AIMC Topic: Neuroimaging

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Diagnostic Prediction for Social Anxiety Disorder via Multivariate Pattern Analysis of the Regional Homogeneity.

BioMed research international
Although decades of efforts have been spent studying the pathogenesis of social anxiety disorder (SAD), there are still no objective biological markers that could be reliably used to identify individuals with SAD. Studies using multivariate pattern a...

The role of machine learning in neuroimaging for drug discovery and development.

Psychopharmacology
Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging ca...

Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach.

Psychological medicine
BACKGROUND: There are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neur...

Feature Selection Based on the SVM Weight Vector for Classification of Dementia.

IEEE journal of biomedical and health informatics
Computer-aided diagnosis of dementia using a support vector machine (SVM) can be improved with feature selection. The relevance of individual features can be quantified from the SVM weights as a significance map (p-map). Although these p-maps previou...

Investigating the use of support vector machine classification on structural brain images of preterm-born teenagers as a biological marker.

PloS one
Preterm birth has been shown to induce an altered developmental trajectory of brain structure and function. With the aid support vector machine (SVM) classification methods we aimed to investigate whether MRI data, collected in adolescence, could be ...

Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction.

International journal of geriatric psychiatry
OBJECTIVE: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate ...

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...

[Towards an equipped psychiatry].

L'Encephale
The article by Moizard and Geoffroy highlights the importance of an integrated approach in psychiatry, emphasizing the need to move beyond the dichotomy between the somatic and the psychic. In their commentary, the authors advocated for precision psy...

Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.

Artificial cells, nanomedicine, and biotechnology
The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multi...