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Riek NT, Susam BT, Hudac CM, Conner CM, Akcakaya M, Yun J, White SW, Mazefsky CA, Gable PA. Feedback Related Negativity Amplitude is Greatest Following Deceptive Feedback in Autistic Adolescents. J Autism Dev Disord 2024; 54:3376-3386. [PMID: 37393370 DOI: 10.1007/s10803-023-06038-y] [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] [Accepted: 06/01/2023] [Indexed: 07/03/2023]
Abstract
The purpose of this study is to investigate if feedback related negativity (FRN) can capture instantaneous elevated emotional reactivity in autistic adolescents. A measurement of elevated reactivity could allow clinicians to better support autistic individuals without the need for self-reporting or verbal conveyance. The study investigated reactivity in 46 autistic adolescents (ages 12-21 years) completing the Affective Posner Task which utilizes deceptive feedback to elicit distress presented as frustration. The FRN event-related potential (ERP) served as an instantaneous quantitative neural measurement of emotional reactivity. We compared deceptive and distressing feedback to both truthful but distressing feedback and truthful and non-distressing feedback using the FRN, response times in the successive trial, and Emotion Dysregulation Inventory (EDI) reactivity scores. Results revealed that FRN values were most negative to deceptive feedback as compared to truthful non-distressing feedback. Furthermore, distressing feedback led to faster response times in the successive trial on average. Lastly, participants with higher EDI reactivity scores had more negative FRN values for non-distressing truthful feedback compared to participants with lower reactivity scores. The FRN amplitude showed changes based on both frustration and reactivity. The findings of this investigation support using the FRN to better understand emotion regulation processes for autistic adolescents in future work. Furthermore, the change in FRN based on reactivity suggests the possible need to subgroup autistic adolescents based on reactivity and adjust interventions accordingly.
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Affiliation(s)
- Nathan T Riek
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Busra T Susam
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA
| | - Caitlin M Hudac
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
- Department of Psychology and Carolina Autism and Neurodevelopment (CAN) Research Center, University of South Carolina, Colombia, SC, USA
| | - Caitlin M Conner
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Murat Akcakaya
- Electrical and Computer Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jane Yun
- Chemical and Petroleum Engineering Department, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan W White
- Department of Psychology, University of Alabama, Tuscaloosa, AL, USA
| | - Carla A Mazefsky
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Philip A Gable
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
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Wu T, Yin X, Xu L, Yu J. Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals. J Neurosci Methods 2024; 409:110157. [PMID: 38705284 DOI: 10.1016/j.jneumeth.2024.110157] [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: 12/17/2023] [Revised: 02/21/2024] [Accepted: 04/27/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. METHOD Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. RESULT A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. CONCLUSION Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.
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Affiliation(s)
- Taoxing Wu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Xiao Yin
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Lingyu Xu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China; Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
| | - Jie Yu
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
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Goel P, Goel A. Exploring the Evolution of Sleep Patterns From Infancy to Adolescence. Cureus 2024; 16:e64759. [PMID: 39156264 PMCID: PMC11329291 DOI: 10.7759/cureus.64759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/17/2024] [Indexed: 08/20/2024] Open
Abstract
Sleep is a critical component of healthy development, particularly during the formative years from infancy through adolescence. Sleep undergoes continuous change throughout life characterized by frequent awakenings and a high proportion of rapid eye movement (REM) sleep during infancy, changes in sleep architecture, an increase in non-rapid eye movement (NREM) sleep during adolescence, and an eventual decrease in REM sleep in old age. Adequate sleep is therefore essential for cognitive development, especially between ages 10 and 16. Sleep deprivation may negatively affect academic performance, attention regulation, and emotional well-being. Biological factors, such as hormonal changes during puberty, significantly influence sleep patterns, leading to later bedtimes and a tendency for chronic sleep deprivation in adolescents. Environmental factors, including light exposure and screen time, also play a critical role in regulating sleep. This paper examines the evolution of sleep patterns across infancy and adolescence, describing changes in sleep architecture, timing, and regulation. The influence of biological, environmental, and socio-cultural factors on sleep is explored, highlighting how these factors collectively shape sleep behaviors and health outcomes. It also addresses the profound role sleep plays in cognitive development, brain maturation, and emotional well-being. The importance of understanding sleep patterns and their developmental trajectories to address sleep-related issues is emphasized. Promoting healthy sleep from an early age can enhance cognitive and emotional outcomes, contributing to better academic performance and overall well-being in children and adolescents. The findings advocate for further standardized sleep intervention programs globally to prioritize sleep health and support optimal development.
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Dmytriw AA, Hadjinicolaou A, Ntolkeras G, Tamilia E, Pesce M, Berto LF, Grant PE, Pang E, Ahtam B. Magnetoencephalography for the pediatric population, indications, acquisition and interpretation for the clinician. Neuroradiol J 2024:19714009241260801. [PMID: 38864180 DOI: 10.1177/19714009241260801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024] Open
Abstract
Magnetoencephalography (MEG) is an imaging technique that enables the assessment of cortical activity via direct measures of neurophysiology. It is a non-invasive and passive technique that is completely painless. MEG has gained increasing prominence in the field of pediatric neuroimaging. This dedicated review article for the pediatric population summarizes the fundamental technical and clinical aspects of MEG for the clinician. We discuss methods tailored for children to improve data quality, including child-friendly MEG facility environments and strategies to mitigate motion artifacts. We provide an in-depth overview on accurate localization of neural sources and different analysis methods, as well as data interpretation. The contemporary platforms and approaches of two quaternary pediatric referral centers are illustrated, shedding light on practical implementations in clinical settings. Finally, we describe the expanding clinical applications of MEG, including its pivotal role in presurgical evaluation of epilepsy patients, presurgical mapping of eloquent cortices (somatosensory and motor cortices, visual and auditory cortices, lateralization of language), its emerging relevance in autism spectrum disorder research and potential future clinical applications, and its utility in assessing mild traumatic brain injury. In conclusion, this review serves as a comprehensive resource of clinicians as well as researchers, offering insights into the evolving landscape of pediatric MEG. It discusses the importance of technical advancements, data acquisition strategies, and expanding clinical applications in harnessing the full potential of MEG to study neurological conditions in the pediatric population.
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Affiliation(s)
- Adam A Dmytriw
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
- Division of Neuroradiology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Aristides Hadjinicolaou
- Division of Neurology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Boston, MA, USA
| | - Georgios Ntolkeras
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Eleonora Tamilia
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Matthew Pesce
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Laura F Berto
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - P Ellen Grant
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
| | - Elizabeth Pang
- Division of Neurology, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Banu Ahtam
- Department of Pediatrics, Division of Newborn Medicine, Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, MA, USA
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Paveenakiattikhun S, Likhitweerawong N, Sanguansermsri C. EEG findings and clinical severity and quality of life in non-epileptic patients with autism spectrum disorders. Child Neuropsychol 2024:1-11. [PMID: 38805362 DOI: 10.1080/09297049.2024.2360651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 05/17/2024] [Indexed: 05/30/2024]
Abstract
Electroencephalogram (EEG) abnormalities could be seen in up to 60% of non-epileptic children with autism spectrum disorder (ASD). They have been used as biomarkers of ASD severity. The objective of our study is to identify EEG abnormalities in children with different degrees of ASD severity based on the Autism Treatment Evaluation Checklist (ATEC). We also want to assess the quality of life for children with ASD. All of the children underwent at least one hour of sleep-deprived EEG. Forty-five children were enrolled, of whom 42 were male. EEG abnormalities were found in 10 (22.2%) children, predominantly in the bilateral frontal areas. There were no differences in EEG findings among the mild, moderate, and severe ASD groups. The severity of ASD was associated with female sex (p-value = 0.013), ASD with attention deficit hyperactivity disorder (ADHD) (p-value = 0.032), ASD children taking medications (p-value = 0.048), and a lower Pediatric Quality of Life Inventory (PedsQL) (p-value <0.001). Social and emotional domains were the most problematic for health-related quality of life in ASD children, according to parent reports of PedsQL. Further studies with a larger sample size will help to clarify the potential associations between EEG abnormalities and the severity of ASD, as well as the impact on quality of life.
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Affiliation(s)
- Sirada Paveenakiattikhun
- Department of Pediatrics, Faculty of Medicine, Chiang Mai University Hospital, Chiang Mai, Thailand
| | - Narueporn Likhitweerawong
- Child and Development Division, Department of Pediatrics, Faculty of Medicine, Chiang Mai University Hospital, Chiang Mai, Thailand
| | - Chinnuwat Sanguansermsri
- Neurology Division, Department of Pediatrics, Faculty of Medicine, Chiang Mai University Hospital, Chiang Mai, Thailand
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Fradkin Y, De Taboada L, Naeser M, Saltmarche A, Snyder W, Steingold E. Transcranial photobiomodulation in children aged 2-6 years: a randomized sham-controlled clinical trial assessing safety, efficacy, and impact on autism spectrum disorder symptoms and brain electrophysiology. Front Neurol 2024; 15:1221193. [PMID: 38737349 PMCID: PMC11086174 DOI: 10.3389/fneur.2024.1221193] [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: 05/12/2023] [Accepted: 03/11/2024] [Indexed: 05/14/2024] Open
Abstract
Background Small pilot studies have suggested that transcranial photobiomodulation (tPBM) could help reduce symptoms of neurological conditions, such as depression, traumatic brain injury, and autism spectrum disorder (ASD). Objective To examine the impact of tPBM on the symptoms of ASD in children aged two to six years. Method We conducted a randomized, sham-controlled clinical trial involving thirty children aged two to six years with a prior diagnosis of ASD. We delivered pulses of near-infrared light (40 Hz, 850 nm) noninvasively to selected brain areas twice a week for eight weeks, using an investigational medical device designed for this purpose (Cognilum™, JelikaLite Corp., New York, United States). We used the Childhood Autism Rating Scale (CARS, 2nd Edition) to assess and compare the ASD symptoms of participants before and after the treatment course. We collected electroencephalogram (EEG) data during each session from those participants who tolerated wearing the EEG cap. Results The difference in the change in CARS scores between the two groups was 7.23 (95% CI 2.357 to 12.107, p = 0.011). Seventeen of the thirty participants completed at least two EEGs and time-dependent trends were detected. In addition, an interaction between Active versus Sham and Scaled Time was observed in delta power (Coefficient = 7.521, 95% CI -0.517 to 15.559, p = 0.07) and theta power (Coefficient = -8.287, 95% CI -17.199 to 0.626, p = 0.07), indicating a potential trend towards a greater reduction in delta power and an increase in theta power over time with treatment in the Active group, compared to the Sham group. Furthermore, there was a significant difference in the condition (Treatment vs. Sham) in the power of theta waves (net_theta) (Coefficient = 9.547, 95% CI 0.027 to 19.067, p = 0.049). No moderate or severe side effects or adverse effects were reported or observed during the trial. Conclusion These results indicate that tPBM may be a safe and effective treatment for ASD and should be studied in more depth in larger studies.Clinical trial registration: https://clinicaltrials.gov/ct2/show/NCT04660552, identifier NCT04660552.
