An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future …

D Sadeghi, A Shoeibi, N Ghassemi, P Moridian… - Computers in Biology …, 2022 - Elsevier
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …

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 …

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 …

FedBrain: A robust multi-site brain network analysis framework based on federated learning for brain disease diagnosis

C Zhang, X Meng, Q Liu, S Wu, L Wang, H Ning - Neurocomputing, 2023 - Elsevier
In recent years, deep learning models have shown their advantages in neuroimage
analysis, such as brain disease diagnosis. Unfortunately, it is usually difficult to acquire …

A new statistical approach for fNIRS hyperscanning to predict brain activity of preschoolers' using teacher's

C Barreto, GA Bruneri, G Brockington… - Frontiers in human …, 2021 - frontiersin.org
Hyperscanning studies using functional Near-Infrared Spectroscopy (fNIRS) have been
performed to understand the neural mechanisms underlying human-human interactions. In …

Depression analysis and recognition based on functional near-infrared spectroscopy

R Wang, Y Hao, Q Yu, M Chen… - IEEE journal of …, 2021 - ieeexplore.ieee.org
Depression is the result of a complex interaction of social, psychological and physiological
elements. Research into the brain disorders of patients suffering from depression can help …

Multimodal mental health digital biomarker analysis from remote interviews using facial, vocal, linguistic, and cardiovascular patterns

Z Jiang, S Seyedi, E Griner, A Abbasi… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
Objective: Psychiatric evaluation suffers from subjectivity and bias, and is hard to scale due
to intensive professional training requirements. In this work, we investigated whether …

Robust discriminant feature extraction for automatic depression recognition

J Zhong, Z Shan, X Zhang, H Lu, H Peng… - … Signal Processing and …, 2023 - Elsevier
The incidence of depression has recently increased significantly. However, the current
manual diagnosis may delay real-time detection and early treatment. Therefore, an …

Hemisphere-separated cross-connectome aggregating learning via VAE-GAN for brain structural connectivity synthesis

Q Zuo, H Tian, R Li, J Guo, J Hu, L Tang, Y Di… - IEEE …, 2023 - ieeexplore.ieee.org
The brain network is an effective tool and has been widely used in the field of brain
neurodegenerative disease analysis. Due to the high cost of accessing medical image data …

Quantitative evaluation of frequency domain measurements in high density diffuse optical tomography

GA Perkins, AT Eggebrecht… - Journal of Biomedical …, 2021 - spiedigitallibrary.org
Significance: High density diffuse optical tomography (HD-DOT) as applied in functional
near-infrared spectroscopy (fNIRS) is largely limited to continuous wave (CW) data. Using a …