMRI language networks across raters with different years of experience.
- Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping
Multilevel fiber tractography may improve the coverage of the motor cortex by corticospinal tract fibers compared with conventional deterministic algorithms.
- Diagnostic Accuracy of Arterial Spin-Labeling, Dynamic Contrast-Enhanced, and DSC Perfusion Imaging in the Diagnosis of Recurrent High-Grade Gliomas: A Prospective Study
In the differentiation of tumor recurrence from radiation necrosis in a newly enhancing lesion, the diagnostic value of arterial spin-labeling-derived CBF is similar to that of DSC and dynamic contrast-enhanced-derived blood volume.
- Connectomic Basis for Tremor Control in Stereotactic Radiosurgical Thalamotomy
Stereotactic radiosurgical targets with a distinct connectivity profile predict improvement in tremor after treatment. Such connectomic fingerprints show promise for developing patient-specific biomarkers to guide therapy with stereotactic radiosurgical thalamotomy.