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Tiwari A, Cassani R, Kshirsagar S, Tobon DP, Zhu Y, Falk TH. Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note. SENSORS 2022; 22:s22124579. [PMID: 35746361 PMCID: PMC9229858 DOI: 10.3390/s22124579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023]
Abstract
Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.
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Affiliation(s)
- Abhishek Tiwari
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
- Myant Inc., Toronto, ON M9W 1B6, Canada
| | - Raymundo Cassani
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada;
| | - Shruti Kshirsagar
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
| | - Diana P. Tobon
- Faculty of Engineering, Universidad de Medellín, Medellín 050026, Colombia;
| | - Yi Zhu
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
| | - Tiago H. Falk
- Institut National de la Recherche Scientifique, University of Quebec, Montréal, QC H5A 1K6, Canada; (A.T.); (S.K.); (Y.Z.)
- Correspondence:
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102
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Abstract
Temporal measures (latencies) in the event-related potentials of the EEG (ERPs) are a valuable tool for estimating the timing of mental processes, one which takes full advantage of the high temporal resolution of the EEG. Especially in larger scale studies using a multitude of individual EEG-based tasks, the quality of latency measures often suffers from high and low frequency noise residuals due to the resulting low trial counts (because of compressed tasks) and because of the limited feasibility of visual inspection of the large-scale data. In the present study, we systematically evaluated two different approaches to latency estimation (peak latencies and fractional area latencies) with respect to their data quality and the application of noise reduction by jackknifing methods. Additionally, we tested the recently introduced method of Standardized Measurement Error (SME) to prune the dataset. We demonstrate that fractional area latency in pruned and jackknifed data may amplify within-subjects effect sizes dramatically in the analyzed data set. Between-subjects effects were less affected by the applied procedures, but remained stable regardless of procedure.
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103
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Hsieh JC, Li Y, Wang H, Perz M, Tang Q, Tang KWK, Pyatnitskiy I, Reyes R, Ding H, Wang H. Design of hydrogel-based wearable EEG electrodes for medical applications. J Mater Chem B 2022; 10:7260-7280. [PMID: 35678148 DOI: 10.1039/d2tb00618a] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The electroencephalogram (EEG) is considered to be a promising method for studying brain disorders. Because of its non-invasive nature, subjects take a lower risk compared to some other invasive methods, while the systems record the brain signal. With the technological advancement of neural and material engineering, we are in the process of achieving continuous monitoring of neural activity through wearable EEG. In this article, we first give a brief introduction to EEG bands, circuits, wired/wireless EEG systems, and analysis algorithms. Then, we review the most recent advances in the interfaces used for EEG recordings, focusing on hydrogel-based EEG electrodes. Specifically, the advances for important figures of merit for EEG electrodes are reviewed. Finally, we summarize the potential medical application of wearable EEG systems.
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Affiliation(s)
- Ju-Chun Hsieh
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Yang Li
- Department of Chemical Engineering, Polytechnique Montréal, Montréal, Québec H3C3J7, Canada
| | - Huiqian Wang
- Department of Mathematics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Matt Perz
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Qiong Tang
- Department of Aerospace Engineering and Engineering Mechanics, The University of Texas at Austin, Austin, TX 78712, USA
| | - Kai Wing Kevin Tang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Ilya Pyatnitskiy
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Raymond Reyes
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Hong Ding
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
| | - Huiliang Wang
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
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104
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Romeo RR, Choi B, Gabard-Durnam LJ, Wilkinson CL, Levin AR, Rowe ML, Tager-Flusberg H, Nelson CA. Parental Language Input Predicts Neuroscillatory Patterns Associated with Language Development in Toddlers at Risk of Autism. J Autism Dev Disord 2022; 52:2717-2731. [PMID: 34185234 PMCID: PMC9594983 DOI: 10.1007/s10803-021-05024-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2021] [Indexed: 12/16/2022]
Abstract
In this study we investigated the impact of parental language input on language development and associated neuroscillatory patterns in toddlers at risk of Autism Spectrum Disorder (ASD). Forty-six mother-toddler dyads at either high (n = 22) or low (n = 24) familial risk of ASD completed a longitudinal, prospective study including free-play, resting electroencephalography, and standardized language assessments. Input quantity/quality at 18 months positively predicted expressive language at 24 months, and relationships were stronger for high-risk toddlers. Moderated mediations revealed that input-language relationships were explained by 24-month frontal and temporal gamma power (30-50 Hz) for high-risk toddlers who would later develop ASD. Results suggest that high-risk toddlers may be cognitively and neurally more sensitive to their language environments, which has implications for early intervention.
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Affiliation(s)
- Rachel R Romeo
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, Boston, MA, 02215, USA. .,Department of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA, 02138, USA.
| | - Boin Choi
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, Boston, MA, 02215, USA
| | - Laurel J Gabard-Durnam
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, Boston, MA, 02215, USA.,Department of Psychology, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA
| | - Carol L Wilkinson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, Boston, MA, 02215, USA
| | - April R Levin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02215, USA
| | - Meredith L Rowe
- Harvard Graduate School of Education, 13 Appian Way, Cambridge, MA, 02138, USA
| | - Helen Tager-Flusberg
- Department of Psychological and Brain Sciences, Boston University, 64 Cummington Mall, Boston, MA, 02215, USA
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, 1 Autumn Street, Boston, MA, 02215, USA.,Harvard Graduate School of Education, 13 Appian Way, Cambridge, MA, 02138, USA.,Department of Pediatrics, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02215, USA
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105
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Cardiac sympathetic-vagal activity initiates a functional brain-body response to emotional arousal. Proc Natl Acad Sci U S A 2022; 119:e2119599119. [PMID: 35588453 PMCID: PMC9173754 DOI: 10.1073/pnas.2119599119] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
We investigate the temporal dynamics of brain and cardiac activities in healthy subjects who underwent an emotional elicitation through videos. We demonstrate that, within the first few seconds, emotional stimuli modulate heartbeat activity, which in turn stimulates an emotion intensity (arousal)–specific cortical response. The emotional processing is then sustained by a bidirectional brain–heart interplay, where the perceived arousal level modulates the amplitude of ascending heart-to-brain neural information flow. These findings may constitute fundamental knowledge linking neurophysiology and psychiatric disorders, including the link between depressive symptoms and cardiovascular disorders. A century-long debate on bodily states and emotions persists. While the involvement of bodily activity in emotion physiology is widely recognized, the specificity and causal role of such activity related to brain dynamics has not yet been demonstrated. We hypothesize that the peripheral neural control on cardiovascular activity prompts and sustains brain dynamics during an emotional experience, so these afferent inputs are processed by the brain by triggering a concurrent efferent information transfer to the body. To this end, we investigated the functional brain–heart interplay under emotion elicitation in publicly available data from 62 healthy subjects using a computational model based on synthetic data generation of electroencephalography and electrocardiography signals. Our findings show that sympathovagal activity plays a leading and causal role in initiating the emotional response, in which ascending modulations from vagal activity precede neural dynamics and correlate to the reported level of arousal. The subsequent dynamic interplay observed between the central and autonomic nervous systems sustains the processing of emotional arousal. These findings should be particularly revealing for the psychophysiology and neuroscience of emotions.
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106
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Srivastava S, Jo B, Zhang B, Frazier T, Gallagher AS, Peck F, Levin AR, Mondal S, Li Z, Filip-Dhima R, Geisel G, Dies KA, Diplock A, Eng C, Hanna R, Sahin M, Hardan A. A randomized controlled trial of Everolimus for neurocognitive symptoms in PTEN hamartoma tumor syndrome. Hum Mol Genet 2022; 31:3393-3404. [PMID: 35594551 PMCID: PMC9558845 DOI: 10.1093/hmg/ddac111] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 03/24/2022] [Accepted: 04/03/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND PTEN hamartoma tumor syndrome (PHTS) is a complex neurodevelopmental disorder characterized by mTOR (mechanistic target of rapamycin) overactivity. Limited data suggest that mTOR inhibitors may be therapeutic. No placebo-controlled studies have examined mTOR inhibition on cognition and behavior in humans with PHTS with/without autism. METHODS We conducted a 6-month phase II, randomized, double-blinded, placebo-controlled trial to examine the safety profile and efficacy of everolimus (4.5 mg/m2) in individuals (5-45 years) with PHTS. We measured several cognitive and behavioral outcomes, and electroencephalography (EEG) biomarkers. The primary endpoint was a neurocognitive composite derived from Stanford Binet-5 (SB-5) nonverbal working memory score, SB-5 verbal working memory, Conners' Continuous Performance Test hit reaction time, and Purdue Pegboard Test score. RESULTS Forty-six participants underwent 1:1 randomization: n = 24 (everolimus) and n = 22 (placebo). Gastrointestinal adverse events were more common in the everolimus group (p < 0.001). Changes in the primary endpoint between groups from baseline to month 6 were not apparent (Cohen's d = -0.10, p = 0.518). However, several measures were associated with modest effect sizes (≥0.2) in the direction of improvement, including measures of nonverbal IQ, verbal learning, autism symptoms, motor skills, adaptive behavior, and global improvement. There was a significant difference in EEG central alpha power (p = 0.049) and central beta power (p = 0.039) six months after everolimus treatment. CONCLUSIONS Everolimus is well tolerated in PHTS; adverse events were similar to previous reports. The primary efficacy endpoint did not reveal improvement. Several secondary efficacy endpoints moved in the direction of improvement. EEG measurements indicate target engagement following 6 months of daily oral everolimus. Trial Registration Information: ClinicalTrials.gov NCT02991807 Classification of Evidence: I.
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Affiliation(s)
- Siddharth Srivastava
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Booil Jo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Bo Zhang
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Thomas Frazier
- Department of Psychology, John Carroll University, University Heights, Ohio, USA
| | - Anne Snow Gallagher
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fleming Peck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - April R Levin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sangeeta Mondal
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Zetan Li
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Rajna Filip-Dhima
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Gregory Geisel
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Kira A Dies
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amelia Diplock
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Charis Eng
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Rabi Hanna
- Department of Pediatrics, Hematology, Oncology, Blood and Marrow Transplantation, Cleveland Clinic, Cleveland, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antonio Hardan
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
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107
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Turk E, Endevelt-Shapira Y, Feldman R, van den Heuvel MI, Levy J. Brains in Sync: Practical Guideline for Parent-Infant EEG During Natural Interaction. Front Psychol 2022; 13:833112. [PMID: 35572249 PMCID: PMC9093685 DOI: 10.3389/fpsyg.2022.833112] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/05/2022] [Indexed: 12/14/2022] Open
Abstract
Parent-infant EEG is a novel hyperscanning paradigm to measure social interaction simultaneously in the brains of parents and infants. The number of studies using parent-infant dual-EEG as a theoretical framework to measure brain-to-brain synchrony during interaction is rapidly growing, while the methodology for measuring synchrony is not yet uniform. While adult dual-EEG methodology is quickly improving, open databases, tutorials, and methodological validations for dual-EEG with infants are largely missing. In this practical guide, we provide a step-by-step manual on how to implement and run parent-infant EEG paradigms in a neurodevelopmental laboratory in naturalistic settings (e.g., free interactions). Next, we highlight insights on the variety of choices that can be made during (pre)processing dual-EEG data, including recommendations on interpersonal neural coupling metrics and interpretations of the results. Moreover, we provide an exemplar dataset of two mother-infant dyads during free interactions ("free play") that may serve as practice material. Instead of providing a critical note, we would like to move the field of parent-infant EEG forward and be transparent about the challenges that come along with the exciting opportunity to study the development of our social brain within the naturalistic context of dual-EEG.
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Affiliation(s)
- Elise Turk
- Department of Cognitive Neuropsychology, Tilburg University, Tilburg, Netherlands
| | - Yaara Endevelt-Shapira
- Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya, Reichman University, Herzliya, Israel
| | - Ruth Feldman
- Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya, Reichman University, Herzliya, Israel
| | | | - Jonathan Levy
- Baruch Ivcher School of Psychology, Interdisciplinary Center Herzliya, Reichman University, Herzliya, Israel.,Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
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108
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de Souza RFL, Mendes TMAS, Lima LABDA, Brandão DS, Laplagne DA, de Sousa MBC. Effect of the Menstrual Cycle on Electroencephalogram Alpha and Beta Bands During Motor Imagery and Action Observation. Front Hum Neurosci 2022; 16:878887. [PMID: 35601901 PMCID: PMC9119141 DOI: 10.3389/fnhum.2022.878887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
Female sex steroids (FSS) can affect the motor system, modulating motor cortex excitability as well as performance in dexterity and coordination tasks. However, it has not yet been explored whether FSS affects the cognitive components of motor behavior. Mu is a sensorimotor rhythm observed by electroencephalography (EEG) in alpha (8–12 Hz) and beta (15–30 Hz) frequency bands in practices such as motor imagery (MI) and action observation (AO). This rhythm represents a window for studying the activity of neural circuits involved in motor cognition. Herein we investigated whether the alpha-mu and beta-mu power in the sensorimotor region (C3 and C4, hypothesis-driven approach) and the alpha and beta power over frontal, parietal, and occipital regions (data-driven approach) are modulated differently in the menstrual, follicular, and luteal phases of menstrual cycles in right-handed dominant women. To do so, these women underwent MI and AO in the three menstrual cycle phases. The spectral activity of the cortical regions for the alpha and beta bands were compared between phases of the menstrual cycle and a correlation analysis was also performed in relation to estrogen and progesterone levels. For the hypothesis-based approach, beta-mu event-related desynchronization (ERD) was significantly stronger in the C3 channel in the follicular phase than in the menstrual and luteal phases. For the data-driven approach, beta ERD during MI was higher in the follicular phase than in the menstrual and luteal phases in the frontal region. These findings suggest the effect of FSS on executive movement control. No effect of menstrual cycle phases was observed in cortical areas investigated during OA, but alpha and beta bands correlated positively with the follicular phase plasma estradiol level. Thus, the attenuation of alpha and beta bands referring to mirror neuron activities appears to be associated with inhibition of cortical activity when estradiol levels are lower, improving cognitive processing of motor action.
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Affiliation(s)
- Rafaela Faustino Lacerda de Souza
- Behavioral Endocrinology Laboratory, Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- *Correspondence: Rafaela Faustino Lacerda de Souza,
| | | | | | - Daniel Soares Brandão
- Electroencephalography Laboratory, Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Diego Andrés Laplagne
- Behavioral Neurophysiology, Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Maria Bernardete Cordeiro de Sousa
- Behavioral Endocrinology Laboratory, Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
- Graduate Program in Psychobiology, Federal University of Rio Grande do Norte, Rio Grande do Norte, Brazil
- Maria Bernardete Cordeiro de Sousa,
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109
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Koshiyama D, Miyakoshi M, Tanaka-Koshiyama K, Sprock J, Light GA. High-power gamma-related delta phase alteration in schizophrenia patients at rest. Psychiatry Clin Neurosci 2022; 76:179-186. [PMID: 35037330 DOI: 10.1111/pcn.13331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 12/12/2021] [Accepted: 01/03/2022] [Indexed: 11/30/2022]
Abstract
AIM Information processing is supported by the cortico-cortical transmission of neural oscillations across brain regions. Recent studies have demonstrated that the rhythmic firing of neural populations is not random but is governed by interactions with other frequency bands. Specifically, the amplitude of gamma-band oscillations is associated with the phase of lower frequency oscillations in support of short and long-range communications among networks. This cross-frequency relation is thought to reflect the temporal coordination of neural communication. While schizophrenia patients show abnormal oscillatory responses across multiple frequencies at rest, it is unclear whether the functional relationships among frequency bands are intact. This study aimed to characterize the lower frequency (delta/theta, 1-8 Hz) phase and the amplitude of gamma oscillations in healthy subjects and schizophrenia patients at rest. METHODS Low frequency-phase (delta- and theta- band) angles and gamma-band amplitude relationships were assessed in 142 schizophrenia patients and 128 healthy subjects. RESULTS Significant low-frequency phase alteration related to high-power gamma was detected across broadly distributed scalp regions in both healthy subjects and patients. In patients, delta phase synchronization related to high-power gamma was significantly decreased at the frontocentral, right middle temporal, and left temporoparietal electrodes but significantly increased at the left parietal electrode. CONCLUSIONS High-power gamma-related delta phase alteration may reflect a core pathophysiologic abnormality in schizophrenia. Data-driven measures of functional relationships among frequency bands may prove useful in the development of novel therapeutics. Future studies are needed to determine whether these alterations are specific to schizophrenia or appear in other neuropsychiatric patient populations.
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Affiliation(s)
- Daisuke Koshiyama
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, La Jolla, California, USA
| | | | - Joyce Sprock
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,VISN-22 Mental Illness, Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, California, USA
| | - Gregory A Light
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA.,VISN-22 Mental Illness, Research, Education and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, California, USA
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110
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Abu Hasan R, Yusoff MSB, Tang TB, Hafeez Y, Mustafa MC, Dzainudin M, Bacotang J, Al-Saggaf UM, Ali SSA. Resilience-Building for Mental Health among Early Childhood Educators: A Systematic Review and Pilot-Study towards an EEG-VR Resilience Building Intervention. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19074413. [PMID: 35410097 PMCID: PMC8998227 DOI: 10.3390/ijerph19074413] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 12/10/2022]
Abstract
Resilience is a key factor that reflects a teacher's ability to utilize their emotional resources and working skills to provide high-quality teaching to children. Resilience-building interventions aim to promote positive psychological functioning and well-being. However, there is lack of evidence on whether these interventions improve the well-being or mental health of teachers in early childhood education (ECE) settings. This review examined the overall effectiveness of resilience-building interventions conducted on teachers working in the ECE field. A systematic approach is used to identify relevant studies that focus on resilience-building in countering work stress among early childhood educators. Findings from this review observed a preference of group approaches and varying durations of interventions. This review highlights the challenges of the group approach which can lead to lengthy interventions and attrition amongst participants. In addition to the concerns regarding response bias from self-report questionnaires, there is also a lack of physiological measures used to evaluate effects on mental health. The large efforts by 11 studies to integrate multiple centres into their intervention and the centre-based assessment performed by four studies highlight the need for a centre-focused approach to build resilience among teachers from various ECE centres. A pilot study is conducted to evaluate the feasibility of an integrated electroencephalography-virtual reality (EEG-VR) approach in building resilience in teachers, where the frontal brain activity can be monitored during a virtual classroom task. Overall, the findings of this review propose the integration of physiological measures to monitor changes in mental health throughout the resilience-building intervention and the use of VR as a tool to design a unique virtual environment.
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Affiliation(s)
- Rumaisa Abu Hasan
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronics Engineering Department, University Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (R.A.H.); (T.B.T.); (Y.H.)
| | - Muhamad Saiful Bahri Yusoff
- Department of Medical Education, School of Medical Sciences, University Sains Malaysia, Kota Bharu 16150, Kelantan, Malaysia;
| | - Tong Boon Tang
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronics Engineering Department, University Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (R.A.H.); (T.B.T.); (Y.H.)
| | - Yasir Hafeez
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronics Engineering Department, University Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (R.A.H.); (T.B.T.); (Y.H.)
| | - Mazlina Che Mustafa
- National Child Development Research Centre, University Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia; (M.C.M.); (M.D.)
| | - Masayu Dzainudin
- National Child Development Research Centre, University Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia; (M.C.M.); (M.D.)
| | - Juppri Bacotang
- Faculty of Psychology and Education, University Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia;
| | - Ubaid M. Al-Saggaf
- Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Syed Saad Azhar Ali
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical and Electronics Engineering Department, University Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (R.A.H.); (T.B.T.); (Y.H.)
- Correspondence:
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111
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Auger E, Berry-Kravis EM, Ethridge LE. Independent evaluation of the harvard automated processing pipeline for Electroencephalography 1.0 using multi-site EEG data from children with Fragile X Syndrome. J Neurosci Methods 2022; 371:109501. [PMID: 35182604 PMCID: PMC8962770 DOI: 10.1016/j.jneumeth.2022.109501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 11/17/2022]
Abstract
BACKGROUND The Harvard Automatic Processing Pipeline for Electroencephalography (HAPPE) is a computerized EEG data processing pipeline designed for multiple site analysis of populations with neurodevelopmental disorders. This pipeline has been validated in-house by the developers but external testing using real-world datasets remains to be done. NEW METHOD Resting and auditory event-related EEG data from 29 children ages 3-6 years with Fragile X Syndrome as well as simulated EEG data was used to evaluate HAPPE's noise reduction techniques, data standardization features, and data integration compared to traditional manualized processing. RESULTS For the real EEG data, HAPPE pipeline showed greater trials retained, greater variance retained through independent component analysis (ICA) component removal, and smaller kurtosis than the manual pipeline; the manual pipeline had a significantly larger signal-to-noise ratio (SNR). For simulated EEG data, correlation between the pure signal and processed data was significantly higher for manually-processed data compared to HAPPE-processed data. Hierarchical linear modeling showed greater signal recovery in the manual pipeline with the exception of the gamma band signal which showed mixed results. COMPARISON WITH EXISTING METHODS SNR and simulated signal retention was significantly greater in the manually-processed data than the HAPPE-processed data. Signal reduction may negatively affect outcome measures. CONCLUSIONS The HAPPE pipeline benefits from less active processing time and artifact reduction without removing segments. However, HAPPE may bias toward elimination of noise at the cost of signal. Recommended implementation of the HAPPE pipeline for neurodevelopmental populations depends on the goals and priorities of the research.
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Affiliation(s)
- Emma Auger
- Department of Psychology, University of Oklahoma, Norman, OK 73019-2007, USA
| | - Elizabeth M Berry-Kravis
- Department of Pediatrics, Neurological Sciences, and Biochemistry, Rush University Medical Center, Chicago, IL 60612, USA
| | - Lauren E Ethridge
- Department of Psychology, University of Oklahoma, Norman, OK 73019-2007, USA; Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.
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112
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Conte S, Richards JE. Cortical Source Analysis of Event-Related Potentials: A Developmental Approach. Dev Cogn Neurosci 2022; 54:101092. [PMID: 35231872 PMCID: PMC8885610 DOI: 10.1016/j.dcn.2022.101092] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 02/16/2022] [Accepted: 02/22/2022] [Indexed: 11/03/2022] Open
Abstract
Cortical source analysis of electroencephalographic (EEG) signals has become an important tool in the analysis of brain activity. The aim of source analysis is to reconstruct the cortical generators (sources) of the EEG signal recorded on the scalp. The quality of the source reconstruction relies on the accuracy of the forward problem, and consequently the inverse problem. An accurate forward solution is obtained when an appropriate imaging modality (i.e., structural magnetic resonance imaging - MRI) is used to describe the head geometry, precise electrode locations are identified with 3D maps of the sensor positions on the scalp, and realistic conductivity values are determined for each tissue type of the head model. Together these parameters contribute to the definition of realistic head models. Here, we describe the steps necessary to reconstruct the cortical generators of the EEG signal recorded on the scalp. We provide an example of source reconstruction of event-related potentials (ERPs) during a face-processing task performed by a 6-month-old infant. We discuss the adjustments necessary to perform source analysis with measures different from the ERPs. The proposed pipeline can be applied to the investigation of different cognitive tasks in both younger and older participants.
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113
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Bick J, Lipschutz R, Tabachnick A, Biekman B, Katz D, Simons R, Dozier M. Timing of adoption is associated with electrophysiological brain activity and externalizing problems among children adopted internationally. Dev Psychobiol 2022; 64:e22249. [PMID: 35452537 PMCID: PMC9038029 DOI: 10.1002/dev.22249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 11/15/2021] [Accepted: 01/03/2022] [Indexed: 11/07/2022]
Abstract
This study investigated middle childhood resting electroencephalography (EEG) and behavioral adjustment in 35 internationally adopted children removed from early caregiving adversity between 6 and 29 months of age. Older age of adoption was associated with more immature or atypical profiles of middle childhood cortical function, based on higher relative theta power (4-6 Hz), lower relative alpha power (7-12 Hz), lower peak alpha frequency, and lower absolute beta (13-20 Hz) and gamma (21-50 Hz) power. More immature or atypical EEG spectral power indirectly linked older age of adoption with increased risk for externalizing problems in middle childhood. The findings add to existing evidence linking duration of early adverse exposures with lasting effects on brain function and behavioral regulation even years after living in a stable adoptive family setting. Findings underscore the need to minimize and prevent children's exposures to early caregiving adversity, especially in the first years of life. They call for innovative interventions to support neurotypical development in internationally adopted children at elevated risk.
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Affiliation(s)
- Johanna Bick
- Department of Psychology University of Houston Houston Texas USA
| | | | - Alexandra Tabachnick
- Department of Psychological & Brain Sciences University of Delaware Newark Delaware USA
| | - Brian Biekman
- Department of Psychology University of Houston Houston Texas USA
| | - Danielle Katz
- Department of Psychological & Brain Sciences University of Delaware Newark Delaware USA
| | - Robert Simons
- Department of Psychological & Brain Sciences University of Delaware Newark Delaware USA
| | - Mary Dozier
- Department of Psychological & Brain Sciences University of Delaware Newark Delaware USA
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114
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Hámori G, Rádosi A, Pászthy B, Réthelyi JM, Ulbert I, Fiáth R, Bunford N. Reliability of reward ERPs in middle-late adolescents using a custom and a standardized preprocessing pipeline. Psychophysiology 2022; 59:e14043. [PMID: 35298041 PMCID: PMC9541384 DOI: 10.1111/psyp.14043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 01/26/2022] [Accepted: 02/19/2022] [Indexed: 11/30/2022]
Abstract
Despite advantage of neuroimaging measures in translational research frameworks, less is known about the psychometric properties thereof, especially in middle-late adolescents. Earlier, we examined evidence of convergent and incremental validity of reward anticipation and response event-related potentials (ERPs) and here we examined, in the same sample of 43 adolescents (Mage = 15.67 years; SD = 1.01; range: 14-18; 32.6% boys), data quality (signal-to-noise ratio [SNR]), stability (mean amplitude across trials), and internal consistency (Cronbach's α and split-half reliability) of the same ERPs. Further, because observed time course and peak amplitude of ERP grand averages and thus findings on SNR, stability, and internal consistency may depend on preprocessing method, we employed a custom and a standardized preprocessing pipeline and compared findings across those. Using our custom pipeline, reward anticipation components were stable by the 40th trial, achieved acceptable internal consistency by the 19th, and all (but the stimulus-preceding negativity [SPN]) achieved acceptable SNR by the 41st trial. Initial response to reward components were stable by the 20th trial and achieved acceptable internal consistency by the 11th and acceptable SNR by the 45th trial. Difference scores had worse psychometric properties than parent measures. Time course and peak amplitudes of ERPs and thus results on SNR, stability, and internal consistency were comparable across preprocessing pipelines. In case of reward anticipation ERPs examined here, 41 trials (+4 artifacted and removed) and, in case of reward response ERPs, 45 trials (+5 artifacted) yielded stable and internally consistent estimates with acceptable SNR. Results are robust across preprocessing methods.
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Affiliation(s)
- György Hámori
- Developmental and Translational Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.,Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics, Budapest, Hungary
| | - Alexandra Rádosi
- Developmental and Translational Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.,Doctoral School of Mental Health Sciences, Semmelweis University, Budapest, Hungary
| | - Bea Pászthy
- 1st Department of Paediatrics, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - János M Réthelyi
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - István Ulbert
- Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Richárd Fiáth
- Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Nóra Bunford
- Developmental and Translational Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
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115
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Cohen AL. Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments. J Neurodev Disord 2022; 14:19. [PMID: 35279095 PMCID: PMC8918299 DOI: 10.1186/s11689-022-09433-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/03/2022] [Indexed: 11/20/2022] Open
Abstract
A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.
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Affiliation(s)
- Alexander Li Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA. .,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Laboratory for Brain Network Imaging and Modulation, Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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116
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Klug M, Kloosterman NA. Zapline‐plus: A Zapline extension for automatic and adaptive removal of frequency‐specific noise artifacts in M/
EEG. Hum Brain Mapp 2022; 43:2743-2758. [PMID: 35278015 PMCID: PMC9120550 DOI: 10.1002/hbm.25832] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/17/2022] [Accepted: 03/01/2022] [Indexed: 11/29/2022] Open
Abstract
Removing power line noise and other frequency‐specific artifacts from electrophysiological data without affecting neural signals remains a challenging task. Recently, an approach was introduced that combines spectral and spatial filtering to effectively remove line noise: Zapline. This algorithm, however, requires manual selection of the noise frequency and the number of spatial components to remove during spatial filtering. Moreover, it assumes that noise frequency and spatial topography are stable over time, which is often not warranted. To overcome these issues, we introduce Zapline‐plus, which allows adaptive and automatic removal of frequency‐specific noise artifacts from M/electroencephalography (EEG) and LFP data. To achieve this, our extension first segments the data into periods (chunks) in which the noise is spatially stable. Then, for each chunk, it searches for peaks in the power spectrum, and finally applies Zapline. The exact noise frequency around the found target frequency is also determined separately for every chunk to allow fluctuations of the peak noise frequency over time. The number of to‐be‐removed components by Zapline is automatically determined using an outlier detection algorithm. Finally, the frequency spectrum after cleaning is analyzed for suboptimal cleaning, and parameters are adapted accordingly if necessary before re‐running the process. The software creates a detailed plot for monitoring the cleaning. We highlight the efficacy of the different features of our algorithm by applying it to four openly available data sets, two EEG sets containing both stationary and mobile task conditions, and two magnetoencephalography sets containing strong line noise.
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Affiliation(s)
- Marius Klug
- Biopsychology and Neuroergonomics, Institute of Psychology and Ergonomics Technische Universität Berlin Berlin Germany
| | - Niels A. Kloosterman
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research Berlin Germany
- Center for Lifespan Psychology Max Planck Institute for Human Development Berlin Germany
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117
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DEEP: A dual EEG pipeline for developmental hyperscanning studies. Dev Cogn Neurosci 2022; 54:101104. [PMID: 35367895 PMCID: PMC8980555 DOI: 10.1016/j.dcn.2022.101104] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 01/31/2022] [Accepted: 03/25/2022] [Indexed: 12/21/2022] Open
Abstract
Cutting-edge hyperscanning methods led to a paradigm shift in social neuroscience. It allowed researchers to measure dynamic mutual alignment of neural processes between two or more individuals in naturalistic contexts. The ever-growing interest in hyperscanning research calls for the development of transparent and validated data analysis methods to further advance the field. We have developed and tested a dual electroencephalography (EEG) analysis pipeline, namely DEEP. Following the preprocessing of the data, DEEP allows users to calculate Phase Locking Values (PLVs) and cross-frequency PLVs as indices of inter-brain phase alignment of dyads as well as time-frequency responses and EEG power for each participant. The pipeline also includes scripts to control for spurious correlations. Our goal is to contribute to open and reproducible science practices by making DEEP publicly available together with an example mother-infant EEG hyperscanning dataset.
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118
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Norton ES, Manning BL, Harriott EM, Nikolaeva JI, Nyabingi OS, Fredian KM, Page JM, McWeeny S, Krogh-Jespersen S, MacNeill LA, Roberts MY, Wakschlag LS. Social EEG: A novel neurodevelopmental approach to studying brain-behavior links and brain-to-brain synchrony during naturalistic toddler-parent interactions. Dev Psychobiol 2022; 64:e22240. [PMID: 35312062 PMCID: PMC9867891 DOI: 10.1002/dev.22240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 10/26/2021] [Accepted: 10/29/2021] [Indexed: 01/26/2023]
Abstract
Despite increasing emphasis on emergent brain-behavior patterns supporting language, cognitive, and socioemotional development in toddlerhood, methodologic challenges impede their characterization. Toddlers are notoriously difficult to engage in brain research, leaving a developmental window in which neural processes are understudied. Further, electroencephalography (EEG) and event-related potential paradigms at this age typically employ structured, experimental tasks that rarely reflect formative naturalistic interactions with caregivers. Here, we introduce and provide proof of concept for a new "Social EEG" paradigm, in which parent-toddler dyads interact naturally during EEG recording. Parents and toddlers sit at a table together and engage in different activities, such as book sharing or watching a movie. EEG is time locked to the video recording of their interaction. Offline, behavioral data are microcoded with mutually exclusive engagement state codes. From 216 sessions to date with 2- and 3-year-old toddlers and their parents, 72% of dyads successfully completed the full Social EEG paradigm, suggesting that it is possible to collect dual EEG from parents and toddlers during naturalistic interactions. In addition to providing naturalistic information about child neural development within the caregiving context, this paradigm holds promise for examination of emerging constructs such as brain-to-brain synchrony in parents and children.
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Affiliation(s)
- Elizabeth S. Norton
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
- Department of Medical Social Sciences and Institute for Innovations in Developmental Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Brittany L. Manning
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Emily M. Harriott
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Julia I. Nikolaeva
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Olufemi S. Nyabingi
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Kaitlyn M. Fredian
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Jessica M. Page
- Department of Medical Social Sciences and Institute for Innovations in Developmental Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Sean McWeeny
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
| | - Sheila Krogh-Jespersen
- Department of Medical Social Sciences and Institute for Innovations in Developmental Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Leigha A. MacNeill
- Department of Medical Social Sciences and Institute for Innovations in Developmental Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Megan Y. Roberts
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois, USA
- Department of Medical Social Sciences and Institute for Innovations in Developmental Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Lauren S. Wakschlag
- Department of Medical Social Sciences and Institute for Innovations in Developmental Sciences, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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119
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Lalgudi Ganesan S, Hahn CD. Spectrograms for Seizure Detection in Critically Ill Children. J Clin Neurophysiol 2022; 39:195-206. [PMID: 34510096 DOI: 10.1097/wnp.0000000000000868] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
SUMMARY Electrographic seizures are common in critically ill children and a significant proportion of these seizures are nonconvulsive. There is an association between electrographic seizures and neurophysiological disturbances, worse short- and long-term neurologic outcomes, and mortality in critically ill patients. In this context, timely diagnosis and treatment of electrographic seizures in critically ill children becomes important. However, most institutions lack the resources to support round-the-clock or frequent review of continuous EEG recordings causing significant delays in seizure diagnosis. Given the current gaps in review of continuous EEG across institutions globally, use of visually simplified, time-compressed quantitative EEG trends such as spectrograms has the potential to enhance timeliness of seizure diagnosis and treatment in critically ill children.
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Affiliation(s)
- Saptharishi Lalgudi Ganesan
- Paediatric Critical Care Medicine, Children's Hospital of Western Ontario, London Health Sciences Centre, London, ON, Canada
- Department of Paediatrics, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada
- Children's Health Research Institute, London, ON, Canada
| | - Cecil D Hahn
- Division of Paediatric Neurology, Department of Paediatrics, The Hospital for Sick Children, Toronto, ON, Canada; and
- Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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120
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Candia-Rivera D, Catrambone V, Barbieri R, Valenza G. Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: a brain-heart study on thermal stress. Neuroimage 2022; 251:119023. [PMID: 35217203 DOI: 10.1016/j.neuroimage.2022.119023] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 12/12/2022] Open
Abstract
The study of functional brain-heart interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control.
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Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy.
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, 20133, Milano, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
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121
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The Functional Interactions between Cortical Regions through Theta-Gamma Coupling during Resting-State and a Visual Working Memory Task. Brain Sci 2022; 12:brainsci12020274. [PMID: 35204038 PMCID: PMC8869925 DOI: 10.3390/brainsci12020274] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
Theta phase-gamma amplitude coupling (TGC) plays an important role in several different cognitive processes. Although spontaneous brain activity at the resting state is crucial in preparing for cognitive performance, the functional role of resting-state TGC remains unclear. To investigate the role of resting-state TGC, electroencephalogram recordings were obtained for 56 healthy volunteers while they were in the resting state, with their eyes closed, and then when they were engaged in a retention interval period in the visual memory task. The TGCs of the two different conditions were calculated and compared. The results indicated that the modulation index of TGC during the retention interval of the visual working memory (VWM) task was not higher than that during the resting state; however, the topographical distribution of TGC during the resting state was negatively correlated with TGC during VWM task at the local level. The topographical distribution of TGC during the resting state was negatively correlated with TGC coordinates’ engagement of brain areas in local and large-scale networks and during task performance at the local level. These findings support the view that TGC reflects information-processing and signal interaction across distant brain areas. These results demonstrate that TGC could explain the efficiency of competing brain networks.
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122
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McLain NJ, Yani MS, Kutch JJ. Analytic consistency and neural correlates of peak alpha frequency in the study of pain. J Neurosci Methods 2022; 368:109460. [PMID: 34958820 PMCID: PMC9236562 DOI: 10.1016/j.jneumeth.2021.109460] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 12/10/2021] [Accepted: 12/21/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Several studies have found evidence of reduced resting-state peak alpha frequency (PAF) in populations with pain. However, the stability of PAF from different analytic pipelines used to study pain has not been determined and underlying neural correlates of PAF have not been validated in humans. NEW METHOD For the first time we compare analytic pipelines and the relationship of PAF to activity in the whole brain and thalamus, a hypothesized generator of PAF. We collected resting-state functional magnetic resonance imaging (rs-fMRI) data and subsequently 64 channel resting-state electroencephalographic (EEG) from 47 healthy men, controls from an ongoing study of chronic prostatitis (a pain condition affecting men). We identified important variations in EEG processing for PAF from a review of 17 papers investigating the relationship between pain and PAF. We tested three progressively complex pre-processing pipelines and varied four postprocessing variables (epoch length, alpha band, calculation method, and region-of-interest [ROI]) that were inconsistent across the literature. RESULTS We found a single principal component, well-represented by the average PAF across all electrodes (grand-average PAF), explained > 95% of the variance across participants. We also found the grand-average PAF was highly correlated among the pre-processing pipelines and primarily impacted by calculation method and ROI. Across methods, interindividual differences in PAF were correlated with rs-fMRI-estimated activity in the thalamus, insula, cingulate, and sensory cortices. CONCLUSIONS These results suggest PAF is a relatively stable marker with respect to common pre and post-processing methods used in pain research and reflects interindividual differences in thalamic and salience network function.
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Affiliation(s)
| | | | - Jason J. Kutch
- Correspondence to: University of Southern California, 1540 E. Alcazar Street, CHP 155, Los Angeles, CA 90033, USA. (J.J. Kutch)
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123
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Natural Infant-Directed Speech Facilitates Neural Tracking of Prosody. Neuroimage 2022; 251:118991. [PMID: 35158023 DOI: 10.1016/j.neuroimage.2022.118991] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 02/02/2022] [Accepted: 02/10/2022] [Indexed: 01/04/2023] Open
Abstract
Infants prefer to be addressed with infant-directed speech (IDS). IDS benefits language acquisition through amplified low-frequency amplitude modulations. It has been reported that this amplification increases electrophysiological tracking of IDS compared to adult-directed speech (ADS). It is still unknown which particular frequency band triggers this effect. Here, we compare tracking at the rates of syllables and prosodic stress, which are both critical to word segmentation and recognition. In mother-infant dyads (n=30), mothers described novel objects to their 9-month-olds while infants' EEG was recorded. For IDS, mothers were instructed to speak to their children as they typically do, while for ADS, mothers described the objects as if speaking with an adult. Phonetic analyses confirmed that pitch features were more prototypically infant-directed in the IDS-condition compared to the ADS-condition. Neural tracking of speech was assessed by speech-brain coherence, which measures the synchronization between speech envelope and EEG. Results revealed significant speech-brain coherence at both syllabic and prosodic stress rates, indicating that infants track speech in IDS and ADS at both rates. We found significantly higher speech-brain coherence for IDS compared to ADS in the prosodic stress rate but not the syllabic rate. This indicates that the IDS benefit arises primarily from enhanced prosodic stress. Thus, neural tracking is sensitive to parents' speech adaptations during natural interactions, possibly facilitating higher-level inferential processes such as word segmentation from continuous speech.
