Dissociation of the neural networks recruited during a haptic object-recognition task: complementary results with a tensorial independent component analysis.

BACKGROUND AND PURPOSE The cerebral and cerebellar networks involved in bimanual object recognition were assessed by blood oxygen level-dependent functional MR imaging by using multivariate model-free analysis, because conventional univariate model-based analysis, such as the general linear model (GLM), does not allow investigation of resting, background, and transiently task-related brain activities. MATERIALS AND METHODS Data from 14 healthy right-handed volunteers, scanned while successively performing bilateral finger movements and a bimanual tactile-tactile matching discrimination task were analyzed by using tensor-independent component analysis (TICA), which computes statistically independent spatiotemporal processes (P > .7) thought to reflect specific and distinct anatomofunctional neural networks. These results were compared with the network obtained in a previous study by using the same paradigm based on GLM to evaluate the advantages of TICA. RESULTS TICA characterized and distinguished the following: 1) resting-state networks such as the default-mode networks, 2) networks transiently synchronized with the beginning and end of the task, such as temporo-pericentral and temporo-pericentral-occipital networks, and 3) task-related networks such as cerebello-fronto-parietal, cerebello-prefrontocingulo-insular, and cerebello-parietal networks. CONCLUSION Bimanual tactile-tactile matching discrimination specifically recruits a complex neural network, which can be dissociated into 3 distinct but cooperative neural subnetworks related to sensorimotor function, salience detection, executive control, and, possibly, sensory expectation. This tripartite network involved in bimanual object recognition could not be demonstrated by GLM. Moreover, TICA allowed monitoring of the temporal succession of the networks recruited during the resting phase, audition of the "go" and "stop" signals, and the tactile discrimination task.