2024
Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges
Jiang S, Pei H, Chen J, Li H, Liu Z, Wang Y, Gong J, Wang S, Li Q, Duan M, Calhoun V, Yao D, Luo C. Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges. International Journal Of Neural Systems 2024, 34: 2450017. PMID: 38372049, DOI: 10.1142/s0129065724500175.Peer-Reviewed Original ResearchConceptsSalience networkSensorimotor cortexFunctional magnetic resonance imaging dataModerating effectInterictal epileptic dischargesIdiopathic generalized epilepsyMagnetic resonance imaging dataInteraction of regionsDecreased connectivityStriatumDMNThalamocortical circuitsCortical interactionsSimultaneous electroencephalogramCortical targetsEpileptic dischargesCerebellumThalamusHierarchical connectionEpileptic networkNeuromodulation techniquesIndirect moderating effectStateCryptogenic etiology
2023
Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls
Verdijk J, van de Mortel L, Doesschate F, Pottkämper J, Stuiver S, Bruin W, Abbott C, Argyelan M, Ousdal O, Bartsch H, Narr K, Tendolkar I, Calhoun V, Lukemire J, Guo Y, Oltedal L, van Wingen G, van Waarde J. Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls. Brain Stimulation 2023, 17: 140-147. PMID: 38101469, PMCID: PMC11145948, DOI: 10.1016/j.brs.2023.12.005.Peer-Reviewed Original ResearchDefault mode networkElectroconvulsive therapyHealthy controlsECT patientsResting-state networksTreatment effectivenessSalience networkElectroconvulsive therapy patientsWhole-brain voxel-wise analysisMajor depressive episodeCanonical resting-state networksRight frontoparietal networkVoxel-wise changesHigh treatment effectivenessVoxel-wise analysisNetwork connectivity changesTest-retest variabilityMulticenter studyDepressive episodeDepressed patientsTherapy patientsMagnetic resonance imaging dataDMN connectivityPatientsConnectivity changesA Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjects