[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 …

Reproducibility in neuroimaging analysis: challenges and solutions

R Botvinik-Nezer, TD Wager - Biological Psychiatry: Cognitive …, 2023 - Elsevier
Recent years have marked a renaissance in efforts to increase research reproducibility in
psychology, neuroscience, and related fields. Reproducibility is the cornerstone of a solid …

Embracing variability in the search for biological mechanisms of psychiatric illness

A Segal, J Tiego, L Parkes, AJ Holmes… - Trends in Cognitive …, 2024 - cell.com
Despite decades of research, we lack objective diagnostic or prognostic biomarkers of
mental health problems. A key reason for this limited progress is a reliance on the traditional …

Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection

M Liu, AH Zhu, P Maiti, SI Thomopoulos… - Human Brain …, 2023 - Wiley Online Library
Recent work within neuroimaging consortia have aimed to identify reproducible, and often
subtle, brain signatures of psychiatric or neurological conditions. To allow for high‐powered …

[HTML][HTML] Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study

Z Zhou, H Li, D Srinivasan, A Abdulkadir, IM Nasrallah… - NeuroImage, 2023 - Elsevier
To learn multiscale functional connectivity patterns of the aging brain, we built a brain age
prediction model of functional connectivity measures at seven scales on a large fMRI …

Brain-based classification of youth with anxiety disorders: transdiagnostic examinations within the ENIGMA-Anxiety database using machine learning

WB Bruin, P Zhutovsky, GA van Wingen… - Nature Mental …, 2024 - nature.com
Neuroanatomical findings on youth anxiety disorders are notoriously difficult to replicate,
small in effect size and have limited clinical relevance. These concerns have prompted a …

Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks

YH Chan, WC Yew, QH Chew, K Sim… - Scientific Reports, 2023 - nature.com
Schizophrenia is a highly heterogeneous disorder and salient functional connectivity (FC)
features have been observed to vary across study sites, warranting the need for methods …

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

V Belov, T Erwin-Grabner, M Aghajani, A Aleman… - Scientific reports, 2024 - nature.com
Abstract Machine learning (ML) techniques have gained popularity in the neuroimaging field
due to their potential for classifying neuropsychiatric disorders. However, the diagnostic …

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

N Jahanshad, P Lenzini, J Bijsterbosch - Neuropsychopharmacology, 2024 - nature.com
Research into the brain basis of psychopathology is challenging due to the heterogeneity of
psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis …

Cortical structure and subcortical volumes in conduct disorder: a coordinated analysis of 15 international cohorts from the ENIGMA-Antisocial Behavior Working Group

Y Gao, M Staginnus, S Townend, C Arango… - The Lancet …, 2024 - thelancet.com
Background Conduct disorder is associated with the highest burden of any mental disorder
in childhood, yet its neurobiology remains unclear. Inconsistent findings limit our …