Modern views of machine learning for precision psychiatry

ZS Chen, IR Galatzer-Levy, B Bigio, C Nasca, Y Zhang - Patterns, 2022 - cell.com
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …

Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy

M Avanzo, M Porzio, L Lorenzon, L Milan, R Sghedoni… - Physica Medica, 2021 - Elsevier
Purpose To perform a systematic review on the research on the application of artificial
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …

[HTML][HTML] Brain correlates of depression, post-traumatic distress, and inflammatory biomarkers in COVID-19 survivors: a multimodal magnetic resonance imaging study

F Benedetti, M Palladini, M Paolini, E Melloni… - Brain, behavior, & …, 2021 - Elsevier
Psychiatric sequelae substantially contribute to the post-acute burden of disease associated
with COVID-19, persisting months after clearance of the virus. Brain imaging shows white …

A peripheral inflammatory signature discriminates bipolar from unipolar depression: a machine learning approach

S Poletti, B Vai, MG Mazza, R Zanardi, C Lorenzi… - Progress in Neuro …, 2021 - Elsevier
Background Mood disorders (major depressive disorder, MDD, and bipolar disorder, BD)
are considered leading causes of life-long disability worldwide, where high rates of no …

Functional and structural brain differences in bipolar disorder: a multimodal meta-analysis of neuroimaging studies

G Chen, J Wang, J Gong, Z Qi, S Fu, G Tang… - Psychological …, 2022 - cambridge.org
BackgroundNumerous studies of resting-state functional imaging and voxel-based
morphometry (VBM) have revealed differences in specific brain regions of patients with …

An insight into diagnosis of depression using machine learning techniques: a systematic review

S Bhadra, CJ Kumar - Current medical research and opinion, 2022 - Taylor & Francis
Background In this modern era, depression is one of the most prevalent mental disorders
from which millions of individuals are affected today. The symptoms of depression are …

Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review

JE Siegel‐Ramsay, MA Bertocci, B Wu… - Bipolar …, 2022 - Wiley Online Library
Objectives Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar
depression characterize pathophysiological differences between these conditions. However …

Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: A systematic review and meta-analysis

F Colombo, F Calesella, MG Mazza… - Neuroscience & …, 2022 - Elsevier
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty
due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA …

Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data

M Ravan, A Noroozi, MM Sanchez, L Borden… - Clinical …, 2023 - Elsevier
Objective Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a
crucial clinical challenge as effective treatment is quite different for each condition. In this …

Lower levels of glutathione in the anterior cingulate cortex associate with depressive symptoms and white matter hyperintensities in COVID-19 survivors

S Poletti, M Paolini, MG Mazza, M Palladini… - European …, 2022 - Elsevier
SARS-CoV-2 is a novel coronavirus that mainly affects the respiratory system. However,
clinical manifestations such as neurological symptoms, psychopathological outcomes and …