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[HTML][HTML] Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications
Deep learning (DL) is a family of machine learning methods that has gained considerable
attention in the scientific community, breaking benchmark records in areas such as speech …
attention in the scientific community, breaking benchmark records in areas such as speech …
Classification and prediction of brain disorders using functional connectivity: promising but challenging
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI)
data, have been employed to reflect functional integration of the brain. Alteration in brain …
data, have been employed to reflect functional integration of the brain. Alteration in brain …
Support vector machine
In this chapter, we explore Support Vector Machine (SVM)—a machine learning method that
has become exceedingly popular for neuroimaging analysis in recent years. Because of …
has become exceedingly popular for neuroimaging analysis in recent years. Because of …
Towards a brain‐based predictome of mental illness
Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …
recent years and have deepened our understanding of both cognitively healthy and …
3D-CNN based discrimination of schizophrenia using resting-state fMRI
Motivation This study reports a framework to discriminate patients with schizophrenia and
normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain …
normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain …
Machine learning techniques in a structural and functional MRI diagnostic approach in schizophrenia: a systematic review
Background Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific
biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) …
biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) …
DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction
Background Genotypes are strongly associated with disease phenotypes, particularly in
brain disorders. However, the molecular and cellular mechanisms behind this association …
brain disorders. However, the molecular and cellular mechanisms behind this association …
Detecting neuroimaging biomarkers for depression: a meta-analysis of multivariate pattern recognition studies
Background Multiple studies have examined functional and structural brain alteration in
patients diagnosed with major depressive disorder (MDD). The introduction of multivariate …
patients diagnosed with major depressive disorder (MDD). The introduction of multivariate …
Structural and functional imaging markers for susceptibility to psychosis
The introduction of clinical criteria for the operationalization of psychosis high risk provided a
basis for early detection and treatment of vulnerable individuals. However, about two-thirds …
basis for early detection and treatment of vulnerable individuals. However, about two-thirds …
Cognitive impairment in schizophrenia: relationships with cortical thickness in fronto-temporal regions, and dissociability from symptom severity
Cognitive impairments are a core and persistent characteristic of schizophrenia with
implications for daily functioning. These show only limited response to antipsychotic …
implications for daily functioning. These show only limited response to antipsychotic …