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124
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Elansary M, Pierce LJ, Wei WS, McCoy DC, Zuckerman B, Nelson CA. Maternal Stress and Early Neurodevelopment: Exploring the Protective Role of Maternal Growth Mindset. J Dev Behav Pediatr 2022; 43:e103-e109. [PMID: 34456304 DOI: 10.1097/dbp.0000000000000998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 07/02/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The purpose of this study was to test associations between maternal stress, maternal mindset, and infant neurodevelopment at 12 months of age. Specifically, we sought to examine the extent to which maternal growth mindsets may serve to attenuate the negative associations between maternal stress and infants' neurodevelopment. METHODS The current exploratory study leverages data from a longitudinal cohort study following mother-infant dyads. Maternal-perceived stress, maternal mindset, and infant electroencephalography (EEG) recordings were collected when infants were 12 months of age. The final analytic sample included 33 dyads. RESULTS Results revealed no statistically significant main effects of maternal stress or maternal mindset for any of the infant EEG frequency band outcomes. After including interactions between maternal stress and mindset, statistically significant positive interactions were detected for all EEG frequency bands. Simple slope tests revealed significant negative associations between maternal stress and each of the 6 EEG frequency bands for mothers with more fixed-oriented mindsets. Associations between maternal stress and infant EEG outcomes for mothers with more growth-oriented mindsets did not differ from 0. CONCLUSION These findings suggest that infants raised by mothers with growth mindsets may be protected against the neurodevelopmental consequences of higher maternal stress.
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Affiliation(s)
- Mei Elansary
- Department of Pediatrics, Boston University School of Medicine, Boston Medical Center, Boston, MA. This work was completed when Dr. Elansary was a member of the Division of Developmental Medicine at Boston Children's Hospital
| | - Lara J Pierce
- Division of Developmental Medicine, Boston Children's Hospital and Harvard Medical School, Harvard University, Boston, MA
| | - Wendy S Wei
- Harvard Graduate School of Education, Cambridge, MA
| | | | - Barry Zuckerman
- Department of Pediatrics, Boston University School of Medicine, Boston Medical Center, Boston, MA. This work was completed when Dr. Elansary was a member of the Division of Developmental Medicine at Boston Children's Hospital
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital and Harvard Medical School, Harvard University, Boston, MA
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Abstract
SUMMARY The field of clinical EEG has had an uneasy relationship with the use of this technology for clinical cognitive applications and often for good reason. However, apart from its clinical use, EEG has had a tradition as a major tool in cognitive psychology and cognitive neuroscience dating back at least to the 1960s. Based on accumulated knowledge from its research application, EEG-based biomarkers are beginning to see applications in clinical trials and may eventually enter clinical care. We address concerns surrounding quality control, the treatment of artifact, and normal variants and how developments in engineering, biomarker validation, and implementation science rigorously applied to these tools can lead to well-justified approaches.
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Affiliation(s)
- Joshua B. Ewen
- Kennedy Krieger Institute, Baltimore, MD
- Johns Hopkins University, Baltimore, MD
| | - April R. Levin
- Harvard Medical School, Boston, MA
- Boston Children’s Hospital, Boston, MA
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126
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Prado P, Birba A, Cruzat J, Santamaría-García H, Parra M, Moguilner S, Tagliazucchi E, Ibáñez A. Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration. Int J Psychophysiol 2022; 172:24-38. [PMID: 34968581 PMCID: PMC9887537 DOI: 10.1016/j.ijpsycho.2021.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 12/19/2021] [Indexed: 02/02/2023]
Abstract
The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Josefina Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Departamento de Física, Universidad de Buenos Aires and Instituto de Fisica de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland,Corresponding author at: Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile., (A. Ibáñez)
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127
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Associations between Heart Rate Variability and Brain Activity during a Working Memory Task: A Preliminary Electroencephalogram Study on Depression and Anxiety Disorder. Brain Sci 2022; 12:brainsci12020172. [PMID: 35203935 PMCID: PMC8870686 DOI: 10.3390/brainsci12020172] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 01/02/2023] Open
Abstract
Heart rate variability (HRV) has been suggested to reflect executive function and related neural activity. Executive dysfunction has been suggested to play an important role in the pathophysiology of emotional disorders. The purpose of this study was to investigate whether HRV showed a significant correlation with electroencephalogram (EEG) during a working memory performance in patients with depressive or anxiety disorder. A retrospective analysis was conducted with data from 61 patients with depressive disorder (43 women and 18 men) and 59 patients with anxiety disorder (35 women and 24 men). HRV was measured in the resting state, and EEG was recorded in the resting state and during the execution of a working memory task. It was performed in patients with depressive and anxiety disorder, and the paired sample t-test between resting state and task performance, as well as the partial correlation analysis between HRV and EEG, was conducted. Both depressed and anxious patients showed weaker beta relative power during the working memory task compared to the rest period. The resting-state EEG did not correlate with HRV parameters in both groups. In depressed patients, HRV showed a positive correlation with delta power during the task and a negative correlation with beta relative power during the task. In patients with anxiety disorder, HRV showed a significant positive correlation with theta power of the right frontal region during the task. Our results suggest that HRV would be related to executive-function-related neural activity in patients with depressive or anxiety disorder. Future studies with more subjects, including healthy controls, are needed to verify the correlation between HRV and EEG and to come up with a more comprehensive picture of neurobiological changes in emotional disorders.
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128
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Glauser J, Wilkinson CL, Gabard-Durnam LJ, Choi B, Tager-Flusberg H, Nelson CA. Neural correlates of face processing associated with development of social communication in 12-month infants with familial risk of autism spectrum disorder. J Neurodev Disord 2022; 14:6. [PMID: 35021990 PMCID: PMC8903527 DOI: 10.1186/s11689-021-09413-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/21/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Differences in face processing in individuals with ASD is hypothesized to impact the development of social communication skills. This study aimed to characterize the neural correlates of face processing in 12-month-old infants at familial risk of developing ASD by (1) comparing face-sensitive event-related potentials (ERP) (Nc, N290, P400) between high-familial-risk infants who develop ASD (HR-ASD), high-familial-risk infants without ASD (HR-NoASD), and low-familial-risk infants (LR), and (2) evaluating how face-sensitive ERP components are associated with development of social communication skills. METHODS 12-month-old infants participated in a study in which they were presented with alternating images of their mother's face and the face of a stranger (LR = 45, HR-NoASD = 41, HR-ASD = 24) as EEG data were collected. Parent-reported and laboratory-observed social communication measures were obtained at 12 and 18 months. Group differences in ERP responses were evaluated using ANOVA, and multiple linear regressions were conducted with maternal education and outcome groups as covariates to assess relationships between ERP and behavioral measures. RESULTS For each of the ERP components (Nc [negative-central], N290, and P400), the amplitude difference between mother and stranger (Mother-Stranger) trials was not statistically different between the three outcome groups (Nc p = 0.72, N290 p = 0.88, P400 p = 0.91). Marginal effects analyses found that within the LR group, a greater Nc Mother-Stranger response was associated with better expressive language skills on the Mullen Scales of Early Learning, controlling for maternal education and outcome group effects (marginal effects dy/dx = 1.15; p < 0.01). No significant associations were observed between the Nc and language or social measures in HR-NoASD or HR-ASD groups. In contrast, specific to the HR-ASD group, amplitude difference between the Mother versus Stranger P400 response was positively associated with expressive (dy/dx = 2.1, p < 0.001) and receptive language skills at 12 months (dy/dx = 1.68, p < 0.005), and negatively associated with social affect scores on the Autism Diagnostic Observation Schedule (dy/dx = - 1.22, p < 0.001) at 18 months. CONCLUSIONS In 12-month-old infant siblings with subsequent ASD, increased P400 response to Mother over Stranger faces is positively associated with concurrent language and future social skills.
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Affiliation(s)
- Joshua Glauser
- Department of Neuroscience, Harvard University, Boston, MA, 02138, USA.,Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Carol L Wilkinson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA. .,Labs of Cognitive Neuroscience, 2 Brookline Place, Brookline, MA, 02445, USA.
| | | | - Boin Choi
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Helen Tager-Flusberg
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.,Harvard Graduate School of Education, Cambridge, MA, 02138, USA
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129
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Fabietti M, Mahmud M, Lotfi A. Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning. Brain Inform 2022; 9:1. [PMID: 34997378 PMCID: PMC8741911 DOI: 10.1186/s40708-021-00149-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/23/2021] [Indexed: 11/13/2022] Open
Abstract
Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the majority of the recording system. Addressing this issue, this paper reports a novel channel-independent machine learning model to accurately identify and replace the artefactual segments present in the signals. Discarding these artifactual segments by the existing approaches causes discontinuities in the reproduced signals which may introduce errors in subsequent analyses. To avoid this, the proposed method predicts multiple values of the artefactual region using long–short term memory network to recreate the temporal and spectral properties of the recorded signal. The method has been tested on two open-access data sets and incorporated into the open-access SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) toolbox for community use.
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Affiliation(s)
- Marcos Fabietti
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK. .,Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK. .,Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK.
| | - Ahmad Lotfi
- Department of Computer Science, Nottingham Trent University, Clifton Lane, NG11 8NS, Nottingham, UK
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130
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Arnett AB, Rutter TM, Stein MA. Neural Markers of Methylphenidate Response in Children With Attention Deficit Hyperactivity Disorder. Front Behav Neurosci 2022; 16:887622. [PMID: 35600991 PMCID: PMC9121006 DOI: 10.3389/fnbeh.2022.887622] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 04/05/2022] [Indexed: 01/09/2023] Open
Abstract
Background Despite widespread use of stimulants to treat ADHD, individual responses vary considerably and few predictors of response have been identified. The identification of reliable and clinically feasible biomarkers would facilitate a precision medicine approach to pharmacological treatment of ADHD. We test the hypothesis that two electroencephalography (EEG) based neural signatures of ADHD, resting aperiodic slope exponent and novelty P3 amplitude, are markers of methylphenidate response in children. We hypothesize that positive response to methylphenidate treatment will be associated with greater abnormality of both neural markers. Methods Twenty-nine 7-11 year-old children with ADHD and a history of methylphenidate treatment, and 30 controls completed resting EEG and visual oddball event related potential (ERP) paradigms. ADHD participants were characterized as methylphenidate responders (n = 16) or non-responders (n = 13) using the clinical global improvement (CGI-I) scale during blinded retrospective interview. All participants abstained from prescribed medications for at least 48 hours prior to the EEG. Results As expected, methylphenidate responders (CGI-I rating < 3) demonstrated attenuated P3 amplitude relative to controls. Unexpectedly, methylphenidate non-responders showed atypically flat aperiodic spectral slope relative to controls, while responders did not differ on this measure. Conclusion ADHD symptoms associated with atypical patterns of intrinsic neural activity may be less responsive to methylphenidate. In contrast, ADHD symptoms associated with abnormal frontal-striatal neural network excitation may be correctable with methylphenidate. Altogether, EEG is a feasible and promising candidate methodology for identifying biomarkers of stimulant response.
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Affiliation(s)
- Anne B Arnett
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Cambridge, MA, United States
| | - Tara M Rutter
- Department of Psychology, Seattle Pacific University, Seattle, WA, United States
| | - Mark A Stein
- Department of Psychiatry & Behavioral Medicine, Seattle Children's Hospital, Seattle, WA, United States.,Department of Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, United States
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131
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Edmunds SR, Fogler J, Braverman Y, Gilbert R, Faja S. Resting frontal alpha asymmetry as a predictor of executive and affective functioning in children with neurodevelopmental differences. Front Psychol 2022; 13:1065598. [PMID: 36710763 PMCID: PMC9880425 DOI: 10.3389/fpsyg.2022.1065598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 12/21/2022] [Indexed: 01/15/2023] Open
Abstract
The relative difference of resting EEG frontal alpha activation between left and right hemispheres (FAA; i.e., asymmetry) correlates with global approach and avoidance tendencies. FAA may relate to problems with executive and affective functioning in children with neurodevelopmental differences, including autism and ADHD. We (1) characterize relative left vs. right FAA in autistic, ADHD, and neurotypical children (NT) and (2) investigate whether FAA predicts "hot" executive function or emotion dysregulation. Participants were 97 7- to 11-year-old autistic, ADHD, and NT Children. Children with ADHD displayed greater left (relative to right) FAA compared to autistic and neurotypical children. Children with ADHD displayed greater challenges with "hot" EF on a gambling task than autistic children, whereas children with co-occurring autism and ADHD had greater parent-reported emotion dysregulation than NT and autism-only groups. Greater left FAA predicted worse hot EF for all children but was not significantly related to emotion dysregulation. Regardless of clinical diagnosis, relatively greater left FAA relates to hot EF. While hot EF deficits may be specific to ADHD rather than autism, both together confer additive risk for emotion dysregulation. Future research should explore the functional relation between FAA, reward processing, and affect for children with different EF-related neurodevelopmental differences.
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Affiliation(s)
- Sarah R Edmunds
- Department of Psychology, University of South Carolina, Columbia, SC, United States.,Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Jason Fogler
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States.,Departments of Pediatrics & Psychiatry and Behavioral Sciences, Harvard Medical School, Boston, MA, United States.,Leadership Education in Neurodevelopmental & Related Disabilities/Institute for Community Inclusion, Boston, MA, United States
| | - Yael Braverman
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Rachel Gilbert
- Department of Pediatrics, Johns Hopkins University, Baltimore, MD, United States
| | - Susan Faja
- Division of Developmental Medicine, Boston Children's Hospital, Boston, MA, United States.,Departments of Pediatrics & Psychiatry and Behavioral Sciences, Harvard Medical School, Boston, MA, United States
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132
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Kumaravel VP, Farella E, Parise E, Buiatti M. NEAR: An artifact removal pipeline for human newborn EEG data. Dev Cogn Neurosci 2022; 54:101068. [PMID: 35085870 PMCID: PMC8800139 DOI: 10.1016/j.dcn.2022.101068] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 11/15/2021] [Accepted: 01/13/2022] [Indexed: 12/17/2022] Open
Abstract
Electroencephalography (EEG) is arising as a valuable method to investigate neurocognitive functions shortly after birth. However, obtaining high-quality EEG data from human newborn recordings is challenging. Compared to adults and older infants, datasets are typically much shorter due to newborns’ limited attentional span and much noisier due to non-stereotyped artifacts mainly caused by uncontrollable movements. We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns. NEAR is based on two key steps: 1) A novel bad channel detection tool based on the Local Outlier Factor (LOF), a robust outlier detection algorithm; 2) A parameter calibration procedure for adapting to newborn EEG data the algorithm Artifacts Subspace Reconstruction (ASR), developed for artifact removal in mobile adult EEG. Tests on simulated data showed that NEAR outperforms existing methods in removing representative newborn non-stereotypical artifacts. NEAR was validated on two developmental populations (newborns and 9-month-old infants) recorded with two different experimental designs (frequency-tagging and ERP). Results show that NEAR artifact removal successfully reproduces established EEG responses from noisy datasets, with a higher statistical significance than the one obtained by existing artifact removal methods. The EEGLAB-based NEAR pipeline is freely available at https://github.com/vpKumaravel/NEAR.
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133
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Fló A, Gennari G, Benjamin L, Dehaene-Lambertz G. Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental cognitive studies. Dev Cogn Neurosci 2022; 54:101077. [PMID: 35093730 PMCID: PMC8804179 DOI: 10.1016/j.dcn.2022.101077] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 01/23/2022] [Accepted: 01/24/2022] [Indexed: 01/01/2023] Open
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Jia W, von Wegner F, Zhao M, Zeng Y. Network oscillations imply the highest cognitive workload and lowest cognitive control during idea generation in open-ended creation tasks. Sci Rep 2021; 11:24277. [PMID: 34930950 PMCID: PMC8688505 DOI: 10.1038/s41598-021-03577-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 12/06/2021] [Indexed: 11/09/2022] Open
Abstract
Design is a ubiquitous, complex, and open-ended creation behaviour that triggers creativity. The brain dynamics underlying design is unclear, since a design process consists of many basic cognitive behaviours, such as problem understanding, idea generation, idea analysis, idea evaluation, and idea evolution. In this present study, we simulated the design process in a loosely controlled setting, aiming to quantify the design-related cognitive workload and control, identify EEG-defined large-scale brain networks, and uncover their temporal dynamics. The effectiveness of this loosely controlled setting was tested through comparing the results with validated findings available in the literature. Task-related power (TRP) analysis of delta, theta, alpha and beta frequency bands revealed that idea generation was associated with the highest cognitive workload and lowest cognitive control, compared to other design activities in the experiment, including problem understanding, idea evaluation, and self-rating. EEG microstate analysis supported this finding as microstate class C, being negatively associated with the cognitive control network, was the most prevalent in idea generation. Furthermore, EEG microstate sequence analysis demonstrated that idea generation was consistently associated with the shortest temporal correlation times concerning finite entropy rate, autoinformation function, and Hurst exponent. This finding suggests that during idea generation the interplay of functional brain networks is less restricted and the brain has more degrees of freedom in choosing the next network configuration than during other design activities. Taken together, the TRP and EEG microstate results lead to the conclusion that idea generation is associated with the highest cognitive workload and lowest cognitive control during open-ended creation task.
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Affiliation(s)
- Wenjun Jia
- Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, H3G 2W1, Canada
| | - Frederic von Wegner
- School of Medical Sciences, University of New South Wales, Wallace Wurth Building, Kensington, NSW, 2052, Australia
| | - Mengting Zhao
- Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, H3G 2W1, Canada
| | - Yong Zeng
- Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, QC, H3G 2W1, Canada.
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135
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WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis. Neuroimage 2021; 245:118713. [PMID: 34798231 DOI: 10.1016/j.neuroimage.2021.118713] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/25/2021] [Accepted: 11/04/2021] [Indexed: 01/06/2023] Open
Abstract
The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.
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Catrambone V, Barbieri R, Wendt H, Abry P, Valenza G. Functional brain-heart interplay extends to the multifractal domain. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200260. [PMID: 34689620 PMCID: PMC8543048 DOI: 10.1098/rsta.2020.0260] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/12/2021] [Indexed: 05/09/2023]
Abstract
The study of functional brain-heart interplay has provided meaningful insights in cardiology and neuroscience. Regarding biosignal processing, this interplay involves predominantly neural and heartbeat linear dynamics expressed via time and frequency domain-related features. However, the dynamics of central and autonomous nervous systems show nonlinear and multifractal behaviours, and the extent to which this behaviour influences brain-heart interactions is currently unknown. Here, we report a novel signal processing framework aimed at quantifying nonlinear functional brain-heart interplay in the non-Gaussian and multifractal domains that combines electroencephalography (EEG) and heart rate variability series. This framework relies on a maximal information coefficient analysis between nonlinear multiscale features derived from EEG spectra and from an inhomogeneous point-process model for heartbeat dynamics. Experimental results were gathered from 24 healthy volunteers during a resting state and a cold pressor test, revealing that synchronous changes between brain and heartbeat multifractal spectra occur at higher EEG frequency bands and through nonlinear/complex cardiovascular control. We conclude that significant bodily, sympathovagal changes such as those elicited by cold-pressure stimuli affect the functional brain-heart interplay beyond second-order statistics, thus extending it to multifractal dynamics. These results provide a platform to define novel nervous-system-targeted biomarkers. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Herwig Wendt
- IRIT–ENSEEIHT, Université de Toulouse, CNRS, Toulouse, France
| | - Patrice Abry
- University of Lyon, ENS de Lyon, University Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
| | - Gaetano Valenza
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
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137
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Ghouse A, Faes L, Valenza G. Inferring directionality of coupled dynamical systems using Gaussian process priors: Application on neurovascular systems. Phys Rev E 2021; 104:064208. [PMID: 35030953 DOI: 10.1103/physreve.104.064208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
Dynamical system theory has recently shown promise for uncovering causality and directionality in complex systems, particularly using the method of convergent cross mapping (CCM). In spite of its success in the literature, the presence of process noise raises concern about CCM's ability to uncover coupling direction. Furthermore, CCM's capacity to detect indirect causal links may be challenged in simulated unidrectionally coupled Rossler-Lorenz systems. To overcome these limitations, we propose a method that places a Gaussian process prior on a cross mapping function (named GP-CCM) to impose constraints on local state space neighborhood comparisons. Bayesian posterior likelihood and evidence ratio tests, as well as surrogate data analyses are performed to obtain a robust statistic for dynamical coupling directionality. We demonstrate GP-CCM's performance with respect to CCM in synthetic data simulation as well as in empirical electroencephelography (EEG) and functional near infrared spectroscopy (fNIRS) activity data. Our findings show that GP-CCM provides a statistic that consistently reports indirect causal structures in non-separable unidirectional system interactions; GP-CCM also provides coupling direction estimates in noisy physiological signals, showing that EEG likely causes, i.e., drives, fNIRS dynamics.