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Affiliation(s)
- Yuliy Fradkin
- Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, United States
| | | | - Margaret Naeser
- Chobanian and Avedisian School of Medicine, Boston University, Boston, MA, United States
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Li J, Kong X, Sun L, Chen X, Ouyang G, Li X, Chen S. Identification of autism spectrum disorder based on electroencephalography: A systematic review. Comput Biol Med 2024; 170:108075. [PMID: 38301514 DOI: 10.1016/j.compbiomed.2024.108075] [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: 09/30/2023] [Revised: 12/22/2023] [Accepted: 01/27/2024] [Indexed: 02/03/2024]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and repetitive and stereotyped behaviors. According to the World Health Organization, about 1 in 100 children worldwide has autism. With the global prevalence of ASD, timely and accurate diagnosis has been essential in enhancing the intervention effectiveness for ASD children. Traditional ASD diagnostic methods rely on clinical observations and behavioral assessment, with the disadvantages of time-consuming and lack of objective biological indicators. Therefore, automated diagnostic methods based on machine learning and deep learning technologies have emerged and become significant since they can achieve more objective, efficient, and accurate ASD diagnosis. Electroencephalography (EEG) is an electrophysiological monitoring method that records changes in brain spontaneous potential activity, which is of great significance for identifying ASD children. By analyzing EEG data, it is possible to detect abnormal synchronous neuronal activity of ASD children. This paper gives a comprehensive review of the EEG-based ASD identification using traditional machine learning methods and deep learning approaches, including their merits and potential pitfalls. Additionally, it highlights the challenges and the opportunities ahead in search of more effective and efficient methods to automatically diagnose autism based on EEG signals, which aims to facilitate automated ASD identification.
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Affiliation(s)
- Jing Li
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Xiaoli Kong
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
| | - Linlin Sun
- Neuroscience Research Institute, Peking University, Beijing, 100191, China; Key Laboratory for Neuroscience, Ministry of Education/National Health Commission of China, Beijing, 100191, China
| | - Xu Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing, 100120, China; The Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, 100032, China
| | - Gaoxiang Ouyang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Shengyong Chen
- School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
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Manjur SM, Diaz LRM, Lee IO, Skuse DH, Thompson DA, Marmolejos-Ramos F, Constable PA, Posada-Quintero HF. Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths. J Autism Dev Disord 2024:10.1007/s10803-024-06290-w. [PMID: 38393437 DOI: 10.1007/s10803-024-06290-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/13/2024] [Indexed: 02/25/2024]
Abstract
PURPOSE Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. METHODS Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. RESULTS Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. CONCLUSION The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300 Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.
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Affiliation(s)
| | | | - Irene O Lee
- Behavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - David H Skuse
- Behavioral and Brain Sciences Unit, Population Policy and Practice Program, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Dorothy A Thompson
- Tony Kriss Visual Electrophysiology Unit, Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
- UCL Great Ormond Street Institute for Child Health, University College London, London, UK
| | | | - Paul A Constable
- College of Nursing and Health Sciences, Flinders University, Caring Futures Institute, Adelaide, Australia
| | - Hugo F Posada-Quintero
- Department of Biomedical Engineering, University of Connecticut, 06269, Storrs, CT, USA.
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Vacy K, Thomson S, Moore A, Eisner A, Tanner S, Pham C, Saffery R, Mansell T, Burgner D, Collier F, Vuillermin P, O'Hely M, Boon WC, Meikle P, Burugupalli S, Ponsonby AL. Cord blood lipid correlation network profiles are associated with subsequent attention-deficit/hyperactivity disorder and autism spectrum disorder symptoms at 2 years: a prospective birth cohort study. EBioMedicine 2024; 100:104949. [PMID: 38199043 PMCID: PMC10825361 DOI: 10.1016/j.ebiom.2023.104949] [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: 07/05/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions with early life origins. Alterations in blood lipids have been linked to ADHD and ASD; however, prospective early life data are limited. This study examined (i) associations between the cord blood lipidome and ADHD/ASD symptoms at 2 years of age, (ii) associations between prenatal and perinatal predictors of ADHD/ASD symptoms and cord blood lipidome, and (iii) mediation by the cord blood lipidome. METHODS From the Barwon Infant Study cohort (1074 mother-child pairs, 52.3% male children), child circulating lipid levels at birth were analysed using ultra-high-performance liquid chromatography-tandem mass spectrometry. These were clustered into lipid network modules via Weighted Gene Correlation Network Analysis. Associations between lipid modules and ADHD/ASD symptoms at 2 years, assessed with the Child Behavior Checklist, were explored via linear regression analyses. Mediation analysis identified indirect effects of prenatal and perinatal risk factors on ADHD/ASD symptoms through lipid modules. FINDINGS The acylcarnitine lipid module is associated with both ADHD and ASD symptoms at 2 years of age. Risk factors of these outcomes such as low income, Apgar score, and maternal inflammation were partly mediated by higher birth acylcarnitine levels. Other cord blood lipid profiles were also associated with ADHD and ASD symptoms. INTERPRETATION This study highlights that elevated cord blood birth acylcarnitine levels, either directly or as a possible marker of disrupted cell energy metabolism, are on the causal pathway of prenatal and perinatal risk factors for ADHD and ASD symptoms in early life. FUNDING The foundational work and infrastructure for the BIS was sponsored by the Murdoch Children's Research Institute, Deakin University, and Barwon Health. Subsequent funding was secured from the Minderoo Foundation, the European Union's Horizon 2020 research and innovation programme (ENDpoiNTs: No 825759), National Health and Medical Research Council of Australia (NHMRC) and Agency for Science, Technology and Research Singapore [APP1149047], The William and Vera Ellen Houston Memorial Trust Fund (via HOMER Hack), The Shepherd Foundation, The Jack Brockhoff Foundation, the Scobie & Claire McKinnon Trust, the Shane O'Brien Memorial Asthma Foundation, the Our Women Our Children's Fund Raising Committee Barwon Health, the Rotary Club of Geelong, the Ilhan Food Allergy Foundation, Geelong Medical and Hospital Benefits Association, Vanguard Investments Australia Ltd, the Percy Baxter Charitable Trust, and Perpetual Trustees.
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Affiliation(s)
- Kristina Vacy
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia; Melbourne School of Population and Global Health, University of Melbourne, Parkville 3010, Australia
| | - Sarah Thomson
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia
| | - Archer Moore
- Melbourne School of Mathematics and Statistics, University of Melbourne, Parkville 3010, Australia
| | - Alex Eisner
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia
| | - Sam Tanner
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia
| | - Cindy Pham
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3010, Australia; Department of Paediatrics, University of Melbourne, Parkville 3010, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3010, Australia; Department of Paediatrics, University of Melbourne, Parkville 3010, Australia
| | - Toby Mansell
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3010, Australia; Department of Paediatrics, University of Melbourne, Parkville 3010, Australia
| | - David Burgner
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3010, Australia; Department of Paediatrics, University of Melbourne, Parkville 3010, Australia; Department of Paediatrics, Monash University, Clayton 3168, Australia
| | - Fiona Collier
- Child Health Research Unit, Barwon Health, Geelong 3220, Australia; School of Medicine, Deakin University, Geelong 3220, Australia
| | - Peter Vuillermin
- Child Health Research Unit, Barwon Health, Geelong 3220, Australia
| | - Martin O'Hely
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3010, Australia; School of Medicine, Deakin University, Geelong 3220, Australia
| | - Wah Chin Boon
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia
| | - Peter Meikle
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne 3004, Australia; Baker Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Bundoora, VIC 3086, Australia
| | - Satvika Burugupalli
- Metabolomics Laboratory, Baker Heart and Diabetes Institute, Melbourne 3004, Australia
| | - Anne-Louise Ponsonby
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville 3010, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, Parkville 3010, Australia; Department of Paediatrics, University of Melbourne, Parkville 3010, Australia.
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10
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Ozernov-Palchik O, Pollack C, Bonawitz E, Christodoulou JA, Gaab N, Gabrieli JD, Kievlan PM, Kirby C, Lin G, Luk G, Nelson CA. Reflections on the past two decades of Mind, Brain, and Education. MIND, BRAIN AND EDUCATION : THE OFFICIAL JOURNAL OF THE INTERNATIONAL MIND, BRAIN, AND EDUCATION SOCIETY 2024; 18:6-16. [PMID: 38745857 PMCID: PMC11090485 DOI: 10.1111/mbe.12407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 01/23/2024] [Indexed: 05/16/2024]
Affiliation(s)
- Ola Ozernov-Palchik
- Harvard Graduate School of Education
- McGovern Institute for Brain Research, MIT
| | | | | | - Joanna A. Christodoulou
- Harvard Graduate School of Education
- McGovern Institute for Brain Research, MIT
- MGH Institute of Health Professions
| | | | - John D.E. Gabrieli
- Harvard Graduate School of Education
- McGovern Institute for Brain Research, MIT
| | | | | | - Grace Lin
- Department of Psychology, Harvard University
- Scheller Teacher Education Program The Education Arcade, MIT
| | - Gigi Luk
- Department of Educational and Counselling Psychology, McGill University
| | - Charles A. Nelson
- Harvard Graduate School of Education
- Department of Pediatrics, Harvard Medical School
- Division of Developmental Medicine, Department of Pediatrics, Boston Children’s Hospital
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11
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Peng Y, Lv B, Yang Q, Peng Y, Jiang L, He M, Yao D, Xu W, Li F, Xu P. Evaluating the depression state during perinatal period by non-invasive scalp EEG. Cereb Cortex 2024; 34:bhae034. [PMID: 38342685 DOI: 10.1093/cercor/bhae034] [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: 12/12/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 02/13/2024] Open
Abstract
Perinatal depression, with a prevalence of 10 to 20% in United States, is usually missed as multiple symptoms of perinatal depression are common in pregnant women. Worse, the diagnosis of perinatal depression still largely relies on questionnaires, leaving the objective biomarker being unveiled yet. This study suggested a safe and non-invasive technique to diagnose perinatal depression and further explore its underlying mechanism. Considering the non-invasiveness and clinical convenience of electroencephalogram for mothers-to-be and fetuses, we collected the resting-state electroencephalogram of pregnant women at the 38th week of gestation. Subsequently, the difference in network topology between perinatal depression patients and healthy mothers-to-be was explored, with related spatial patterns being adopted to achieve the classification of pregnant women with perinatal depression from those healthy ones. We found that the perinatal depression patients had decreased brain network connectivity, which indexed impaired efficiency of information processing. By adopting the spatial patterns, the perinatal depression could be accurately recognized with an accuracy of 87.88%; meanwhile, the depression severity at the individual level was effectively predicted, as well. These findings consistently illustrated that the resting-state electroencephalogram network could be a reliable tool for investigating the depression state across pregnant women, and will further facilitate the clinical diagnosis of perinatal depression.
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Affiliation(s)
- Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bin Lv
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610040, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610040, Sichuan, China
| | - Qingqing Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yan Peng
- Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Chengdu 610040, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610040, Sichuan, China
| | - Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Mengling He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
| | - Wenming Xu
- Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu 610054, China
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12
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Fields MC, Marsh C, Eka O, Johnson EA, Marcuse LV, Kwon CS, Young JJ, LaVega-Talbott M, Kurukumbi M, Von Allmen G, Zempel J, Friedman D, Jette N, Singh A, Yoo JY, Blank L, Panov F, Ghatan S. Responsive Neurostimulation for People With Drug-Resistant Epilepsy and Autism Spectrum Disorder. J Clin Neurophysiol 2024; 41:64-71. [PMID: 35512185 PMCID: PMC10756699 DOI: 10.1097/wnp.0000000000000939] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Individuals with autism spectrum disorder (ASD) have comorbid epilepsy at much higher rates than the general population, and about 30% will be refractory to medication. Patients with drug-resistant epilepsy (DRE) should be referred for surgical evaluation, yet many with ASD and DRE are not resective surgical candidates. The aim of this study was to examine the response of this population to the responsive neurostimulator (RNS) System. METHODS This multicenter study evaluated patients with ASD and DRE who underwent RNS System placement. Patients were included if they had the RNS System placed for 1 year or more. Seizure reduction and behavioral outcomes were reported. Descriptive statistics were used for analysis. RESULTS Nineteen patients with ASD and DRE had the RNS System placed at 5 centers. Patients were between the ages of 11 and 29 (median 20) years. Fourteen patients were male, whereas five were female. The device was implanted from 1 to 5 years. Sixty-three percent of all patients experienced a >50% seizure reduction, with 21% of those patients being classified as super responders (seizure reduction >90%). For the super responders, two of the four patients had the device implanted for >2 years. The response rate was 70% for those in whom the device was implanted for >2 years. Improvements in behaviors as measured by the Clinical Global Impression Scale-Improvement scale were noted in 79%. No complications from the surgery were reported. CONCLUSIONS Based on the authors' experience in this small cohort of patients, the RNS System seems to be a promising surgical option in people with ASD-DRE.
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Affiliation(s)
- Madeline C. Fields
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Christina Marsh
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Onome Eka
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Emily A. Johnson
- Washington University School of Medicine, St. Louis, Missouri, U.S.A.
| | - Lara V. Marcuse
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Churl-Su Kwon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - James J. Young
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Maite LaVega-Talbott
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | | | - Gretchen Von Allmen
- Division of Pediatric Epilepsy, McGovern Medical School, UTHealth, Houston, Texas, U.S.A.
| | - John Zempel
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, U.S.A.; and
| | - Daniel Friedman
- Department of Neurology, New York University Langone Medical Center, New York, New York, U.S.A.
| | - Nathalie Jette
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Anuradha Singh
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Ji Yeoun Yoo
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Leah Blank
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Fedor Panov
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
| | - Saadi Ghatan
- Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
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13
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Sathyanarayana A, El Atrache R, Jackson M, Cantley S, Reece L, Ufongene C, Loddenkemper T, Mandl KD, Bosl WJ. Measuring Real-Time Medication Effects From Electroencephalography. J Clin Neurophysiol 2024; 41:72-82. [PMID: 35583401 PMCID: PMC9669285 DOI: 10.1097/wnp.0000000000000946] [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] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Evaluating the effects of antiseizure medication (ASM) on patients with epilepsy remains a slow and challenging process. Quantifiable noninvasive markers that are measurable in real-time and provide objective and useful information could guide clinical decision-making. We examined whether the effect of ASM on patients with epilepsy can be quantitatively measured in real-time from EEGs. METHODS This retrospective analysis was conducted on 67 patients in the long-term monitoring unit at Boston Children's Hospital. Two 30-second EEG segments were selected from each patient premedication and postmedication weaning for analysis. Nonlinear measures including entropy and recurrence quantitative analysis values were computed for each segment and compared before and after medication weaning. RESULTS Our study found that ASM effects on the brain were measurable by nonlinear recurrence quantitative analysis on EEGs. Highly significant differences ( P < 1e-11) were found in several nonlinear measures within the seizure zone in response to antiseizure medication. Moreover, the size of the medication effect correlated with a patient's seizure frequency, seizure localization, number of medications, and reported seizure frequency reduction on medication. CONCLUSIONS Our findings show the promise of digital biomarkers to measure medication effects and epileptogenicity.
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Affiliation(s)
- Aarti Sathyanarayana
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, U.S.A.;
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, U.S.A.;
| | - Rima El Atrache
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Michele Jackson
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Sarah Cantley
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Latania Reece
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Claire Ufongene
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Tobias Loddenkemper
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, U.S.A.; and
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, U.S.A.;
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
| | - William J. Bosl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, U.S.A.;
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, U.S.A.;
- Department of Health Professions, University of San Francisco, San Francisco, California, U.S.A
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14
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Sun B, Wang B, Wei Z, Feng Z, Wu ZL, Yassin W, Stone WS, Lin Y, Kong XJ. Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking. Front Neurosci 2023; 17:1236637. [PMID: 37886678 PMCID: PMC10598595 DOI: 10.3389/fnins.2023.1236637] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.
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Affiliation(s)
- Binbin Sun
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Bryan Wang
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of English and Creative Writing, Brandeis University, Waltham, MA, United States
| | - Zhen Wei
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhe Feng
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhi-Liu Wu
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Walid Yassin
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- McLean Hospital, Harvard Medical School, Belmont, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - William S. Stone
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yan Lin
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Xue-Jun Kong
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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15
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Mayor Torres JM, Medina-DeVilliers S, Clarkson T, Lerner MD, Riccardi G. Evaluation of interpretability for deep learning algorithms in EEG emotion recognition: A case study in autism. Artif Intell Med 2023; 143:102545. [PMID: 37673554 DOI: 10.1016/j.artmed.2023.102545] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 04/03/2023] [Accepted: 04/03/2023] [Indexed: 09/08/2023]
Abstract
Current models on Explainable Artificial Intelligence (XAI) have shown a lack of reliability when evaluating feature-relevance for deep neural biomarker classifiers. The inclusion of reliable saliency-maps for obtaining trustworthy and interpretable neural activity is still insufficiently mature for practical applications. These limitations impede the development of clinical applications of Deep Learning. To address, these limitations we propose the RemOve-And-Retrain (ROAR) algorithm which supports the recovery of highly relevant features from any pre-trained deep neural network. In this study we evaluated the ROAR methodology and algorithm for the Face Emotion Recognition (FER) task, which is clinically applicable in the study of Autism Spectrum Disorder (ASD). We trained a Convolutional Neural Network (CNN) from electroencephalography (EEG) signals and assessed the relevance of FER-elicited EEG features from individuals diagnosed with and without ASD. Specifically, we compared the ROAR reliability from well-known relevance maps such as Layer-Wise Relevance Propagation, PatternNet, Pattern-Attribution, and Smooth-Grad Squared. This study is the first to bridge previous neuroscience and ASD research findings to feature-relevance calculation for EEG-based emotion recognition with CNN in typically-development (TD) and in ASD individuals.
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Affiliation(s)
- Juan Manuel Mayor Torres
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, Povo, Trento, 1328, Italy.
| | | | - Tessa Clarkson
- Department of Psychology, Temple University, 1801 N Broad St, Philadelphia, 19122, PA, USA
| | - Matthew D Lerner
- Department of Psychology, StonyBrook University, 100 Nicolls Rd, 11794, NY, USA
| | - Giuseppe Riccardi
- Department of Information Engineering and Computer Science, University of Trento, Via Sommarive, Povo, Trento, 1328, Italy
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16
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Tang Y, Tong G, Xiong X, Zhang C, Zhang H, Yang Y. Multi-site diagnostic classification of Autism spectrum disorder using adversarial deep learning on resting-state fMRI. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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17
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Tang H, Liang J, Chai K, Gu H, Ye W, Cao P, Chen S, Shen D. Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism spectrum disorder. Front Neurol 2023; 14:1203375. [PMID: 37528852 PMCID: PMC10390071 DOI: 10.3389/fneur.2023.1203375] [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: 04/10/2023] [Accepted: 06/26/2023] [Indexed: 08/03/2023] Open
Abstract
Introduction Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however. Methods In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified. Results We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88. Discussion Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.
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Affiliation(s)
- Huitao Tang
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Jiawei Liang
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Keping Chai
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Huaqian Gu
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Weiping Ye
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Panlong Cao
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Shufang Chen
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
| | - Daojiang Shen
- Department of Pediatrics, Zhejiang Hospital, Hangzhou, China
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18
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Bosl WJ, Bosquet Enlow M, Lock EF, Nelson CA. A biomarker discovery framework for childhood anxiety. Front Psychiatry 2023; 14:1158569. [PMID: 37533889 PMCID: PMC10393248 DOI: 10.3389/fpsyt.2023.1158569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 07/04/2023] [Indexed: 08/04/2023] Open
Abstract
Introduction Anxiety is the most common manifestation of psychopathology in youth, negatively affecting academic, social, and adaptive functioning and increasing risk for mental health problems into adulthood. Anxiety disorders are diagnosed only after clinical symptoms emerge, potentially missing opportunities to intervene during critical early prodromal periods. In this study, we used a new empirical approach to extracting nonlinear features of the electroencephalogram (EEG), with the goal of discovering differences in brain electrodynamics that distinguish children with anxiety disorders from healthy children. Additionally, we examined whether this approach could distinguish children with externalizing disorders from healthy children and children with anxiety. Methods We used a novel supervised tensor factorization method to extract latent factors from repeated multifrequency nonlinear EEG measures in a longitudinal sample of children assessed in infancy and at ages 3, 5, and 7 years of age. We first examined the validity of this method by showing that calendar age is highly correlated with latent EEG complexity factors (r = 0.77). We then computed latent factors separately for distinguishing children with anxiety disorders from healthy controls using a 5-fold cross validation scheme and similarly for distinguishing children with externalizing disorders from healthy controls. Results We found that latent factors derived from EEG recordings at age 7 years were required to distinguish children with an anxiety disorder from healthy controls; recordings from infancy, 3 years, or 5 years alone were insufficient. However, recordings from two (5, 7 years) or three (3, 5, 7 years) recordings gave much better results than 7 year recordings alone. Externalizing disorders could be detected using 3- and 5 years EEG data, also giving better results with two or three recordings than any single snapshot. Further, sex assigned at birth was an important covariate that improved accuracy for both disorder groups, and birthweight as a covariate modestly improved accuracy for externalizing disorders. Recordings from infant EEG did not contribute to the classification accuracy for either anxiety or externalizing disorders. Conclusion This study suggests that latent factors extracted from EEG recordings in childhood are promising candidate biomarkers for anxiety and for externalizing disorders if chosen at appropriate ages.
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Affiliation(s)
- William J. Bosl
- Center for AI & Medicine, University of San Francisco, San Francisco, CA, United States
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, MA, United States
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Michelle Bosquet Enlow
- Department of Psychiatry and Behavioral Sciences, Boston Children’s Hospital, Boston, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Eric F. Lock
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, United States
| | - Charles A. Nelson
- Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Boston Children’s Hospital, Boston, MA, United States
- Harvard Graduate School of Education, Cambridge, MA, United States
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19
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Tilwani D, Bradshaw J, Sheth A, O'Reilly C. ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach. Bioengineering (Basel) 2023; 10:827. [PMID: 37508854 PMCID: PMC10376813 DOI: 10.3390/bioengineering10070827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/22/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1-2 years of age, but ASD diagnoses are not typically made until ages 2-5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3-6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.
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Affiliation(s)
- Deepa Tilwani
- Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA
- Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA
| | - Jessica Bradshaw
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA
- Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA
- Department of Psychology, University of South Carolina, Columbia, SC 29208, USA
| | - Amit Sheth
- Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Christian O'Reilly
- Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA
- Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA
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20
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Scheinost D, Pollatou A, Dufford AJ, Jiang R, Farruggia MC, Rosenblatt M, Peterson H, Rodriguez RX, Dadashkarimi J, Liang Q, Dai W, Foster ML, Camp CC, Tejavibulya L, Adkinson BD, Sun H, Ye J, Cheng Q, Spann MN, Rolison M, Noble S, Westwater ML. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biol Psychiatry 2023; 93:893-904. [PMID: 36759257 PMCID: PMC10259670 DOI: 10.1016/j.biopsych.2022.10.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Revised: 09/10/2022] [Accepted: 10/07/2022] [Indexed: 12/01/2022]
Abstract
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.
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Affiliation(s)
- Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut; Department of Statistics and Data Science, Yale University, New Haven, Connecticut; Child Study Center, Yale School of Medicine, New Haven, Connecticut; Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut.
| | - Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Michael C Farruggia
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Hannah Peterson
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | | | | | - Qinghao Liang
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut
| | - Maya L Foster
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, Connecticut
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut
| | - Qi Cheng
- Departments of Neuroscience and Psychology, Smith College, Northampton, Massachusetts
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York
| | - Max Rolison
- Child Study Center, Yale School of Medicine, New Haven, Connecticut
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
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21
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Balathay D, Narasimhan U, Belo D, Anandan K. Quantitative assessment of cognitive profile and brain asymmetry in the characterization of autism spectrum in children: A task-based EEG study. Proc Inst Mech Eng H 2023:9544119231170683. [PMID: 37096354 DOI: 10.1177/09544119231170683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2023]
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.
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Affiliation(s)
- Divya Balathay
- Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India
| | - Udayakumar Narasimhan
- Department of Pediatrics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India
| | - David Belo
- Machine Learning for Time Series at Fraunhofer Portugal AICOS, Seixal, Setubal, Portugal
| | - Kavitha Anandan
- Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India
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22
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Das S, Zomorrodi R, Mirjalili M, Kirkovski M, Blumberger DM, Rajji TK, Desarkar P. Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2023; 123:110705. [PMID: 36574922 DOI: 10.1016/j.pnpbp.2022.110705] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 12/04/2022] [Accepted: 12/21/2022] [Indexed: 12/26/2022]
Abstract
There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.
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Affiliation(s)
- Sushmit Das
- Centre for Addiction and Mental Health, Toronto, Canada; Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Reza Zomorrodi
- Centre for Addiction and Mental Health, Toronto, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mina Mirjalili
- Centre for Addiction and Mental Health, Toronto, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Melissa Kirkovski
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia; Insitute for Health and Sport, Victoria University, Melbourne, Australia
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Pushpal Desarkar
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.
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23
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Demchick BB, Flanagan J, Li CY, Cassidy R, Golding J. Early Indicators of Autism in Infants: Development of the IMES Screening Tool. OTJR-OCCUPATION PARTICIPATION AND HEALTH 2023; 43:255-263. [PMID: 36495161 DOI: 10.1177/15394492221134910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
We developed the Infant, Motor, and Engagement Scale (IMES) to address the public health goal of early identification of autism spectrum disorder (ASD). The IMES is a screening tool that assesses quality of infants' interaction with people and objects during early play. We aimed to examine the IMES' preliminary psychometric properties and its value in discriminating between infants later diagnosed with ASD and typically developing infants. We used the IMES to score retrospective home videos of 15 male infants, 7 who were later diagnosed with autism. We examined interrater reliability using Cohen's Kappa, internal consistency with Cronbach's alpha and content validity through expert review. Preliminary data support validity and reliability of the IMES for early identification for infants at 6 to 9 months. With further research, the IMES has the potential to identify at risk infants at a young age that may have long-term impact on child and family outcomes.
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Affiliation(s)
| | | | - Chih-Ying Li
- The University of Texas Medical Branch, Galveston, USA
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24
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Bosl WJ. Ellen R. Grass Lecture: The Future of Neurodiagnostics and Emergence of a New Science. Neurodiagn J 2023; 63:1-13. [PMID: 37023375 DOI: 10.1080/21646821.2023.2183012] [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] [Accepted: 01/12/2023] [Indexed: 06/19/2023]
Abstract
Electroencepholography (EEG) is the oldest and original brain measurement technology. Since EEG was first used in clinical settings, the role of neurodiagnostic professionals has focused on two principal tasks that require specialized training. These include collecting the EEG recording, performed primarily by EEG Technologists, and interpreting the recording, generally done by physicians with proper specialization. Emerging technology appears to enable non-specialists to contribute to these tasks. Neurotechnologists may feel vulnerable to being displaced by new technology. A similar shift occurred in the last century when human "computers," employed to perform repetitive calculations needed to solve complex mathematics for the Manhattan and Apollo Projects, were displaced by new electronic computing machines. Many human "computers" seized on the opportunity created by the new computing technology to become the first computer programmers and create the new field of computer science. That transition offers insights for the future of neurodiagnostics. From its inception, neurodiagnostics has been an information processing discipline. Advances in dynamical systems theory, cognitive neuroscience, and biomedical informatics have created an opportunity for neurodiagnostic professionals to help create a new science of functional brain monitoring. A new generation of advanced neurodiagnostic professionals that bring together knowledge and skills in clinical neuroscience and biomedical informatics will benefit psychiatry, neurology, and precision healthcare, lead to preventive brain health through the lifespan, and lead the establishment of a new science of clinical neuroinformatics.
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Affiliation(s)
- William J Bosl
- Health Informatics Program, University of San Francisco, San Francisco, California
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
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25
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Dawson G, Rieder AD, Johnson MH. Prediction of autism in infants: progress and challenges. Lancet Neurol 2023; 22:244-254. [PMID: 36427512 PMCID: PMC10100853 DOI: 10.1016/s1474-4422(22)00407-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 09/17/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022]
Abstract
Autism spectrum disorder (henceforth autism) is a neurodevelopmental condition that can be reliably diagnosed in children by age 18-24 months. Prospective longitudinal studies of infants aged 1 year and younger who are later diagnosed with autism are elucidating the early developmental course of autism and identifying ways of predicting autism before diagnosis is possible. Studies that use MRI, EEG, and near-infrared spectroscopy have identified differences in brain development in infants later diagnosed with autism compared with infants without autism. Retrospective studies of infants younger than 1 year who received a later diagnosis of autism have also showed an increased prevalence of health conditions, such as sleep disorders, gastrointestinal disorders, and vision problems. Behavioural features of infants later diagnosed with autism include differences in attention, vocalisations, gestures, affect, temperament, social engagement, sensory processing, and motor abilities. Although research findings offer insight on promising screening approaches for predicting autism in infants, individual-level predictions remain a future goal. Multiple scientific challenges and ethical questions remain to be addressed to translate research on early brain-based and behavioural predictors of autism into feasible and reliable screening tools for clinical practice.
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Affiliation(s)
- Geraldine Dawson
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA.
| | - Amber D Rieder
- Duke Center for Autism and Brain Development, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA; Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, Cambridge, UK; Centre for Brain and Cognitive Development, Birkbeck, University of London, London, UK
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26
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Alhassan S, Soudani A, Almusallam M. Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:2228. [PMID: 36850829 PMCID: PMC9962521 DOI: 10.3390/s23042228] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 06/15/2023]
Abstract
The deployment of wearable wireless systems that collect physiological indicators to aid in diagnosing neurological disorders represents a potential solution for the new generation of e-health systems. Electroencephalography (EEG), a recording of the brain's electrical activity, is a promising physiological test for the diagnosis of autism spectrum disorders. It can identify the abnormalities of the neural system that are associated with autism spectrum disorders. However, streaming EEG samples remotely for classification can reduce the wireless sensor's lifespan and creates doubt regarding the application's feasibility. Therefore, decreasing data transmission may conserve sensor energy and extend the lifespan of wireless sensor networks. This paper suggests the development of a sensor-based scheme for early age autism detection. The proposed scheme implements an energy-efficient method for signal transformation allowing relevant feature extraction for accurate classification using machine learning algorithms. The experimental results indicate an accuracy of 96%, a sensitivity of 100%, and around 95% of F1 score for all used machine learning models. The results also show that our scheme energy consumption is 97% lower than streaming the raw EEG samples.
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Affiliation(s)
- Sarah Alhassan
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia
- Department of Computer Science, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
| | - Adel Soudani
- Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11362, Saudi Arabia
| | - Manan Almusallam
- Department of Computer Science, College of Computer and Information Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia
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27
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Delgado CF, Simpson EA, Zeng G, Delgado RE, Miron O. Newborn Auditory Brainstem Responses in Children with Developmental Disabilities. J Autism Dev Disord 2023; 53:776-788. [PMID: 34181140 PMCID: PMC9549590 DOI: 10.1007/s10803-021-05126-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2021] [Indexed: 12/30/2022]
Abstract
We integrated data from a newborn hearing screening database and a preschool disability database to examine the relationship between newborn click evoked auditory brainstem responses (ABRs) and developmental disabilities. This sample included children with developmental delay (n = 2992), speech impairment (SI, n = 905), language impairment (n = 566), autism spectrum disorder (ASD, n = 370), and comparison children (n = 128,181). We compared the phase of the ABR waveform, a measure of sound processing latency, across groups. Children with SI and children with ASD had greater newborn ABR phase values than both the comparison group and the developmental delay group. Newborns later diagnosed with SI or ASD have slower neurological responses to auditory stimuli, suggesting sensory differences at birth.
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Affiliation(s)
- Christine F Delgado
- Department of Psychology, University of Miami, PO Box 248185, Coral Gables, FL, 33124-0721, USA.
| | - Elizabeth A Simpson
- Department of Psychology, University of Miami, PO Box 248185, Coral Gables, FL, 33124-0721, USA
| | - Guangyu Zeng
- Department of Psychology, University of Miami, PO Box 248185, Coral Gables, FL, 33124-0721, USA
| | - Rafael E Delgado
- Biomedical Engineering, University of Miami, Coral Gables, FL, USA
- Intelligent Hearing Systems Corp., Miami, FL, USA
| | - Oren Miron
- Department of Health Systems Management, Ben-Gurion University of the Negev, Beer Sheva, Israel
- Department of Biomedical-Informatics, Harvard Medical School, Boston, MA, USA
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28
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Desowska A, Berde CB, Cornelissen L. Emerging functional connectivity patterns during sevoflurane anaesthesia in the developing human brain. Br J Anaesth 2023; 130:e381-e390. [PMID: 35803755 DOI: 10.1016/j.bja.2022.05.033] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Spectral-based EEG is used to monitor anaesthetic state during surgical procedures in adults. Spectral EEG features that can resemble the patterns seen in adults emerge in children after the age of 10 months and cannot distinguish wakefulness and anaesthesia in the youngest children. There is a need to explore alternative EEG measures. We hypothesise that functional connectivity is one of the measures that can help distinguish between consciousness states in children. METHODS An EEG data set of children undergoing sevoflurane general anaesthesia (age 0-3 yr) was reanalysed using debiased weighted phase lag index as a measure of functional connectivity in wakefulness (n=38) and anaesthesia (n=73). Network topology measures were compared between states in 0- to 6-, 6- to 10-, and >10-month-old children. RESULTS Functional connectivity was reduced in anaesthesia vs wakefulness in delta band (n=cluster of 17 significant connections; P=0.013; 58% connections surviving thresholding in wakefulness and 49% in anaesthesia). Network density and node degree were lower in anaesthesia even in the youngest children (0.57 in wakefulness; 0.48 in anaesthesia; t [9]=3.39; P=0.029; G=0.98; confidence interval [CI] [0.25-1.77]). Modularity was higher in anaesthesia (0-6 months: 0.16 in wakefulness and 0.19 in anaesthesia, t [9]=-2.95, P=0.04, G=-0.85, CI [-1.60 to -0.16]; >10 months: 0.16 vs 0.21, t [13]=-6.45, P<0.001, G=-1.62, CI [-2.49 to -0.85]) and decreased with age (ρ [73]=-0.456; P<0.001). CONCLUSIONS Anaesthesia modulates functional connectivity. Increased segregation into a more modular structure in anaesthesia decreases with age as adult-like features develop. These findings advance our understanding of the network architecture underlying the effects of anaesthesia on the developing brain.
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Affiliation(s)
- Adela Desowska
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Charles B Berde
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura Cornelissen
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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29
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Hüpen P, Kumar H, Shymanskaya A, Swaminathan R, Habel U. Impulsivity Classification Using EEG Power and Explainable Machine Learning. Int J Neural Syst 2023; 33:2350006. [PMID: 36632032 DOI: 10.1142/s0129065723500065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified [Formula: see text]-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ([Formula: see text]-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.
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Affiliation(s)
- Philippa Hüpen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.,JARA - Translational Brain Medicine, Aachen, Germany
| | - Himanshu Kumar
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India
| | - Aliaksandra Shymanskaya
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
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30
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Chawla P, Rana SB, Kaur H, Singh K. Computer-aided diagnosis of autism spectrum disorder from EEG signals using deep learning with FAWT and multiscale permutation entropy features. Proc Inst Mech Eng H 2023; 237:282-294. [PMID: 36515392 DOI: 10.1177/09544119221141751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Autism spectrum disorder (ASD), a neurodevelopment disorder, is characterized by significant difficulties in social interaction and emerges as a major threat to children. Its computer-aided diagnosis used by neurologists improves the detection process and has a favorable impact on patients' health. Currently, a biomarker termed electroencephalography (EEG) is considered as vital tool to detect abnormal electrical activity in the brain. In this context, the present paper brings forth a novel approach for automated diagnosis of ASD from multichannel EEG signals using flexible analytic wavelet transform (FAWT). Firstly, this approach processes the acquired EEG signals with filtering and segmentation into short-duration EEG segments in the range of 5-20 s. These segmented EEG signals are decomposed into five levels using FAWT technique to obtain various sub-bands. Further, multiscale permutation entropy values are extracted from decomposed sub-bands which are used as feature vectors in the present work. Afterwards, these feature vectors are evaluated by traditional machine learning algorithms viz., k-nearest neighbor, logistic regression, support vector machine, and random forest, as well as convolutional neural network (CNN) as deep learning algorithm with different segment durations. The analysis of results reveals that CNN provides maximum accuracy, sensitivity, specificity, and area under the curve of 99.19%, 99.34%, 99.21%, and 0.9997, respectively, for 10 s duration EEG segment to identify ASD patients among healthy individuals. Thus, the proposed CNN architecture would be extremely helpful during diagnostic process of autism disease for neurologists.
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Affiliation(s)
- Parikha Chawla
- Department of Engineering & Technology, Guru Nanak Dev University Regional Campus, Gurdaspur, Punjab, India
| | - Shashi B Rana
- Department of Engineering & Technology, Guru Nanak Dev University Regional Campus, Gurdaspur, Punjab, India
| | - Hardeep Kaur
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
| | - Kuldeep Singh
- Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India
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31
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Thomson S, Drummond K, O'Hely M, Symeonides C, Chandran C, Mansell T, Saffery R, Sly P, Mueller J, Vuillermin P, Ponsonby AL. Increased maternal non-oxidative energy metabolism mediates association between prenatal di-(2-ethylhexyl) phthalate (DEHP) exposure and offspring autism spectrum disorder symptoms in early life: A birth cohort study. ENVIRONMENT INTERNATIONAL 2023; 171:107678. [PMID: 36516674 DOI: 10.1016/j.envint.2022.107678] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 11/08/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Prenatal phthalate exposure has previously been linked to the development of autism spectrum disorder (ASD). However, the underlying biological mechanisms remain unclear. We investigated whether maternal and child central carbon metabolism is involved as part of the Barwon Infant Study (BIS), a population-based birth cohort of 1,074 Australian children. We estimated phthalate daily intakes using third-trimester urinary phthalate metabolite concentrations and other relevant indices. The metabolome of maternal serum in the third trimester, cord serum at birth and child plasma at 1 year were measured by nuclear magnetic resonance. We used the Small Molecule Pathway Database and principal component analysis to construct composite metabolite scores reflecting metabolic pathways. ASD symptoms at 2 and 4 years were measured in 596 and 674 children by subscales of the Child Behavior Checklist and the Strengths and Difficulties Questionnaire, respectively. Multivariable linear regression analyses demonstrated (i) prospective associations between higher prenatal di-(2-ethylhexyl) phthalate (DEHP) levels and upregulation of maternal non-oxidative energy metabolism pathways, and (ii) prospective associations between upregulation of these pathways and increased offspring ASD symptoms at 2 and 4 years of age. Counterfactual mediation analyses indicated that part of the mechanism by which higher prenatal DEHP exposure influences the development of ASD symptoms in early childhood is through a maternal metabolic shift in pregnancy towards non-oxidative energy pathways, which are inefficient compared to oxidative metabolism. These results highlight the importance of the prenatal period and suggest that further investigation of maternal energy metabolism as a molecular mediator of the adverse impact of prenatal environmental exposures such as phthalates is warranted.
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Affiliation(s)
- Sarah Thomson
- Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC 3052, Australia
| | - Katherine Drummond
- Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC 3052, Australia
| | - Martin O'Hely
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, 299 Ryrie Street, Geelong, VIC 3220, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia
| | - Christos Symeonides
- Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia
| | - Chitra Chandran
- Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC 3052, Australia
| | - Toby Mansell
- Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia
| | - Richard Saffery
- Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia
| | - Peter Sly
- Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia; Child Health Research Centre, The University of Queensland, 62 Graham St, South Brisbane, QLD 4101, Australia
| | - Jochen Mueller
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Peter Vuillermin
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, 299 Ryrie Street, Geelong, VIC 3220, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia
| | - Anne-Louise Ponsonby
- Florey Institute of Neuroscience and Mental Health, 30 Royal Parade, Parkville, VIC 3052, Australia; Murdoch Children's Research Institute, Royal Children's Hospital, The University of Melbourne, 50 Flemington Rd, Parkville, VIC 3052, Australia.
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32
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Li K, Ma X, Chen T, Xin J, Wang C, Wu B, Ogihara A, Zhou S, Liu J, Huang S, Wang Y, Li S, Chen Z, Xu R. A new early warning method for mild cognitive impairment due to Alzheimer's disease based on dynamic evaluation of the "spatial executive process". Digit Health 2023; 9:20552076231194938. [PMID: 37654709 PMCID: PMC10467230 DOI: 10.1177/20552076231194938] [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: 02/21/2023] [Accepted: 07/28/2023] [Indexed: 09/02/2023] Open
Abstract
Objective Mild cognitive impairment (MCI) due to Alzheimer's disease (AD), as an early stage of AD, is an important point for early warning of AD. Neuropathological studies have shown that AD pathology in pre-dementia patients involves the hippocampus and caudate nucleus, which are responsible for controlling cognitive mechanisms such as the spatial executive process (SEP). The aim of this study is to design a new method for early warning of MCI due to AD by dynamically evaluating SEP. Methods We designed fingertip interaction handwriting digital evaluation paradigms and analyzed the dynamic trajectory of fingertip interaction and image data during "clock drawing" and "repetitive writing" tasks. Extracted fingertip interaction digital biomarkers were used to assess participants' SEP disorders, ultimately enabling intelligent diagnosis of MCI due to AD. A cross-sectional study demonstrated the predictive performance of this new method. Results We enrolled 30 normal cognitive (NC) elderly and 30 MCI due to AD patients, and clinical research results showed that there may be neurobehavioral differences between the two groups in digital biomarkers captured during SEP. The early warning performance for MCI due to AD of this new method (areas under the curve (AUC) = 0.880) is better than that of the Minimum Mental State Examination (MMSE) neuropsychological scale (AUC = 0.856) assessed by physicians. Conclusion Patients with MCI due to AD may have SEP disorders, and this new method based on dynamic evaluation of SEP will provide a novel human-computer interaction and intelligent early warning method for home and community screening of MCI due to AD.
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Affiliation(s)
- Kai Li
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
- Joint Laboratory of Police Health Smart Surveillance, Zhejiang Police College, Hangzhou, China
| | - Xiaowen Ma
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Tong Chen
- Department of Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Junyi Xin
- School of Information Engineering, Hangzhou Medical College, Hangzhou, China
| | - Chen Wang
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Bo Wu
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Computer Science, Tokyo University of Technology, Hachioji City, Tokyo, Japan
| | - Atsushi Ogihara
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Health Sciences and Social Welfare, Faculty of Human Sciences, Waseda University, Tokorozawa, Japan
| | - Siyu Zhou
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Public health, Hangzhou Normal University, Hangzhou, China
| | - Jiakang Liu
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shouqiang Huang
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yujia Wang
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuwu Li
- Zhejiang-Japan Digital Diagnosis and Treatment and Equipment of Integrated Traditional Chinese Medicine and Western Medicine for Major Brain Diseases Joint Laboratory, Zhejiang Chinese Medical University, Hangzhou, China
- School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zeyuan Chen
- Joint Laboratory of Police Health Smart Surveillance, Zhejiang Police College, Hangzhou, China
- School of International Studies and Cooperation, Zhejiang Police College, Hangzhou, China
| | - Runlong Xu
- School of Information Engineering, Hangzhou Medical College, Hangzhou, China
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Colombi C, Chericoni N, Bargagna S, Costanzo V, Devescovi R, Lecciso F, Pierotti C, Prosperi M, Contaldo A. Case report: Preemptive intervention for an infant with early signs of autism spectrum disorder during the first year of life. Front Psychiatry 2023; 14:1105253. [PMID: 37205979 PMCID: PMC10189150 DOI: 10.3389/fpsyt.2023.1105253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/07/2023] [Indexed: 05/21/2023] Open
Abstract
Autism spectrum disorder (ASD) includes neurodevelopmental conditions traditionally considered to bring life long disabilities, severely impacting individuals and their families. Very early identification and intervention during the very first phases of life have shown to significantly diminish symptom severity and disability, and improve developmental trajectories. Here we report the case of a young child showing early behavioral signs of ASD during the first months of life, including diminished eye contact, reduced social reciprocity, repetitive movements. The child received a pre-emptive parent mediated intervention based on the Infant Start, an adaptation of the Early Start Denver Model (ESDM), specifically developed for children with ASD signs during the first year of life. The child here described received intervention from 6 to 32 months of age, in combination with educational services. Diagnostic evaluations performed at several time points (8, 14, 19, and 32 months) showed progressive improvements in his developmental level and ASD symptoms. Our case study supports the possibility of identifying ASD symptoms and providing services as soon as concerns emerge even during the first year of life. Our report, in combination with recent infant identification and intervention studies, suggests the need for very early screening and preemptive intervention to promote optimal outcomes.
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Affiliation(s)
- Costanza Colombi
- Stella Maris Foundation (IRCCS), Calambrone, Italy
- *Correspondence: Costanza Colombi,
| | | | | | | | - Raffaella Devescovi
- Institute for Maternal and Child Health Burlo Garofolo (IRCCS), Trieste, Friuli-Venezia Giulia, Italy
| | - Flavia Lecciso
- Department of History, Society and Human Studies, University of Salento, Lecce, Apulia, Italy
| | | | - Margherita Prosperi
- UFSMIA Valdera-Alta Val di Cecina, Azienda USL Toscana Nord Ovest, Pisa, Tuscany, Italy
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Wei Q, Cao H, Shi Y, Xu X, Li T. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. J Biomed Inform 2023; 137:104254. [PMID: 36509416 DOI: 10.1016/j.jbi.2022.104254] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Machine learning has been widely used to identify Autism Spectrum Disorder (ASD) based on eye-tracking, but its accuracy is uncertain. We aimed to summarize the available evidence on the performances of machine learning algorithms in classifying ASD and typically developing (TD) individuals based on eye-tracking data. METHODS We searched Medline, Embase, Web of Science, Scopus, Cochrane Library, IEEE Xplore Digital Library, Wan Fang Database, China National Knowledge Infrastructure, Chinese BioMedical Literature Database, VIP Database for Chinese Technical Periodicals, from database inception to December 24, 2021. Studies using machine learning methods to classify ASD and TD individuals based on eye-tracking technologies were included. We extracted the data on study population, model performances, algorithms of machine learning, and paradigms of eye-tracking. This study is registered with PROSPERO, CRD42022296037. RESULTS 261 articles were identified, of which 24 studies with sample sizes ranging from 28 to 141 were included (n = 1396 individuals). Machine learning based on eye-tracking yielded the pooled classified accuracy of 81 % (I2 = 73 %), specificity of 79 % (I2 = 61 %), and sensitivity of 84 % (I2 = 61 %) in classifying ASD and TD individuals. In subgroup analysis, the accuracy was 88 % (95 % CI: 85-91 %), 79 % (95 % CI: 72-84 %), 71 % (95 % CI: 59-91 %) for preschool-aged, school-aged, and adolescent-adult group. Eye-tracking stimuli and machine learning algorithms varied widely across studies, with social, static, and active stimuli and Support Vector Machine and Random Forest most commonly reported. Regarding the model performance evaluation, 15 studies reported their final results on validation datasets, four based on testing datasets, and five did not report whether they used validation datasets. Most studies failed to report the information on eye-tracking hardware and the implementation process. CONCLUSION Using eye-tracking data, machine learning has shown potential in identifying ASD individuals with high accuracy, especially in preschool-aged children. However, the heterogeneity between studies, the absence of test set-based performance evaluations, the small sample size, and the non-standardized implementation of eye-tracking might deteriorate the reliability of results. Further well-designed and well-executed studies with comprehensive and transparent reporting are needed to determine the optimal eye-tracking paradigms and machine learning algorithms.
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Affiliation(s)
- Qiuhong Wei
- Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Childhood Nutrition and Health, Chongqing, China
| | - Huiling Cao
- Department of Neonatology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Yuan Shi
- Department of Neonatology, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Ximing Xu
- Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, Chongqing, China.
| | - Tingyu Li
- Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Childhood Nutrition and Health, Chongqing, China.
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Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques. Sci Rep 2022; 12:22547. [PMID: 36581646 PMCID: PMC9800369 DOI: 10.1038/s41598-022-26644-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 12/19/2022] [Indexed: 12/30/2022] Open
Abstract
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.
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EEG Signals to Digit Classification Using Deep Learning-Based One-Dimensional Convolutional Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-07313-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Oda K, Colman R, Koshiba M. Simplified Attachable EEG Revealed Child Development Dependent Neurofeedback Brain Acute Activities in Comparison with Visual Numerical Discrimination Task and Resting. SENSORS (BASEL, SWITZERLAND) 2022; 22:7207. [PMID: 36236305 PMCID: PMC9572555 DOI: 10.3390/s22197207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
The development of an easy-to-attach electroencephalograph (EEG) would enable its frequent use for the assessment of neurodevelopment and clinical monitoring. In this study, we designed a two-channel EEG headband measurement device that could be used safely and was easily attachable and removable without the need for restraint or electrode paste or gel. Next, we explored the use of this device for neurofeedback applications relevant to education or neurocognitive development. We developed a prototype visual neurofeedback game in which the size of a familiar local mascot changes in the PC display depending on the user's brain wave activity. We tested this application at a local children's play event. Children at the event were invited to experience the game and, upon agreement, were provided with an explanation of the game and support in attaching the EEG device. The game began with a consecutive number visual discrimination task which was followed by an open-eye resting condition and then a neurofeedback task. Preliminary linear regression analyses by the least-squares method of the acquired EEG and age data in 30 participants from 5 to 20 years old suggested an age-dependent left brain lateralization of beta waves at the neurofeedback stage (p = 0.052) and of alpha waves at the open-eye resting stage (p = 0.044) with potential involvement of other wave bands. These results require further validation.
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Affiliation(s)
- Kazuyuki Oda
- Engineering Department, Graduate School of Sciences and Technology for Innovation Yamaguchi University, Yamaguchi 755-8611, Japan
| | - Ricki Colman
- Department of Cell and Regenerative Biology, University of Wisconsin, Madison, Madison, WI 53706, USA
| | - Mamiko Koshiba
- Engineering Department, Graduate School of Sciences and Technology for Innovation Yamaguchi University, Yamaguchi 755-8611, Japan
- Department of Pediatrics, Saitama Medical University, Saitama 350-0495, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan
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Fioranelli M, Aru OE, Roccia MG, Beesham A, Flavin D. A model for analyzing evolutions of neurons by using EEG waves. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12936-12949. [PMID: 36654029 DOI: 10.3934/mbe.2022604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
It is known that differences between potentials of soma, dendrites and different parts of neural structures may be the origin of electroencephalogram (EEG) waves. These potentials may be produced by some excitatory synapses and currents of charges between neurons and then thereafter may themselves cause the emergence of new synapses and electrical currents. These currents within and between neurons emit some electromagnetic waves which could be absorbed by electrodes on the scalp, and form topographic images. In this research, a model is proposed which formulates EEG topographic parameters in terms of the charge and mass of exchanged particles within neurons, those which move between neurons, the number of neurons and the length of neurons and synapses. In this model, by knowing the densities of the frequencies in different regions of the brain, one can predict the type, charge and velocity of particles which are moving along neurons or are exchanged between neurons.
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Affiliation(s)
- Massimo Fioranelli
- Department of Human Sciences, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, Italy
| | - O Eze Aru
- Department of Computer Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike Umuahia, Abia State, Nigeria
| | - Maria Grazia Roccia
- Department of Human Sciences, Guglielmo Marconi University, Via Plinio 44, 00193 Rome, Italy
| | - Aroonkumar Beesham
- Faculty of Natural Sciences, Mangosuthu University of Technology, PO Box 12363, Jacobs 4026, South Africa
- Department of Mathematical Sciences, University of Zululand, Private Bag X1001, Kwa-Dlangezwa 3886, South Africa
| | - Dana Flavin
- President, Foundation for Collaborative Medicine and Research, Greenwich CT, USA
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Wang Z, Liu J, Zhang W, Nie W, Liu H. Diagnosis and Intervention for Children With Autism Spectrum Disorder: A Survey. IEEE Trans Cogn Dev Syst 2022. [DOI: 10.1109/tcds.2021.3093040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Zhiyong Wang
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Jingjing Liu
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Wanqi Zhang
- State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China
| | - Wei Nie
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology Shenzhen, Shenzhen, China
| | - Honghai Liu
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology Shenzhen, Shenzhen, China
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Peng Y, Huang Y, Chen B, He M, Jiang L, Li Y, Huang X, Pei C, Zhang S, Li C, Zhang X, Zhang T, Zheng Y, Yao D, Li F, Xu P. Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients with Major Depressive Disorder. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2577-2588. [PMID: 36044502 DOI: 10.1109/tnsre.2022.3203073] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.
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Tye C, Bussu G, Gliga T, Elsabbagh M, Pasco G, Johnsen K, Charman T, Jones EJH, Buitelaar J, Johnson MH. Understanding the nature of face processing in early autism: A prospective study. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2022; 131:542-555. [PMID: 35901386 PMCID: PMC9330670 DOI: 10.1037/abn0000648] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 08/08/2020] [Accepted: 09/22/2020] [Indexed: 11/21/2022]
Abstract
Dimensional approaches to psychopathology interrogate the core neurocognitive domains interacting at the individual level to shape diagnostic symptoms. Embedding this approach in prospective longitudinal studies could transform our understanding of the mechanisms underlying neurodevelopmental disorders. Such designs require us to move beyond traditional group comparisons and determine which domain-specific alterations apply at the level of the individual, and whether they vary across distinct phenotypic subgroups. As a proof of principle, this study examines how the domain of face processing contributes to the emergence of autism spectrum disorder (ASD). We used an event-related potentials (ERPs) task in a cohort of 8-month-old infants with (n = 148) and without (n = 68) an older sibling with ASD, and combined traditional case-control comparisons with machine-learning techniques for prediction of social traits and ASD diagnosis at 36 months, and Bayesian hierarchical clustering for stratification into subgroups. A broad profile of alterations in the time-course of neural processing of faces in infancy was predictive of later ASD, with a strong convergence in ERP features predicting social traits and diagnosis. We identified two main subgroups in ASD, defined by distinct patterns of neural responses to faces, which differed on later sensory sensitivity. Taken together, our findings suggest that individual differences between infants contribute to the diffuse pattern of alterations predictive of ASD in the first year of life. Moving from group-level comparisons to pattern recognition and stratification can help to understand and reduce heterogeneity in clinical cohorts, and improve our understanding of the mechanisms that lead to later neurodevelopmental outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
- Charlotte Tye
- Department of Child and Adolescent Psychiatry and MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London
| | - Giorgia Bussu
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center
| | - Teodora Gliga
- Centre for Brain and Cognitive Development, Birkbeck College, University of London
| | | | - Greg Pasco
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London
| | | | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck College, University of London
| | - Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Birkbeck College, University of London
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Jagadapillai R, Qiu X, Ojha K, Li Z, El-Baz A, Zou S, Gozal E, Barnes GN. Potential Cross Talk between Autism Risk Genes and Neurovascular Molecules: A Pilot Study on Impact of Blood Brain Barrier Integrity. Cells 2022; 11:2211. [PMID: 35883654 PMCID: PMC9315816 DOI: 10.3390/cells11142211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 07/08/2022] [Accepted: 07/11/2022] [Indexed: 12/10/2022] Open
Abstract
Autism Spectrum Disorder (ASD) is a common pediatric neurobiological disorder with up to 80% of genetic etiologies. Systems biology approaches may make it possible to test novel therapeutic strategies targeting molecular pathways to alleviate ASD symptoms. A clinical database of autism subjects was queried for individuals with a copy number variation (CNV) on microarray, Vineland, and Parent Concern Questionnaire scores. Pathway analyses of genes from pathogenic CNVs yielded 659 genes whose protein-protein interactions and mRNA expression mapped 121 genes with maximal antenatal expression in 12 brain regions. A Research Domain Criteria (RDoC)-derived neural circuits map revealed significant differences in anxiety, motor, and activities of daily living skills scores between altered CNV genes and normal microarrays subjects, involving Positive Valence (reward), Cognition (IQ), and Social Processes. Vascular signaling was identified as a biological process that may influence these neural circuits. Neuroinflammation, microglial activation, iNOS and 3-nitrotyrosine increase in the brain of Semaphorin 3F- Neuropilin 2 (Sema 3F-NRP2) KO, an ASD mouse model, agree with previous reports in the brain of ASD individuals. Signs of platelet deposition, activation, release of serotonin, and albumin leakage in ASD-relevant brain regions suggest possible blood brain barrier (BBB) deficits. Disruption of neurovascular signaling and BBB with neuroinflammation may mediate causative pathophysiology in some ASD subgroups. Although preliminary, these data demonstrate the potential for developing novel therapeutic strategies based on clinically derived data, genomics, cognitive neuroscience, and basic neuroscience methods.
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Affiliation(s)
- Rekha Jagadapillai
- Department of Neurology, Pediatric Research Institute, Louisville, KY 40202, USA; (R.J.); (X.Q.); (K.O.)
- University of Louisville Autism Center, Louisville, KY 40217, USA
| | - Xiaolu Qiu
- Department of Neurology, Pediatric Research Institute, Louisville, KY 40202, USA; (R.J.); (X.Q.); (K.O.)
- University of Louisville Autism Center, Louisville, KY 40217, USA
- Department of Pediatrics, Pediatric Research Institute, University of Louisville School of Medicine, Louisville, KY 40202, USA
- Department of Child Health, Jiangxi Provincial Children’s Hospital, Donghu District, Nanchang 330006, China;
| | - Kshama Ojha
- Department of Neurology, Pediatric Research Institute, Louisville, KY 40202, USA; (R.J.); (X.Q.); (K.O.)
- University of Louisville Autism Center, Louisville, KY 40217, USA
| | - Zhu Li
- Department of Neurology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA;
| | - Ayman El-Baz
- Department of Bioengineering, University of Louisville Speed School, Louisville, KY 40292, USA;
| | - Shipu Zou
- Department of Child Health, Jiangxi Provincial Children’s Hospital, Donghu District, Nanchang 330006, China;
| | - Evelyne Gozal
- Department of Pediatrics, Pediatric Research Institute, University of Louisville School of Medicine, Louisville, KY 40202, USA
| | - Gregory N. Barnes
- Department of Neurology, Pediatric Research Institute, Louisville, KY 40202, USA; (R.J.); (X.Q.); (K.O.)
- University of Louisville Autism Center, Louisville, KY 40217, USA
- Department of Pediatrics, Pediatric Research Institute, University of Louisville School of Medicine, Louisville, KY 40202, USA
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Forbes O, Schwenn PE, Wu PPY, Santos-Fernandez E, Xie HB, Lagopoulos J, McLoughlin LT, Sacks DD, Mengersen K, Hermens DF. EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents. Biol Psychol 2022; 173:108403. [DOI: 10.1016/j.biopsycho.2022.108403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/27/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
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Shuffrey LC, Pini N, Potter M, Springer P, Lucchini M, Rayport Y, Sania A, Firestein M, Brink L, Isler JR, Odendaal H, Fifer WP. Aperiodic electrophysiological activity in preterm infants is linked to subsequent autism risk. Dev Psychobiol 2022; 64:e22271. [PMID: 35452546 PMCID: PMC9169229 DOI: 10.1002/dev.22271] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/31/2022] [Accepted: 03/04/2022] [Indexed: 12/31/2022]
Abstract
Approximately 7% of preterm infants receive an autism spectrum disorder (ASD) diagnosis. Yet, there is a significant gap in the literature in identifying prospective markers of neurodevelopmental risk in preterm infants. The present study examined two electroencephalography (EEG) parameters during infancy, absolute EEG power and aperiodic activity of the power spectral density (PSD) slope, in association with subsequent autism risk and cognitive ability in a diverse cohort of children born preterm in South Africa. Participants were 71 preterm infants born between 25 and 36 weeks gestation (34.60 ± 2.34 weeks). EEG was collected during sleep between 39 and 41 weeks postmenstrual age adjusted (40.00 ± 0.42 weeks). The Bayley Scales of Infant Development and Brief Infant Toddler Social Emotional Assessment (BITSEA) were administered at approximately 3 years of age adjusted (34 ± 2.7 months). Aperiodic activity, but not the rhythmic oscillatory activity, at multiple electrode sites was associated with subsequent increased autism risk on the BITSEA at three years of age. No associations were found between the PSD slope or absolute EEG power and cognitive development. Our findings highlight the need to examine potential markers of subsequent autism risk in high-risk populations other than infants at familial risk.
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Affiliation(s)
- Lauren C Shuffrey
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Mandy Potter
- Department of Obstetrics and Gynaecology, Stellenbosch University, Tygerberg, Western Cape, South Africa
| | - Priscilla Springer
- Paediatrics and Child Health, Stellenbosch University, Tygerberg, Western Cape, South Africa
| | - Maristella Lucchini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Yael Rayport
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA
| | - Ayesha Sania
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Morgan Firestein
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Lucy Brink
- Department of Obstetrics and Gynaecology, Stellenbosch University, Tygerberg, Western Cape, South Africa
| | - Joseph R Isler
- Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA
| | - Hein Odendaal
- Department of Obstetrics and Gynaecology, Stellenbosch University, Tygerberg, Western Cape, South Africa
| | - William P Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, USA.,Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, USA.,Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, USA
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45
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Bhavnani S, Parameshwaran D, Sharma KK, Mukherjee D, Divan G, Patel V, Thiagarajan TC. The Acceptability, Feasibility, and Utility of Portable Electroencephalography to Study Resting-State Neurophysiology in Rural Communities. Front Hum Neurosci 2022; 16:802764. [PMID: 35386581 PMCID: PMC8978891 DOI: 10.3389/fnhum.2022.802764] [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] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/17/2022] [Indexed: 11/23/2022] Open
Abstract
Electroencephalography (EEG) provides a non-invasive means to advancing our understanding of the development and function of the brain. However, the majority of the world’s population residing in low and middle income countries has historically been limited from contributing to, and thereby benefiting from, such neurophysiological research, due to lack of scalable validated methods of EEG data collection. In this study, we establish a standard operating protocol to collect approximately 3 min each of eyes-open and eyes-closed resting-state EEG data using a low-cost portable EEG device in rural households through formative work in the community. We then evaluate the acceptability of these EEG assessments to young children and feasibility of administering them through non-specialist workers. Finally, we describe properties of the EEG recordings obtained using this novel approach to EEG data collection. The formative phase was conducted with 9 families which informed protocols for consenting, child engagement strategies and data collection. The protocol was then implemented on 1265 families. 977 children (Mean age = 38.8 months, SD = 0.9) and 1199 adults (Mean age = 27.0 years, SD = 4) provided resting-state data for this study. 259 children refused to wear the EEG cap or removed it, and 58 children refused the eyes-closed recording session. Hardware or software issues were experienced during 30 and 25 recordings in eyes-open and eyes-closed conditions respectively. Disturbances during the recording sessions were rare and included participants moving their heads, touching the EEG headset with their hands, opening their eyes within the eyes-closed recording session, and presence of loud sounds in the testing environment. Similar to findings in laboratory-based studies from high-income settings, the percentage of recordings which showed an alpha peak was higher in eyes-closed than eyes-open condition, with the peak occurring most frequently in electrodes at O1 and O2 positions, and the mean frequency of the alpha peak was found to be lower in children (8.43 Hz, SD = 1.73) as compared to adults (10.71 Hz, SD = 3.96). We observed a deterioration in the EEG signal with prolonged device usage. This study demonstrates the acceptability, feasibility and utility of conducting EEG research at scale in a rural low-resource community, while highlighting its potential limitations, and offers the impetus needed to further refine the methods and devices and validate such scalable methods to overcome existing research inequity.
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Affiliation(s)
- Supriya Bhavnani
- Child Development Group, Sangath, Goa, India.,Public Health Foundation of India, New Delhi, India
| | | | | | - Debarati Mukherjee
- Indian Institute of Public Health-Bengaluru, Public Health Foundation of India, Bengaluru, India
| | - Gauri Divan
- Child Development Group, Sangath, Goa, India
| | - Vikram Patel
- Child Development Group, Sangath, Goa, India.,Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States.,Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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46
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Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:9340027. [PMID: 35368925 PMCID: PMC8975630 DOI: 10.1155/2022/9340027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/05/2022] [Accepted: 03/08/2022] [Indexed: 11/17/2022]
Abstract
Early detection of autism spectrum disorder (ASD) is highly beneficial to the health sustainability of children. Existing detection methods depend on the assessment of experts, which are subjective and costly. In this study, we proposed a machine learning approach that fuses physiological data (electroencephalography, EEG) and behavioral data (eye fixation and facial expression) to detect children with ASD. Its implementation can improve detection efficiency and reduce costs. First, we used an innovative approach to extract features of eye fixation, facial expression, and EEG data. Then, a hybrid fusion approach based on a weighted naive Bayes algorithm was presented for multimodal data fusion with a classification accuracy of 87.50%. Results suggest that the machine learning classification approach in this study is effective for the early detection of ASD. Confusion matrices and graphs demonstrate that eye fixation, facial expression, and EEG have different discriminative powers for the detection of ASD and typically developing children, and EEG may be the most discriminative information. The physiological and behavioral data have important complementary characteristics. Thus, the machine learning approach proposed in this study, which combines the complementary information, can significantly improve classification accuracy.
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47
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Abdulhay E, Alafeef M, Hadoush H, Venkataraman V, Arunkumar N. EMD-based analysis of complexity with dissociated EEG amplitude and frequency information: a data-driven robust tool -for Autism diagnosis- compared to multi-scale entropy approach. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:5031-5054. [PMID: 35430852 DOI: 10.3934/mbe.2022235] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is usually characterised by altered social skills, repetitive behaviours, and difficulties in verbal/nonverbal communication. It has been reported that electroencephalograms (EEGs) in ASD are characterised by atypical complexity. The most commonly applied method in studies of ASD EEG complexity is multiscale entropy (MSE), where the sample entropy is evaluated across several scales. However, the accuracy of MSE-based classifications between ASD and neurotypical EEG activities is poor owing to several shortcomings in scale extraction and length, the overlap between amplitude and frequency information, and sensitivity to frequency. The present study proposes a novel, nonlinear, non-stationary, adaptive, data-driven, and accurate method for the classification of ASD and neurotypical groups based on EEG complexity and entropy without the shortcomings of MSE. APPROACH The proposed method is as follows: (a) each ASD and neurotypical EEG (122 subjects × 64 channels) is decomposed using empirical mode decomposition (EMD) to obtain the intrinsic components (intrinsic mode functions). (b) The extracted components are normalised through the direct quadrature procedure. (c) The Hilbert transforms of the components are computed. (d) The analytic counterparts of components (and normalised components) are found. (e) The instantaneous frequency function of each analytic normalised component is calculated. (f) The instantaneous amplitude function of each analytic component is calculated. (g) The Shannon entropy values of the instantaneous frequency and amplitude vectors are computed. (h) The entropy values are classified using a neural network (NN). (i) The achieved accuracy is compared to that obtained with MSE-based classification. (j) The consistency of the results of entropy 3D mapping with clinical data is assessed. MAIN RESULTS The results demonstrate that the proposed method outperforms MSE (accuracy: 66.4%), with an accuracy of 93.5%. Moreover, the entropy 3D mapping results are more consistent with the available clinical data regarding brain topography in ASD. SIGNIFICANCE This study presents a more robust alternative to MSE, which can be used for accurate classification of ASD/neurotypical as well as for the examination of EEG entropy across brain zones in ASD.
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Affiliation(s)
- Enas Abdulhay
- Biomedical Engineering department, Jordan University of Science and Technology, 22110 Irbid, Jordan
| | - Maha Alafeef
- Biomedical Engineering department, Jordan University of Science and Technology, 22110 Irbid, Jordan
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
| | - Hikmat Hadoush
- Rehabilitation Sciences department, Jordan University of Science and Technology, 22110 Irbid, Jordan
| | - V Venkataraman
- Department of Mathematics, School of Arts, Science and Humanities, SASTRA Deemed University, Thanjavur, 613401, India
| | - N Arunkumar
- Biomedical Engineering department, Rathinam Technical Campus, Coimbatore, India
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48
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Park G, Jeon SJ, Ko IO, Park JH, Lee KC, Kim MS, Shin CY, Kim H, Lee YS. Decreased in vivo glutamate/GABA ratio correlates with the social behavior deficit in a mouse model of autism spectrum disorder. Mol Brain 2022; 15:19. [PMID: 35183218 PMCID: PMC8858545 DOI: 10.1186/s13041-022-00904-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/08/2022] [Indexed: 12/29/2022] Open
Abstract
To diagnose autism spectrum disorder (ASD), researchers have sought biomarkers whose alterations correlate with the susceptibility to ASD. However, biomarkers closely related to the pathophysiology of ASD are lacking. Even though excitation/inhibition (E/I) imbalance has been suggested as an underlying mechanism of ASD, few studies have investigated the actual ratio of glutamate (Glu) to γ-aminobutyric acid (GABA) concentration in vivo. Moreover, there are controversies in the directions of E/I ratio alterations even in extensively studied ASD animal models. Here, using proton magnetic resonance spectroscopy (1H-MRS) at 9.4T, we found significant differences in the levels of different metabolites or their ratios in the prefrontal cortex and hippocampus of Cntnap2−/− mice compared to their wild-type littermates. The Glu/GABA ratio, N-acetylaspartate (NAA)/total creatine (tCr) ratio, and tCr level in the prefrontal cortex were significantly different in Cntnap2−/− mice compared to those in wild-type mice, and they significantly correlated with the sociability of mice. Moreover, receiver operating characteristic (ROC) analyses indicated high specificity and selectivity of these metabolites in discriminating genotypes. These results suggest that the lowered Glu/GABA ratio in the prefrontal cortex along with the changes in the other metabolites might contribute to the social behavior deficit in Cntnap2−/− mice. Our results also demonstrate the utility of 1H-MRS in investigating the underlying mechanisms or the diagnosis of ASD.
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49
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Stuart N, Whitehouse A, Palermo R, Bothe E, Badcock N. Eye Gaze in Autism Spectrum Disorder: A Review of Neural Evidence for the Eye Avoidance Hypothesis. J Autism Dev Disord 2022; 53:1884-1905. [PMID: 35119604 PMCID: PMC10123036 DOI: 10.1007/s10803-022-05443-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2022] [Indexed: 12/27/2022]
Abstract
Reduced eye contact early in life may play a role in the developmental pathways that culminate in a diagnosis of autism spectrum disorder. However, there are contradictory theories regarding the neural mechanisms involved. According to the amygdala theory of autism, reduced eye contact results from a hypoactive amygdala that fails to flag eyes as salient. However, the eye avoidance hypothesis proposes the opposite-that amygdala hyperactivity causes eye avoidance. This review evaluated studies that measured the relationship between eye gaze and activity in the 'social brain' when viewing facial stimuli. Of the reviewed studies, eight of eleven supported the eye avoidance hypothesis. These results suggest eye avoidance may be used to reduce amygdala-related hyperarousal among people on the autism spectrum.
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Affiliation(s)
- Nicole Stuart
- University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia.
| | - Andrew Whitehouse
- Telethon Kids Institute, Perth Children's Hospital, 15 Hospital Avenue, Nedlands, WA, 6009, Australia
| | - Romina Palermo
- University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia
| | - Ellen Bothe
- University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia
| | - Nicholas Badcock
- University of Western Australia, 35 Stirling Highway, Crawley, WA, 6009, Australia
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50
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Clairmont C, Wang J, Tariq S, Sherman HT, Zhao M, Kong XJ. The Value of Brain Imaging and Electrophysiological Testing for Early Screening of Autism Spectrum Disorder: A Systematic Review. Front Neurosci 2022; 15:812946. [PMID: 35185452 PMCID: PMC8851356 DOI: 10.3389/fnins.2021.812946] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 12/09/2021] [Indexed: 11/13/2022] Open
Abstract
Given the significance of validating reliable tests for the early detection of autism spectrum disorder (ASD), this systematic review aims to summarize available evidence of neuroimaging and neurophysiological changes in high-risk infants to improve ASD early diagnosis. We included peer-reviewed, primary research in English published before May 21, 2021, involving the use of magnetic resonance imaging (MRI), electroencephalogram (EEG), or functional near-infrared spectroscopy (fNIRS) in children with high risk for ASD under 24 months of age. The main exclusion criteria includes diagnosis of a genetic disorder and gestation age of less the 36 weeks. Online research was performed on PubMed, Web of Science, PsycINFO, and CINAHL. Article selection was conducted by two reviewers to minimize bias. This research was funded by Massachusetts General Hospital Sundry funding. IRB approval was not submitted as it was deemed unnecessary. We included 75 primary research articles. Studies showed that high-risk infants had divergent developmental trajectories for fractional anisotropy and regional brain volumes, increased CSF volume, and global connectivity abnormalities on MRI, decreased sensitivity for familiar faces, atypical lateralization during facial and auditory processing, and different spectral powers across multiple band frequencies on EEG, and distinct developmental trajectories in functional connectivity and regional oxyhemoglobin concentrations in fNIRS. These findings in infants were found to be correlated with the core ASD symptoms and diagnosis at toddler age. Despite the lack of quantitative analysis of the research database, neuroimaging and electrophysiological biomarkers have promising value for the screening of ASD as early as infancy with high accuracy, which warrants further investigation.
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Affiliation(s)
- Cullen Clairmont
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Jiuju Wang
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
- NHC Key Laboratory of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing, China
| | - Samia Tariq
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Hannah Tayla Sherman
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
| | - Mingxuan Zhao
- Department of Business Analytics, Bentley University, Waltham, MA, United States
| | - Xue-Jun Kong
- Synapse Lab, Athinoula A. Martinos Center, Massachusetts General Hospital, Boston, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, United States
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