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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [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: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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Yang L, Wang S, Chen Y, Liang Y, Chen T, Wang Y, Fu X, Wang S. Effects of Age on the Auditory Cortex During Speech Perception in Noise: Evidence From Functional Near-Infrared Spectroscopy. Ear Hear 2024; 45:742-752. [PMID: 38268081 PMCID: PMC11008455 DOI: 10.1097/aud.0000000000001460] [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: 09/08/2022] [Accepted: 11/23/2023] [Indexed: 01/26/2024]
Abstract
OBJECTIVES Age-related speech perception difficulties may be related to a decline in central auditory processing abilities, particularly in noisy or challenging environments. However, how the activation patterns related to speech stimulation in different noise situations change with normal aging has yet to be elucidated. In this study, we aimed to investigate the effects of noisy environments and aging on patterns of auditory cortical activation. DESIGN We analyzed the functional near-infrared spectroscopy signals of 20 young adults, 21 middle-aged adults, and 21 elderly adults, and evaluated their cortical response patterns to speech stimuli under five different signal to noise ratios (SNRs). In addition, we analyzed the behavior score, activation intensity, oxyhemoglobin variability, and dominant hemisphere, to investigate the effects of aging and noisy environments on auditory cortical activation. RESULTS Activation intensity and oxyhemoglobin variability both showed a decreasing trend with aging at an SNR of 0 dB; we also identified a strong correlation between activation intensity and age under this condition. However, we observed an inconsistent activation pattern when the SNR was 5 dB. Furthermore, our analysis revealed that the left hemisphere may be more susceptible to aging than the right hemisphere. Activation in the right hemisphere was more evident in older adults than in the left hemisphere; in contrast, younger adults showed leftward lateralization. CONCLUSIONS Our analysis showed that with aging, auditory cortical regions gradually become inflexible in noisy environments. Furthermore, changes in cortical activation patterns with aging may be related to SNR conditions, and that understandable speech with a low SNR ratio but still understandable may induce the highest level of activation. We also found that the left hemisphere was more affected by aging than the right hemisphere in speech perception tasks; the left-sided dominance observed in younger individuals gradually shifted to the right hemisphere with aging.
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Affiliation(s)
- Liu Yang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- These authors contributed equally to this work
| | - Songjian Wang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- These authors contributed equally to this work
| | - Younuo Chen
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Ting Chen
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China
| | - Yuan Wang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Xinxing Fu
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Shuo Wang
- Beijing Institute of Otolaryngology, Otolaryngology-Head and Neck Surgery, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, Beijing Tongren Hospital, Capital Medical University, Beijing, China
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3
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Finn CE, Han GT, Naples AJ, Wolf JM, McPartland JC. Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children. Autism Res 2023; 16:2077-2089. [PMID: 37638733 DOI: 10.1002/aur.3017] [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: 01/07/2023] [Accepted: 08/05/2023] [Indexed: 08/29/2023]
Abstract
Electroencephalographic peak alpha frequency (PAF) is a marker of neural maturation that increases with age throughout childhood. Distinct maturation of PAF is observed in children with autism spectrum disorder such that PAF does not increase with age and is instead positively associated with cognitive ability. The current study clarifies and extends previous findings by characterizing the effects of age and cognitive ability on PAF between diagnostic groups in a sample of children and adolescents with and without autism spectrum disorder. Resting EEG data and behavioral measures were collected from 45 autistic children and 34 neurotypical controls aged 8 to 18 years. Utilizing generalized additive models to account for nonlinear relations, we examined differences in the joint effect of age and nonverbal IQ by diagnosis as well as bivariate relations between age, nonverbal IQ, and PAF across diagnostic groups. Age was positively associated with PAF among neurotypical children but not among autistic children. In contrast, nonverbal IQ but not age was positively associated with PAF among autistic children. Models accounting for nonlinear relations revealed different developmental trajectories as a function of age and cognitive ability based on diagnostic status. Results align with prior evidence indicating that typical age-related increases in PAF are absent in autistic children and that PAF instead increases with cognitive ability in these children. Findings suggest the potential of PAF to index distinct trajectories of neural maturation in autistic children.
