2024
In 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
2001
Cerebral energetics and the glycogen shunt: Neurochemical basis of functional imaging
Shulman R, Hyder F, Rothman D. Cerebral energetics and the glycogen shunt: Neurochemical basis of functional imaging. Proceedings Of The National Academy Of Sciences Of The United States Of America 2001, 98: 6417-6422. PMID: 11344262, PMCID: PMC33483, DOI: 10.1073/pnas.101129298.Peer-Reviewed Original ResearchConceptsLactate concentrationBrain activationBrain lactate concentrationCerebral blood flowAdult mammalian brainBasal glucose oxidationPositron emission tomographyGlucose oxidationGlucose indexCerebral energeticsFunctional imaging dataNeurophysiological valuesBlood flowGlucose metabolismNeurotransmitter clearanceNeurochemical basisLactate effluxMammalian brainBasal physiological conditionsNeurotransmitter releaseAnaerobic glucose metabolismFunctional imagingComplete dataBrain activityMetabolic consumption rate