Literature DB >> 17928451

Posteromedial parietal cortical activity and inputs predict tactile spatial acuity.

Randall Stilla1, Gopikrishna Deshpande, Stephen LaConte, Xiaoping Hu, K Sathian.   

Abstract

We used functional magnetic resonance imaging (fMRI) to investigate the neural circuitry underlying tactile spatial acuity at the human finger pad. Stimuli were linear, three-dot arrays, applied to the immobilized right index finger pad using a computer-controlled, MRI-compatible, pneumatic stimulator. Activity specific for spatial processing was isolated by contrasting discrimination of left-right offsets of the central dot in the array with discrimination of the duration of stimulation by an array without a spatial offset. This contrast revealed activity in a distributed frontoparietal cortical network, within which the levels of activity in right posteromedial parietal cortical foci [right posterior intraparietal sulcus (pIPS) and right precuneus] significantly predicted individual acuity thresholds. Connectivity patterns were assessed using both bivariate analysis of Granger causality with the right pIPS as a reference region and multivariate analysis of Granger causality for a selected set of regions. The strength of inputs into the right pIPS was significantly greater in subjects with better acuity than those with poorer acuity. In the better group, the paths predicting acuity converged from the left postcentral sulcus and right frontal eye field onto the right pIPS and were selective for the spatial task, and their weights predicted the level of right pIPS activity. We propose that the optimal strategy for fine tactile spatial discrimination involves interaction in the pIPS of a top-down control signal, possibly attentional, with somatosensory cortical inputs, reflecting either visualization of the spatial configurations of tactile stimuli or engagement of modality-independent circuits specialized for fine spatial processing.

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Mesh:

Year:  2007        PMID: 17928451      PMCID: PMC6672842          DOI: 10.1523/JNEUROSCI.1808-07.2007

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  46 in total

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2.  Effective connectivity of the multiplication network: a functional MRI and multivariate Granger Causality Mapping study.

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Journal:  Hum Brain Mapp       Date:  2010-08-16       Impact factor: 5.038

3.  Effective connectivity during haptic perception: a study using Granger causality analysis of functional magnetic resonance imaging data.

Authors:  Gopikrishna Deshpande; Xiaoping Hu; Randall Stilla; K Sathian
Journal:  Neuroimage       Date:  2008-02-09       Impact factor: 6.556

4.  Brain networks shaping religious belief.

Authors:  Dimitrios Kapogiannis; Gopikrishna Deshpande; Frank Krueger; Matthew P Thornburg; Jordan Henry Grafman
Journal:  Brain Connect       Date:  2014-01-15

Review 5.  Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis.

Authors:  Gopikrishna Deshpande; Xiaoping Hu
Journal:  Brain Connect       Date:  2012

6.  Dynamic brain connectivity is a better predictor of PTSD than static connectivity.

Authors:  Changfeng Jin; Hao Jia; Pradyumna Lanka; D Rangaprakash; Lingjiang Li; Tianming Liu; Xiaoping Hu; Gopikrishna Deshpande
Journal:  Hum Brain Mapp       Date:  2017-06-12       Impact factor: 5.038

7.  Oscillatory activity in neocortical networks during tactile discrimination near the limit of spatial acuity.

Authors:  Bhim M Adhikari; K Sathian; Charles M Epstein; Bidhan Lamichhane; Mukesh Dhamala
Journal:  Neuroimage       Date:  2014-01-13       Impact factor: 6.556

8.  Effect of hemodynamic variability on Granger causality analysis of fMRI.

Authors:  Gopikrishna Deshpande; K Sathian; Xiaoping Hu
Journal:  Neuroimage       Date:  2009-12-11       Impact factor: 6.556

9.  Neural processing underlying tactile microspatial discrimination in the blind: a functional magnetic resonance imaging study.

Authors:  Randall Stilla; Rebecca Hanna; Xiaoping Hu; Erica Mariola; Gopikrishna Deshpande; K Sathian
Journal:  J Vis       Date:  2008-12-17       Impact factor: 2.240

10.  Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity.

Authors:  Gopikrishna Deshpande; Zhihao Li; Priya Santhanam; Claire D Coles; Mary Ellen Lynch; Stephan Hamann; Xiaoping Hu
Journal:  PLoS One       Date:  2010-12-09       Impact factor: 3.240

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