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Affiliation(s)
- Caroline E Finn
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - Gloria T Han
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Adam J Naples
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - Julie M Wolf
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
| | - James C McPartland
- Child Study Center, Yale School of Medicine, New Haven, Connecticut, USA
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Angulo-Ruiz BY, Ruiz-Martínez FJ, Rodríguez-Martínez EI, Ionescu A, Saldaña D, Gómez CM. Linear and Non-linear Analyses of EEG in a Group of ASD Children During Resting State Condition. Brain Topogr 2023; 36:736-749. [PMID: 37330940 PMCID: PMC10415465 DOI: 10.1007/s10548-023-00976-7] [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: 02/07/2023] [Accepted: 06/06/2023] [Indexed: 06/20/2023]
Abstract
This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5-11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5-45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.
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Affiliation(s)
- Brenda Y. Angulo-Ruiz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Francisco J. Ruiz-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Elena I. Rodríguez-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Anca Ionescu
- Département de Psychologie, Université de Montréal, Montréal, Canada
| | - David Saldaña
- Laboratorio de Diversidad, Cognición y Lenguaje, Departamento de Psicología Evolutiva y de la Educación, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
| | - Carlos M. Gómez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/ Camilo José Cela S/N 41018, Seville, Spain
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5
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Angulo-Ruiz BY, Muñoz V, Rodríguez-Martínez EI, Cabello-Navarro C, Gómez CM. Multiscale entropy of ADHD children during resting state condition. Cogn Neurodyn 2023; 17:869-891. [PMID: 37522046 PMCID: PMC10374506 DOI: 10.1007/s11571-022-09869-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/18/2022] [Accepted: 08/05/2022] [Indexed: 11/28/2022] Open
Abstract
This present study aims to investigate neural mechanisms underlying ADHD compared to healthy children through the analysis of the complexity and the variability of the EEG brain signal using multiscale entropy (MSE), EEG signal standard deviation (SDs), as well as the mean, standard deviation (SDp) and coefficient of variation (CV) of absolute spectral power (PSD). For this purpose, a sample of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) between 6 and 17 years old were selected based on the number of trials and diagnostic agreement, 32 for the open-eyes (OE) experimental condition and 25 children for the close-eyes (CE) experimental condition. Healthy control subjects were age- and gender-matched with the ADHD group. The MSE and SDs of resting-state EEG activity were calculated on 34 time scales using a coarse-grained procedure. In addition, the PSD was averaged in delta, theta, alpha, and beta frequency bands, and its mean, SDp, and CV were calculated. The results show that the MSE changes with age during development, increases as the number of scales increases and has a higher amplitude in controls than in ADHD. The absolute PSD results show CV differences between subjects in low and beta frequency bands, with higher variability values in the ADHD group. All these results suggest an increased EEG variability and reduced complexity in ADHD compared to controls. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09869-0.
