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      Low-Frequency Oscillations Are a Biomarker of Injury and Recovery After Stroke

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          Abstract

          Background and Purpose—

          Low-frequency oscillations reflect brain injury but also contribute to normal behaviors. We examined hypotheses relating electroencephalography measures, including low-frequency oscillations, to injury and motor recovery poststroke.

          Methods—

          Patients with stroke completed structural neuroimaging, a resting-state electroencephalography recording and clinical testing. A subset admitted to an inpatient rehabilitation facility also underwent serial electroencephalography recordings. The relationship that electroencephalography measures (power and coherence with leads overlying ipsilesional primary motor cortex [iM1]) had with injury and motor status was assessed, focusing on delta (1–3 Hz) and high-beta (20–30 Hz) bands.

          Results—

          Across all patients (n=62), larger infarct volume was related to higher delta band power in bilateral hemispheres and to higher delta band coherence between iM1 and bilateral regions. In chronic stroke, higher delta power bilaterally correlated with better motor status. In subacute stroke, higher delta coherence between iM1 and bilateral areas correlated with poorer motor status. These coherence findings were confirmed in serial recordings from 18 patients in an inpatient rehabilitation facility. Here, interhemispheric coherence between leads overlying iM1 and contralesional M1 was elevated at inpatient rehabilitation facility admission compared with healthy controls (n=22), declining to control levels over time. Decreases in interhemispheric coherence between iM1 and contralesional M1 correlated with better motor recovery.

          Conclusions—

          Delta band coherence with iM1 related to greater injury and poorer motor status subacutely, while delta band power related to greater injury and better motor status chronically. Low-frequency oscillations reflect both injury and recovery after stroke and may be useful biomarkers in stroke recovery and rehabilitation.

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          Most cited references29

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          Small-world brain networks.

          Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.
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            A mechanism for cognitive dynamics: neuronal communication through neuronal coherence.

            At any one moment, many neuronal groups in our brain are active. Microelectrode recordings have characterized the activation of single neurons and fMRI has unveiled brain-wide activation patterns. Now it is time to understand how the many active neuronal groups interact with each other and how their communication is flexibly modulated to bring about our cognitive dynamics. I hypothesize that neuronal communication is mechanistically subserved by neuronal coherence. Activated neuronal groups oscillate and thereby undergo rhythmic excitability fluctuations that produce temporal windows for communication. Only coherently oscillating neuronal groups can interact effectively, because their communication windows for input and for output are open at the same times. Thus, a flexible pattern of coherence defines a flexible communication structure, which subserves our cognitive flexibility.
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              Beta-band oscillations--signalling the status quo?

              In this review, we consider the potential functional role of beta-band oscillations, which at present is not yet well understood. We discuss evidence from recent studies on top-down mechanisms involved in cognitive processing, on the motor system and on the pathophysiology of movement disorders that suggest a unifying hypothesis: beta-band activity seems related to the maintenance of the current sensorimotor or cognitive state. We hypothesize that beta oscillations and/or coupling in the beta-band are expressed more strongly if the maintenance of the status quo is intended or predicted, than if a change is expected. Moreover, we suggest that pathological enhancement of beta-band activity is likely to result in an abnormal persistence of the status quo and a deterioration of flexible behavioural and cognitive control. (c) 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Stroke
                Stroke
                Ovid Technologies (Wolters Kluwer Health)
                0039-2499
                1524-4628
                May 2020
                May 2020
                : 51
                : 5
                : 1442-1450
                Affiliations
                [1 ]From the Department of Allied Health Sciences, University of North Carolina, Chapel Hill (J.M.C.)
                [2 ]Department of Cognitive Sciences (A.W., R.S.), University of California, Irvine
                [3 ]Department of Neurology (J.W., K.K., A.K.M.), University of California, Irvine
                [4 ]Department of Physical Medicine & Rehabilitation (J.W.), University of California, Irvine
                [5 ]Department of Biomedical Engineering, University of California, Irvine (R.S.)
                [6 ]Department of Neurology, University of California, Los Angeles (S.C.C.)
                [7 ]California Rehabilitation Institute, Los Angeles (S.C.C.).
                Article
                10.1161/STROKEAHA.120.028932
                32299324
                34b2cdfa-9277-4141-ba44-b50eacd2ceb8
                © 2020
                History

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