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Affiliation(s)
- Ameer Ghouse
- Bioengineering and Robotics Research Centre "E Piaggio" & Department of Engineering, University of Pisa, 56122 Pisa, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90133 Palermo, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre "E Piaggio" & Department of Engineering, University of Pisa, 56122 Pisa, Italy
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138
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Peck FC, Gabard-Durnam LJ, Wilkinson CL, Bosl W, Tager-Flusberg H, Nelson CA. Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months. J Neurodev Disord 2021; 13:57. [PMID: 34847887 PMCID: PMC8903497 DOI: 10.1186/s11689-021-09405-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 11/05/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.
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Affiliation(s)
- Fleming C Peck
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, 08544, USA
| | - Laurel J Gabard-Durnam
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, Northeastern University, Boston, MA, 02118, USA
| | - Carol L Wilkinson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - William Bosl
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Health Informatics Program, University of San Francisco, San Francisco, CA, 94117, USA
| | - Helen Tager-Flusberg
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - Charles A Nelson
- Division of Developmental Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Harvard Graduate School of Education, Cambridge, MA, 02138, USA
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139
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Miyakoshi M, Nariai H, Rajaraman RR, Bernardo D, Shrey DW, Lopour BA, Sim MS, Staba RJ, Hussain SA. Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness. Epilepsy Res 2021; 178:106809. [PMID: 34823159 DOI: 10.1016/j.eplepsyres.2021.106809] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 10/26/2021] [Accepted: 11/02/2021] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. METHODS Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). RESULTS The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). CONCLUSIONS We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.
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Affiliation(s)
- Makoto Miyakoshi
- Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California San Diego, United States
| | - Hiroki Nariai
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States.
| | - Rajsekar R Rajaraman
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States
| | | | - Daniel W Shrey
- Children's Hospital of Orange County, Neurology, University of California, Irvine, Pediatrics, United States
| | - Beth A Lopour
- Henry Samueli School of Engineering, University of California Irvine, United States
| | - Myung Shin Sim
- Division of General Internal Medicine and Health Services Research, Department of Medicine Statistics Core, University of California Los Angeles, United States
| | - Richard J Staba
- David Geffen School of Medicine, Department of Neurology, University of California Los Angeles, United States
| | - Shaun A Hussain
- David Geffen School of Medicine, Department of Pediatrics, University of California Los Angeles, United States
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140
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Sandre A, Panier L, O'Brien A, Weinberg A. Internal consistency reliability of the P300 to novelty in infants: The influence of trial number and data loss due to artifacts. Dev Psychobiol 2021; 63:e22208. [PMID: 34813097 DOI: 10.1002/dev.22208] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 09/23/2021] [Accepted: 09/27/2021] [Indexed: 12/13/2022]
Abstract
The P300 is an event-related potential component that reflects attention to motivationally salient stimuli and may be a promising tool to examine individual differences in cognitive-affective processing very early in development. However, the psychometric properties of the P300 in infancy are unknown, a fact that limits the component's utility as an individual difference measure in developmental research. To address this gap, 38 infants completed an auditory three-stimulus oddball task that included frequent standard, infrequent deviant, and novel stimuli. We quantified the P300 at a single electrode site and at region of interest (ROI) and examined the internal consistency reliability of the component, both via split-half reliability and as a function of trial number. Results indicated that the P300 to standard, deviant, and novel stimuli fell within moderate to high internal consistency reliability thresholds, and that scoring the component at an ROI led to slightly higher estimates of reliability. However, the percentage of data loss due to artifacts increased across the course of the task, suggesting that including more trials will not necessarily improve the reliability of the P300. Together, these results suggest that robust and reliable measurement of the P300 will require designing tasks that minimize trial number and maximize infant tolerability.
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Affiliation(s)
- Aislinn Sandre
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Lidia Panier
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Ashley O'Brien
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Anna Weinberg
- Department of Psychology, McGill University, Montreal, Quebec, Canada
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141
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Monroy C, Domínguez-Martínez E, Taylor B, Marin OP, Parise E, Reid VM. Understanding the causes and consequences of variability in infant ERP editing practices. Dev Psychobiol 2021; 63:e22217. [PMID: 34813094 DOI: 10.1002/dev.22217] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/11/2022]
Abstract
The current study examined the effects of variability on infant event-related potential (ERP) data editing methods. A widespread approach for analyzing infant ERPs is through a trial-by-trial editing process. Researchers identify electroencephalogram (EEG) channels containing artifacts and reject trials that are judged to contain excessive noise. This process can be performed manually by experienced researchers, partially automated by specialized software, or completely automated using an artifact-detection algorithm. Here, we compared the editing process from four different editors-three human experts and an automated algorithm-on the final ERP from an existing infant EEG dataset. Findings reveal that agreement between editors was low, for both the numbers of included trials and of interpolated channels. Critically, variability resulted in differences in the final ERP morphology and in the statistical results of the target ERP that each editor obtained. We also analyzed sources of disagreement by estimating the EEG characteristics that each human editor considered for accepting an ERP trial. In sum, our study reveals significant variability in ERP data editing pipelines, which has important consequences for the final ERP results. These findings represent an important step toward developing best practices for ERP editing methods in infancy research.
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Affiliation(s)
| | | | - Benjamin Taylor
- Department of Psychology, Lancaster University, Lancaster, UK.,Blackpool Teaching Hospitals NHS Foundation Trust, Blackpool, UK
| | - Oscar Portolés Marin
- Department of Artificial Intelligence and Cognitive Modelling, University of Groningen, Groningen, the Netherlands
| | - Eugenio Parise
- Department of Psychology, Lancaster University, Lancaster, UK.,Department of Psychology and Cognitive Science, CIMeC, Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Vincent M Reid
- Department of Psychology, Lancaster University, Lancaster, UK.,School of Psychology, University of Waikato, Waikato, New Zealand
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142
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Meyer M, Lamers D, Kayhan E, Hunnius S, Oostenveld R. Enhancing reproducibility in developmental EEG research: BIDS, cluster-based permutation tests, and effect sizes. Dev Cogn Neurosci 2021; 52:101036. [PMID: 34801856 PMCID: PMC8607163 DOI: 10.1016/j.dcn.2021.101036] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 10/29/2021] [Accepted: 11/11/2021] [Indexed: 01/02/2023] Open
Abstract
Developmental research using electroencephalography (EEG) offers valuable insights in brain processes early in life, but at the same time, applying this sensitive technique to young children who are often non-compliant and have short attention spans comes with practical limitations. It is thus of particular importance to optimally use the limited resources to advance our understanding of development through reproducible and replicable research practices. Here, we describe methodological approaches that help maximize the reproducibility of developmental EEG research. We discuss how to transform EEG data into the standardized Brain Imaging Data Structure (BIDS) which organizes data according to the FAIR data sharing principles. We provide a tutorial on how to use cluster-based permutation testing to analyze developmental EEG data. This versatile test statistic solves the multiple comparison problem omnipresent in EEG analysis and thereby substantially decreases the risk of reporting false discoveries. Finally, we describe how to quantify effect sizes, in particular of cluster-based permutation results. Reporting effect sizes conveys a finding’s impact and robustness which in turn informs future research. To demonstrate these methodological approaches to data organization, analysis and report, we use a publicly accessible infant EEG dataset and provide a complete copy of the analysis code. Methods for enhancing reproducibility in developmental EEG research. Tutorial for converting EEG data into BIDS to adopt FAIR data sharing principles. How to use cluster-based permutation testing to analyze developmental EEG data. How to quantify effect sizes, particularly of cluster-based permutation results.
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Affiliation(s)
- Marlene Meyer
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, NL, USA; Department of Psychology, University of Chicago, Chicago, IL, USA
| | - Didi Lamers
- Radboud University Library, Radboud University, Nijmegen, NL, USA
| | - Ezgi Kayhan
- Department of Developmental Psychology, University of Potsdam, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Sabine Hunnius
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, NL, USA
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, NL, USA; NatMEG, Karolinska Institutet, Stockholm, SE, USA
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143
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Song S, Nordin AD. Mobile Electroencephalography for Studying Neural Control of Human Locomotion. Front Hum Neurosci 2021; 15:749017. [PMID: 34858154 PMCID: PMC8631362 DOI: 10.3389/fnhum.2021.749017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/05/2021] [Indexed: 01/09/2023] Open
Abstract
Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.
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Affiliation(s)
- Seongmi Song
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States
| | - Andrew D Nordin
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, United States
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, United States
- Texas A&M Institute for Neuroscience, College Station, TX, United States
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144
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Miljevic A, Bailey NW, Vila-Rodriguez F, Herring SE, Fitzgerald PB. EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:546-554. [PMID: 34740847 DOI: 10.1016/j.bpsc.2021.10.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 10/19/2022]
Abstract
Brain connectivity can be estimated through many analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.
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Affiliation(s)
- Aleksandra Miljevic
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia.
| | - Neil W Bailey
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Dept. Psychiatry, The University of British Columbia, Vancouver, BC, Canada
| | - Sally E Herring
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
| | - Paul B Fitzgerald
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
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145
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Cavaliere L, Catrambone V, Bianchi M, Rocha AP, Valenza G. Characterization of upper limb movement-related EEG dynamics through fractional integrated autoregressive modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5987-5990. [PMID: 34892482 DOI: 10.1109/embc46164.2021.9630051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The analysis of electroencephalographic (EEG) series associated with movement performance is important for understanding the cortical neural control on motor tasks. While the existence of long-range correlations in physiological dynamics has been reported in previous studies, such a characterization in EEG series gathered during upper-limb movements has not been performed yet. To this end, here we report on a fractional integrated autoregressive analysis of EEG series during different functional classes of motor actions and resting phase, and data were gathered from 33 healthy volunteers. Results show significant differences in EEG long-range correlations on EEG series from characteristic topography.
