[HTML][HTML] Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

F Hu, AA Chen, H Horng, V Bashyam, C Davatzikos… - NeuroImage, 2023 - Elsevier
Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

[HTML][HTML] Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges

XJ Cao, XQ Liu - World Journal of Psychiatry, 2022 - ncbi.nlm.nih.gov
Artificial intelligence-based technologies are gradually being applied to psych-iatric
research and practice. This paper reviews the primary literature concerning artificial …

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

S Gallo, A El-Gazzar, P Zhutovsky, RM Thomas… - Molecular …, 2023 - nature.com
The promise of machine learning has fueled the hope for developing diagnostic tools for
psychiatry. Initial studies showed high accuracy for the identification of major depressive …

Using graph convolutional network to characterize individuals with major depressive disorder across multiple imaging sites

K Qin, D Lei, WHL Pinaya, N Pan, W Li, Z Zhu… - …, 2022 - thelancet.com
Background Establishing objective and quantitative neuroimaging biomarkers at individual
level can assist in early and accurate diagnosis of major depressive disorder (MDD) …

A systematic review on the potential use of machine learning to classify major depressive disorder from healthy controls using resting state fMRI measures

E Bondi, E Maggioni, P Brambilla… - … & Biobehavioral Reviews, 2023 - Elsevier
Abstract Background Major Depressive Disorder (MDD) is a psychiatric disorder
characterized by functional brain deficits, as documented by resting-state functional …

Non-invasively targeting, probing and modulating a deep brain circuit for depression alleviation

DJ Oathes, RJP Duprat, J Reber, X Liang… - Nature Mental …, 2023 - nature.com
Effective repetitive transcranial magnetic stimulation (rTMS) treatment for depression has
often focused on modulating the subgenual anterior cingulate cortex (sgACC), but no …

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 …

Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review

Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback

JE Taylor, T Yamada, T Kawashima, Y Kobayashi… - Scientific reports, 2022 - nature.com
Depressive disorders contribute heavily to global disease burden; This is possibly because
patients are often treated homogeneously, despite having heterogeneous symptoms with …

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

Z Chen, B Hu, X Liu, B Becker, SB Eickhoff, K Miao… - BMC medicine, 2023 - Springer
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …