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Arutiunian V, Arcara G, Buyanova I, Fedorov M, Davydova E, Pereverzeva D, Sorokin A, Tyushkevich S, Mamokhina U, Danilina K, Dragoy O. Abnormalities in both stimulus-induced and baseline MEG alpha oscillations in the auditory cortex of children with Autism Spectrum Disorder. Brain Struct Funct 2024; 229:1225-1242. [PMID: 38683212 DOI: 10.1007/s00429-024-02802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 04/22/2024] [Indexed: 05/01/2024]
Abstract
The neurobiology of Autism Spectrum Disorder (ASD) is hypothetically related to the imbalance between neural excitation (E) and inhibition (I). Different studies have revealed that alpha-band (8-12 Hz) activity in magneto- and electroencephalography (MEG and EEG) may reflect E and I processes and, thus, can be of particular interest in ASD research. Previous findings indicated alterations in event-related and baseline alpha activity in different cortical systems in individuals with ASD, and these abnormalities were associated with core and co-occurring conditions of ASD. However, the knowledge on auditory alpha oscillations in this population is limited. This MEG study investigated stimulus-induced (Event-Related Desynchronization, ERD) and baseline alpha-band activity (both periodic and aperiodic) in the auditory cortex and also the relationships between these neural activities and behavioral measures of children with ASD. Ninety amplitude-modulated tones were presented to two groups of children: 20 children with ASD (5 girls, Mage = 10.03, SD = 1.7) and 20 typically developing controls (9 girls, Mage = 9.11, SD = 1.3). Children with ASD had a bilateral reduction of alpha-band ERD, reduced baseline aperiodic-adjusted alpha power, and flattened aperiodic exponent in comparison to TD children. Moreover, lower raw baseline alpha power and aperiodic offset in the language-dominant left auditory cortex were associated with better language skills of children with ASD measured in formal assessment. The findings highlighted the alterations of E / I balance metrics in response to basic auditory stimuli in children with ASD and also provided evidence for the contribution of low-level processing to language difficulties in ASD.
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Affiliation(s)
- Vardan Arutiunian
- Center for Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, Seattle, WA, 98101, United States of America.
| | | | - Irina Buyanova
- Center for Language and Brain, HSE University, Moscow, Russia
- University of Otago, Dunedin, New Zealand
| | - Makar Fedorov
- Center for Language and Brain, HSE University, Nizhny Novgorod, Russia
| | - Elizaveta Davydova
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Chair of Differential Psychology and Psychophysiology, Moscow State University of Psychology and Education, Moscow, Russia
| | - Darya Pereverzeva
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Alexander Sorokin
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Haskins Laboratories, New Haven, CT, United States of America
| | - Svetlana Tyushkevich
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Uliana Mamokhina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
| | - Kamilla Danilina
- Federal Resource Center for ASD, Moscow State University of Psychology and Education, Moscow, Russia
- Scientific Research and Practical Center of Pediatric Psychoneurology, Moscow, Russia
| | - Olga Dragoy
- Center for Language and Brain, HSE University, Moscow, Russia
- Institute of Linguistics, Russian Academy of Sciences, Moscow, Russia
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Del Bianco T, Haartsen R, Mason L, Leno VC, Springer C, Potter M, Mackay W, Smit P, Plessis CD, Brink L, Johnson MH, Murphy D, Loth E, Odendaal H, Jones EJH. The importance of decomposing periodic and aperiodic EEG signals for assessment of brain function in a global context. Dev Psychobiol 2024; 66:e22484. [PMID: 38528816 DOI: 10.1002/dev.22484] [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: 12/20/2022] [Revised: 01/31/2024] [Accepted: 02/26/2024] [Indexed: 03/27/2024]
Abstract
Measures of early neuro-cognitive development that are suitable for use in low-resource settings are needed to enable studies of the effects of early adversity on the developing brain in a global context. These measures should have high acquisition rates and good face and construct validity. Here, we investigated the feasibility of a naturalistic electroencephalography (EEG) paradigm in a low-resource context during childhood. Additionally, we examined the sensitivity of periodic and aperiodic EEG metrics to social and non-social stimuli. We recorded simultaneous 20-channel EEG and eye-tracking in 72 children aged 4-12 years (45 females) while they watched videos of women singing nursery rhymes and moving toys, selected to represent familiar childhood experiences. These measures were part of a feasibility study that assessed the feasibility and acceptability of a follow-up data collection of the South African Safe Passage Study, which tracks environmental adversity and brain and cognitive development from before birth up until childhood. We examined whether data quantity and quality varied with child characteristics and the sensitivity of varying EEG metrics (canonical band power in the theta and alpha band and periodic and aperiodic features of the power spectra). We found that children who completed the EEG and eye-tracking assessment were, in general, representative of the full cohort. Data quantity was higher in children with greater visual attention to the stimuli. Out of the tested EEG metrics, periodic measures in the theta frequency range were most sensitive to condition differences, compared to alpha range measures and canonical and aperiodic EEG measures. Our results show that measuring EEG during ecologically valid social and non-social stimuli is feasible in low-resource settings, is feasible for most children, and produces robust indices of social brain function. This work provides preliminary support for testing longitudinal links between social brain function, environmental factors, and emerging behaviors.
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Affiliation(s)
- Teresa Del Bianco
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
| | - Rianne Haartsen
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
| | - Luke Mason
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
- Institute of Psychiatry, Psychology & Neuroscience, King's College, London, London, UK
| | - Virginia Carter Leno
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Cilla Springer
- Department of Paediatrics and Child Health, Stellenbosch University, Cape Town, South Africa
| | - Mandy Potter
- Department of Obstetrics and Gynaecology, Stellenbosch University, Cape Town, South Africa
| | - Wendy Mackay
- Department of Obstetrics and Gynaecology, Stellenbosch University, Cape Town, South Africa
| | - Petrusa Smit
- Department of Obstetrics and Gynaecology, Stellenbosch University, Cape Town, South Africa
| | - Carlie Du Plessis
- Department of Obstetrics and Gynaecology, Stellenbosch University, Cape Town, South Africa
| | - Lucy Brink
- Department of Obstetrics and Gynaecology, Stellenbosch University, Cape Town, South Africa
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Declan Murphy
- Institute of Psychiatry, Psychology & Neuroscience, King's College, London, London, UK
| | - Eva Loth
- Institute of Psychiatry, Psychology & Neuroscience, King's College, London, London, UK
| | - Hein Odendaal
- Department of Obstetrics and Gynaecology, Stellenbosch University, Cape Town, South Africa
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck University of London, London, UK
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Neo WS, Foti D, Keehn B, Kelleher B. Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis. Transl Psychiatry 2023; 13:389. [PMID: 38097538 PMCID: PMC10721649 DOI: 10.1038/s41398-023-02681-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023] Open
Abstract
Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.
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Affiliation(s)
- Wei Siong Neo
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA.
| | - Dan Foti
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Brandon Keehn
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA
| | - Bridgette Kelleher
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
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4
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Deng L, He WZ, Zhang QL, Wei L, Dai Y, Liu YQ, Chen ZL, Ren T, Zhang LL, Gong JB, Li F. Caregiver-child interaction as an effective tool for identifying autism spectrum disorder: evidence from EEG analysis. Child Adolesc Psychiatry Ment Health 2023; 17:138. [PMID: 38098032 PMCID: PMC10722789 DOI: 10.1186/s13034-023-00690-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 12/04/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects individuals across their lifespan. Early diagnosis and intervention are crucial for improving outcomes. However, current diagnostic methods are often time-consuming, and costly, making them inaccessible to many families. In the current study, we aim to test caregiver-child interaction as a potential tool for screening children with ASD in clinic. METHODS We enrolled 85 preschool children (Mean age: 4.90 ± 0.65 years, 70.6% male), including ASD children with or without developmental delay (DD), and typical development (TD) children, along with their caregivers. ASD core symptoms were evaluated by Childhood Autism Rating Scale (CARS) and Autism Diagnostic Observation Schedule-Calibrated Severity Scores (ADOS-CSS). Behavioral indicators were derived from video encoding of caregiver-child interaction, including social involvement of children (SIC), interaction time (IT), response of children to social cues (RSC), time for caregiver initiated social interactions (GIS) and time for children initiated social interactions (CIS)). Power spectral density (PSD) values were calculated by EEG signals simultaneously recorded. Partial Pearson correlation analysis was used in both ASD groups to investigate the correlation among behavioral indicators scores and ASD symptom severity and PSD values. Receiver operating characteristic (ROC) analysis was used to describe the discrimination accuracy of behavioral indicators. RESULTS Compared to TD group, both ASD groups demonstrated significant lower scores of SIC, IT, RSC, CIS (all p values < 0.05), and significant higher time for GIS (all p values < 0.01). SIC scores negatively correlated with CARS (p = 0.006) and ADOS-CSS (p = 0.023) in the ASD with DD group. Compared to TD group, PSD values elevated in ASD groups (all p values < 0.05), and was associated with SIC (theta band: p = 0.005; alpha band: p = 0.003) but not IQ levels. SIC was effective in identifying both ASD groups (sensitivity/specificity: ASD children with DD, 76.5%/66.7%; ASD children without DD, 82.6%/82.2%). CONCLUSION Our results verified the behavioral paradigm of caregiver-child interaction as an efficient tool for early ASD screening.
