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Peterson EJ, Rosen BQ, Belger A, Voytek B, Campbell AM. Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations. Clin EEG Neurosci 2023; 54:434-445. [PMID: 37287239 DOI: 10.1177/15500594231165589] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants' behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.
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Affiliation(s)
- Erik J Peterson
- University of California, San Diego, La Jolla, CA, USA
- Carnegie Mellon University, Pittsburgh, PA, USA
| | - Burke Q Rosen
- Neurosciences Graduate Program, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Aysenil Belger
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bradley Voytek
- University of California, San Diego, La Jolla, CA, USA
- Neurosciences Graduate Program, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Alana M Campbell
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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2
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Shah-Basak P, Sivaratnam G, Teti S, Deschamps T, Kielar A, Jokel R, Meltzer JA. Electrophysiological connectivity markers of preserved language functions in post-stroke aphasia. Neuroimage Clin 2022; 34:103036. [PMID: 35561556 PMCID: PMC9111985 DOI: 10.1016/j.nicl.2022.103036] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 04/19/2022] [Accepted: 05/04/2022] [Indexed: 11/17/2022]
Abstract
Post-stroke aphasia is a consequence of localized stroke-related damage as well as global disturbances in a highly interactive and bilaterally-distributed language network. Aphasia is increasingly accepted as a network disorder and it should be treated as such when examining the reorganization and recovery mechanisms after stroke. In the current study, we sought to investigate reorganized patterns of electrophysiological connectivity, derived from resting-state magnetoencephalography (rsMEG), in post-stroke chronic (>6 months after onset) aphasia. We implemented amplitude envelope correlations (AEC), a metric of connectivity commonly used to describe slower aspects of interregional communication in resting-state electrophysiological data. The main focus was on identifying the oscillatory frequency bands and frequency-specific spatial topology of connections associated with preserved language abilities after stroke. RsMEG was recorded for 5 min in 21 chronic stroke survivors with aphasia and in 20 matched healthy controls. Source-level MEG activity was reconstructed and summarized within 72 atlas-defined brain regions (or nodes). A 72 × 72 leakage-corrected connectivity (of AEC) matrix was obtained for frequencies from theta to low-gamma (4–50 Hz). Connectivity was compared between groups, and, the correlations between connectivity and subscale scores from the Western Aphasia Battery (WAB) were evaluated in the stroke group, using partial least squares analyses. Posthoc multiple regression analyses were also conducted on a graph theory measure of node strengths, derived from significant connectivity results, to control for node-wise properties (local spectral power and lesion sizes) and demographic and stroke-related variables. Connectivity among the left hemisphere regions, i.e. those ipsilateral to the stroke lesion, was greatly reduced in stroke survivors with aphasia compared to matched healthy controls in the alpha (8–13 Hz; p = 0.011) and beta (15–30 Hz; p = 0.001) bands. The spatial topology of hypoconnectivity in the alpha vs. beta bands was distinct, revealing a greater involvement of ventral frontal, temporal and parietal areas in alpha, and dorsal frontal and parietal areas in beta. The node strengths from alpha and beta group differences remained significant after controlling for nodal spectral power. AEC correlations with WAB subscales of object naming and fluency were significant. Greater alpha connectivity was associated with better naming performance (p = 0.045), and greater connectivity in both the alpha (p = 0.033) and beta (p = 0.007) bands was associated with better speech fluency performance. The spatial topology was distinct between these frequency bands. The node strengths remained significant after controlling for age, time post stroke onset, nodal spectral power and nodal lesion sizes. Our findings provide important insights into the electrophysiological connectivity profiles (frequency and spatial topology) potentially underpinning preserved language abilities in stroke survivors with aphasia.
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Affiliation(s)
- Priyanka Shah-Basak
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada.
| | - Gayatri Sivaratnam
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Selina Teti
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Tiffany Deschamps
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Aneta Kielar
- Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson, AZ, USA
| | - Regina Jokel
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada; Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
| | - Jed A Meltzer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada; Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada; Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada; Department of Psychology, University of Toronto, Toronto, ON, Canada
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3
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Hommelsen M, Viswanathan S, Daun S. Robustness of individualized inferences from longitudinal resting state EEG dynamics. Eur J Neurosci 2022; 56:3613-3644. [PMID: 35445438 DOI: 10.1111/ejn.15673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 03/21/2022] [Accepted: 04/08/2022] [Indexed: 11/27/2022]
Abstract
Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification. We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-EEG was acquired over five consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision-rule, an individual could be re-identified from 2-second samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded-RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day vs same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of longitudinal RS for robust individualized inferences about neurophysiological change in health and disease.
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Affiliation(s)
- Maximilian Hommelsen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany
| | | | - Silvia Daun
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich, Germany.,Institute of Zoology, University of Cologne, Cologne, Germany
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钟 文, 安 兴, 狄 洋, 张 力, 明 东. [Review on identity feature extraction methods based on electroencephalogram signals]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2021; 38:1203-1210. [PMID: 34970904 PMCID: PMC9927118 DOI: 10.7507/1001-5515.202102057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 09/16/2021] [Indexed: 06/14/2023]
Abstract
Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.
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Affiliation(s)
- 文潇 钟
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China
- 天津大学 精密仪器与光电子工程学院 生物医学工程系(天津 300072)Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, TianJin 300072, P.R.China
| | - 兴伟 安
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China
| | - 洋 狄
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China
| | - 力新 张
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China
- 天津大学 精密仪器与光电子工程学院 生物医学工程系(天津 300072)Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, TianJin 300072, P.R.China
| | - 东 明
- 天津大学 医学工程与转化医学研究院(天津 300072)Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China
- 天津大学 精密仪器与光电子工程学院 生物医学工程系(天津 300072)Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, TianJin 300072, P.R.China
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5
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Leuchter AF, Wilson AC, Vince-Cruz N, Corlier J. Novel method for identification of individualized resonant frequencies for treatment of Major Depressive Disorder (MDD) using repetitive Transcranial Magnetic Stimulation (rTMS): A proof-of-concept study. Brain Stimul 2021; 14:1373-1383. [PMID: 34425244 DOI: 10.1016/j.brs.2021.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 07/28/2021] [Accepted: 08/11/2021] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Repetitive Transcranial Magnetic Stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD), but therapeutic benefit is highly variable. Clinical improvement is related to changes in brain circuits, which have preferred resonant frequencies (RFs) and vary across individuals. OBJECTIVE We developed a novel rTMS-electroencephalography (rTMS-EEG) interrogation paradigm to identify RFs using the association of power/connectivity measures with symptom severity and treatment outcome. METHODS 35 subjects underwent rTMS interrogation at 71 frequencies ranging from 3 to 17 Hz administered to left dorsolateral prefrontal cortex (DLPFC). rTMS-EEG was used to assess resonance in oscillatory power/connectivity changes (phase coherence [PC], envelope correlation [EC], and spectral correlation coefficient [SCC]) after each frequency. Multiple regression was used to detect relationships between 10 Hz resonance and baseline symptoms as well as clinical improvement after 10 sessions of 10 Hz rTMS treatment. RESULTS Baseline symptom severity was significantly associated with SCC resonance in left sensorimotor (SM; p < 0.0004), PC resonance in fronto-parietal (p = 0.001), and EC resonance in centro-posterior channels (p = 0.002). Subjects significantly improved with 10 sessions of rTMS treatment. Only decreased SCC SM resonance was significantly associated with clinical improvement (r = 0.35, p = 0.04). Subjects for whom 10 Hz SM SCC was highly ranked as an RF among all stimulation frequencies had better outcomes from 10 Hz treatment. CONCLUSIONS Resonance of 10 Hz stimulation measured using SCC correlated with both symptom severity and improvement with 10 Hz rTMS treatment. Research should determine whether this interrogation paradigm can identify individualized rTMS treatment frequencies.
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Affiliation(s)
- Andrew F Leuchter
- From the TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, And the Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
| | - Andrew C Wilson
- From the TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, And the Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Nikita Vince-Cruz
- From the TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, And the Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Juliana Corlier
- From the TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, And the Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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Jo Y, Faskowitz J, Esfahlani FZ, Sporns O, Betzel RF. Subject identification using edge-centric functional connectivity. Neuroimage 2021; 238:118204. [PMID: 34087363 DOI: 10.1016/j.neuroimage.2021.118204] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 05/23/2021] [Accepted: 05/24/2021] [Indexed: 12/11/2022] Open
Abstract
Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.
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Affiliation(s)
- Youngheun Jo
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA
| | - Farnaz Zamani Esfahlani
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
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7
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Yuk V, Dunkley BT, Anagnostou E, Taylor MJ. Alpha connectivity and inhibitory control in adults with autism spectrum disorder. Mol Autism 2020; 11:95. [PMID: 33287904 PMCID: PMC7722440 DOI: 10.1186/s13229-020-00400-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Accepted: 11/18/2020] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Individuals with autism spectrum disorder (ASD) often report difficulties with inhibition in everyday life. During inhibition tasks, adults with ASD show reduced activation of and connectivity between brain areas implicated in inhibition, suggesting impairments in inhibitory control at the neural level. Our study further investigated these differences by using magnetoencephalography (MEG) to examine the frequency band(s) in which functional connectivity underlying response inhibition occurs, as brain functions are frequency specific, and whether connectivity in certain frequency bands differs between adults with and without ASD. METHODS We analysed MEG data from 40 adults with ASD (27 males; 26.94 ± 6.08 years old) and 39 control adults (27 males; 27.29 ± 5.94 years old) who performed a Go/No-go task. The task involved two blocks with different proportions of No-go trials: Inhibition (25% No-go) and Vigilance (75% No-go). We compared whole-brain connectivity in the two groups during correct No-go trials in the Inhibition vs. Vigilance blocks between 0 and 400 ms. RESULTS Despite comparable performance on the Go/No-go task, adults with ASD showed reduced connectivity compared to controls in the alpha band (8-14 Hz) in a network with a main hub in the right inferior frontal gyrus. Decreased connectivity in this network predicted more self-reported difficulties on a measure of inhibition in everyday life. LIMITATIONS Measures of everyday inhibitory control were not available for all participants, so this relationship between reduced network connectivity and inhibitory control abilities may not be necessarily representative of all adults with ASD or the larger ASD population. Further research with independent samples of adults with ASD, including those with a wider range of cognitive abilities, would be valuable. CONCLUSIONS Our findings demonstrate reduced functional brain connectivity during response inhibition in adults with ASD. As alpha-band synchrony has been linked to top-down control mechanisms, we propose that the lack of alpha synchrony observed in our ASD group may reflect difficulties in suppressing task-irrelevant information, interfering with inhibition in real-life situations.
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Affiliation(s)
- Veronica Yuk
- Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada. .,Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada. .,Department of Psychology, University of Toronto, Toronto, ON, Canada.
| | - Benjamin T Dunkley
- Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.,Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Evdokia Anagnostou
- Department of Neurology, The Hospital for Sick Children, Toronto, ON, Canada.,Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, 555 University Avenue, Toronto, ON, M5G 1X8, Canada.,Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Psychology, University of Toronto, Toronto, ON, Canada
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Wriessnegger SC, Müller-Putz GR, Brunner C, Sburlea AI. Inter- and Intra-individual Variability in Brain Oscillations During Sports Motor Imagery. Front Hum Neurosci 2020; 14:576241. [PMID: 33192406 PMCID: PMC7662155 DOI: 10.3389/fnhum.2020.576241] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 09/24/2020] [Indexed: 11/23/2022] Open
Abstract
The aim of this work was to re-evaluate electrophysiological data from a previous study on motor imagery (MI) with a special focus on observed inter- and intra-individual differences. More concretely, we investigated event-related desynchronization/synchronization patterns during sports MI (playing tennis) compared with simple MI (squeezing a ball) and discovered high variability across participants. Thirty healthy volunteers were divided in two groups; the experimental group (EG) performed a physical exercise between two imagery sessions, and the control group (CG) watched a landscape movie without physical activity. We computed inter-individual differences by assessing the dissimilarities among subjects for each group, condition, time period, and frequency band. In the alpha band, we observe some clustering in the ranking of the subjects, therefore showing smaller distances than others. Moreover, in our statistical evaluation, we observed a consistency in ranking across time periods both for the EG and for the CG. For the latter, we also observed similar rankings across conditions. On the contrary, in the beta band, the ranking of the subjects was more similar for the EG across conditions and time periods than for the subjects of the CG. With this study, we would like to draw attention to variability measures instead of primarily focusing on the identification of common patterns across participants, which often do not reflect the whole neurophysiological reality.
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Affiliation(s)
- Selina C Wriessnegger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.,BioTechMed-Graz, Graz, Austria
| | | | - Andreea I Sburlea
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
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Yuk V, Anagnostou E, Taylor MJ. Altered Connectivity During a False-Belief Task in Adults With Autism Spectrum Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:901-912. [PMID: 32600899 DOI: 10.1016/j.bpsc.2020.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 04/15/2020] [Accepted: 04/15/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND Deficits in social communication are one of the main features of autism spectrum disorder (ASD). Adults with ASD show atypical brain activity during false-belief understanding, an aspect of social communication involving the ability to infer that an individual can have an incorrect belief about a situation. Our study is the first to investigate whether adults with ASD exhibit differences in frequency-specific functional connectivity patterns during false-belief reasoning. METHODS We used magnetoencephalography to contrast functional connectivity underlying false-belief understanding between 40 adults with ASD and 39 control adults. We examined whole-brain phase synchrony measures during a false-belief task in 3 frequency bands: theta (4-7 Hz), alpha (8-14 Hz), and beta (15-30 Hz). RESULTS Adults with ASD demonstrated reduced theta-band connectivity compared with control adults between several right-lateralized and midline regions such as the medial prefrontal cortex, right temporoparietal junction, right inferior frontal gyrus, and right superior temporal gyrus. During false-belief trials, they also recruited a network in the beta band that included primary visual regions such as the bilateral inferior occipital gyri and the left anterior temporoparietal junction. CONCLUSIONS Reduced theta-band synchrony between areas associated with mentalizing, inhibition, and visual processing implies some difficulty in communication among these functions in ASD. This impairment in top-down control in the theta band may be counterbalanced by their engagement of a beta-band network because both the left anterior temporoparietal junction and beta-band oscillations are associated with attentional processes. Thus, adults with ASD demonstrate alternative neural mechanisms for successful false-belief reasoning.
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Affiliation(s)
- Veronica Yuk
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada.
| | - Evdokia Anagnostou
- Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada; Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Margot J Taylor
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
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Wang J, Gu Y, Dong W, Zhao M, Tian J, Sun T, Yu X, Ouyang G, Wang H. Lower Small-Worldness of Intrinsic Brain Networks Facilitates the Cognitive Protection of Intellectual Engagement in Elderly People Without Dementia: A Near-Infrared Spectroscopy Study. Am J Geriatr Psychiatry 2020; 28:722-731. [PMID: 32173205 DOI: 10.1016/j.jagp.2020.02.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 02/04/2020] [Accepted: 02/14/2020] [Indexed: 11/26/2022]
Abstract
OBJECTIVE Lifetime intellectual engagement may be associated with cognitive ability late in life. However, the current evidence on whether cognitive activities will improve and/or maintain cognitive function is heterogeneous. Drawing on knowledge of the brain's intrinsic small-world organization which combines regional specialization and efficient global information transfer, we aimed to explore that whether individual differences in the small-worldness of resting-state functional connectivity (rsFC) networks would explain the variability in the strength of the association between intellectual engagement and cognitive functioning. METHODS Sixty-five elderly people without dementia were enrolled and scanned with a 52-channel near-infrared spectroscopy system. The number, frequency, and participation hours of intellectual activities were investigated to measure intellectual engagement. Global cognition was assessed by the Montreal Cognitive Assessment. The general linear models and the simple slope analysis were employed to measure the modulatory role of network properties. RESULTS The small-worldness of the brain network emerged as a moderator of the association between intellectual engagement and cognition. Exclusively among elderly people with lower small-worldness, greater intellectual engagement, including the frequency and participation hours of activities, was associated with greater global cognitive function. Furthermore, we observed that elderly people with lower small-worldness exhibited decreased rsFC across the bilateral frontopolar areas and increased rsFC across the bilateral parietal cortex. CONCLUSION The individual differences in the small-worldness of rsFC networks might explain the varying strength of the association between intellectual engagement and cognitive functioning. Our findings imply that the intrinsic small-worldness of the brain network might be a potential neurobiological contributor that interacts with the intellectual engagement in enhancing the cognitive ability in late life.
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Affiliation(s)
- Jing Wang
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China; Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia (JW, MZ, TS, XY, HW), Beijing, China
| | - Yue Gu
- Key Laboratory of Computer Vision and System (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology (YG), Tianjin, China
| | - Wentian Dong
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China
| | - Mei Zhao
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China; Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia (JW, MZ, TS, XY, HW), Beijing, China; Department of Psychiatry, University of Melbourne (MZ), Melbourne, Australia
| | - Ju Tian
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China
| | - Tingting Sun
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China; Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia (JW, MZ, TS, XY, HW), Beijing, China
| | - Xin Yu
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China; Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia (JW, MZ, TS, XY, HW), Beijing, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University (GO), Beijing, China
| | - Huali Wang
- Peking University Institute of Mental Health (Sixth Hospital) (JW, WD, MZ, JT, TS, XY, HW), Beijing, China; National Clinical Research Center for Mental Disorders, NHC Key Laboratory of Mental Health (Peking University) (JW, WD, MZ, JT, TS, XY HW), Beijing, China; Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia (JW, MZ, TS, XY, HW), Beijing, China.
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Pani SM, Ciuffi M, Demuru M, La Cava SM, Bazzano G, D’Aloja E, Fraschini M. Subject, session and task effects on power, connectivity and network centrality: A source-based EEG study. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101891] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Riha C, Güntensperger D, Kleinjung T, Meyer M. Accounting for Heterogeneity: Mixed-Effects Models in Resting-State EEG Data in a Sample of Tinnitus Sufferers. Brain Topogr 2020; 33:413-424. [PMID: 32328859 PMCID: PMC7293675 DOI: 10.1007/s10548-020-00772-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 04/13/2020] [Indexed: 12/26/2022]
Abstract
In neuroscience, neural oscillations and other features of brain activity recorded by electroencephalography (EEG) are typically statistically assessed on the basis of the study’s population mean to identify possible blueprints for healthy subjects, or subjects with diagnosable neurological or psychiatric disorders. Despite some inter-individual similarities, there is reason to believe that a discernible portion of the individual brain activity is subject-specific. In order to encompass the potential individual source of variance in EEG data and psychometric parameters, we introduce an innovative application of linear mixed-effects models (LMM) as an alternative procedure for the analysis of resting-state EEG data. Using LMM, individual differences can be modelled through the assumptions of idiosyncrasy of all responses and dependency among data points (e.g., from the same subject within and across units of time) via random effects parameters. This report provides an example of how LMM can be used for the statistical analysis of resting-state EEG data in a heterogeneous group of subjects; namely, people who suffer from tinnitus (ringing in the ear/s). Results from 49 participants (38 male, mean age of 46.69 ± 12.65 years) revealed that EEG signals were not only associated with specific recording sites, but exhibited regional specific oscillations in conjunction to symptom severity. Tinnitus distress targeted the frequency bands beta3 (23.5–35 Hz) and gamma (35.5–45 Hz) in right frontal regions, whereas delta (0.5–4 Hz) exhibited significant changes in temporal-parietal sources. Further, 57.8% of the total variance in EEG power was subject-specific and acknowledged by the LMM framework and its prediction. Thus, a deeper understanding of both the underlying statistical and physiological patterns of EEG data was gained.
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Affiliation(s)
- Constanze Riha
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Binzmühlestr. 14/25, 8050, Zurich, Switzerland. .,Research Priority Program "ESIT - European School of Interdisciplinary Tinnitus Research", Zurich, Switzerland.
| | - Dominik Güntensperger
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Binzmühlestr. 14/25, 8050, Zurich, Switzerland
| | - Tobias Kleinjung
- Department of Otorhinolaryngology, University Hospital Zurich, Zurich, Switzerland
| | - Martin Meyer
- Chair of Neuropsychology, Department of Psychology, University of Zurich, Binzmühlestr. 14/25, 8050, Zurich, Switzerland
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Cox R, Schapiro AC, Stickgold R. Variability and stability of large-scale cortical oscillation patterns. Netw Neurosci 2018; 2:481-512. [PMID: 30320295 PMCID: PMC6175693 DOI: 10.1162/netn_a_00046] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 01/26/2018] [Indexed: 11/08/2022] Open
Abstract
Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior. Neural oscillations are critical for the human brain’s ability to optimally respond to complex environmental input. However, relatively little is known about the network properties of these oscillatory rhythms. We used electroencephalography (EEG) to analyze large-scale brain wave patterns, focusing on multiple frequency bands and several key features of oscillatory communication. We show that networks defined in this manner are, in fact, distinct, suggesting that EEG activity encompasses multiple, parallel information processing streams. Remarkably, the same networks can be used to uniquely identify individuals over a period of approximately half a year, thus serving as neural fingerprints. These findings indicate that investigating oscillatory dynamics from a network perspective holds considerable promise as a tool to understand human cognition and behavior.
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Affiliation(s)
- Roy Cox
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, USA
| | - Anna C Schapiro
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston MA, USA
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