Search results for "Tensor PICA"

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Multi-subject fMRI analysis via combined independent component analysis and shift-invariant canonical polyadic decomposition

2014

Canonical polyadic decomposition (CPD) may face a local optimal problem when analyzing multi-subject fMRI data with inter-subject variability. Beckmann and Smith proposed a tensor PICA approach that incorporated an independence constraint to the spatial modality by combining CPD with ICA, and alleviated the problem of inter-subject spatial map (SM) variability.This study extends tensor PICA to incorporate additional inter-subject time course (TC) variability and to connect CPD and ICA in a new way. Assuming multiple subjects share common TCs but with different time delays, we accommodate subject-dependent TC delays into the CP model based on the idea of shift-invariant CP (SCP). We use ICA …

Independent component analysis (ICA)Speech recognitionModels NeurologicalMotor ActivityNeuropsychological TestsInter-subject variabilityta3112TimeMulti-subject fMRI dataFingersHumansCanonical polyadic decomposition (CPD)Computer SimulationMotor activityInvariant (mathematics)ta217ta113Brain MappingShift-invariant CP (SCP)General NeuroscienceBrainMagnetic Resonance ImagingIndependent component analysisAuditory PerceptionTensor PICASpatial mapsPsychologyAlgorithmJournal of Neuroscience Methods
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