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146
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Nardelli M, Catrambone V, Grandi G, Banfi T, Bruno RM, Scilingo EP, Faraguna U, Valenza G. Activation of brain-heart axis during REM sleep: a trigger for dreaming. Am J Physiol Regul Integr Comp Physiol 2021; 321:R951-R959. [PMID: 34704848 DOI: 10.1152/ajpregu.00306.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Dreams may be recalled after awakening from sleep following a defined electroencephalographic pattern that involves local decreases in low-frequency activity in the posterior cortical regions. While a dreaming experience implies bodily changes at many organ-, system-, and timescale-levels, the entity and causal role of such peripheral changes in a conscious dream experience are unknown. We performed a comprehensive, causal, multivariate analysis of physiological signals acquired during REM sleep at night, including high-density EEG and peripheral dynamics including electrocardiography and blood pressure. In this preliminary study, we investigated multiple recalls and non-recalls of dream experiences using data from nine healthy volunteers. The aim was not only to investigate the changes in central and autonomic dynamics associated with dream recalls and non-recalls, but also to characterize the central-peripheral dynamical and (causal) directional interactions, and the temporal relations of the related arousals upon awakening. We uncovered a brain-body network that drives a conscious dreaming experience that acts with specific interaction and time delays. Such a network is sustained by the blood pressure dynamics and the increasing functional information transfer from the neural heartbeat regulation to the brain. We conclude that bodily changes play a crucial and causative role in a conscious dream experience during REM sleep.
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Affiliation(s)
- Mimma Nardelli
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Giulia Grandi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy
| | - Tommaso Banfi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy
| | - Rosa Maria Bruno
- INSERM U970 Team 7, Paris Cardiovascular Research Centre - PARCC, University Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Enzo Pasquale Scilingo
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy.,Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
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147
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Haartsen R, Mason L, Braithwaite EK, Del Bianco T, Johnson MH, Jones EJH. Reliability of an automated gaze-controlled paradigm for capturing neural responses during visual and face processing in toddlerhood. Dev Psychobiol 2021; 63:e22157. [PMID: 34674242 PMCID: PMC9293026 DOI: 10.1002/dev.22157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 11/19/2022]
Abstract
Electroencephalography (EEG) has substantial potential value for examining individual differences during early development. Current challenges in developmental EEG research include high dropout rates and low trial numbers, which may in part be due to passive stimulus presentation. Comparability is challenged by idiosyncratic processing pipelines. We present a novel toolbox (“Braintools”) that uses gaze‐contingent stimulus presentation and an automated processing pipeline suitable for measuring visual processing through low‐density EEG recordings in the field. We tested the feasibility of this toolbox in 61 2.5‐ to 4‐year olds, and computed test–retest reliability (1‐ to 2‐week interval) of event‐related potentials (ERP) associated with visual (P1) and face processing (N290, P400). Feasibility was good, with 52 toddlers providing some EEG data at the first session. Reliability values for ERP features were moderate when derived from 20 trials; this would allow inclusion of 79% of the 61 toddlers for the P1 and 82% for the N290 and P400. P1 amplitude/latency were more reliable across sessions than for the N290 and P400. Amplitudes were generally more reliable than latencies. Automated and standardized solutions to collection and analysis of event‐related EEG data would allow efficient application in large‐scale global health studies, opening significant potential for examining individual differences in development.
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Affiliation(s)
- Rianne Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Luke Mason
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Eleanor K Braithwaite
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Teresa Del Bianco
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK.,Department of Psychology, University of Cambridge, Cambridge, UK
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, UK
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148
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Marriott Haresign I, Phillips E, Whitehorn M, Noreika V, Jones EJH, Leong V, Wass SV. Automatic classification of ICA components from infant EEG using MARA. Dev Cogn Neurosci 2021; 52:101024. [PMID: 34715619 PMCID: PMC8556604 DOI: 10.1016/j.dcn.2021.101024] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 09/05/2021] [Accepted: 10/18/2021] [Indexed: 11/29/2022] Open
Abstract
Automated systems for identifying and removing non-neural ICA components are growing in popularity among EEG researchers of adult populations. Infant EEG data differs in many ways from adult EEG data, but there exists almost no specific system for automated classification of source components from paediatric populations. Here, we adapt one of the most popular systems for adult ICA component classification for use with infant EEG data. Our adapted classifier significantly outperformed the original adult classifier on samples of naturalistic free play EEG data recorded from 10 to 12-month-old infants, achieving agreement rates with the manual classification of over 75% across two validation studies (n = 44, n = 25). Additionally, we examined both classifiers’ ability to remove stereotyped ocular artifact from a basic visual processing ERP dataset compared to manual ICA data cleaning. Here, the new classifier performed on level with expert manual cleaning and was again significantly better than the adult classifier at removing artifact whilst retaining a greater amount of genuine neural signal operationalised through comparing ERP activations in time and space. Our new system (iMARA) offers developmental EEG researchers a flexible tool for automatic identification and removal of artifactual ICA components. Currently, few tools are available for ICA based data correction that are designed around infant EEG. ICA correction is necessary for naturalistic paradigms when movement artifacts covary with cognitive processes. We present a system for automated ICA classification designed around infant EEG data. This system offers increased performance compared to systems of automatic ICA classification designed around adult EEG.
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Affiliation(s)
| | - E Phillips
- Department of Psychology, University of East London, London, UK
| | - M Whitehorn
- Department of Psychology, University of East London, London, UK
| | - V Noreika
- Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, UK
| | - E J H Jones
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, UK
| | - V Leong
- Department of Experimental Psychology, University of Cambridge, Cambridge, UK; School of Social Sciences, Nanyang Technological University, Singapore
| | - S V Wass
- Department of Psychology, University of East London, London, UK
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149
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Stylianou O, Racz FS, Kim K, Kaposzta Z, Czoch A, Yabluchanskiy A, Eke A, Mukli P. Multifractal Functional Connectivity Analysis of Electroencephalogram Reveals Reorganization of Brain Networks in a Visual Pattern Recognition Paradigm. Front Hum Neurosci 2021; 15:740225. [PMID: 34733145 PMCID: PMC8558231 DOI: 10.3389/fnhum.2021.740225] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/23/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain consists of anatomically distant neuronal assemblies that are interconnected via a myriad of synapses. This anatomical network provides the neurophysiological wiring framework for functional connectivity (FC), which is essential for higher-order brain functions. While several studies have explored the scale-specific FC, the scale-free (i.e., multifractal) aspect of brain connectivity remains largely neglected. Here we examined the brain reorganization during a visual pattern recognition paradigm, using bivariate focus-based multifractal (BFMF) analysis. For this study, 58 young, healthy volunteers were recruited. Before the task, 3-3 min of resting EEG was recorded in eyes-closed (EC) and eyes-open (EO) states, respectively. The subsequent part of the measurement protocol consisted of 30 visual pattern recognition trials of 3 difficulty levels graded as Easy, Medium, and Hard. Multifractal FC was estimated with BFMF analysis of preprocessed EEG signals yielding two generalized Hurst exponent-based multifractal connectivity endpoint parameters, H(2) and ΔH 15; with the former indicating the long-term cross-correlation between two brain regions, while the latter captures the degree of multifractality of their functional coupling. Accordingly, H(2) and ΔH 15 networks were constructed for every participant and state, and they were characterized by their weighted local and global node degrees. Then, we investigated the between- and within-state variability of multifractal FC, as well as the relationship between global node degree and task performance captured in average success rate and reaction time. Multifractal FC increased when visual pattern recognition was administered with no differences regarding difficulty level. The observed regional heterogeneity was greater for ΔH 15 networks compared to H(2) networks. These results show that reorganization of scale-free coupled dynamics takes place during visual pattern recognition independent of difficulty level. Additionally, the observed regional variability illustrates that multifractal FC is region-specific both during rest and task. Our findings indicate that investigating multifractal FC under various conditions - such as mental workload in healthy and potentially in diseased populations - is a promising direction for future research.
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Affiliation(s)
- Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | | | - Keumbi Kim
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Akos Czoch
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment and Neurodegeneration Program, Department of Biochemistry and Molecular Biology, Oklahoma Center for Geroscience and Healthy Brain Aging, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,The Peggy and Charles Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,Department of Health Promotion Sciences, College of Public Health, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States,Andras Eke,
| | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary,Vascular Cognitive Impairment and Neurodegeneration Program, Department of Biochemistry and Molecular Biology, Oklahoma Center for Geroscience and Healthy Brain Aging, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States,*Correspondence: Peter Mukli,
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Norton ES, MacNeill LA, Harriott EM, Allen N, Krogh-Jespersen S, Smyser CD, Rogers CE, Smyser TA, Luby J, Wakschlag L. EEG/ERP as a pragmatic method to expand the reach of infant-toddler neuroimaging in HBCD: Promises and challenges. Dev Cogn Neurosci 2021; 51:100988. [PMID: 34280739 PMCID: PMC8318873 DOI: 10.1016/j.dcn.2021.100988] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 06/14/2021] [Accepted: 07/12/2021] [Indexed: 01/12/2023] Open
Abstract
Though electrophysiological measures (EEG and ERP) offer complementary information to MRI and a variety of advantages for studying infants and young children, these measures have not yet been included in large cohort studies of neurodevelopment. This review summarizes the types of EEG and ERP measures that could be used in the HEALthy Brain and Cognitive Development (HBCD) study, and the promises and challenges in doing so. First, we provide brief overview of the use of EEG/ERP for studying the developing brain and discuss exemplar findings, using resting or baseline EEG measures as well as the ERP mismatch negativity (MMN) as exemplars. We then discuss the promises of EEG/ERP such as feasibility, while balancing challenges such as ensuring good signal quality in diverse children with different hair types. We then describe an ongoing multi-site EEG data harmonization from our groups. We discuss the process of alignment and provide preliminary usability data for both resting state EEG data and auditory ERP MMN in diverse samples including over 300 infants and toddlers. Finally, we provide recommendations and considerations for the HBCD study and other studies of neurodevelopment.
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Affiliation(s)
- Elizabeth S Norton
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, United States; Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, United States; Institute for Innovations in Developmental Sciences, Northwestern University, United States.
| | - Leigha A MacNeill
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, United States; Institute for Innovations in Developmental Sciences, Northwestern University, United States
| | - Emily M Harriott
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, United States
| | - Norrina Allen
- Institute for Innovations in Developmental Sciences, Northwestern University, United States; Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, United States
| | - Sheila Krogh-Jespersen
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, United States; Institute for Innovations in Developmental Sciences, Northwestern University, United States
| | - Christopher D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, United States; Department of Psychiatry, Washington University School of Medicine, United States
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Tara A Smyser
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Joan Luby
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Lauren Wakschlag
- Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, United States; Institute for Innovations in Developmental Sciences, Northwestern University, United States
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