[HTML][HTML] One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry
Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
across individuals, and no two patients with a shared diagnosis exhibit identical symptom …
Predicting the future of neuroimaging predictive models in mental health
Predictive modeling using neuroimaging data has the potential to improve our
understanding of the neurobiology underlying psychiatric disorders and putatively …
understanding of the neurobiology underlying psychiatric disorders and putatively …
Making individual prognoses in psychiatry using neuroimaging and machine learning
Psychiatric prognosis is a difficult problem. Making a prognosis requires looking far into the
future, as opposed to making a diagnosis, which is concerned with the current state. During …
future, as opposed to making a diagnosis, which is concerned with the current state. During …
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 …
Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification
Psychiatric research is in crisis. We highlight efforts to overcome current challenges by
focusing on the emerging field of computational psychiatry, which might enable the field to …
focusing on the emerging field of computational psychiatry, which might enable the field to …
Computational approaches and machine learning for individual-level treatment predictions
MP Paulus, WK Thompson - Psychopharmacology, 2021 - Springer
Rationale The impact of neuroscience-based approaches for psychiatry on pragmatic
clinical decision-making has been limited. Although neuroscience has provided insights into …
clinical decision-making has been limited. Although neuroscience has provided insights into …
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 …
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 …
TAPAS: an open-source software package for translational neuromodeling and computational psychiatry
Psychiatry faces fundamental challenges with regard to mechanistically guided differential
diagnosis, as well as prediction of clinical trajectories and treatment response of individual …
diagnosis, as well as prediction of clinical trajectories and treatment response of individual …