Leveraging machine learning for gaining neurobiological and nosological insights in psychiatric research
Much attention is currently devoted to developing diagnostic classifiers for mental disorders.
Complementing these efforts, we highlight the potential of machine learning to gain …
Complementing these efforts, we highlight the potential of machine learning to gain …
Machine learning for psychiatry: getting doctors at the black box?
DM Hedderich, SB Eickhoff - Molecular psychiatry, 2021 - nature.com
Recent developments in the field of machine learning have spurred high hopes for
diagnostic support for psychiatric patients based on brain MRI. But while technical advances …
diagnostic support for psychiatric patients based on brain MRI. But while technical advances …
Current approaches in computational psychiatry for the data-driven identification of brain-based subtypes
The ability of our current psychiatric nosology to accurately delineate clinical populations
and inform effective treatment plans has reached a critical point with only moderately …
and inform effective treatment plans has reached a critical point with only moderately …
Machine learning with neuroimaging: evaluating its applications in psychiatry
Psychiatric disorders are complex, involving heterogeneous symptomatology and
neurobiology that rarely involves the disruption of single, isolated brain structures. In an …
neurobiology that rarely involves the disruption of single, isolated brain structures. In an …
Machine learning for precision psychiatry
D Bzdok, A Meyer-Lindenberg - arXiv preprint arXiv:1705.10553, 2017 - arxiv.org
The nature of mental illness remains a conundrum. Traditional disease categories are
increasingly suspected to mis-represent the causes underlying mental disturbance. Yet …
increasingly suspected to mis-represent the causes underlying mental disturbance. Yet …
Machine learning for precision psychiatry: opportunities and challenges
D Bzdok, A Meyer-Lindenberg - Biological Psychiatry: Cognitive …, 2018 - Elsevier
The nature of mental illness remains a conundrum. Traditional disease categories are
increasingly suspected to misrepresent the causes underlying mental disturbance. Yet …
increasingly suspected to misrepresent the causes underlying mental disturbance. Yet …
Anatomical brain images alone can accurately diagnose chronic neuropsychiatric illnesses
Objective Diagnoses using imaging-based measures alone offer the hope of improving the
accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect …
accuracy of clinical diagnosis, thereby reducing the costs associated with incorrect …
[HTML][HTML] Beyond lumping and splitting: a review of computational approaches for stratifying psychiatric disorders
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels,
including symptoms, disease course, and biological underpinnings. These form a …
including symptoms, disease course, and biological underpinnings. These form a …
The heterogeneity problem: approaches to identify psychiatric subtypes
The imprecise nature of psychiatric nosology restricts progress towards characterizing and
treating mental health disorders. One issue is the 'heterogeneity problem': different causal …
treating mental health disorders. One issue is the 'heterogeneity problem': different causal …
A systematic evaluation of machine learning-based biomarkers for major depressive disorder across modalities
Background Biological psychiatry aims to understand mental disorders in terms of altered
neurobiological pathways. However, for one of the most prevalent and disabling mental …
neurobiological pathways. However, for one of the most prevalent and disabling mental …