Neuroimaging Biomarkers For Psychiatry PDF Download

"Diagnosis in psychiatry relies on the subjective assessment of symptoms, both reported by patients and observed by clinicians. This purely symptom-based classification has its limitations. Patients with the same diagnosis express different symptoms, and there is considerable overlap in symptoms across different mental disorders, leading to the possibility of over- or misdiagnosis. Moreover, these diagnoses tell us little about the individual patient, as treatment guidelines tend to follow a one-size-fits-all approach that may not be effective for every patient, resulting in delayed or ineffective treatment for some. To address these issues, there has been a shift towards more objective approaches to diagnosis and personalized treatment in psychiatry . An important means to this end is the search for objective biomarkers , which are measurable characteristics that indicate normal (neuro)biological processes or responses to therapeutic intervention. These biomarkers could help clinicians make more evidence-based decisions that are tailored to individual patients, potentially leading to more efficient and effective psychiatric care. The aim of this thesis was to investigate whether machine learning techniques could be used to find generalizable biomarkers for various psychiatric disorders. We used neuroimaging data to develop diagnostic biomarkers for OCD, (trans)diagnostic biomarkers for anxiety disorders in juveniles, and biomarkers predictive for ECT treatment outcome in patients with depression. This thesis showed that machine learning techniques can be applied to predict diagnosis and treatment outcome with variable success. Future studies should carefully consider sources of heterogeneity to facilitate the development of more accurate and clinically relevant biomarkers, ultimately improving psychiatric care."--