EEG Functional Connectivity is a Weak Predictor of Causal Brain Interactions

Vink, Jord; Klooster, Debby; Ozdemir, Recep A.; Westover, M. Brandon; Pascual‐Leone, Álvaro; Shafi, Mouhsin M.

doi:10.1007/s10548-020-00757-6

Cited by 12 publications

(10 citation statements)

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“…This method can be used in studying the weak signals in time series of the measurable processes of dynamical systems in diverse fields [45–50]. Consequently, we understand that this method is more efficient in weak periodic signal detection compared to the conventional method that relies on state‐space analysis of the Duffing oscillator.…”

Section: Discussionmentioning

confidence: 99%

Automated system for weak periodic signal detection based on Duffing oscillator

Akıllı

Yılmaz

Akdeniz³

2020

IET signal process.

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The periodic signals that have predictable and deterministic characteristics are used in the analysis and modelling of dynamical systems in diverse fields. These signals can be detected as the weak signals within the time series obtained from the measurable processes of dynamical systems. The Duffing oscillator is effective in detecting weak periodic signals with a very low signal‐to‐noise ratio. In this study, the authors present a method to automate the weak periodic signal detection of the Duffing oscillator using a quantitative index for the classification of the periodic and non‐periodic signals. In this method, the authors use the wavelet scale index as the quantitative index in the classification of signals. Thus, they are able to plot the wavelet scale index spectrum of the Duffing oscillator where the frequency values of the weak periodic signals correspond to near‐zero wavelet scale index parameters. First, the authors perform simulations using the method and detect weak periodic signals embedded in noise. Then, they employ two electroencephalogram signals to demonstrate the feasibility of the proposed method in the empirical data. Lastly, they compare the method to the periodogram power spectral density estimate based on fast Fourier transform.

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Section: Discussionmentioning

confidence: 99%

Automated system for weak periodic signal detection based on Duffing oscillator

Akıllı

Yılmaz

Akdeniz³

2020

IET signal process.

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“…An index of change in the tACS-induced modulation of cortical excitability was defined for each participant, and brain functional connectivity and interregional coordination were directly estimated from EEG. We used established tACS/EEG procedures to measure neuroplasticity [ 16 , 17 ]. We used the noninvasive brain stimulation/EEG hybrid Starstim-Home system (Neuroelectrics) and recorded EEG before (5 minutes), during (3 minutes), and after (5 minutes) tACS stimulation.…”

Section: Methodsmentioning

confidence: 99%

“…Participants received tACS stimulation (3 minutes, 40-60 Hz, 1-2 mA) targeting the motor and prefrontal cortices through remote neurophysiologic monitoring by the study team. We used published methods of artifact removal and event detection algorithms to extract the magnitude and duration of post-tACS gamma EEG power changes from the EEG signals [ 16 , 17 ]. In accordance with the safe application of neurophysiological measures recommended by the International Federation of Clinical Neurophysiology, we collected and monitored adverse events.…”

Section: Methodsmentioning

confidence: 99%

“…In this paradigm, small electrodes placed on the scalp deliver a short burst of tACS, which induces a controlled perturbation in the brain that is measured by EEG changes. The magnitude and duration of tACS-induced spectral EEG changes in EEG enable an assessment of neuroplastic mechanisms that is akin to the TMS/intermittent theta-burst stimulation (iTBS) approach [ 16 , 17 ]. tACS/EEG is uniquely suited for a home-based intervention as it can be delivered with a device designed for home use.…”

Section: Introductionmentioning

confidence: 99%

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Harnessing Neuroplasticity to Promote Brain Health in Aging Adults: Protocol for the MOVE-Cog Intervention Study

Cabral¹,

Hinchman²,

Nunez³

et al. 2021

JMIR Res Protoc

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Background Extensive evidence supports a link between aerobic exercise and cognitive improvements in aging adults. A major limitation with existing research is the high variability in cognitive response to exercise. Our incomplete understanding of the mechanisms that influence this variability and the low adherence to exercise are critical knowledge gaps and major barriers for the systematic implementation of exercise for promoting cognitive health in aging. Objective We aimed to provide an in-person and remotely delivered intervention study protocol with the main goal of informing the knowledge gap on the mechanistic action of exercise on the brain by characterizing important mechanisms of neuroplasticity, cardiorespiratory fitness response, and genetics proposed to underlie cognitive response to exercise. Methods This is an open-label, 2-month, interventional study protocol in neurologically healthy sedentary adults. This study was delivered fully in-person and in remote options. Participants underwent a total of 30 sessions, including the screening session, 3 pretest (baseline) assessments, 24 moderate-to-vigorous aerobic exercise sessions, and 3 posttest assessments. We recruited participants aged 55 years and above, sedentary, and cognitively healthy. Primary outcomes were neuroplasticity, cognitive function, and cardiorespiratory fitness. Secondary outcomes included genetic factors, endothelium function, functional mobility and postural control, exercise questionnaires, depression, and sleep. We also explored study feasibility, exercise adherence, technology adaptability, and compliance of both in-person and remote protocols. Results The recruitment phase and data collection of this study have concluded. Results are expected to be published by the end of 2021 or in early 2022. Conclusions The data generated in these studies will introduce tangible parameters to guide the development of personalized exercise prescription models for maximal cognitive benefit in aging adults. Successful completion of the specific aims will enable researchers to acquire the appropriate expertise to design and conduct studies by testing personalized exercise interventions in person and remotely delivered, likely to be more effective at promoting cognitive health in aging adults. Trial Registration ClinicalTrials.gov NCT03804528; http://clinicaltrials.gov/ct2/show/NCT03804528 International Registered Report Identifier (IRRID) RR1-10.2196/33589

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“…Digitized EEG electrode locations on the scalp were also co-registered to individual MRI scans using Nexstim Brainsight™ TMS Frameless Navigation system. See Vink et al [40] for more details.…”

Section: Electroencephalography Acquisition and Preprocessingmentioning

confidence: 99%

A Structured ICA-based Process for Removing Auditory Evoked Potentials Reveals TMS-evoked Potentials and TMS-modulated Oscillations

Ross

Ozdemir

Lian³

et al. 2021

Preprint

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Background: Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (EEG), reflect a combination of TMS-induced cortical activity and multi-sensory responses to TMS. The auditory evoked potential (AEP) is a high-amplitude sensory potential—evoked by the “click” sound produced by every TMS pulse—that can dominate the TEP and obscure observation of other neural components. The AEP is peripherally evoked and therefore should not be stimulation site specificObjectives/Methods: We address the problem of disentangling the peripherally evoked AEP of the TEP from components evoked by cortical stimulation and ask whether removal of AEP enables more accurate isolation of TEP. We hypothesized that isolation of the AEP using Independent Components Analysis (ICA) would reveal features that are stimulation site specific and unique individual features. In order to improve the effectiveness of ICA for removal of AEP from the TEP, and thus more clearly separate the transcranial-evoked and non-specific TMS-modulated potentials, we merged sham and active TMS datasets representing multiple stimulation conditions, removed the resulting AEP component, and evaluated performance across different sham protocols and clinical populations using reduction in Global and Local Mean Field Potentials (GMFA/LMFA) and cosine similarity analysis.Results: We show that removing AEPs significantly reduced GMFA and LMFA in the post-stimulation TEP (14 to 400 ms), driven by time windows consistent with the N100 and P200 temporal characteristics of AEPs. Cosine similarity analysis supports that removing AEPs reduces TEP similarity between subjects and reduces TEP similarity between stimulation conditions. Similarity is reduced most in a mid-latency window consistent with the N100 time-course, but nevertheless remains high in this time window. Residual TEP in this window has a time-course and topography unique from AEPs, which follow-up analyses suggest could be a modulation in the alpha band that is not stimulation site specific but is unique to individual subject.Conclusion(s): We show, using two datasets and two implementations of sham, evidence in cortical topography, TEP time-course, GMFA/LMFA and cosine similarity analyses that this procedure is effective and conservative in removing the AEP from TEP, and thereby reveals better defined TMS-evoked activity. We show TEP remaining in early, mid and late latencies. The early response is site and subject specific. Later responses are consistent with TMS-modulated alpha activity that is not site specific but is unique to the individual. TEP remaining after removal of AEP is unique and can provide insight into TMS-evoked potentials and other modulated oscillatory dynamics.

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EEG Functional Connectivity is a Weak Predictor of Causal Brain Interactions

Cited by 12 publications

References 58 publications

Automated system for weak periodic signal detection based on Duffing oscillator

Automated system for weak periodic signal detection based on Duffing oscillator

Harnessing Neuroplasticity to Promote Brain Health in Aging Adults: Protocol for the MOVE-Cog Intervention Study

A Structured ICA-based Process for Removing Auditory Evoked Potentials Reveals TMS-evoked Potentials and TMS-modulated Oscillations

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