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      Oscillatory motor network activity during rest and movement: an fNIRS study

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          Abstract

          Coherent network oscillations (<0.1 Hz) linking distributed brain regions are commonly observed in the brain during both rest and task conditions. What oscillatory network exists and how network oscillations change in connectivity strength, frequency and direction when going from rest to explicit task are topics of recent inquiry. Here, we study network oscillations within the sensorimotor regions of able-bodied individuals using hemodynamic activity as measured by functional near-infrared spectroscopy (fNIRS). Using spectral interdependency methods, we examined how the supplementary motor area (SMA), the left premotor cortex (LPMC) and the left primary motor cortex (LM1) are bound as a network during extended resting state (RS) and between-tasks resting state (btRS), and how the activity of the network changes as participants execute left, right, and bilateral hand (LH, RH, and BH) finger movements. We found: (i) power, coherence and Granger causality (GC) spectra had significant peaks within the frequency band (0.01–0.04 Hz) during RS whereas the peaks shifted to a bit higher frequency range (0.04–0.08 Hz) during btRS and finger movement tasks, (ii) there was significant bidirectional connectivity between all the nodes during RS and unidirectional connectivity from the LM1 to SMA and LM1 to LPMC during btRS, and (iii) the connections from SMA to LM1 and from LPMC to LM1 were significantly modulated in LH, RH, and BH finger movements relative to btRS. The unidirectional connectivity from SMA to LM1 just before the actual task changed to the bidirectional connectivity during LH and BH finger movement. The uni-directionality could be associated with movement suppression and the bi-directionality with preparation, sensorimotor update and controlled execution. These results underscore that fNIRS is an effective tool for monitoring spectral signatures of brain activity, which may serve as an important precursor before monitoring the recovery progress following brain injury.

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          Dynamic causal modelling.

          In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.
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            Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data.

            In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.
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              Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses.

              Evoked activity in the mammalian cortex and the resulting behavioral responses exhibit a large variability to repeated presentations of the same stimulus. This study examined whether the variability can be attributed to ongoing activity. Ongoing and evoked spatiotemporal activity patterns in the cat visual cortex were measured with real-time optical imaging; local field potentials and discharges of single neurons were recorded simultaneously, by electrophysiological techniques. The evoked activity appeared deterministic, and the variability resulted from the dynamics of ongoing activity, presumably reflecting the instantaneous state of cortical networks. In spite of the large variability, evoked responses in single trials could be predicted by linear summation of the deterministic response and the preceding ongoing activity. Ongoing activity must play an important role in cortical function and cannot be ignored in exploration of cognitive processes.
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                Author and article information

                Journal
                Front Syst Neurosci
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Media S.A.
                1662-5137
                04 February 2014
                2014
                : 8
                : 13
                Affiliations
                [1] 1Department of Physics and Astronomy, Georgia State University Atlanta, GA, USA
                [2] 2Department of Physical Therapy, Byrdine F. Lewis School of Nursing and Health Professions, Georgia State University Atlanta, GA, USA
                [3] 3Department of Veteran's Affairs, Atlanta Rehabilitation Research and Development Center of Excellence Decatur, GA, USA
                [4] 4Joint Center for Advanced Brain Imaging, Center for Behavioral Neuroscience, Neuroscience Institute, Georgia State University Atlanta, GA, USA
                Author notes

                Edited by: Linda Douw, VU University Medical Center, Netherlands

                Reviewed by: Fahad Sultan, University Tübingen, Germany; Behnam Molavi, University of British Columbia, Canada

                *Correspondence: Andrew J. Butler, Department of Physical Therapy, Byrdine F. Lewis School of Nursing and Health Professions, Georgia State University, Urban Life Building, Suite 819, Atlanta, GA 30303, USA e-mail: andrewbutler@ 123456gsu.edu

                This article was submitted to the journal Frontiers in Systems Neuroscience.

                Article
                10.3389/fnsys.2014.00013
                3912434
                24550793
                9a1c6613-c5a0-4d0a-8ca6-d9cd30b7cc43
                Copyright © 2014 Bajaj, Drake, Butler and Dhamala.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 01 October 2013
                : 20 January 2014
                Page count
                Figures: 6, Tables: 0, Equations: 6, References: 67, Pages: 12, Words: 8433
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                fnir,resting state,brain connectivity,slow oscillations,motor networks,granger causality
                Neurosciences
                fnir, resting state, brain connectivity, slow oscillations, motor networks, granger causality

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