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Jiao M, Xian X, Wang B, Zhang Y, Yang S, Chen S, Sun H, Liu F. XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG. Neuroimage 2024:120802. [PMID: 39173694 DOI: 10.1016/j.neuroimage.2024.120802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 08/17/2024] [Accepted: 08/19/2024] [Indexed: 08/24/2024] Open
Abstract
Electroencephalography (EEG) or Magnetoencephalography (MEG) source imaging aims to estimate the underlying activated brain sources to explain the observed EEG/MEG recordings. Solving the inverse problem of EEG/MEG Source Imaging (ESI) is challenging due to its ill-posed nature. To achieve a unique solution, it is essential to apply sophisticated regularization constraints to restrict the solution space. Traditionally, the design of regularization terms is based on assumptions about the spatiotemporal structure of the underlying source dynamics. In this paper, we propose a novel paradigm for ESI via Explainable Deep Learning Framework, termed as XDL-ESI, which connects the iterative optimization algorithm with deep learning architecture by unfolding the iterative updates with neural network modules. The proposed framework has the advantages of (1) establishing a data-driven approach to model the source solution structure instead of using hand-crafted regularization terms; (2) improving the robustness of source solutions by introducing a topological loss that leverages the geometric spatial information applying varying penalties on distinct localization errors; (3) improving the reconstruction efficiency and interpretability as it inherits the advantages from both the iterative optimization algorithms (interpretability) and deep learning approaches (function approximation). The proposed XDL-ESI framework provides an efficient, accurate, and interpretable paradigm to solve the ESI inverse problem with satisfactory performance in both simulated data and real clinical data. Specially, this approach is further validated using simultaneous EEG and intracranial EEG (iEEG).
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Affiliation(s)
- Meng Jiao
- Department of Systems and Enterprises Stevens Institute of Technology, Hoboken, NJ, 07030, United States
| | - Xiaochen Xian
- H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology, Atlanta, GA, 30332, United States
| | - Boyu Wang
- Department of Computer Science, University of Western Ontario, Ontario, N6A 3K7, Canada
| | - Yu Zhang
- Department of Bioengineering Lehigh University, Bethlehem, PA, 18015, United States
| | - Shihao Yang
- Department of Systems and Enterprises Stevens Institute of Technology, Hoboken, NJ, 07030, United States
| | - Spencer Chen
- Department of Neurosurgery Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, United States
| | - Hai Sun
- Department of Neurosurgery Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, 08901, United States
| | - Feng Liu
- Department of Systems and Enterprises Stevens Institute of Technology, Hoboken, NJ, 07030, United States; Semcer Center for Healthcare Innovation Stevens Institute of Technology, Hoboken, NJ, 07030, United States.
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Wei Q, Dong W, Yu D, Wang K, Yang T, Xiao Y, Long D, Xiong H, Chen J, Xu X, Li T. Early identification of autism spectrum disorder based on machine learning with eye-tracking data. J Affect Disord 2024; 358:326-334. [PMID: 38615846 DOI: 10.1016/j.jad.2024.04.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 02/15/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Early identification of autism spectrum disorder (ASD) improves long-term outcomes, yet significant diagnostic delays persist. METHODS A retrospective cohort of 449 children (ASD: 246, typically developing [TD]: 203) was used for model development. Eye-movement data were collected from the participants watching videos that featured eye-tracking paradigms for assessing social and non-social cognition. Five machine learning algorithms, namely random forest, support vector machine, logistic regression, artificial neural network, and extreme gradient boosting, were trained to classify children with ASD and TD. The best-performing algorithm was selected to build the final model which was further evaluated in a prospective cohort of 80 children. The Shapley values interpreted important eye-tracking features. RESULTS Random forest outperformed other algorithms during model development and achieved an area under the curve of 0.849 (< 3 years: 0.832, ≥ 3 years: 0.868) on the external validation set. Of the ten most important eye-tracking features, three measured social cognition, and the rest were related to non-social cognition. A deterioration in model performance was observed using only the social or non-social cognition-related eye-tracking features. LIMITATIONS The sample size of this study, although larger than that of existing studies of ASD based on eye-tracking data, was still relatively small compared to the number of features. CONCLUSIONS Machine learning models based on eye-tracking data have the potential to be cost- and time-efficient digital tools for the early identification of ASD. Eye-tracking phenotypes related to social and non-social cognition play an important role in distinguishing children with ASD from TD children.