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Affiliation(s)
- Lin Deng
- Department of Developmental and Behavioral Pediatric and Child Primary Care & Ministry of Education, Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Wei-Zhong He
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China
| | - Qing-Li Zhang
- Ministry of Education - Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Ling Wei
- College of Medical Imaging, Shanghai University of Medicine & Health Science, Shanghai, China
| | - Yuan Dai
- Department of Developmental and Behavioral Pediatric and Child Primary Care & Ministry of Education, Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Yu-Qi Liu
- Department of Developmental and Behavioral Pediatric and Child Primary Care & Ministry of Education, Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Zi-Lin Chen
- Department of Developmental and Behavioral Pediatric and Child Primary Care & Ministry of Education, Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Tai Ren
- Ministry of Education - Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Lin-Li Zhang
- Department of Developmental and Behavioral Pediatric and Child Primary Care & Ministry of Education, Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Jing-Bo Gong
- Shanghai Changning Mental Health Center, Shanghai, 200335, China.
| | - Fei Li
- Department of Developmental and Behavioral Pediatric and Child Primary Care & Ministry of Education, Shanghai Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
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Alho J, Samuelsson JG, Khan S, Mamashli F, Bharadwaj H, Losh A, McGuiggan NM, Graham S, Nayal Z, Perrachione TK, Joseph RM, Stoodley CJ, Hämäläinen MS, Kenet T. Both stronger and weaker cerebro-cerebellar functional connectivity patterns during processing of spoken sentences in autism spectrum disorder. Hum Brain Mapp 2023; 44:5810-5827. [PMID: 37688547 PMCID: PMC10619366 DOI: 10.1002/hbm.26478] [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: 02/17/2023] [Revised: 08/11/2023] [Accepted: 08/20/2023] [Indexed: 09/11/2023] Open
Abstract
Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age-matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left-hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro-cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus-driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI.
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Affiliation(s)
- Jussi Alho
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - John G. Samuelsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Harvard‐MIT Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Sheraz Khan
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fahimeh Mamashli
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Hari Bharadwaj
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Speech, Language, and Hearing Sciences, and Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Ainsley Losh
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nicole M. McGuiggan
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Steven Graham
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Zein Nayal
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Tyler K. Perrachione
- Department of Speech, Language, and Hearing SciencesBoston UniversityBostonMassachusettsUSA
| | - Robert M. Joseph
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Catherine J. Stoodley
- Department of PsychologyCollege of Arts and Sciences, American UniversityWashingtonDCUSA
| | - Matti S. Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Tal Kenet
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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6
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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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Shan J, Gu Y, Zhang J, Hu X, Wu H, Yuan T, Zhao D. A scoping review of physiological biomarkers in autism. Front Neurosci 2023; 17:1269880. [PMID: 37746140 PMCID: PMC10512710 DOI: 10.3389/fnins.2023.1269880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by pervasive deficits in social interaction, communication impairments, and the presence of restricted and repetitive behaviors. This complex disorder is a significant public health concern due to its escalating incidence and detrimental impact on quality of life. Currently, extensive investigations are underway to identify prospective susceptibility or predictive biomarkers, employing a physiological biomarker-based framework. However, knowledge regarding physiological biomarkers in relation to Autism is sparse. We performed a scoping review to explore putative changes in physiological activities associated with behaviors in individuals with Autism. We identified studies published between January 2000 and June 2023 from online databases, and searched keywords included electroencephalography (EEG), magnetoencephalography (MEG), electrodermal activity markers (EDA), eye-tracking markers. We specifically detected social-related symptoms such as impaired social communication in ASD patients. Our results indicated that the EEG/ERP N170 signal has undergone the most rigorous testing as a potential biomarker, showing promise in identifying subgroups within ASD and displaying potential as an indicator of treatment response. By gathering current data from various physiological biomarkers, we can obtain a comprehensive understanding of the physiological profiles of individuals with ASD, offering potential for subgrouping and targeted intervention strategies.
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Affiliation(s)
- Jiatong Shan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Arts and Sciences, New York University Shanghai, Shanghai, China
| | - Yunhao Gu
- Graduate School of Education, University of Pennsylvania, Philadelphia, PA, United States
| | - Jie Zhang
- Department of Neurology, Institute of Neurology, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoqing Hu
- Department of Psychology, The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- HKU, Shenzhen Institute of Research and Innovation, Shenzhen, China
| | - Haiyan Wu
- Center for Cognitive and Brain Sciences and Department of Psychology, Macau, China
| | - Tifei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Di Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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8
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Khadidos AO, Alyoubi KH, Mahato S, Khadidos AO, Nandan Mohanty S. Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression. Neurosci Lett 2023; 809:137313. [PMID: 37257682 DOI: 10.1016/j.neulet.2023.137313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/12/2023] [Accepted: 05/22/2023] [Indexed: 06/02/2023]
Abstract
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicide. Electroencephalogram (EEG) signals can be used to diagnose depression using machine learning techniques. The dataset studied is public dataset which consists of 30 healthy people and 34 depression patients. The methods used for detection of depression are Decision Tree, Random Forest, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long-Short Term Memory (Bi-LSTM), Gradient Boosting, Extreme Gradient Boosting (XGBoost) along with band power. Among Deep Learning techniques, CNN model got the highest accuracy with 98.13%, specificity of 99%, and sensitivity of 97% using band power features.
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Affiliation(s)
- Adil O Khadidos
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Khaled H Alyoubi
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia; Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.
| | - Shalini Mahato
- Department of Computer Science and Engineering, Indian Institute of Information Technology (IIIT), Ranchi, Jharkhand, India.
| | - Alaa O Khadidos
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Sachi Nandan Mohanty
- Department of Computer Science & Engineering, Vardhaman College of Engineering(Autonomous), Hyderabad, India.
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9
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Zhao Q, Luo Y, Mei X, Shao Z. Resting-state EEG patterns of preschool-aged boys with autism spectrum disorder: A pilot study. APPLIED NEUROPSYCHOLOGY. CHILD 2023:1-8. [PMID: 37172019 DOI: 10.1080/21622965.2023.2211702] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Defective cognition development during preschool years is believed to be linked with core symptoms of autism spectrum disorder (ASD). Neurophysiological research on mechanisms underly the cognitive disabilities of preschool-aged children with ASD is scarce currently. This pilot study aimed to compare the resting spectral EEG power of preschool-aged boys with ASD with their matched typically developing peers. Children in the ASD group demonstrated reduced central and posterior absolute delta (1-4 Hz) and enhanced frontal absolute beta (12-30 Hz) and gamma (30-45 Hz). The relative power of the ASD group was elevated in delta, theta (4-8 Hz), alpha (8-12 Hz), beta, and gamma bands as compared to the controls. The theta/beta ratio decreased in the frontal regions and enhanced at Cz and Pz electrodes in the ASD group. Correlations between the inhibition and metacognition indices of the behavior rating inventory of executive function-preschool version (BRIEF-P) and the theta/beta ratio for children of both groups were significant. In conclusion, the present study revealed atypical resting spectral characteristics of boys with ASD at preschool ages. Future large-sampled studies for the generalization of our findings and a better understanding of the relationships between brain oscillations and phenotypes of ASD are warranted.
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Affiliation(s)
- Qin Zhao
- Rehabilitation Center for Children with Autism of Chongqing, Department of Child Health Care, Ninth People's Hospital of Chongqing, Beibei, Chongqing, China
| | - Yan Luo
- Department of Child Health Care, Guiyang Maternal and Child Health Care Hospital, Guiyang, China
| | - Xinjie Mei
- Department of Child Health Care, Guiyang Maternal and Child Health Care Hospital, Guiyang, China
| | - Zhi Shao
- Rehabilitation Center for Children with Autism of Chongqing, Department of Child Health Care, Ninth People's Hospital of Chongqing, Beibei, Chongqing, China
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10
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Gallina J, Marsicano G, Romei V, Bertini C. Electrophysiological and Behavioral Effects of Alpha-Band Sensory Entrainment: Neural Mechanisms and Clinical Applications. Biomedicines 2023; 11:biomedicines11051399. [PMID: 37239069 DOI: 10.3390/biomedicines11051399] [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: 03/30/2023] [Revised: 04/28/2023] [Accepted: 05/04/2023] [Indexed: 05/28/2023] Open
Abstract
Alpha-band (7-13 Hz) activity has been linked to visuo-attentional performance in healthy participants and to impaired functionality of the visual system in a variety of clinical populations including patients with acquired posterior brain lesion and neurodevelopmental and psychiatric disorders. Crucially, several studies suggested that short uni- and multi-sensory rhythmic stimulation (i.e., visual, auditory and audio-visual) administered in the alpha-band effectively induces transient changes in alpha oscillatory activity and improvements in visuo-attentional performance by synchronizing the intrinsic brain oscillations to the external stimulation (neural entrainment). The present review aims to address the current state of the art on the alpha-band sensory entrainment, outlining its potential functional effects and current limitations. Indeed, the results of the alpha-band entrainment studies are currently mixed, possibly due to the different stimulation modalities, task features and behavioral and physiological measures employed in the various paradigms. Furthermore, it is still unknown whether prolonged alpha-band sensory entrainment might lead to long-lasting effects at a neural and behavioral level. Overall, despite the limitations emerging from the current literature, alpha-band sensory entrainment may represent a promising and valuable tool, inducing functionally relevant changes in oscillatory activity, with potential rehabilitative applications in individuals characterized by impaired alpha activity.
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Affiliation(s)
- Jessica Gallina
- Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, 47521 Cesena, Italy
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, 40121 Bologna, Italy
| | - Gianluca Marsicano
- Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, 47521 Cesena, Italy
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, 40121 Bologna, Italy
| | - Vincenzo Romei
- Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, 47521 Cesena, Italy
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, 40121 Bologna, Italy
| | - Caterina Bertini
- Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Via Rasi e Spinelli 176, 47521 Cesena, Italy
- Department of Psychology, University of Bologna, Viale Berti Pichat 5, 40121 Bologna, Italy
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11
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Han C, Guo M, Ke X, Zeng L, Li M, Haihambo N, Lu J, Wang L, Wei P. Oscillatory biomarkers of autism: evidence from the innate visual fear evoking paradigm. Cogn Neurodyn 2023; 17:459-466. [PMID: 37007195 PMCID: PMC10050250 DOI: 10.1007/s11571-022-09839-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 11/03/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with multiple associated deficits in both social and cognitive functioning. Diagnosing ASD usually relies on subjective clinical competencies, and research on objective criteria for diagnosing ASD in the early stage is still in its infancy. A recent animal study showed that the looming-evoked defensive response was impaired in mice with ASD, but whether the effect will be observed in human and contribute to finding a robust clinical neural biomarker remain unclear. Here, to investigate the looming-evoked defense response in humans, electroencephalogram responses toward looming and corresponding control stimuli (far and missing type) were recorded in children with ASD and typical developed (TD) children. Results revealed that alpha-band activity in the posterior brain region was strongly suppressed after looming stimuli in the TD group, but remained unchanged in the ASD group. This method could be a novel, objective way to detect ASD earlier. These findings suggest that further investigation of the neural mechanism underlying innate fear from the oscillatory view could be a helpful direction in the future. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09839-6.
