ArticleAIMS neuroscience2021
ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia.
Article in AIMS neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Brain network functional connectivity changes in long illness duration chronic schizophrenia.Frontiers in psychiatry · 2024Article
- Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Manifold Learning for fMRI time-varying FC.bioRxiv : the preprint server for biology · 2023Article
- Manifold learning for fMRI time-varying functional connectivity.Frontiers in human neuroscience · 2023Article
- Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia.Frontiers in psychiatry · 2023Article
- Aberrant resting-state functional connectivity and topological properties of the subcortical network in functional dyspepsia patients.Frontiers in molecular neuroscience · 2022Article
- A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning.Frontiers in neuroscience · 2022Article
- The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity.Neuroimage. Reports · 2021Article
- Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals.Frontiers in neuroinformatics · 2021Article
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Abstract
We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.
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