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Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines.

NeuroImage
A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decodin...

Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span.

Medical engineering & physics
In this study, we present a method for measuring functional magnetic resonance imaging (fMRI) signal complexity using fuzzy approximate entropy (fApEn) and compare it with the established sample entropy (SampEn). Here we use resting state fMRI datase...

Spatially regularized machine learning for task and resting-state fMRI.

Journal of neuroscience methods
BACKGROUND: Reliable mapping of brain function across sessions and/or subjects in task- and resting-state has been a critical challenge for quantitative fMRI studies although it has been intensively addressed in the past decades.

Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

Journal of neural engineering
OBJECTIVE: Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these inter...

Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity--A multi-center study.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Resting-state functional magnetic resonance imaging studies examining low frequency fluctuations (0.01-0.08 Hz) have revealed atypical whole brain functional connectivity patterns in adolescents with autism spectrum disorder (ASD), and th...

Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest.

Medical image analysis
Cardiac computed tomography angiography (CTA) is a non-invasive method for anatomic evaluation of coronary artery stenoses. However, CTA is prone to artifacts that reduce the diagnostic accuracy to identify stenoses. Further, CTA does not allow for d...

Predicting Methylphenidate Response in ADHD Using Machine Learning Approaches.

The international journal of neuropsychopharmacology
BACKGROUND: There are no objective, biological markers that can robustly predict methylphenidate response in attention deficit hyperactivity disorder. This study aimed to examine whether applying machine learning approaches to pretreatment demographi...

Multimodal analysis of functional and structural disconnection in Alzheimer's disease using multiple kernel SVM.

Human brain mapping
Alzheimer's disease (AD) patients exhibit alterations in the functional connectivity between spatially segregated brain regions which may be related to both local gray matter (GM) atrophy as well as a decline in the fiber integrity of the underlying ...

Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

International journal of neural systems
Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mecha...

Identifying Neuroimaging Markers of Motor Disability in Acute Stroke by Machine Learning Techniques.

Cerebral cortex (New York, N.Y. : 1991)
Conventional mass-univariate analyses have been previously used to test for group differences in neural signals. However, machine learning algorithms represent a multivariate decoding approach that may help to identify neuroimaging patterns associate...