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Affiliation(s)
- Chuanliang Han
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
| | - Mingrou Guo
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
| | - Xiaoyin Ke
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Lanting Zeng
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
| | - Meijia Li
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Naem Haihambo
- Faculty of Psychology and Center for Neuroscience, Vrije Universiteit Brussel, 1050 Brussels, Belgium
| | - Jianping Lu
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China
| | - Liping Wang
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Pengfei Wei
- Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen Fundamental Research Institutions, Shenzhen, 518055 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
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12
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Bogéa Ribeiro L, da Silva Filho M. Systematic Review on EEG Analysis to Diagnose and Treat Autism by Evaluating Functional Connectivity and Spectral Power. Neuropsychiatr Dis Treat 2023; 19:415-424. [PMID: 36861010 PMCID: PMC9968781 DOI: 10.2147/ndt.s394363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/05/2023] [Indexed: 02/24/2023] Open
Abstract
An abnormality in neural connectivity is linked to autism spectrum disorder (ASD). There is no way to test the concept of neural connectivity empirically. According to recent network theory and time series analysis findings, electroencephalography (EEG) can assess neural network architecture, a sign of activity in the brain. This systematic review aims to evaluate functional connectivity and spectral power using EEG signals. EEG records the brain activity of an individual by displaying wavy lines that depict brain cells' communication through electrical impulses. EEG can diagnose various brain disorders, including epilepsy and related seizure illness, brain dysfunction, tumors, and damage. We found 21 studies using two of the most common EEG analysis methods: functional connectivity and spectral power. ASD and non-ASD individuals were found to differ significantly in all selected papers. Due to high heterogeneity in the outcomes, generalizations cannot be drawn, and no single method is currently beneficial as a diagnostic tool. For ASD subtype delineation, the lack of research prevented the evaluation of these techniques as diagnostic tools. These findings confirm the presence of abnormalities in the EEG in ASD, but they are insufficient to diagnose. Our study suggests that EEG is useful in diagnosing ASD by evaluating entropy in the brain. Researchers may be able to develop new diagnostic methods for ASD which focuses on particular stimuli and brainwaves if they conduct more extensive studies with higher numbers and more rigorous study designs.
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13
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Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity. Sci Rep 2022; 12:19016. [PMID: 36347938 PMCID: PMC9643446 DOI: 10.1038/s41598-022-22597-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] Open
Abstract
There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.
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14
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Prany W, Patrice C, Franck D, Fabrice W, Mahdi M, Pierre D, Christian M, Jean-Marc G, Fabian G, Francis E, Jean-Marc B, Bérengère GG. EEG resting-state functional connectivity: evidence for an imbalance of external/internal information integration in autism. J Neurodev Disord 2022; 14:47. [PMID: 36030210 PMCID: PMC9419397 DOI: 10.1186/s11689-022-09456-8] [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: 11/23/2021] [Accepted: 08/04/2022] [Indexed: 01/12/2023] Open
Abstract
Background Autism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD. Methods Using high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched). Results We observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band. Conclusion These results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts. Supplementary Information The online version contains supplementary material available at 10.1186/s11689-022-09456-8.
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Affiliation(s)
- Wantzen Prany
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.,Université de Paris, LaPsyDÉ, CNRS, F-75005, Paris, France
| | - Clochon Patrice
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Doidy Franck
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Wallois Fabrice
- INSERM UMR-S 1105, GRAMFC, Université de Picardie-Jules Verne, CHU Sud, 80025, Amiens, France
| | - Mahmoudzadeh Mahdi
- INSERM UMR-S 1105, GRAMFC, Université de Picardie-Jules Verne, CHU Sud, 80025, Amiens, France
| | - Desaunay Pierre
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Mille Christian
- Centre Ressources Autisme Picardie, Service de Psychopathologie Enfants et Adolescents, CHU, 4 rue Grenier et Bernard, 80000, Amiens, France
| | - Guilé Jean-Marc
- INSERM UMR-S 1105, GRAMFC, Université de Picardie-Jules Verne, CHU Sud, 80025, Amiens, France.,Centre Ressources Autisme Picardie, Service de Psychopathologie Enfants et Adolescents, CHU, 4 rue Grenier et Bernard, 80000, Amiens, France
| | - Guénolé Fabian
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Eustache Francis
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France
| | - Baleyte Jean-Marc
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.,Service de Psychiatrie de l'enfant et de l'adolescent, Centre Hospitalier Interuniversitaire de Créteil, 94000, Créteil, France
| | - Guillery-Girard Bérengère
- Normandie univ, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14000, Caen, France.
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15
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Krause MR, Vieira PG, Thivierge JP, Pack CC. Brain stimulation competes with ongoing oscillations for control of spike timing in the primate brain. PLoS Biol 2022; 20:e3001650. [PMID: 35613140 PMCID: PMC9132296 DOI: 10.1371/journal.pbio.3001650] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/27/2022] [Indexed: 11/19/2022] Open
Abstract
Transcranial alternating current stimulation (tACS) is a popular method for modulating brain activity noninvasively. In particular, tACS is often used as a targeted intervention that enhances a neural oscillation at a specific frequency to affect a particular behavior. However, these interventions often yield highly variable results. Here, we provide a potential explanation for this variability: tACS competes with the brain's ongoing oscillations. Using neural recordings from alert nonhuman primates, we find that when neural firing is independent of ongoing brain oscillations, tACS readily entrains spiking activity, but when neurons are strongly entrained to ongoing oscillations, tACS often causes a decrease in entrainment instead. Consequently, tACS can yield categorically different results on neural activity, even when the stimulation protocol is fixed. Mathematical analysis suggests that this competition is likely to occur under many experimental conditions. Attempting to impose an external rhythm on the brain may therefore often yield precisely the opposite effect.
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Affiliation(s)
- Matthew R. Krause
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Pedro G. Vieira
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jean-Philippe Thivierge
- School of Psychology, University of Ottawa, Ottawa, Ontario, Canada
- Brain and Mind Research Institute University of Ottawa, Ottawa, Ontario, Canada
| | - Christopher C. Pack
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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16
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Williams OOF, Coppolino M, Perreault ML. Sex differences in neuronal systems function and behaviour: beyond a single diagnosis in autism spectrum disorders. Transl Psychiatry 2021; 11:625. [PMID: 34887388 PMCID: PMC8660826 DOI: 10.1038/s41398-021-01757-1] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/30/2021] [Indexed: 12/12/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is associated with functional brain alterations that underlie the expression of behaviour. Males are diagnosed up to four times more than females, and sex differences have been identified in memory, cognitive flexibility, verbal fluency, and social communication. Unfortunately, there exists a lack of information on the sex-dependent mechanisms of ASD, as well as biological markers to distinguish sex-specific symptoms in ASD. This can often result in a standardized diagnosis for individuals across the spectrum, despite significant differences in the various ASD subtypes. Alterations in neuronal connectivity and oscillatory activity, such as is observed in ASD, are highly coupled to behavioural states. Yet, despite the well-identified sexual dimorphisms that exist in ASD, these functional patterns have rarely been analyzed in the context of sex differences or symptomology. This review summarizes alterations in neuronal oscillatory function in ASD, discusses the age, region, symptom and sex-specific differences that are currently observed across the spectrum, and potential targets for regulating neuronal oscillatory activity in ASD. The need to identify sex-specific biomarkers, in order to facilitate specific diagnostic criteria and allow for more targeted therapeutic approaches for ASD will also be discussed.
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Affiliation(s)
| | | | - Melissa L Perreault
- Department of Biomedical Sciences, University of Guelph, Guelph, ON, Canada.
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17
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Tarasi L, Trajkovic J, Diciotti S, di Pellegrino G, Ferri F, Ursino M, Romei V. Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model. Neurosci Biobehav Rev 2021; 132:1-22. [PMID: 34774901 DOI: 10.1016/j.neubiorev.2021.11.006] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/29/2021] [Accepted: 11/07/2021] [Indexed: 12/14/2022]
Abstract
The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.
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Affiliation(s)
- Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy.
| | - Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy; Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | - Giuseppe di Pellegrino
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Mauro Ursino
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521 Cesena, Italy; IRCCS Fondazione Santa Lucia, 00179 Rome, Italy.
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18
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Neuhaus E, Lowry SJ, Santhosh M, Kresse A, Edwards LA, Keller J, Libsack EJ, Kang VY, Naples A, Jack A, Jeste S, McPartland JC, Aylward E, Bernier R, Bookheimer S, Dapretto M, Van Horn JD, Pelphrey K, Webb SJ. Resting state EEG in youth with ASD: age, sex, and relation to phenotype. J Neurodev Disord 2021; 13:33. [PMID: 34517813 PMCID: PMC8439051 DOI: 10.1186/s11689-021-09390-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/17/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy. METHODS We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations. RESULTS Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD. CONCLUSIONS Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.
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Affiliation(s)
- Emily Neuhaus
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Sarah J Lowry
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA
| | - Megha Santhosh
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA
| | - Anna Kresse
- Mailman School of Public Health, Columbia University, New York, USA
| | - Laura A Edwards
- School of Medicine, Emory University, Atlanta, GA, USA
- Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Jack Keller
- Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, USA
| | - Erin J Libsack
- Department of Psychology, Stony Brook University, Stony Brook, USA
| | - Veronica Y Kang
- Department of Special Education, University of Illinois at Chicago, Chicago, USA
| | - Adam Naples
- Yale Child Study Center, Yale University, New Haven, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, USA
| | - Shafali Jeste
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, USA
- Intellectual and Developmental Disabilities Research Center, University of California Los Angeles, Los Angeles, USA
| | | | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, USA
| | - Raphael Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA
| | - Susan Bookheimer
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, USA
- Intellectual and Developmental Disabilities Research Center, University of California Los Angeles, Los Angeles, USA
| | - Mirella Dapretto
- Department of Psychiatry & Biobehavioral Sciences, University of California Los Angeles School of Medicine, Los Angeles, USA
- Intellectual and Developmental Disabilities Research Center, University of California Los Angeles, Los Angeles, USA
| | - John D Van Horn
- Department of Psychology, University of Virginia, Charlottesville, USA
- School of Data Science, University of Virginia, Charlottesville, USA
| | - Kevin Pelphrey
- Department of Psychology, University of Virginia, Charlottesville, USA
- Department of Neurology, Brain Institute and School of Education and Human Development, University of Virginia, Charlottesville, USA
| | - Sara Jane Webb
- Center on Child Health, Behavior and Development, Seattle Children's Research Institute, 1920 Terry Ave, CURE-03, Seattle, WA, 98101, USA.
