Machine learning approaches for clinical psychology and psychiatry

DB Dwyer, P Falkai… - Annual review of clinical …, 2018‏ - annualreviews.org
Machine learning approaches for clinical psychology and psychiatry explicitly focus on
learning statistical functions from multidimensional data sets to make generalizable …

Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions

S Verma, T Goel, M Tanveer, W Ding, R Sharma… - Journal of Ambient …, 2023‏ - Springer
Schizophrenia (SCZ) is a brain disorder where different people experience different
symptoms, such as hallucination, delusion, flat-talk, disorganized thinking, etc. In the long …

[HTML][HTML] Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression

M Cai, Y Ji, Q Zhao, H Xue, Z Sun, H Wang, Y Zhang… - Neuroimage, 2024‏ - Elsevier
It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is
present in patients with schizophrenia, yet there are inconsistencies in the relevant findings …

Cerebellar volume and cerebellocerebral structural covariance in schizophrenia: a multisite mega-analysis of 983 patients and 1349 healthy controls

T Moberget, NT Doan, D Alnæs, T Kaufmann… - Molecular …, 2018‏ - nature.com
Although cerebellar involvement across a wide range of cognitive and neuropsychiatric
phenotypes is increasingly being recognized, previous large-scale studies in schizophrenia …

Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia

Y Jiang, J Wang, E Zhou, L Palaniyappan, C Luo… - Nature Mental …, 2023‏ - nature.com
Technical developments and improved access to neuroimaging techniques have brought us
closer to understanding the neuropathological origins of schizophrenia. Using data-driven …

Identifying schizophrenia using structural MRI with a deep learning algorithm

J Oh, BL Oh, KU Lee, JH Chae, K Yun - Frontiers in psychiatry, 2020‏ - frontiersin.org
Objective Although distinctive structural abnormalities occur in patients with schizophrenia,
detecting schizophrenia with magnetic resonance imaging (MRI) remains challenging. This …

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study

WHL Pinaya, A Mechelli, JR Sato - Human brain map**, 2019‏ - Wiley Online Library
Abstract Machine learning is becoming an increasingly popular approach for investigating
spatially distributed and subtle neuroanatomical alterations in brain‐based disorders …

The function biomedical informatics research network data repository

DB Keator, TGM van Erp, JA Turner, GH Glover… - Neuroimage, 2016‏ - Elsevier
Abstract The Function Biomedical Informatics Research Network (FBIRN) developed
methods and tools for conducting multi-scanner functional magnetic resonance imaging …

Estimating brain age based on a uniform healthy population with deep learning and structural magnetic resonance imaging

X Feng, ZC Lipton, J Yang, SA Small… - Neurobiology of …, 2020‏ - Elsevier
Numerous studies have established that estimated brain age constitutes a valuable
biomarker that is predictive of cognitive decline and various neurological diseases. In this …

Disrupted local functional connectivity in schizophrenia: an updated and extended meta-analysis

M Cai, R Wang, M Liu, X Du, K Xue, Y Ji, Z Wang… - Schizophrenia, 2022‏ - nature.com
Neuroimaging studies have shown that schizophrenia is associated with disruption of
resting-state local functional connectivity. However, these findings vary considerably, which …