ArticleFrontiers in human neuroscience2019
Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety).
Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Machine Learning-Derived Neural Signatures of Itch and Pain That Reliably Distinguish the Two Sensations in Humans: A Proof-Of-Concept Study.European journal of pain (London, England) · 2026Article
- Influence of Functional Magnetic Resonance Imaging Data Preprocessing Pipelines on the Accuracy of Schizophrenia Classification Using Machine Learning Methods.Sovremennye tekhnologii v meditsine · 2026Article
- Article
- Regulation of craving for real-time fMRI neurofeedback based on individual classification.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.Frontiers in human neuroscience · 2024Article
- Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment.Scientific reports · 2023Article
- SenseHunger: Machine Learning Approach to Hunger Detection Using Wearable Sensors.Sensors (Basel, Switzerland) · 2022Article
- A machine-learning approach for predicting impaired consciousness in absence epilepsy.Annals of clinical and translational neurology · 2022Article
- Left DLPFC activity is associated with plasma kynurenine levels and can predict treatment response to escitalopram in major depressive disorder.Psychiatry and clinical neurosciences · 2022Article
- Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study.Neuroimage. Reports · 2021Article
- Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI.Molecular autism · 2021Article
- Vector Phase Analysis Approach for Sleep Stage Classification: A Functional Near-Infrared Spectroscopy-Based Passive Brain-Computer Interface.Frontiers in human neuroscience · 2021Article
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
- Current Challenges in Translational and Clinical fMRI and Future Directions.Frontiers in psychiatry · 2019Article
Corrections and comments
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveResting-state functional magnetic resonance imaging (rs-fMRI) has become an essential measure to investigate the human brain's spontaneous activity and intrinsic functional connectivity. Several studies including our own previous work have shown that the brain controls the regulation of energy expenditure and food intake behavior. Accordingly, we expected different metabolic states to influence connectivity and activity patterns in neuronal networks.
methodsThe influence of hunger and satiety on rs-fMRI was investigated using three connectivity models (local connectivity, global connectivity and amplitude rs-fMRI signals). After extracting the connectivity parameters of 90 brain regions for each model, we used sequential forward floating selection strategy in conjunction with a linear support vector machine classifier and permutation tests to reveal which connectivity model differentiates best between metabolic states (hunger vs. satiety).
resultsWe found that the amplitude of rs-fMRI signals is slightly more precise than local and global connectivity models in order to detect resting brain changes during hunger and satiety with a classification accuracy of 81%.
conclusionThe amplitude of rs-fMRI signals serves as a suitable basis for machine learning based classification of brain activity. This opens up the possibility to apply this combination of algorithms to similar research questions, such as the characterization of brain states (e.g., sleep stages) or disease conditions (e.g., Alzheimer's disease, minimal cognitive impairment).
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