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, USA.
- Intellectual and Developmental Disabilities Research Center, University of Washington, Seattle, USA.
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19
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Wired to Punish? Electroencephalographic Study of the Resting-state Neuronal Oscillations Underlying Third-party Punishment. Neuroscience 2021; 471:1-10. [PMID: 34302905 DOI: 10.1016/j.neuroscience.2021.07.012] [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: 12/08/2020] [Revised: 07/08/2021] [Accepted: 07/13/2021] [Indexed: 11/22/2022]
Abstract
For over a decade, neuroimaging and brain stimulation studies have investigated neural mechanisms of third-party punishment, a key instrument for social norms enforcement. However, the neural dynamics underlying these mechanisms are still unclear. Previous electroencephalographic studies on third-party punishment have shown that inter-brain connectivity is linked to punishment behavior. However, no clear evidence was provided regarding whether the effect of inter-brain connectivity on third-party punishment is mediated by local neuronal states. In this study, we further investigate whether resting-state neuronal activity in the alpha frequency range can predict individual differences in third-party punishment. More specifically, we show that the global resting-state connectivity between the right dorsolateral prefrontal and right temporo-parietal regions is negatively correlated with the level of third-party punishment. Additionally, individuals with stronger local resting-state long-range temporal correlations in the right temporo-parietal cortices demonstrated a lower level of third-party punishment. Thus, our results further support the idea that global and local neuronal dynamics can contribute to individual differences in third-party punishment.
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McCracken JT, Anagnostou E, Arango C, Dawson G, Farchione T, Mantua V, McPartland J, Murphy D, Pandina G, Veenstra-VanderWeele J. Drug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions. Eur Neuropsychopharmacol 2021; 48:3-31. [PMID: 34158222 PMCID: PMC10062405 DOI: 10.1016/j.euroneuro.2021.05.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 05/13/2021] [Accepted: 05/18/2021] [Indexed: 12/11/2022]
Abstract
In 2017, facing lack of progress and failures encountered in targeted drug development for Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders, the ISCTM with the ECNP created the ASD Working Group charged to identify barriers to progress and recommending research strategies for the field to gain traction. Working Group international academic, regulatory and industry representatives held multiple in-person meetings, teleconferences, and subgroup communications to gather a wide range of perspectives on lessons learned from extant studies, current challenges, and paths for fundamental advances in ASD therapeutics. This overview delineates the barriers identified, and outlines major goals for next generation biomedical intervention development in ASD. Current challenges for ASD research are many: heterogeneity, lack of validated biomarkers, need for improved endpoints, prioritizing molecular targets, comorbidities, and more. The Working Group emphasized cautious but unwavering optimism for therapeutic progress for ASD core features given advances in the basic neuroscience of ASD and related disorders. Leveraging genetic data, intermediate phenotypes, digital phenotyping, big database discovery, refined endpoints, and earlier intervention, the prospects for breakthrough treatments are substantial. Recommendations include new priorities for expanded research funding to overcome challenges in translational clinical ASD therapeutic research.
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Affiliation(s)
- James T McCracken
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, United States.
| | | | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Univesitario Gregorio Maranon, and School of Medicine, Universidad Complutense de Madrid, CIBERSAM, Madrid, Spain
| | - Geraldine Dawson
- Duke University Medical Center, Durham, North Carolina, United States
| | - Tiffany Farchione
- Food and Drug Administration, Silver Spring, Maryland, United States
| | - Valentina Mantua
- Food and Drug Administration, Silver Spring, Maryland, United States
| | | | - Declan Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King's College De Crespigny Park, Denmark Hill, London SE5 8AF, United Kingdom
| | - Gahan Pandina
- Neuroscience Therapeutic Area, Janssen Research & Development, Pennington, New Jersey, United States
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21
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Gagnon K, Bolduc C, Bastien L, Godbout R. REM Sleep EEG Activity and Clinical Correlates in Adults With Autism. Front Psychiatry 2021; 12:659006. [PMID: 34168578 PMCID: PMC8217632 DOI: 10.3389/fpsyt.2021.659006] [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: 01/26/2021] [Accepted: 05/06/2021] [Indexed: 12/02/2022] Open
Abstract
We tested the hypothesis of an atypical scalp distribution of electroencephalography (EEG) activity during Rapid Eye Movement (REM) sleep in young autistic adults. EEG spectral activity and ratios along the anteroposterior axis and across hemispheres were compared in 16 neurotypical (NT) young adults and 17 individuals with autism spectrum disorder (ASD). EEG spectral power was lower in the ASD group over the bilateral central and right parietal (beta activity) as well as bilateral occipital (beta, theta, and total activity) recording sites. The NT group displayed a significant posterior polarity of intra-hemispheric EEG activity while EEG activity was more evenly or anteriorly distributed in ASD participants. No significant inter-hemispheric EEG lateralization was found. Correlations between EEG distribution and ASD symptoms using the Autism Diagnostic Interview-Revised (ADI-R) showed that a higher posterior ratio was associated with a better ADI-R score on communication skills, whereas a higher anterior ratio was related to more restricted interests and repetitive behaviors. EEG activity thus appears to be atypically distributed over the scalp surface in young adults with autism during REM sleep within cerebral hemispheres, and this correlates with some ASD symptoms. These suggests the existence in autism of a common substrate between some of the symptoms of ASD and an atypical organization and/or functioning of the thalamo-cortical loop during REM sleep.
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Affiliation(s)
- Katia Gagnon
- Sleep Laboratory and Clinic, Hôpital en santé mentale Rivière-des-Prairies, Montréal, QC, Canada.,Departement of Psychiatry, Université de Montréal, Montréal, QC, Canada
| | - Christianne Bolduc
- Sleep Laboratory and Clinic, Hôpital en santé mentale Rivière-des-Prairies, Montréal, QC, Canada
| | - Laurianne Bastien
- Sleep Laboratory and Clinic, Hôpital en santé mentale Rivière-des-Prairies, Montréal, QC, Canada.,Departement of Psychology, Université de Montréal, Montréal, QC, Canada
| | - Roger Godbout
- Sleep Laboratory and Clinic, Hôpital en santé mentale Rivière-des-Prairies, Montréal, QC, Canada.,Departement of Psychiatry, Université de Montréal, Montréal, QC, Canada
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22
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Pierce S, Kadlaskar G, Edmondson DA, McNally Keehn R, Dydak U, Keehn B. Associations between sensory processing and electrophysiological and neurochemical measures in children with ASD: an EEG-MRS study. J Neurodev Disord 2021; 13:5. [PMID: 33407072 PMCID: PMC7788714 DOI: 10.1186/s11689-020-09351-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 12/14/2020] [Indexed: 12/15/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is associated with hyper- and/or hypo-sensitivity to sensory input. Spontaneous alpha power, which plays an important role in shaping responsivity to sensory information, is reduced across the lifespan in individuals with ASD. Furthermore, an excitatory/inhibitory imbalance has also been linked to sensory dysfunction in ASD and has been hypothesized to underlie atypical patterns of spontaneous brain activity. The present study examined whether resting-state alpha power differed in children with ASD as compared to TD children, and investigated the relationships between alpha levels, concentrations of excitatory and inhibitory neurotransmitters, and atypical sensory processing in ASD. Methods Participants included thirty-one children and adolescents with ASD and thirty-one age- and IQ-matched typically developing (TD) participants. Resting-state electroencephalography (EEG) was used to obtain measures of alpha power. A subset of participants (ASD = 16; TD = 16) also completed a magnetic resonance spectroscopy (MRS) protocol in order to measure concentrations of excitatory (glutamate + glutamine; Glx) and inhibitory (GABA) neurotransmitters. Results Children with ASD evidenced significantly decreased resting alpha power compared to their TD peers. MRS estimates of GABA and Glx did not differ between groups with the exception of Glx in the temporal-parietal junction. Inter-individual differences in alpha power within the ASD group were not associated with region-specific concentrations of GABA or Glx, nor were they associated with sensory processing differences. However, atypically decreased Glx was associated with increased sensory impairment in children with ASD. Conclusions Although we replicated prior reports of decreased alpha power in ASD, atypically reduced alpha was not related to neurochemical differences or sensory symptoms in ASD. Instead, reduced Glx in the temporal-parietal cortex was associated with greater hyper-sensitivity in ASD. Together, these findings may provide insight into the neural underpinnings of sensory processing differences present in ASD. Supplementary Information The online version contains supplementary material available at 10.1186/s11689-020-09351-0.
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Affiliation(s)
- Sarah Pierce
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Girija Kadlaskar
- Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA
| | - David A Edmondson
- Cincinnati Children's Hospital Medical Center, Imaging Research Center, Cincinnati, OH, USA
| | - Rebecca McNally Keehn
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ulrike Dydak
- School of Health Sciences, Purdue University, West Lafayette, IN, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brandon Keehn
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA. .,Department of Speech, Language, and Hearing Sciences, Purdue University, West Lafayette, IN, USA.
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23
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Haendel AD, Barrington A, Magnus B, Arias AA, McVey A, Pleiss S, Carson A, Vogt EM, Van Hecke AV. Changes in Electroencephalogram Coherence in Adolescents With Autism Spectrum Disorder After a Social Skills Intervention. Autism Res 2021; 14:787-803. [PMID: 33398936 DOI: 10.1002/aur.2459] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 11/11/2022]
Abstract
Autism spectrum disorder (ASD) is a developmental condition that affects social communication and behavior. There is consensus that neurological differences are present in ASD. Further, theories emphasize the mixture of hypo- and hyper-connectivity as a neuropathologies in ASD [O'Reilly, Lewis, & Elsabbagh, 2017]; however, there is a paucity of studies specifically testing neurological underpinnings as predictors of success on social skills interventions. This study examined functional neural connectivity (electroencephalogram [EEG], coherence) of adolescents with ASD before and after the Program for the Education and Enrichment of Relational Skills (PEERS®) intervention, using a randomized controlled trial of two groups: an Experimental ASD (EXP) Group and a Waitlist Control ASD (WL) Group. The study had two purposes. First, the study aimed to determine whether changes in EEG coherence differed for adolescents that received PEERS® versus those that did not receive PEERS®. Results revealed a significant increase in connectivity in the occipital left to temporal left pair for the EXP group after intervention. Second, the study aimed to determine if changes in EEG coherence related to changes in behavior, friendships, and social skills measured by questionnaires. At post-intervention, results indicated: (a) positive change in frontal right to parietal right coherence was linked to an increase in social skills scores; and (b) positive changes in occipital right to temporal right coherence and occipital left to parietal left coherence were linked to an increase in the total number of get-togethers. Results of this study support utilizing neurobehavioral domains as indicators of treatment outcome. Lay Summary: This study examined how well various areas of the brain communicate in adolescents with ASD before and after a social skills intervention. Results revealed increased connectivity in the adolescents that received the intervention. Secondly, the study aimed to determine if changes in connectivity of brain areas related to changes in behavior, friendships, and social skills. Results indicated that changes in connectivity were also linked to increased social skills. Autism Res 2021, 14: 787-803. © 2021 International Society for Autism Research and Wiley Periodicals LLC.
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Affiliation(s)
- Angela D Haendel
- Department of Speech-Language Pathology, Concordia University Wisconsin, Grafton, Wisconsin, USA
| | - Alexander Barrington
- Department of Biomedical Engineering, Marquette University, Milwaukee, Wisconsin, USA
| | - Brooke Magnus
- Department of Psychology, Boston College, Boston, Massachusetts, USA
| | - Alexis A Arias
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
| | - Alana McVey
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA.,Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, California, Los Angeles, USA
| | - Sheryl Pleiss
- Great Lakes Neurobehavioral Center, Edina, Minnesota, USA
| | | | - Elisabeth M Vogt
- Medical College of Wisconsin, Neurology, Wauwatosa, Wisconsin, USA
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24
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Wang J, Wang X, Wang X, Zhang H, Zhou Y, Chen L, Li Y, Wu L. Increased EEG coherence in long-distance and short-distance connectivity in children with autism spectrum disorders. Brain Behav 2020; 10:e01796. [PMID: 32815287 PMCID: PMC7559606 DOI: 10.1002/brb3.1796] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 07/20/2020] [Accepted: 07/21/2020] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Autism spectrum disorder (ASD) is a complex and prevalent neurodevelopmental disorder characterized by deficits in social communication and social interaction as well as repetitive behaviors. Alterations in function connectivity are widely recognized in recent electroencephalogram (EEG) studies. However, most studies have not reached consistent conclusions, which could be due to the developmental nature and the heterogeneity of ASD. METHODS Here, EEG coherence analysis was used in a cohort of children with ASD (n = 13) and matched typically developing controls (TD, n = 15) to examine the functional connectivity characteristics in long-distance and short-distance electrode pairs. Subsequently, we explore the association between the connectivity strength of coherence and symptom severity in children with ASD. RESULTS Compared with TD group, individuals with ASD showed increased coherence in short-distance electrode pairs in the right temporal-parietal region (delta, alpha, beta bands), left temporal-parietal region (all frequency bands), occipital region (theta, alpha, beta bands), right central-parietal region (delta, alpha, beta bands), and the prefrontal region (only beta band). In the long-distance coherence analysis, the ASD group showed increased coherence in bilateral frontal region, temporal region, parietal region, and frontal-occipital region in alpha and beta bands. The strength of such connections was associated with symptom severity. DISCUSSION Our study indicates that abnormal connectivity patterns in neuroelectrophysiology may be of critical importance to acknowledge the underlying brain mechanism.
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Affiliation(s)
- Jia Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Xiaomin Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Xuelai Wang
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huiying Zhang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Yong Zhou
- Heilongjiang Province Center for Disease Control and Prevention, Harbin, China
| | - Lei Chen
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Yutong Li
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
| | - Lijie Wu
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, China
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25
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Yates L, Hobson H. Continuing to look in the mirror: A review of neuroscientific evidence for the broken mirror hypothesis, EP-M model and STORM model of autism spectrum conditions. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2020; 24:1945-1959. [PMID: 32668956 PMCID: PMC7539595 DOI: 10.1177/1362361320936945] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The mirror neuron system has been argued to be a key brain system responsible for action understanding and imitation. Subsequently, mirror neuron system dysfunction has therefore been proposed to explain the social deficits manifested within autism spectrum condition, an approach referred to as the broken mirror hypothesis. Despite excitement surrounding this hypothesis, extensive research has produced insufficient evidence to support the broken mirror hypothesis in its pure form, and instead two alternative models have been formulated: EP-M model and the social top-down response modulation (STORM) model. All models suggest some dysfunction regarding the mirror neuron system in autism spectrum condition, be that within the mirror neuron system itself or systems that regulate the mirror neuron system. This literature review compares these three models in regard to recent neuroscientific investigations. This review concludes that there is insufficient support for the broken mirror hypothesis, but converging evidence supports an integrated EP-M and STORM model.
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26
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Comparing different EEG connectivity methods in young males with ASD. Behav Brain Res 2020; 383:112482. [PMID: 31972185 DOI: 10.1016/j.bbr.2020.112482] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 12/24/2019] [Accepted: 01/13/2020] [Indexed: 12/27/2022]
Abstract
Although EEG connectivity data are often used to build models of the association between overt behavioural signs of Autism Spectrum Disorder (ASD) and underlying brain connectivity indices, use of a large number of possible connectivity methods across studies has produced a fairly inconsistent set of results regarding this association. To explore the level of agreement between results from five commonly-used EEG connectivity models (i.e., Coherence, Weighted Phased Lag Index- Debiased, Phase Locking Value, Phase Slope Index, Granger Causality), a sample of 41 young males with ASD provided EEG data under eyes-opened and eyes-closed conditions. There were relatively few statistically significant and/or meaningful correlations between the results obtained from the five connectivity methods, arguing for a re-estimation of the methodology used in such studies so that specific connectivity methods may be matched to particular research questions regarding the links between neural connectivity and overt behaviour within this population.
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27
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Sarmukadam K, Sharpley CF, Bitsika V, McMillan MME, Agnew LL. A review of the use of EEG connectivity to measure the neurological characteristics of the sensory features in young people with autism. Rev Neurosci 2020; 30:497-510. [PMID: 30269108 DOI: 10.1515/revneuro-2018-0070] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Accepted: 08/03/2018] [Indexed: 11/15/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting about 1 in 100 children and is currently incurable. ASD represents a challenge to traditional methods of assessment and diagnosis, and it has been suggested that direct measures of brain activity and connectivity between brain regions during demanding tasks represents a potential pathway to building more accurate models of underlying brain function and ASD. One of the key behavioural diagnostic indicators of ASD consists of sensory features (SF), often characterised by over- or under-reactivity to environmental stimuli. SF are associated with behavioural difficulties that impede social and education success in these children as well as anxiety and depression. This review examines the previous literature on the measurement of EEG connectivity and SF observed in individuals with ASD.
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Affiliation(s)
- Kimaya Sarmukadam
- Brain-Behaviour Research Group, University of New England, Armidale 2350, New South Wales, Australia
| | - Christopher F Sharpley
- Brain-Behaviour Research Group, University of New England, Armidale 2350, New South Wales, Australia
| | - Vicki Bitsika
- Centre for Autism Spectrum Disorder, Bond University, Gold Coast 4229, Queensland, Australia
| | - Mary M E McMillan
- Brain-Behaviour Research Group, University of New England, Armidale 2350, New South Wales, Australia
| | - Linda L Agnew
- Brain-Behaviour Research Group, University of New England, Armidale 2350, New South Wales, Australia
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28
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Kang J, Han X, Song J, Niu Z, Li X. The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data. Comput Biol Med 2020; 120:103722. [PMID: 32250854 DOI: 10.1016/j.compbiomed.2020.103722] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 03/11/2020] [Accepted: 03/21/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE To identify autistic children, we used features extracted from two modalities (EEG and eye-tracking) as input to a machine learning approach (SVM). METHODS A total of 97 children aged from 3 to 6 were enrolled in the present study. After resting-state EEG data recording, the children performed eye-tracking tests individually on own-race and other-race stranger faces stimuli. Power spectrum analysis was used for EEG analysis and areas of interest (AOI) were selected for face gaze analysis of eye-tracking data. The minimum redundancy maximum relevance (MRMR) feature selection method combined with SVM classifiers were used for classification of autistic versus typically developing children. RESULTS Results showed that classification accuracy from combining two types of data reached a maximum of 85.44%, with AUC = 0.93, when 32 features were selected. LIMITATIONS The sample consisted of children aged from 3 to 6, and no younger patients were included. CONCLUSIONS Our machine learning approach, combining EEG and eye-tracking data, may be a useful tool for the identification of children with ASD, and may help for diagnostic processes.
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Affiliation(s)
- Jiannan Kang
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Xiaoya Han
- School of Information Science & Engineering, Yanshan University, Qinhuangdao, China
| | - Jiajia Song
- College of Electronic & Information Engineering, Hebei University, Baoding, China
| | - Zikang Niu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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29
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Sadeghi S, Pouretemad H, Khosrowabadi R, Fathabadi J, Nikbakht S. Behavioral and electrophysiological evidence for parent training in young children with autism symptoms and excessive screen-time. Asian J Psychiatr 2019; 45:7-12. [PMID: 31430692 DOI: 10.1016/j.ajp.2019.08.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 06/09/2019] [Accepted: 08/03/2019] [Indexed: 11/27/2022]
Abstract
Recent studies have shown the relationship between excessive screen time and autism symptoms. Unfortunately, there are no studies that evaluated the interventions for children with autism symptoms and excessive screen-time. This paper is a preliminary attempt to examine the effects of parent training on the duration of screen-time, repetitive behaviors and brain electrophysiological characteristics in young children with subthreshold autism symptoms and excessive screen time. Results showed that after the 2 months' parent-child interaction, children's screen-time and repetitive behaviors decreased and EEG ratio power in some channels changed. Our findings suggest that parent training have positive effects on young children with excessive screen-time and autism symptoms.
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Affiliation(s)
- Saeid Sadeghi
- Dept. of Clinical & Health Psychology, Shahid Beheshti University, Tehran, Iran.
| | - Hamidreza Pouretemad
- Dept. of Clinical & Health Psychology, Shahid Beheshti University, Tehran, Iran; Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
| | - Jalil Fathabadi
- Department of Educational and Developmental Psychology, Shahid Beheshti University, Tehran, Iran.
| | - Sedighe Nikbakht
- Department of Pediatric Neurology, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.
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30
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Mehdizadefar V, Ghassemi F, Fallah A. Brain Connectivity Reflected in Electroencephalogram Coherence in Individuals With Autism: A Meta-Analysis. Basic Clin Neurosci 2019; 10:409-417. [PMID: 32284830 PMCID: PMC7149956 DOI: 10.32598/bcn.9.10.375] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 05/15/2018] [Accepted: 10/07/2018] [Indexed: 01/24/2023] Open
Abstract
INTRODUCTION Many theories have been proposed about the etiology of autism. One is related to brain connectivity in patients with autism. Several studies have reported brain connectivity changes in autism disease. This study was performed on Electroencephalogram (EEG) studies that evaluated patients with autism, using functional brain connectivity, and compared them with typically-developing individuals. METHODS Three scientific databases of ScienceDirect, Medline (PubMed), and BioMed Central were systematically searched through their online search engines. Comprehensive Meta-analysis software analyzed the obtained data. RESULTS The systematic search led to 10 papers, in which EEG coherence was used to obtain the brain connectivity of people with autism. To determine the effect size, Cohen's d parameter was used. In the first meta-analysis, the study of the maximum effect size was considered, and all significant effect sizes were evaluated in the second meta-analysis. The effect size was assessed using a random-effects model in both meta-analyses. The results of the first meta-analysis indicated that heterogeneity was not present among the studies (Q=13.345, P>0.1). The evaluation of all effect sizes in the second meta-analysis showed a significant lack of homogeneity among the studies (Q=56.984, P=0.0001). CONCLUSION On the whole, autism was found to be related to neural connectivity, and the present research showed the difference in the EEG coherence of people with autism and healthy people. These conclusions require further studies with more extensive data, considering different brain regions, and novel analysis techniques for assessing brain connectivity.
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Affiliation(s)
- Vida Mehdizadefar
- Department of Biomedical Engineering, School of Electrical, Computer & Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Fanaz Ghassemi
- Department of Biomedical Engineering, School of Electrical, Computer & Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Ali Fallah
- Department of Biomedical Engineering, School of Electrical, Computer & Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
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31
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Tanu, Kakkar D. Diagnostic Assessment Techniques and Non-Invasive Biomarkers for Autism Spectrum Disorder. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2019. [DOI: 10.4018/ijehmc.2019070105] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Autism spectrum disorder (ASD) is a complex heterogeneous neurological disorder that has led to a spectrum of diagnosis techniques. The screening instruments, medical and technological tools initiate the diagnosis process. Clinicians and psychologists propose therapies depending on the examination done by these methodologies. The literature has accounted dozens of diagnostic methods and alternative and complementary therapies but still lack in highlighting the proper biomarker for early detection and intervention. The emerging multi-modal neuro-imaging techniques have correlated the brain's functional and structural measures and diagnosed ASD with more sensitivity than individual approaches. The purpose of this review article is: (i) to provide an overview of the emerging ASD diagnosis methods and different markers and; (ii) to present the idea of integrating all the individual methods in to a multi-modal diagnostic system to enhance detection sensitivity. This system possesses the potential to diagnose and predict ASD clinically, neurologically & objectively with high detection sensitivity.
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Affiliation(s)
- Tanu
- Dr. B R Ambedkar National Institute of Technology, Jalandhar, India
| | - Deepti Kakkar
- Dr B R Ambedkar National institute of Technology, Jalandhar, India
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Abstract
Altered power of resting-state neurophysiological activity has been associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. We compared resting-state neurophysiological power in children with ASD, ADHD, co-occurring ASD + ADHD, and typically developing controls. Children with ASD (ASD/ASD + ADHD) showed reduced theta and alpha power compared to children without ASD (controls/ADHD). Children with ADHD (ADHD/ASD + ADHD) displayed decreased delta power compared to children without ADHD (ASD/controls). Children with ASD + ADHD largely presented as an additive co-occurrence with deficits of both disorders, although reduced theta compared to ADHD-only and reduced delta compared to controls suggested some unique markers. Identifying specific neurophysiological profiles in ASD and ADHD may assist in characterising more homogeneous subgroups to inform treatment approaches and aetiological investigations.
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33
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Newson JJ, Thiagarajan TC. EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies. Front Hum Neurosci 2019; 12:521. [PMID: 30687041 PMCID: PMC6333694 DOI: 10.3389/fnhum.2018.00521] [Citation(s) in RCA: 351] [Impact Index Per Article: 70.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 12/11/2018] [Indexed: 12/19/2022] Open
Abstract
A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.
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Lefebvre A, Delorme R, Delanoë C, Amsellem F, Beggiato A, Germanaud D, Bourgeron T, Toro R, Dumas G. Alpha Waves as a Neuromarker of Autism Spectrum Disorder: The Challenge of Reproducibility and Heterogeneity. Front Neurosci 2018; 12:662. [PMID: 30327586 PMCID: PMC6174243 DOI: 10.3389/fnins.2018.00662] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 09/04/2018] [Indexed: 11/13/2022] Open
Abstract
Background: There is no consensus in the literature concerning the presence of abnormal alpha wave profiles in patients with autism spectrum disorder (ASD). This may be due to phenotypic heterogeneity among patients as well as the limited sample sizes utilized. Here we present our results of alpha wave profile analysis based on a sample larger than most of those in the field, performed using a robust processing pipeline. Methods: We compared the alpha waves profiles at rest in children with ASD to those of age-, sex-, and IQ-matched control individuals. We used linear regression and non-parametric normative models using age as covariate forparsing the clinical heterogeneity. We explored the correlation between EEG profiles and the patient's brain volumes, obtained from structural MRI. We automatized the detection of the alpha peak and visually quality controled our MRI measurements. We assessed the robustness of our results by running the EEG preprocessing with two different versions of Matlab as well as Python. Results: A simple linear regression between peak power or frequency of the alpha waves and the status or age of the participants did not allow to identify any statistically significant relationship. The non-parametric normative model (which took account the non-linear effect of age on the alpha profiles) suggested that participants with ASD displayed more variability than control participants for both frequency and amplitude of the alpha peak (p < 0.05). Independent of the status of the individual, we also observed weak associations (uncorrected p < 0.05) between the alpha frequency, and the volumes of several cortical and subcortical structures (in particular the striatum), but which did not survive correction for multiple testing and changed between analysis pelines. Discussions: Our study did not find evidence for abnormal alpha wave profiles in ASD. We propose, however, an analysis pipeline to perform standardized and automatized EEG analyses on large cohorts. These should help the community to address the challenge of clinical heterogeneity of ASD and to tackle the problems of reproducibility.
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Affiliation(s)
- Aline Lefebvre
- Department of Child and Adolescent Psychiatry, Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Paris, France.,Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
| | - Richard Delorme
- Department of Child and Adolescent Psychiatry, Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Paris, France.,Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
| | - Catherine Delanoë
- Neurophysiology Department, Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Paris, France
| | - Frederique Amsellem
- Department of Child and Adolescent Psychiatry, Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Paris, France.,Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
| | - Anita Beggiato
- Department of Child and Adolescent Psychiatry, Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Paris, France.,Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
| | - David Germanaud
- Pediatric Neurology Department, Assistance Publique-Hôpitaux de Paris, Robert Debré Hospital, Paris, France
| | - Thomas Bourgeron
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
| | - Guillaume Dumas
- Human Genetics and Cognitive Functions Unit, Institut Pasteur, Paris, France.,CNRS UMR 3571 Genes, Synapses and Cognition, Institut Pasteur, Paris, France.,Sorbonne Paris Cité, Human Genetics and Cognitive Functions, University Paris Diderot, Paris, France
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Xia L, Malik AS, Subhani AR. A physiological signal-based method for early mental-stress detection. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.06.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Carter Leno V, Tomlinson SB, Chang SAA, Naples AJ, McPartland JC. Resting-state alpha power is selectively associated with autistic traits reflecting behavioral rigidity. Sci Rep 2018; 8:11982. [PMID: 30097597 PMCID: PMC6086866 DOI: 10.1038/s41598-018-30445-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 07/29/2018] [Indexed: 11/12/2022] Open
Abstract
Previous research suggests that variation in at-rest neural activity correlates with specific domains of the ASD phenotype; however, few studies have linked patterns of brain activity with autistic trait expression in typically developing populations. The purpose of this study was to examine associations between resting-state electroencephalography (EEG) and three domains of the broader autism phenotype (social interest, rigidity, and pragmatic language) in typically developing individuals. High-density scalp EEG was recorded in thirty-seven typically developing adult participants (13 male, aged 18-52 years). The Broad Autism Phenotype Questionnaire (BAP-Q) was used to measure autistic trait expression. Absolute alpha power (8-13 Hz) was extracted from eyes-closed epochs using spectral decomposition techniques. Analyses revealed a specific positive association between scores on the BAP-Q Rigidity subscale and alpha power in the parietal scalp region. No significant associations were found between alpha power and the BAP-Q Aloofness or Pragmatic Language subscales. Furthermore, the association between EEG power and behavioral rigidity was specific to the alpha frequency band. This study demonstrates that specific traits within the broader autism phenotype are associated with dissociable patterns of at-rest neural activity.
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Affiliation(s)
- Virginia Carter Leno
- Yale Child Study Center, 230 South Frontage Road, New Haven, 06520, CT, USA
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AF, UK
| | - Samuel B Tomlinson
- Yale Child Study Center, 230 South Frontage Road, New Haven, 06520, CT, USA
- School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, 14642, NY, USA
| | - Shou-An A Chang
- Yale Child Study Center, 230 South Frontage Road, New Haven, 06520, CT, USA
| | - Adam J Naples
- Yale Child Study Center, 230 South Frontage Road, New Haven, 06520, CT, USA
| | - James C McPartland
- Yale Child Study Center, 230 South Frontage Road, New Haven, 06520, CT, USA.
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den Bakker H, Sidorov MS, Fan Z, Lee DJ, Bird LM, Chu CJ, Philpot BD. Abnormal coherence and sleep composition in children with Angelman syndrome: a retrospective EEG study. Mol Autism 2018. [PMID: 29719672 DOI: 10.1186/s13229-018-0214-8.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS. Methods We analyzed retrospective wake and sleep EEGs from children with AS (age 4-11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches. Results During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts. Conclusions We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS-gamma coherence and spindles-and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.
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Affiliation(s)
- Hanna den Bakker
- 1Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.,2Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.,3Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Michael S Sidorov
- 1Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.,2Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.,3Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Zheng Fan
- 4Department of Neurology, University of North Carolina, Chapel Hill, NC 27599 USA
| | - David J Lee
- 5Department of Neurosciences, University of California, San Diego, CA USA
| | - Lynne M Bird
- 6Department of Pediatrics, University of California, San Diego, CA USA.,7Division of Dysmorphology/Genetics, Rady Children's Hospital, San Diego, CA USA
| | - Catherine J Chu
- 8Department of Neurology, Massachusetts General Hospital, Boston, MA 02114 USA.,9Harvard Medical School, Boston, MA 02215 USA
| | - Benjamin D Philpot
- 1Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.,2Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.,3Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA
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den Bakker H, Sidorov MS, Fan Z, Lee DJ, Bird LM, Chu CJ, Philpot BD. Abnormal coherence and sleep composition in children with Angelman syndrome: a retrospective EEG study. Mol Autism 2018; 9:32. [PMID: 29719672 PMCID: PMC5924514 DOI: 10.1186/s13229-018-0214-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 04/11/2018] [Indexed: 12/28/2022] Open
Abstract
Background Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS. Methods We analyzed retrospective wake and sleep EEGs from children with AS (age 4–11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches. Results During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts. Conclusions We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS—gamma coherence and spindles—and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS. Electronic supplementary material The online version of this article (10.1186/s13229-018-0214-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hanna den Bakker
- 1Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.,2Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.,3Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Michael S Sidorov
- 1Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.,2Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.,3Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Zheng Fan
- 4Department of Neurology, University of North Carolina, Chapel Hill, NC 27599 USA
| | - David J Lee
- 5Department of Neurosciences, University of California, San Diego, CA USA
| | - Lynne M Bird
- 6Department of Pediatrics, University of California, San Diego, CA USA.,7Division of Dysmorphology/Genetics, Rady Children's Hospital, San Diego, CA USA
| | - Catherine J Chu
- 8Department of Neurology, Massachusetts General Hospital, Boston, MA 02114 USA.,9Harvard Medical School, Boston, MA 02215 USA
| | - Benjamin D Philpot
- 1Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC 27599 USA.,2Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC 27599 USA.,3Neuroscience Center, University of North Carolina, Chapel Hill, NC 27599 USA
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Kozhushko NJ, Nagornova ZV, Evdokimov SA, Shemyakina NV, Ponomarev VA, Tereshchenko EP, Kropotov JD. Specificity of spontaneous EEG associated with different levels of cognitive and communicative dysfunctions in children. Int J Psychophysiol 2018; 128:22-30. [PMID: 29577946 DOI: 10.1016/j.ijpsycho.2018.03.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Revised: 02/06/2018] [Accepted: 03/19/2018] [Indexed: 10/17/2022]
Abstract
This study aimed to reveal electrophysiological markers of communicative and cognitive dysfunctions of different severity in children with autism spectrum disorder (ASD). Eyes-opened electroencephalograms (EEGs) of 42 children with ASD, divided into two groups according to the severity of their communicative and cognitive dysfunctions (24 with severe and 18 children with less severe ASD), and 70 age-matched controls aged 4-9 years were examined by means of spectral and group independent component (gIC) analyses. A predominance of theta and beta EEG activity in both groups of children with ASD compared to the activity in the control group was found in the global gIC together with a predominance of beta EEG activity in the right occipital region. The quantity of local gICs with enhanced slow and high-frequency EEG activity (within the frontal, temporal, and parietal cortex areas) in children 4-9 years of age might be considered a marker of cognitive and communicative dysfunction severity.
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Affiliation(s)
- Nadezhda Ju Kozhushko
- N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, 197376, akad. Pavlova str., 9, Saint Petersburg, Russia
| | - Zhanna V Nagornova
- I.M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, 194223, pr. Torez, 44, Saint Petersburg, Russia
| | - Sergey A Evdokimov
- N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, 197376, akad. Pavlova str., 9, Saint Petersburg, Russia
| | - Natalia V Shemyakina
- I.M. Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, 194223, pr. Torez, 44, Saint Petersburg, Russia.
| | - Valery A Ponomarev
- N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, 197376, akad. Pavlova str., 9, Saint Petersburg, Russia
| | - Ekaterina P Tereshchenko
- N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, 197376, akad. Pavlova str., 9, Saint Petersburg, Russia
| | - Jury D Kropotov
- N.P. Bechtereva Institute of the Human Brain, Russian Academy of Sciences, 197376, akad. Pavlova str., 9, Saint Petersburg, Russia; Department of Psychology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway; Department of Neuropsychology, Andrzej Frycz Modrzewski Krakow University, Herlinga-Grudzinskiego 1, 30-705 Kraków, Poland
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Dickinson A, DiStefano C, Senturk D, Jeste SS. Peak alpha frequency is a neural marker of cognitive function across the autism spectrum. Eur J Neurosci 2018; 47:643-651. [PMID: 28700096 PMCID: PMC5766439 DOI: 10.1111/ejn.13645] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/28/2017] [Accepted: 06/30/2017] [Indexed: 01/19/2023]
Abstract
Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants.
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Affiliation(s)
- Abigail Dickinson
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States of America
| | - Charlotte DiStefano
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States of America
| | - Damla Senturk
- Department of Biostatistics, UCLA School of Public Health, Room 21-254C, CHS, Los Angeles, CA, 90095, United States of America
| | - Shafali Spurling Jeste
- Center for Autism Research and Treatment, University of California, Semel Institute for Neuroscience, 760 Westwood Plaza, Suite A7-452 Los Angeles, CA, 90095, United States of America
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Picci G, Gotts SJ, Scherf KS. A theoretical rut: revisiting and critically evaluating the generalized under/over-connectivity hypothesis of autism. Dev Sci 2018; 19:524-49. [PMID: 27412228 DOI: 10.1111/desc.12467] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 05/28/2016] [Indexed: 11/29/2022]
Abstract
In 2004, two papers proposed that pervasive functional under-connectivity (Just et al., ) or a trade-off between excessive local connectivity at the cost of distal under-connectivity (Belmonte et al., ) characterizes atypical brain organization in autism. Here, we take stock of the most recent and rigorous functional and structural connectivity findings with a careful eye toward evaluating the extent to which they support these original hypotheses. Indeed, the empirical data do not support them. From rsfMRI studies in adolescents and adults, there is an emerging consensus regarding long-range functional connections indicating cortico-cortical under-connectivity, specifically involving the temporal lobes, combined with subcortical-cortical over-connectivity. In contrast, there is little to no consensus regarding local functional connectivity or findings from task-based functional connectivity studies. The structural connectivity data suggest that white matter tracts are pervasively weak, particularly in the temporal lobe. Together, these findings are revealing how deeply complex the story is regarding atypical neural network organization in autism. In other words, distance and strength of connectivity as individual factors or as interacting factors do not consistently explain the patterns of atypical neural connectivity in autism. Therefore, we make several methodological recommendations and highlight developmental considerations that will help researchers in the field cultivate new hypotheses about the nature and mechanisms of potentially aberrant functional and structural connectivity in autism.
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Affiliation(s)
- Giorgia Picci
- Department of Psychology, Pennsylvania State University, USA
| | - Stephen J Gotts
- Department of Psychology, Pennsylvania State University, USA
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Abstract
The underlying neural mechanisms of implicit and explicit facial emotion recognition (FER) were studied in children and adolescents with autism spectrum disorder (ASD) compared to matched typically developing controls (TDC). EEG was obtained from N = 21 ASD and N = 16 TDC. Task performance, visual (P100, N170) and cognitive (late positive potential) event-related-potentials, as well as coherence were compared across groups. TDC showed a task-dependent increase and a stronger lateralization of P100 amplitude during the explicit task and task-dependent modulation of intra-hemispheric coherence in the beta band. In contrast, the ASD group showed no task dependent modulation. Results indicate disruptions in early visual processing and top-down attentional processes as contributing factors to FER deficits in ASD.
