rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis
Abstract Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria
through a lengthy and time-consuming process. Much effort is being made to identify brain …
through a lengthy and time-consuming process. Much effort is being made to identify brain …
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review
Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and
clinical observations. However, these diagnoses are not perfect, and additional diagnostic …
clinical observations. However, these diagnoses are not perfect, and additional diagnostic …
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
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 …
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
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 …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
Consistent connectome landscape mining for cross-site brain disease identification using functional MRI
Many human brain disorders are associated with characteristic alterations in functional
connectivity of the brain. A lot of efforts have been devoted to mining disease-related …
connectivity of the brain. A lot of efforts have been devoted to mining disease-related …
Temporal segmentation of EEG based on functional connectivity network structure
Z Xu, S Tang, C Liu, Q Zhang, H Gu, X Li, Z Di, Z Li - Scientific Reports, 2023 - nature.com
In the study of brain functional connectivity networks, it is assumed that a network is built
from a data window in which activity is stationary. However, brain activity is non-stationary …
from a data window in which activity is stationary. However, brain activity is non-stationary …
Automated diagnosis of autism with artificial intelligence: State of the art
Autism spectrum disorder (ASD) represents a panel of conditions that begin during the
developmental period and result in impairments of personal, social, academic, or …
developmental period and result in impairments of personal, social, academic, or …
Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient …
B Song, S Yoshida… - Plos one, 2024 - journals.plos.org
Currently, numerous studies focus on employing fMRI-based deep neural networks to
diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have …
diagnose neurological disorders such as Alzheimer's Disease (AD), yet only a handful have …
Concordance of intrinsic brain connectivity measures is disrupted in alzheimer's disease
X Chen, OA Onur, N Richter, R Fassbender… - Brain …, 2023 - liebertpub.com
Background: Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI)
measure to evaluate the concordance between different rs-fMRI metrics has been proposed …
measure to evaluate the concordance between different rs-fMRI metrics has been proposed …
Iterative consensus spectral clustering improves detection of subject and group level brain functional modules
Specialized processing in the brain is performed by multiple groups of brain regions
organized as functional modules. Although, in vivo studies of brain functional modules …
organized as functional modules. Although, in vivo studies of brain functional modules …