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Affiliation(s)
- Brenda Y. Angulo-Ruiz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Vanesa Muñoz
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Elena I. Rodríguez-Martínez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Celia Cabello-Navarro
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
| | - Carlos M. Gómez
- Human Psychobiology Laboratory, Experimental Psychology Department, University of Seville, C/Camilo José Cela S/N, 41018 Seville, Spain
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Favaro J, Colombo MA, Mikulan E, Sartori S, Nosadini M, Pelizza MF, Rosanova M, Sarasso S, Massimini M, Toldo I. The maturation of aperiodic EEG activity across development reveals a progressive differentiation of wakefulness from sleep. Neuroimage 2023:120264. [PMID: 37399931 DOI: 10.1016/j.neuroimage.2023.120264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/04/2023] [Accepted: 07/01/2023] [Indexed: 07/05/2023] Open
Abstract
During development, the brain undergoes radical structural and functional changes following a posterior-to-anterior gradient, associated with profound changes of cortical electrical activity during both wakefulness and sleep. However, a systematic assessment of the developmental effects on aperiodic EEG activity maturation across vigilance states is lacking, particularly regarding its topographical aspects. Here, in a population of 160 healthy infants, children and teenagers (from 2 to 17 years, 10 subjects for each year), we investigated the development of aperiodic EEG activity in wakefulness and sleep. Specifically, we parameterized the shape of the aperiodic background of the EEG Power Spectral Density (PSD) by means of the spectral exponent and offset; the exponent reflects the rate of exponential decay of power over increasing frequencies and the offset reflects an estimate of the y-intercept of the PSD. We found that sleep and development caused the EEG-PSD to rotate over opposite directions: during wakefulness the PSD showed a flatter decay and reduced offset over development, while during sleep it showed a steeper decay and a higher offset as sleep becomes deeper. During deep sleep (N2, N3) only the spectral offset decreased over age, indexing a broad-band voltage reduction. As a result, the difference between values in deep sleep and those in both light sleep (N1) and wakefulness increased with age, suggesting a progressive differentiation of wakefulness from sleep EEG activity, most prominent over the frontal regions, the latest to complete maturation. Notably, the broad-band spectral exponent values during deep sleep stages were entirely separated from wakefulness values, consistently across developmental ages and in line with previous findings in adults. Concerning topographical development, the location showing the steepest PSD decay and largest offset shifted from posterior to anterior regions with age. This shift, particularly evident during deep sleep, paralleled the migration of sleep slow wave activity and was consistent with neuroanatomical and cognitive development. Overall, aperiodic EEG activity distinguishes wakefulness from sleep regardless of age; while, during development, it reveals a postero-anterior topographical maturation and a progressive differentiation of wakefulness from sleep. Our study could help to interpret changes due to pathological conditions and may elucidate the neurophysiological processes underlying the development of wakefulness and sleep.
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Affiliation(s)
- Jacopo Favaro
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy.
| | - Michele A Colombo
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy.
| | - Ezequiel Mikulan
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy
| | - Stefano Sartori
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy; Neuroimmunology Group, Pediatric Research Institute "Città della Speranza", 35127, Padua, Italy; Department of Neuroscience, University of Padua, 35121, Padua, Italy
| | - Margherita Nosadini
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy; Neuroimmunology Group, Pediatric Research Institute "Città della Speranza", 35127, Padua, Italy
| | - Maria Federica Pelizza
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy
| | - Mario Rosanova
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy
| | - Simone Sarasso
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy
| | - Marcello Massimini
- Department of Clinical and Biomedical Sciences "L. Sacco", University of Milan, 20157, Milan, Italy; IRCCS, Fondazione Don Carlo Gnocchi Onlus, 20148, Milan, Italy.
| | - Irene Toldo
- Pediatric Neurology and Neurophysiology Unit, Department of Women's and Children Health, University of Padua, 35128, Padua, Italy
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7
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Davoudi S, Schwartz T, Labbe A, Trainor L, Lippé S. Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants. Cereb Cortex 2023; 33:8734-8747. [PMID: 37143183 PMCID: PMC10321121 DOI: 10.1093/cercor/bhad154] [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: 11/15/2022] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023] Open
Abstract
Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.
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Affiliation(s)
- Saeideh Davoudi
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Neuroscience, Université de Montréal, Montréal H3T 1J4, Canada
| | - Tyler Schwartz
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal H3T 2A7, Canada
| | - Laurel Trainor
- Department of Psychology, Neuroscience and Behavior, McMaster University, Hamilton L8S 4K1, Canada
| | - Sarah Lippé
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal H3T 1C5, Canada
- Department of Psychology, Université de Montréal, Montréal H2V 2S9, Canada
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8
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Jia G, Hubbard CS, Hu Z, Xu J, Dong Q, Niu H, Liu H. Intrinsic brain activity is increasingly complex and develops asymmetrically during childhood and early adolescence. Neuroimage 2023:120225. [PMID: 37336421 DOI: 10.1016/j.neuroimage.2023.120225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/18/2023] [Accepted: 06/11/2023] [Indexed: 06/21/2023] Open
Abstract
A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.
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Affiliation(s)
- Gaoding Jia
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Catherine S Hubbard
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Zhenyan Hu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Jingping Xu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China
| | - Haijing Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875 China.
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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9
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Lopez KL, Monachino AD, Vincent KM, Peck FC, Gabard-Durnam LJ. Stability, change, and reliable individual differences in electroencephalography measures: a lifespan perspective on progress and opportunities. Neuroimage 2023; 275:120116. [PMID: 37169118 DOI: 10.1016/j.neuroimage.2023.120116] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/27/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023] Open
Abstract
Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.
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Affiliation(s)
- K L Lopez
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - A D Monachino
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - K M Vincent
- Northeastern University, 360 Huntington Ave, Boston, MA, United States
| | - F C Peck
- University of California, Los Angeles, Los Angeles, CA, United States
| | - L J Gabard-Durnam
- Northeastern University, 360 Huntington Ave, Boston, MA, United States.
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10
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Pelc K, Gajewska A, Napiórkowski N, Dan J, Verhoeven C, Dan B. Multiscale entropy as a metric of brain maturation in a large cohort of typically developing children born preterm using longitudinal high-density EEG in the first two years of life. Physiol Meas 2022; 43. [PMID: 36374000 DOI: 10.1088/1361-6579/aca26c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/14/2022] [Indexed: 11/16/2022]
Abstract
Objective.We aimed to analyze whether complexity of brain electrical activity (EEG) measured by multiscale entropy (MSE) increases with brain maturation during the first two years of life. We also aimed to investigate whether this complexity shows regional differences across the brain, and whether changes in complexity are influenced by extrauterine life experience duration.Approach.We measured MSE of EEG signals recorded longitudinally using a high-density setup (64 or 128 electrodes) in 84 typically developing infants born preterm (<32 weeks' gestation) from term age to two years. We analyzed the complexity index and maximum value of MSE over increasing age, across brain regions, and in function of extrauterine life duration, and used correlation matrices as a metric of functional connectivity of the cerebral cortex.Main results.We found an increase of strong inter-channel correlation of MSE (R > 0.8) with increasing age. Regional analysis showed significantly increased MSE between 3 and 24 months of corrected age in the posterior and middle regions with respect to the anterior region. We found a weak relationship (adjusted R2= 0.135) between MSE and extrauterine life duration.Significance.These findings suggest that brain functional connectivity increases with maturation during the first two years of life. EEG complexity shows regional differences with earlier maturation of the visual cortex and brain regions involved in joint attention than of regions involved in cognitive analysis, abstract thought, and social behavior regulation. Finally, our MSE analysis suggested only a weak influence of early extrauterine life experiences (prior to term age) on EEG complexity.