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Thomas AM, Schwartz MD, Saxe MD, Kilduff TS. Cntnap2 Knockout Rats and Mice Exhibit Epileptiform Activity and Abnormal Sleep-Wake Physiology. Sleep 2017; 40:2661545. [PMID: 28364455 DOI: 10.1093/sleep/zsw026] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2016] [Indexed: 11/12/2022] Open
Abstract
Study Objectives Although recent innovations have enabled modification of the rat genome, it is unclear whether enhanced utility of rodents as human disease models will result. We compared electroencephalogram (EEG) and behavioral phenotypes of rats and mice with homozygous deletion of Cntnap2, a gene associated with cortical dysplasia-focal epilepsy (CDFE) and autism spectrum disorders (ASD). Methods Male contactin-associated protein-like 2 (Cntnap2) knockout (KO) and wild-type (WT) rats and male Cntnap2 KO and WT mice were implanted with telemeters to record EEG, electromyogram, body temperature, and locomotor activity. Animals were subjected to a test battery for ASD-related behaviors, followed by 24-hr EEG recordings that were analyzed for sleep-wake parameters and subjected to spectral analysis. Results Cntnap2 KO rats exhibited severe motor seizures, hyperactivity, and increased consolidation of wakefulness and REM sleep. By contrast, Cntnap2 KO mice demonstrated absence seizure-like events, hypoactivity, and wake fragmentation. Although seizures observed in Cntnap2 KO rats were more similar to those in CDFE patients than in KO mice, neither model fully recapitulated the full spectrum of disease symptoms. However, KOs in both species had reduced spectral power in the alpha (9-12 Hz) range during wake, suggesting a conserved EEG biomarker. Conclusions Deletion of Cntnap2 impacts similar behaviors and EEG measures in rats and mice, but with profound differences in nature and phenotypic severity. These observations highlight the importance of cross-species comparisons to understand conserved gene functions and the limitations of single- species models to provide translational insights relevant to human diseases.
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Affiliation(s)
- Alexia M Thomas
- Biosciences Division, Center for Neuroscience, SRI International, Menlo Park, CA
| | - Michael D Schwartz
- Biosciences Division, Center for Neuroscience, SRI International, Menlo Park, CA
| | - Michael D Saxe
- Pharma Research and Early Development, Neuroscience, Ophthalmology and Rare Disease DTA, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Thomas S Kilduff
- Biosciences Division, Center for Neuroscience, SRI International, Menlo Park, CA
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Moore MR, Franz EA. Resting-state mu activity modulations are associated with aloofness. PERSONALITY AND INDIVIDUAL DIFFERENCES 2017. [DOI: 10.1016/j.paid.2017.05.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Autism, Attention, and Alpha Oscillations: An Electrophysiological Study of Attentional Capture. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:528-536. [PMID: 29170759 DOI: 10.1016/j.bpsc.2017.06.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background Autism spectrum disorder (ASD) is associated with deficits in adaptively orienting attention to behaviorally-relevant information. Neural oscillatory activity plays a key role in brain function and provides a high-resolution temporal marker of attention dynamics. Alpha band (8-12 Hz) activity is associated with both selecting task-relevant stimuli and filtering task-irrelevant information. Methods The present study used electroencephalography (EEG) to examine alpha-band oscillatory activity associated with attentional capture in nineteen children with ASD and twenty-one age- and IQ-matched typically developing (TD) children. Participants completed a rapid serial visual presentation paradigm designed to investigate responses to behaviorally-relevant targets and contingent attention capture by task-irrelevant distractors, which either did or did not share a behaviorally-relevant feature. Participants also completed six minutes of eyes-open resting EEG. Results In contrast to their TD peers, children with ASD did not evidence posterior alpha desynchronization to behaviorally-relevant targets. Additionally, reduced target-related desynchronization and poorer target detection were associated with increased ASD symptomatology. TD children also showed behavioral and electrophysiological evidence of contingent attention capture, whereas children with ASD showed no behavioral facilitation or alpha desynchronization to distractors that shared a task-relevant feature. Lastly, children with ASD had significantly decreased resting alpha power, and for all participants increased resting alpha levels were associated with greater task-related alpha desynchronization. Conclusions These results suggest that in ASD under-responsivity and impairments in orienting to salient events within their environment are reflected by atypical EEG oscillatory neurodynamics, which may signify atypical arousal levels and/or an excitatory/inhibitory imbalance.
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Gurau O, Bosl WJ, Newton CR. How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review. Front Psychiatry 2017; 8:121. [PMID: 28747892 PMCID: PMC5506073 DOI: 10.3389/fpsyt.2017.00121] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 06/23/2017] [Indexed: 01/29/2023] Open
Abstract
Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.
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Affiliation(s)
- Oana Gurau
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - William J. Bosl
- School of Nursing and Health Professions, University of San Francisco, San Francisco, CA, United States
- Benioff UCSF Children’s Hospital Oakland Research Institute, Oakland, CA, United States
| | - Charles R. Newton
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
- KEMRI-Wellcome Trust Research Program, Centre for Geographic Medicine Research (Coast), Kilifi, Kenya
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O’Reilly C, Lewis JD, Elsabbagh M. Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies. PLoS One 2017; 12:e0175870. [PMID: 28467487 PMCID: PMC5414938 DOI: 10.1371/journal.pone.0175870] [Citation(s) in RCA: 176] [Impact Index Per Article: 25.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 03/31/2017] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings. OBJECTIVES 1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization). METHODS Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies. RESULTS Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies. CONCLUSIONS The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.
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Affiliation(s)
- Christian O’Reilly
- Douglas Mental Health University Institute, 6875 Boulevard Lasalle, Verdun, Canada
- Department of Psychiatry, McGill University, 1033 Pine Avenue West, Montreal, QC, Canada
| | - John D. Lewis
- McGill Center for Integrative Neuroscience, Montreal Neurological Institute, McGill University, 3801 University Street, Montréal, QC, Canada
| | - Mayada Elsabbagh
- Douglas Mental Health University Institute, 6875 Boulevard Lasalle, Verdun, Canada
- Department of Psychiatry, McGill University, 1033 Pine Avenue West, Montreal, QC, Canada
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48
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Hobson HM, Bishop DVM. The interpretation of mu suppression as an index of mirror neuron activity: past, present and future. ROYAL SOCIETY OPEN SCIENCE 2017; 4:160662. [PMID: 28405354 PMCID: PMC5383811 DOI: 10.1098/rsos.160662] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2016] [Accepted: 02/01/2017] [Indexed: 06/01/2023]
Abstract
Mu suppression studies have been widely used to infer the activity of the human mirror neuron system (MNS) in a number of processes, ranging from action understanding, language, empathy and the development of autism spectrum disorders (ASDs). Although mu suppression is enjoying a resurgence of interest, it has a long history. This review aimed to revisit mu's past, and examine its recent use to investigate MNS involvement in language, social processes and ASDs. Mu suppression studies have largely failed to produce robust evidence for the role of the MNS in these domains. Several key potential shortcomings with the use and interpretation of mu suppression, documented in the older literature and highlighted by more recent reports, are explored here.
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Schwartz S, Kessler R, Gaughan T, Buckley AW. Electroencephalogram Coherence Patterns in Autism: An Updated Review. Pediatr Neurol 2017; 67:7-22. [PMID: 28065825 PMCID: PMC6127859 DOI: 10.1016/j.pediatrneurol.2016.10.018] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 09/21/2016] [Accepted: 10/19/2016] [Indexed: 01/06/2023]
Abstract
Electrophysiologic studies suggest that autism spectrum disorder is characterized by aberrant anatomic and functional neural circuitry. During normal brain development, pruning and synaptogenesis facilitate ongoing changes in both short- and long-range neural wiring. In developmental disorders such as autism, this process may be perturbed and lead to abnormal neural connectivity. Careful analysis of electrophysiologic connectivity patterns using EEG coherence may provide a way to probe the resulting differences in neurological function between people with and without autism. There is general consensus that electroencephalogram coherence patterns differ between individuals with and without autism spectrum disorders; however, the exact nature of the differences and their clinical significance remain unclear. Here we review recent literature comparing electroencephalogram coherence patterns between patients with autism spectrum disorders or at high risk for autism and their nonautistic or low-risk for autism peers.
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Affiliation(s)
- Sophie Schwartz
- Graduate Program for Neuroscience, Boston University, Boston, Massachusetts
| | - Riley Kessler
- Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Thomas Gaughan
- Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Ashura W. Buckley
- Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
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50
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Nowicka A, Cygan HB, Tacikowski P, Ostaszewski P, Kuś R. Name recognition in autism: EEG evidence of altered patterns of brain activity and connectivity. Mol Autism 2016. [PMID: 27602201 DOI: 10.1186/s13229‐016‐0102‐z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Impaired orienting to social stimuli is one of the core early symptoms of autism spectrum disorder (ASD). However, in contrast to faces, name processing has rarely been studied in individuals with ASD. Here, we investigated brain activity and functional connectivity associated with recognition of names in the high-functioning ASD group and in the control group. METHODS EEG was recorded in 15 young males with ASD and 15 matched one-to-one control individuals. EEG data were analyzed with the event-related potential (ERP), event-related desynchronization and event-related synchronization (ERD/S), as well as coherence and direct transfer function (DTF) methods. Four categories of names were presented visually: one's own, close-other's, famous, and unknown. RESULTS Differences between the ASD and control groups were found for ERP, coherence, and DTF. In individuals with ASD, P300 (a positive ERP component) to own-name and to a close-other's name were similar whereas in control participants, P300 to own-name was enhanced when compared to all other names. Analysis of coherence and DTF revealed disruption of fronto-posterior task-related connectivity in individuals with ASD within the beta range frequencies. Moreover, DTF indicated the directionality of those impaired connections-they were going from parieto-occipital to frontal regions. DTF also showed inter-group differences in short-range connectivity: weaker connections within the frontal region and stronger connections within the occipital region in the ASD group in comparison to the control group. CONCLUSIONS Our findings suggest a lack of the self-preference effect and impaired functioning of the attentional network during recognition of visually presented names in individuals with ASD.
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Affiliation(s)
- Anna Nowicka
- Laboratory of Psychophysiology, Department of Neurophysiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland
| | - Hanna B Cygan
- Laboratory of Psychophysiology, Department of Neurophysiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland ; Central Institute for Labour Protection - National Research Institute, Czerniakowska 16, 00-701 Warsaw, Poland
| | - Paweł Tacikowski
- Laboratory of Psychophysiology, Department of Neurophysiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, 3 Pasteur Street, 02-093 Warsaw, Poland ; Brain, Body, and Self Laboratory, Department of Neuroscience, Karolinska Institute, Retzius väg 8, SE-17177 Stockholm, Sweden
| | - Paweł Ostaszewski
- Department of Psychology, University of Social Sciences and Humanities, 19/31 Chodakowska Street, 03-815 Warsaw, Poland
| | - Rafał Kuś
- Faculty of Physics, University of Warsaw, 5 Pasteur Street, 02-093 Warsaw, Poland
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