Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve
clinically relevant single-subject predictions. An alternative to machine learning, which tries …

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 …

Computational theories of brain function have become very influential in neuroscience. They
have facilitated the growth of formal approaches to disease, particularly in psychiatric …

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating
individual differences in various behavioral phenotypes and clinical outcomes is of growing …

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 …

Research in clinical neuroscience is founded on the idea that a better understanding of
brain (dys) function will improve our ability to diagnose and treat neurological and …

Rationale The impact of neuroscience-based approaches for psychiatry on pragmatic
clinical decision-making has been limited. Although neuroscience has provided insights into …

The neuronal substrates that implement the free-energy principle and ensuing active
inference at the neuron and synapse level have not been fully elucidated. This Review …

Highlights•We summarize recent progress in computational and physiological
models.•These advances provide a basis for future diagnostic applications in psychiatry.•A …

The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain mapping approaches to multivariate predictive models …