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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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China; College of Medical Informatics, Medical Data Science Academy, Chongqing Engineering Research Center for Clinical Big-data and Drug Evaluation, Chongqing Medical University, Chongqing, China
| | - Wenxin Dong
- College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China; Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science (Ministry of Education), Research Center for Learning Science, School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Ke Wang
- Big Data Center for Children's Medical Care, Children's Hospital of Chongqing Medical University, Chongqing, China
| | - Ting Yang
- 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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Yuanjie Xiao
- 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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Dan Long
- 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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Haiyi Xiong
- 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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China
| | - Jie Chen
- 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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, 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, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China.
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3
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Jia S, Mei X, Chen L, Chan LH, Tsang C, Suen V, Li T, Zaw MW, Liu A, Thompson B, Sabel B, Woo G, Leung CKS, Yip SP, Chang DHF, Cheong AMY. Glaucoma Rehabilitation using ElectricAI Transcranial Stimulation (GREAT)-study protocol for randomized controlled trial using combined perceptual learning and transcranial electrical stimulation for vision enhancement. Trials 2024; 25:501. [PMID: 39039582 PMCID: PMC11264395 DOI: 10.1186/s13063-024-08314-3] [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: 04/10/2024] [Accepted: 07/03/2024] [Indexed: 07/24/2024] Open
Abstract
BACKGROUND Glaucoma patients with irreversible visual field loss often experience decreased quality of life, impaired mobility, and mental health challenges. Perceptual learning (PL) and transcranial electrical stimulation (tES) have emerged as promising interventions for vision rehabilitation, showing potential in restoring residual visual functions. The Glaucoma Rehabilitation using ElectricAI Transcranial stimulation (GREAT) project aims to investigate whether combining PL and tES is more effective than using either method alone in maximizing the visual function of glaucoma patients. Additionally, the study will assess the impact of these interventions on brain neural activity, blood biomarkers, mobility, mental health, quality of life, and fear of falling. METHODS The study employs a three-arm, double-blind, randomized, superiority-controlled design. Participants are randomly allocated in a 1:1:1 ratio to one of three groups receiving: (1) real PL and real tES, (2) real PL and sham tES, and (3) placebo PL and sham tES. Each participant undergoes 10 sessions per block (~ 1 h each), with a total of three blocks. Assessments are conducted at six time points: baseline, interim 1, interim 2, post-intervention, 1-month post-intervention, and 2-month post-intervention. The primary outcome is the mean deviation of the 24-2 visual field measured by the Humphrey visual field analyzer. Secondary outcomes include detection rate in the suprathreshold visual field, balance and gait functions, and electrophysiological and biological responses. This study also investigates changes in neurotransmitter metabolism, biomarkers, self-perceived quality of life, and psychological status before and after the intervention. DISCUSSION The GREAT project is the first study to assess the effectiveness of PL and tES in the rehabilitation of glaucoma. Our findings will offer comprehensive assessments of the impact of these treatments on a wide range of brain and vision-related metrics including visual field, neural activity, biomarkers, mobility, mental health, fear of falling, and quality of life. TRIAL REGISTRATION ClinicalTrials.gov NCT05874258 . Registered on May 15, 2023.
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Affiliation(s)
- Shuwen Jia
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Xiaolin Mei
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Lilin Chen
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Lok Hin Chan
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Celia Tsang
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Venus Suen
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Tingni Li
- Centre for Eye and Vision Research Limited, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Myo Win Zaw
- Centre for Eye and Vision Research Limited, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Amanda Liu
- Centre for Eye and Vision Research Limited, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Ben Thompson
- Centre for Eye and Vision Research Limited, Hong Kong Science Park, Hong Kong Special Administrative Region, China
- School of Optometry and Vision Science, University of Waterloo, Waterloo, Canada
| | - Bernhard Sabel
- Institute of Medical Psychology, University of Magdeburg, Magdeburg, Germany
| | - George Woo
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Christopher K S Leung
- Department of Ophthalmology, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Shea-Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China
| | - Dorita H F Chang
- Department of Psychology, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Allen M Y Cheong
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
- Centre for Eye and Vision Research Limited, Hong Kong Science Park, Hong Kong Special Administrative Region, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region, China.
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Tigga NP, Garg S, Goyal N, Raj J, Das B. Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network. Technol Health Care 2024:THC240550. [PMID: 38943414 DOI: 10.3233/thc-240550] [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: 07/01/2024]
Abstract
BACKGROUND Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities. OBJECTIVE This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals. METHODS In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction. RESULTS The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection. CONCLUSION The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.
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Affiliation(s)
- Neha Prerna Tigga
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Shruti Garg
- Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India
| | - Nishant Goyal
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
| | - Justin Raj
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
| | - Basudeb Das
- Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, India
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Szakács H, Mutlu MC, Balestrieri G, Gombos F, Braun J, Kringelbach ML, Deco G, Kovács I. Navigating Pubertal Goldilocks: The Optimal Pace for Hierarchical Brain Organization. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308364. [PMID: 38489748 DOI: 10.1002/advs.202308364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/22/2024] [Indexed: 03/17/2024]
Abstract
Adolescence is a timed process with an onset, tempo, and duration. Nevertheless, the temporal dimension, especially the pace of maturation, remains an insufficiently studied aspect of developmental progression. The primary objective is to estimate the precise influence of pubertal maturational tempo on the configuration of associative brain regions. To this end, the connection between maturational stages and the level of hierarchical organization of large-scale brain networks in 12-13-year-old females is analyzed. Skeletal maturity is used as a proxy for pubertal progress. The degree of maturity is defined by the difference between bone age and chronological age. To assess the level of hierarchical organization in the brain, the temporal dynamic of closed eye resting state high-density electroencephalography (EEG) in the alpha frequency range is analyzed. Different levels of hierarchical order are captured by the measured asymmetry in the directionality of information flow between different regions. The calculated EEG-based entropy production of participant groups is then compared with accelerated, average, and decelerated maturity. Results indicate that an average maturational trajectory optimally aligns with cerebral hierarchical order, and both accelerated and decelerated timelines result in diminished cortical organization. This suggests that a "Goldilocks rule" of brain development is favoring a particular maturational tempo.
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Affiliation(s)
- Hanna Szakács
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Square, Budapest, 1088, Hungary
- Semmelweis University Doctoral School, Division of Mental Health Sciences, 26 Üllői road, Budapest, 1085, Hungary
| | - Murat Can Mutlu
- Institute of Biology, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
| | - Giulio Balestrieri
- Center for Brain and Cognition, Universitat Pompeu Fabra, 25-27 Ramon Trias Fargas, Barcelona, 08005, Spain
| | - Ferenc Gombos
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Square, Budapest, 1088, Hungary
- HUN-REN-ELTE-PPKE Adolescent Development Research Group, 1 Mikszáth Kálmán Square, Budapest, 1088, Hungary
| | - Jochen Braun
- Institute of Biology, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Otto-von-Guericke University, 44 Leipziger Straße, 39120, Magdeburg, Germany
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Wellington Square, Oxford, OX3 9BX, UK
- Department of Psychiatry, University of Oxford, Wellington Square, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Nordre Ringgade 1, Aarhus, 8000, Denmark
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, 25-27 Ramon Trias Fargas, Barcelona, 08005, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 122-140 Carrer de Tànger, Barcelona, 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), 23 Passeig de Lluís Companys, Barcelona, 08010, Spain
| | - Ilona Kovács
- HUN-REN-ELTE-PPKE Adolescent Development Research Group, 1 Mikszáth Kálmán Square, Budapest, 1088, Hungary
- Institute of Psychology, Faculty of Education and Psychology, Eötvös Loránd University, 25-27 Kazinczy Street, Budapest, 1075, Hungary
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Xu Y, Yu Z, Li Y, Liu Y, Li Y, Wang Y. Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108196. [PMID: 38678958 DOI: 10.1016/j.cmpb.2024.108196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 01/30/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND OBJECTIVE People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. METHODS This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. RESULTS Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. CONCLUSIONS This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.
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Affiliation(s)
- Yongjie Xu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zengjie Yu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yisheng Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuehan Liu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ye Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yishan Wang
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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Qian S, Yang Q, Cai C, Dong J, Cai S. Spatial-Temporal Characteristics of Brain Activity in Autism Spectrum Disorder Based on Hidden Markov Model and Dynamic Graph Theory: A Resting-State fMRI Study. Brain Sci 2024; 14:507. [PMID: 38790485 PMCID: PMC11118919 DOI: 10.3390/brainsci14050507] [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: 04/22/2024] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain's intrinsic connectivity and capture dynamic changes in the brain. In this study, the hidden Markov model (HMM) and dynamic graph (DG) theory are used to study the spatial-temporal characteristics and dynamics of brain networks based on dynamic functional connectivity (DFC). By using HMM, we identified three typical brain states for ASD and healthy control (HC). Furthermore, we explored the correlation between HMM time-varying properties and clinical autism scale scores. Differences in brain topological characteristics and dynamics between ASD and HC were compared by DG analysis. The experimental results indicate that ASD is more inclined to enter a strongly connected HMM brain state, leading to the isolation of brain networks and alterations in the topological characteristics of brain networks, such as default mode network (DMN), ventral attention network (VAN), and visual network (VN). This work suggests that using different data-driven methods based on DFC to study brain network dynamics would have better information complementarity, which can provide a new direction for the extraction of neuro-biomarkers in the early diagnosis of ASD.
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Affiliation(s)
| | | | | | | | - Shuhui Cai
- Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Department of Electronic Science, Xiamen University, Xiamen 361005, China; (S.Q.); (Q.Y.); (C.C.); (J.D.)
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Desowska A, Coffman S, Kim I, Underwood E, Tao A, Lopez KL, Nelson CA, Hensch TK, Gabard-Durnam L, Cornelissen L, Berde CB. Neurodevelopment of children exposed to prolonged anesthesia in infancy: GABA study interim analysis of resting-state brain networks at 2, 4, and 10-months old. Neuroimage Clin 2024; 42:103614. [PMID: 38754325 PMCID: PMC11126539 DOI: 10.1016/j.nicl.2024.103614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Previous studies have raised concerns regarding neurodevelopmental impacts of early exposures to general anesthesia and surgery. Electroencephalography (EEG) can be used to study ontogeny of brain networks during infancy. As a substudy of an ongoing study, we examined measures of functional connectivity in awake infants with prior early and prolonged anesthetic exposures and in control infants. METHODS EEG functional connectivity was assessed using debiased weighted phase lag index at source and sensor levels and graph theoretical measures for resting state activity in awake infants in the early anesthesia (n = 26 at 10 month visit, median duration of anesthesia = 4 [2, 7 h]) and control (n = 38 at 10 month visit) groups at ages approximately 2, 4 and 10 months. Theta and low alpha frequency bands were of primary interest. Linear mixed models incorporated impact of age and cumulative hours of general anesthesia exposure. RESULTS Models showed no significant impact of cumulative hours of general anesthesia exposure on debiased weighted phase lag index, characteristic path length, clustering coefficient or small-worldness (conditional R2 0.05-0.34). An effect of age was apparent in many of these measures. CONCLUSIONS We could not demonstrate significant impact of general anesthesia in the first months of life on early development of resting state brain networks over the first postnatal year. Future studies will explore these networks as these infants grow older.
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Affiliation(s)
- Adela Desowska
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Siobhan Coffman
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Isabelle Kim
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Ellen Underwood
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Alice Tao
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Kelsie L Lopez
- Center for Cognitive and Brain Health, Northeastern University, Boston, USA
| | - Charles A Nelson
- Laboratories of Cognitive Neurosciences, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Takao K Hensch
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | | | - Laura Cornelissen
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Charles B Berde
- Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, USA.
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Shen G, Green HL, Franzen RE, Berman JI, Dipiero M, Mowad TG, Bloy L, Liu S, Airey M, Goldin S, Ku M, McBride E, Blaskey L, Kuschner ES, Kim M, Konka K, Roberts TPL, Edgar JC. Resting-State Activity in Children: Replicating and Extending Findings of Early Maturation of Alpha Rhythms in Autism Spectrum Disorder. J Autism Dev Disord 2024; 54:1961-1976. [PMID: 36932271 DOI: 10.1007/s10803-023-05926-7] [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] [Accepted: 02/07/2023] [Indexed: 03/19/2023]
Abstract
Resting-state alpha brain rhythms provide a foundation for basic as well as higher-order brain processes. Research suggests atypical maturation of the peak frequency of resting-state alpha activity (= PAF) in autism spectrum disorder (ASD). The present study examined resting-state alpha activity in young school-aged children, obtaining magnetoencephalographic (MEG) eyes-closed resting-state data from 47 typically developing (TD) males and 45 ASD males 6.0 to 9.3 years old. Results confirmed a higher PAF in ASD versus TD, and demonstrated that alpha power differences between groups were linked to the shift of PAF in ASD. Additionally, a higher PAF was associated with better cognitive performance in TD but not ASD. Finding thus suggested functional consequences of group differences in resting-state alpha activity.
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Affiliation(s)
- Guannan Shen
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Radiology, Children's Hospital of Philadelphia, 19104, Philadelphia, PA, USA.
| | - Heather L Green
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Rose E Franzen
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jeffrey I Berman
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marissa Dipiero
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Theresa G Mowad
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Luke Bloy
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Song Liu
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Megan Airey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sophia Goldin
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthew Ku
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Emma McBride
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lisa Blaskey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Autism Research, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Emily S Kuschner
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Autism Research, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mina Kim
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kimberly Konka
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Timothy P L Roberts
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - J Christopher Edgar
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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10
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Ke SY, Wu H, Sun H, Zhou A, Liu J, Zheng X, Liu K, Westover MB, Xu H, Kong XJ. Classification of autism spectrum disorder using electroencephalography in Chinese children: a cross-sectional retrospective study. Front Neurosci 2024; 18:1330556. [PMID: 38332856 PMCID: PMC10850305 DOI: 10.3389/fnins.2024.1330556] [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: 10/31/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by diverse clinical features. EEG biomarkers such as spectral power and functional connectivity have emerged as potential tools for enhancing early diagnosis and understanding of the neural processes underlying ASD. However, existing studies yield conflicting results, necessitating a comprehensive, data-driven analysis. We conducted a retrospective cross-sectional study involving 246 children with ASD and 42 control children. EEG was collected, and diverse EEG features, including spectral power and spectral coherence were extracted. Statistical inference methods, coupled with machine learning models, were employed to identify differences in EEG features between ASD and control groups and develop classification models for diagnostic purposes. Our analysis revealed statistically significant differences in spectral coherence, particularly in gamma and beta frequency bands, indicating elevated long range functional connectivity between frontal and parietal regions in the ASD group. Machine learning models achieved modest classification performance of ROC-AUC at 0.65. While machine learning approaches offer some discriminative power classifying individuals with ASD from controls, they also indicate the need for further refinement.
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Affiliation(s)
- Si Yang Ke
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
| | - Huiwen Wu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Haoqi Sun
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Aiqin Zhou
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Jianhua Liu
- Huangshi Maternity and Child Health Care Hospital, Huangshi, Hubei, China
| | - Xiaoyun Zheng
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Kevin Liu
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - M. Brandon Westover
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Haiqing Xu
- Hubei Maternity and Child Health Hospital, Wuhan, Hubei, China
| | - Xue-jun Kong
- Anthinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Beth Israel Deaconess Medical Center, Boston, MA, United States
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11
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Chen B, Jiang L, Lu G, Li Y, Zhang S, Huang X, Xu P, Li F, Yao D. Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks. Cereb Cortex 2023; 33:11170-11180. [PMID: 37750334 DOI: 10.1093/cercor/bhad355] [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: 07/05/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/27/2023] Open
Abstract
Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.
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Affiliation(s)
- Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, 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 610054, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Guoqing Lu
- School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 600054, China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Shu Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xunan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, China
- School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu 611731, 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 610054, 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 Neuro Information, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China
- Radiation Oncology Key Laboratory of Sichuan Province, 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 610054, 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 Neuro Information, Chinese Academy of Medical Sciences, Chengdu 2019RU035, 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 610054, 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 Neuro Information, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China
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12
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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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13
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O'Reilly C, Huberty S, van Noordt S, Desjardins J, Wright N, Scorah J, Webb SJ, Elsabbagh M. EEG functional connectivity in infants at elevated familial likelihood for autism spectrum disorder. Mol Autism 2023; 14:37. [PMID: 37805500 PMCID: PMC10559476 DOI: 10.1186/s13229-023-00570-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 09/29/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy. METHODS We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between cortical sources of EEG during video watching using the corrected imaginary part of phase-locking values. RESULTS Our main analysis found no significant association between functional connectivity and ASD, showing only significant effects for age, sex, age-sex interaction, and site. Given these null results, we performed an exploratory analysis and observed, at 12 months, a negative correlation between functional connectivity and ADOS calibrated severity scores for restrictive and repetitive behaviors (RRB). LIMITATIONS The small sample of ASD participants inherent to sibling studies limits diagnostic group comparisons. Also, results from our secondary exploratory analysis should be considered only as potential relationships to further explore, given their increased vulnerability to false positives. CONCLUSIONS These results are inconclusive concerning an association between EEG functional connectivity and ASD in infancy. Exploratory analyses provided preliminary support for a relationship between RRB and functional connectivity specifically, but these preliminary observations need corroboration on larger samples.
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Affiliation(s)
- Christian O'Reilly
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA.
- Artificial Intelligence Institute of South Carolina, University of South Carolina, 1112 Greene St, Columbia, SC, 29208, USA.
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC, USA.
| | - Scott Huberty
- Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | - Stefon van Noordt
- Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada
| | | | - Nicky Wright
- Department of Psychology, Manchester Metropolitan University, Manchester, UK
| | - Julie Scorah
- Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
| | | | - Mayada Elsabbagh
- Azrieli Centre for Autism Research, Montreal Neurological Institute-Hospital, McGill University, Montreal, Canada
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14
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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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15
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Barik K, Watanabe K, Bhattacharya J, Saha G. Functional connectivity based machine learning approach for autism detection in young children using MEG signals. J Neural Eng 2023; 20. [PMID: 36812588 DOI: 10.1088/1741-2552/acbe1f] [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: 09/03/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023]
Abstract
Objective.Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.Approach.We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique.Main results.To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.Significance.Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.
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Affiliation(s)
- Kasturi Barik
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
| | - Katsumi Watanabe
- Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Joydeep Bhattacharya
- Department of Psychology, Goldsmiths, University of London, London, United Kingdom
| | - Goutam Saha
- Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
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16
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Geng X, Fan X, Zhong Y, Casanova MF, Sokhadze EM, Li X, Kang J. Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder. Brain Sci 2023; 13:brainsci13010130. [PMID: 36672111 PMCID: PMC9857308 DOI: 10.3390/brainsci13010130] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/05/2023] [Accepted: 01/10/2023] [Indexed: 01/13/2023] Open
Abstract
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3-10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD.
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Affiliation(s)
- Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Xiwang Fan
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 200124, China
| | - Yiwen Zhong
- Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai 200124, China
| | - Manuel F. Casanova
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, 701 Grove Rd, Greenville, SC 29605, USA
| | - Estate M. Sokhadze
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, 701 Grove Rd, Greenville, SC 29605, USA
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100859, China
- Correspondence: (X.L.); (J.K.)
| | - Jiannan Kang
- College of Electronic & Information Engineering, Hebei University, Baoding 071000, China
- Correspondence: (X.L.); (J.K.)
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17
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Loth E. Does the current state of biomarker discovery in autism reflect the limits of reductionism in precision medicine? Suggestions for an integrative approach that considers dynamic mechanisms between brain, body, and the social environment. Front Psychiatry 2023; 14:1085445. [PMID: 36911126 PMCID: PMC9992810 DOI: 10.3389/fpsyt.2023.1085445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 01/23/2023] [Indexed: 02/24/2023] Open
Abstract
Over the past decade, precision medicine has become one of the most influential approaches in biomedical research to improve early detection, diagnosis, and prognosis of clinical conditions and develop mechanism-based therapies tailored to individual characteristics using biomarkers. This perspective article first reviews the origins and concept of precision medicine approaches to autism and summarises recent findings from the first "generation" of biomarker studies. Multi-disciplinary research initiatives created substantially larger, comprehensively characterised cohorts, shifted the focus from group-comparisons to individual variability and subgroups, increased methodological rigour and advanced analytic innovations. However, although several candidate markers with probabilistic value have been identified, separate efforts to divide autism by molecular, brain structural/functional or cognitive markers have not identified a validated diagnostic subgroup. Conversely, studies of specific monogenic subgroups revealed substantial variability in biology and behaviour. The second part discusses both conceptual and methodological factors in these findings. It is argued that the predominant reductionist approach, which seeks to parse complex issues into simpler, more tractable units, let us to neglect the interactions between brain and body, and divorce individuals from their social environment. The third part draws on insights from systems biology, developmental psychology and neurodiversity approaches to outline an integrative approach that considers the dynamic interaction between biological (brain, body) and social mechanisms (stress, stigma) to understanding the origins of autistic features in particular conditions and contexts. This requires 1) closer collaboration with autistic people to increase face validity of concepts and methodologies; (2) development of measures/technologies that enable repeat assessment of social and biological factors in different (naturalistic) conditions and contexts, (3) new analytic methods to study (simulate) these interactions (including emergent properties), and (4) cross-condition designs to understand which mechanisms are transdiagnostic or specific for particular autistic sub-populations. Tailored support may entail both creating more favourable conditions in the social environment and interventions for some autistic people to increase well-being.
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Affiliation(s)
- Eva Loth
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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18
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Bogéa Ribeiro L, da Silva Filho M. Systematic Review on EEG Analysis to Diagnose and Treat Autism by Evaluating Functional Connectivity and Spectral Power. Neuropsychiatr Dis Treat 2023; 19:415-424. [PMID: 36861010 PMCID: PMC9968781 DOI: 10.2147/ndt.s394363] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/05/2023] [Indexed: 02/24/2023] Open
Abstract
An abnormality in neural connectivity is linked to autism spectrum disorder (ASD). There is no way to test the concept of neural connectivity empirically. According to recent network theory and time series analysis findings, electroencephalography (EEG) can assess neural network architecture, a sign of activity in the brain. This systematic review aims to evaluate functional connectivity and spectral power using EEG signals. EEG records the brain activity of an individual by displaying wavy lines that depict brain cells' communication through electrical impulses. EEG can diagnose various brain disorders, including epilepsy and related seizure illness, brain dysfunction, tumors, and damage. We found 21 studies using two of the most common EEG analysis methods: functional connectivity and spectral power. ASD and non-ASD individuals were found to differ significantly in all selected papers. Due to high heterogeneity in the outcomes, generalizations cannot be drawn, and no single method is currently beneficial as a diagnostic tool. For ASD subtype delineation, the lack of research prevented the evaluation of these techniques as diagnostic tools. These findings confirm the presence of abnormalities in the EEG in ASD, but they are insufficient to diagnose. Our study suggests that EEG is useful in diagnosing ASD by evaluating entropy in the brain. Researchers may be able to develop new diagnostic methods for ASD which focuses on particular stimuli and brainwaves if they conduct more extensive studies with higher numbers and more rigorous study designs.
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19
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Intellectually able adults with autism spectrum disorder show typical resting-state EEG activity. Sci Rep 2022; 12:19016. [PMID: 36347938 PMCID: PMC9643446 DOI: 10.1038/s41598-022-22597-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] Open
Abstract
There is broad interest in discovering quantifiable physiological biomarkers for psychiatric disorders to aid diagnostic assessment. However, finding biomarkers for autism spectrum disorder (ASD) has proven particularly difficult, partly due to high heterogeneity. Here, we recorded five minutes eyes-closed rest electroencephalography (EEG) from 186 adults (51% with ASD and 49% without ASD) and investigated the potential of EEG biomarkers to classify ASD using three conventional machine learning models with two-layer cross-validation. Comprehensive characterization of spectral, temporal and spatial dimensions of source-modelled EEG resulted in 3443 biomarkers per recording. We found no significant group-mean or group-variance differences for any of the EEG features. Interestingly, we obtained validation accuracies above 80%; however, the best machine learning model merely distinguished ASD from the non-autistic comparison group with a mean balanced test accuracy of 56% on the entirely unseen test set. The large drop in model performance between validation and testing, stress the importance of rigorous model evaluation, and further highlights the high heterogeneity in ASD. Overall, the lack of significant differences and weak classification indicates that, at the group level, intellectually able adults with ASD show remarkably typical resting-state EEG.