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Affiliation(s)
- Karine Pelc
- Université libre de Bruxelles (ULB), Facuty of Motor Sciences, Brussels, Belgium.,Inkendaal Rehabilitation Hospital, Vlezenbeek, Belgium
| | | | | | - Jonathan Dan
- KU Leuven, Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, Belgium.,Byteflies, Berchem, Belgium
| | - Caroline Verhoeven
- Université libre de Bruxelles (ULB), Facuty of Medicine, Department of Mathematics Education, Brussels, Belgium
| | - Bernard Dan
- Inkendaal Rehabilitation Hospital, Vlezenbeek, Belgium.,Université libre de Bruxelles (ULB), Faculty of Psychology and Education Sciences, Brussels, Belgium
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11
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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12
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Murray CH, Huang Z, Lee R, de Wit H. Adolescents are more sensitive than adults to acute behavioral and cognitive effects of THC. Neuropsychopharmacology 2022; 47:1331-1338. [PMID: 35110688 PMCID: PMC9117219 DOI: 10.1038/s41386-022-01281-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 01/16/2022] [Accepted: 01/19/2022] [Indexed: 11/09/2022]
Abstract
Increased cannabis availability has contributed to increased use with concomitant incidence of adverse effects. One risk factor for adverse drug reactions may be age. There is preclinical evidence that acute effects of delta-9-tetrahydrocannabinol (THC), the primary active constituent of cannabis, are greater during adolescence, but this has not been fully studied in humans. The present study sought to determine whether adolescent men and women are more sensitive than adults to acute THC. Adolescents aged 18-20 (N = 12) and adults aged 30-40 (N = 12), with less than 20 total lifetime uses of THC-containing products, received capsules of THC (7.5, 15 mg) and placebo across three study sessions in randomized order under double blind conditions. During each session, subjective, cardiovascular, behavioral, and EEG measures were obtained. Behavioral measures included Simple Reaction Time, Stop Task, Time Production and N-back and EEG measures included P300 amplitudes during an auditory oddball task and eyes-closed resting state. THC affected subjective state and heart rate similarly in both age groups. However, adolescents were more sensitive to performance impairing effects, exhibiting dose-dependent impairments on reaction time, response accuracy, and time perception. On EEG measures, THC dose-dependently decreased P300 amplitude in adolescents but not adults. Adolescents were more sensitive to behavioral and cognitive effects of THC, but not to cardiovascular effects or subjective measures. Thus, at doses that produce comparable ratings of intoxication, adolescents may exhibit greater cognitive impairment and alterations in brain function.
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Affiliation(s)
- Conor H. Murray
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 USA
| | - Zhengyi Huang
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 USA
| | - Royce Lee
- grid.170205.10000 0004 1936 7822Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL 60637 USA
| | - Harriet de Wit
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA.
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13
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Liang Z, Wang Y, Tian H, Gu Y, Arimitsu T, Takahashi T, Minagawa Y, Niu H, Tong Y. Spatial complexity method for tracking brain development and degeneration using functional near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:1718-1736. [PMID: 35414994 PMCID: PMC8973163 DOI: 10.1364/boe.449341] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/07/2022] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (Δ[HbO]) or two deoxy-hemoglobin (Δ[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE's performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDEHbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yuxi Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Hao Tian
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yue Gu
- Key Laboratory of Computer Vision and System (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Takeshi Arimitsu
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Takao Takahashi
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Yasuyo Minagawa
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Haijing Niu
- Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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14
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Evidence of a maturational disruption in non-rapid eye movement sleep slow wave activity in youth with attention-deficit/hyperactivity, learning and internalizing disorders. Sleep Med 2022; 90:230-237. [PMID: 35217303 PMCID: PMC8923949 DOI: 10.1016/j.sleep.2022.01.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/13/2022] [Accepted: 01/31/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND Sleep slow wave activity (SWA) peaks during childhood and declines in the transition to adolescence during typical development (TD). It remains unknown whether this trajectory differs in youth with neuropsychiatric disorders. METHODS We analyzed sleep EEGs of 664 subjects 6 to 21 y (449 TD, 123 unmedicated, 92 medicated) and 114 subjects 7-12 y (median 10.5 y) followed-up at 18-22 y (median 19 y). SWA (0.4-4 Hz) power was calculated during non-rapid eye movement sleep. RESULTS TD and unmedicated youth showed cubic central and frontal SWA trajectories from 6 to 21 y (p-cubic<0.05), with TD youth showing peaks in central SWA at 6.8 y and frontal at 8.2 y. Unmedicated attention-deficit/hyperactivity (ADHD) and/or learning disorders (LD) showed peak central SWA 2 y later (at 9.6 y, coinciding with peak frontal SWA) than TD, followed by a 67% steeper slope by 19 y. Frontal SWA peak and slope in unmedicated ADHD/LD, and that of central and frontal in internalizing disorders (ID), were similar to TD. Unmedicated ADHD/LD did not differ in the longitudinal SWA percent change by 18-22 y; unmedicated ID showed a lower longitudinal change in frontal SWA than TD. Medicated youth showed a linear decline in central and frontal SWA from 6 to 21 y (p-linear<0.05). CONCLUSIONS ADHD/LD youth show a maturational delay and potential topographical disruption in SWA during childhood and steeper decline throughout adolescence, suggesting faster synaptic pruning. Youth with ID experience less changes in frontal SWA by late adolescence. Psychotropic medications may impact the maturational trajectory of SWA, but not the magnitude of developmental decline by late adolescence.
