A Convolutional Neural Network Approach to Personalized Neuropil Density Prediction
Chang B, Akif A, Onofrey J, Hyder F. A Convolutional Neural Network Approach to Personalized Neuropil Density Prediction. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2024 DOI: 10.58530/2024/4846.Peer-Reviewed Original ResearchConvolutional neural networkSuccess of machine learning methodsConvolutional neural network approachCell countNeural network approachMachine learning methodsNeural cell countNeural networkLearning methodsMetabolic imagingMulti-modal MRINetwork approachSynaptic densityPatient-specificBrain energeticsQuantify electrical activityNeural cellsEnergy budgetDensity predictionElectrical activityIn vivo neuropil density from anatomical MRI and machine learning
Akif A, Staib L, Herman P, Rothman D, Yu Y, Hyder F. In vivo neuropil density from anatomical MRI and machine learning. Cerebral Cortex 2024, 34: bhae200. PMID: 38771239, PMCID: PMC11107380, DOI: 10.1093/cercor/bhae200.Peer-Reviewed Original ResearchConceptsMagnetic resonance imagingSynaptic densityNeuropil densityCellular densityArtificial neural networkNeural networkPositron emission tomographyAnatomical magnetic resonance imagingHealthy subjectsSynaptic activityMRI scansMachine learning algorithmsBrain's energy budgetEmission tomographyIn vivo MRI scansResonance imagingTissue cellularityLearning algorithmsDiffusion magnetic resonance imagingMachine learningMicroscopic interpretationInterpretation of functional neuroimaging dataIndividual predictionsSubjects