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Portnova G, Nekrashevich M, Morozova M, Martynova O, Sharaev M. New approaches to Clinical Electroencephalography analysis in typically developing children and children with autism. COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.11.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Haartsen R, Mason L, Garces P, Gui A, Charman T, Tillmann J, Johnson MH, Buitelaar JK, Loth E, Murphy D, Jones EJH. Qualitative differences in the spatiotemporal brain states supporting configural face processing emerge in adolescence in autism. Cortex 2022; 155:13-29. [PMID: 35961249 DOI: 10.1016/j.cortex.2022.06.010] [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: 05/28/2021] [Revised: 12/17/2021] [Accepted: 06/29/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Studying the neural processing of faces can illuminate the mechanisms of compromised social expertise in autism. To resolve a longstanding debate, we examined whether differences in configural face processing in autism are underpinned by quantitative differences in the activation of typical face processing pathways, or the recruitment of non-typical neural systems. METHODS We investigated spatial and temporal characteristics of event-related EEG responses to upright and inverted faces in a large sample of children, adolescents, and adults with and without autism. We examined topographic analyses of variance and global field power to identify group differences in the spatial and temporal response to face inversion. We then examined how quasi-stable spatiotemporal profiles - microstates - are modulated by face orientation and diagnostic group. RESULTS Upright and inverted faces produced distinct profiles of topography and strength in the topographical analyses. These topographical profiles differed between diagnostic groups in adolescents, but not in children or adults. In the microstate analysis, the autistic group showed differences in the activation strength of normative microstates during early-stage processing at all ages, suggesting consistent quantitative differences in the operation of typical processing pathways; qualitative differences in microstate topographies during late-stage processing became prominent in adults, suggesting the increasing involvement of non-typical neural systems with processing time and over development. CONCLUSIONS These findings suggest that early difficulties with configural face processing may trigger later compensatory processes in autism that emerge in later development.
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Affiliation(s)
- Rianne Haartsen
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom.
| | - Luke Mason
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom
| | - Pilar Garces
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Anna Gui
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Kent, United Kingdom
| | - Julian Tillmann
- Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Jan K Buitelaar
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, the Netherlands
| | - Eva Loth
- Department of Forensic and Neurodevelopmental Science, King's College London, United Kingdom
| | - Declan Murphy
- Department of Forensic and Neurodevelopmental Science, King's College London, United Kingdom
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, United Kingdom
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Freschl J, Azizi LA, Balboa L, Kaldy Z, Blaser E. The development of peak alpha frequency from infancy to adolescence and its role in visual temporal processing: A meta-analysis. Dev Cogn Neurosci 2022; 57:101146. [PMID: 35973361 PMCID: PMC9399966 DOI: 10.1016/j.dcn.2022.101146] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/21/2022] [Accepted: 08/08/2022] [Indexed: 01/19/2023] Open
Abstract
While it has been shown that alpha frequency increases over development (Stroganova et al., 1999), a precise trajectory has not yet been specified, making it challenging to constrain theories linking alpha rhythms to perceptual development. We conducted a comprehensive review of studies measuring resting-state occipital peak alpha frequency (PAF, the frequency exhibiting maximum power) from birth to 18 years of age. From 889 potentially relevant studies, we identified 40 reporting PAF (109 samples; 3882 subjects). A nonlinear regression revealed that PAF increases quickly in early childhood (from 6.1 Hz at 6 months to 8.4 Hz at 5 years) and levels off in adolescence (9.7 Hz at 13 years), with an asymptote at 10.1 Hz. We found no effect of resting state procedure (eyes-open versus eyes-closed) or biological sex. PAF has been implicated as a clock on visual temporal processing, with faster frequencies associated with higher visual temporal resolution. Psychophysical studies have shown that temporal resolution reaches adult levels by 5 years of age (Freschl et al., 2019, 2020). The fact that PAF reaches the adult range of 8-12 Hz by that age strengthens the link between PAF and temporal resolution.
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Affiliation(s)
- Julie Freschl
- University of Massachusetts Boston, Boston, MA, USA; Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA.
| | | | | | - Zsuzsa Kaldy
- University of Massachusetts Boston, Boston, MA, USA
| | - Erik Blaser
- University of Massachusetts Boston, Boston, MA, USA
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