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15
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Marcos-Martínez D, Martínez-Cagigal V, Santamaría-Vázquez E, Pérez-Velasco S, Hornero R. Neurofeedback Training Based on Motor Imagery Strategies Increases EEG Complexity in Elderly Population. ENTROPY (BASEL, SWITZERLAND) 2021; 23:1574. [PMID: 34945880 PMCID: PMC8700498 DOI: 10.3390/e23121574] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 11/22/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022]
Abstract
Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI-NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject's scores from Luria tests performed before and after MI-NFT. We found that MI-NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI-NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI-NFT.
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Affiliation(s)
- Diego Marcos-Martínez
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (V.M.-C.); (E.S.-V.); (S.P.-V.); (R.H.)
| | - Víctor Martínez-Cagigal
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (V.M.-C.); (E.S.-V.); (S.P.-V.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Eduardo Santamaría-Vázquez
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (V.M.-C.); (E.S.-V.); (S.P.-V.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
| | - Sergio Pérez-Velasco
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (V.M.-C.); (E.S.-V.); (S.P.-V.); (R.H.)
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. Telecomunicación, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; (V.M.-C.); (E.S.-V.); (S.P.-V.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 28029 Madrid, Spain
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16
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Proteau-Lemieux M, Knoth IS, Agbogba K, Côté V, Barlahan Biag HM, Thurman AJ, Martin CO, Bélanger AM, Rosenfelt C, Tassone F, Abbeduto LJ, Jacquemont S, Hagerman R, Bolduc F, Hessl D, Schneider A, Lippé S. EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome. Front Psychiatry 2021; 12:716707. [PMID: 34858220 PMCID: PMC8632368 DOI: 10.3389/fpsyt.2021.716707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/06/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied. Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site. Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS. Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.
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Affiliation(s)
- Mélodie Proteau-Lemieux
- Department of Psychology, University of Montreal, Montreal, QC, Canada.,Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Inga Sophia Knoth
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Kristian Agbogba
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Valérie Côté
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Hazel Maridith Barlahan Biag
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States
| | - Angela John Thurman
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States
| | | | - Anne-Marie Bélanger
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
| | - Cory Rosenfelt
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - Flora Tassone
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,Department of Biochemistry and Molecular Medicine, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Leonard J Abbeduto
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Sébastien Jacquemont
- Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada.,Department of Pediatrics, University of Montreal, Montreal, QC, Canada
| | - Randi Hagerman
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States
| | - François Bolduc
- Department of Pediatric Neurology, University of Alberta, Edmonton, AB, Canada
| | - David Hessl
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,Department of Psychiatry and Behavioral Sciences, University of California Davis School of Medicine, Sacramento, CA, United States
| | - Andrea Schneider
- University of California Davis Medical Investigation of Neurodevelopmental Disorders (MIND) Institute, Sacramento, CA, United States.,California North State University, College of Psychology, Rancho Cordova, CA, United States
| | - Sarah Lippé
- Department of Psychology, University of Montreal, Montreal, QC, Canada.,Research Center of the Sainte-Justine University Hospital, Montreal, QC, Canada
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