1
|
Taddei M, Cuesta P, Annunziata S, Bulgheroni S, Esposito S, Visani E, Granvillano A, Dotta S, Rossi DS, Panzica F, Franceschetti S, Varotto G, Riva D. Correlation between autistic traits and brain functional connectivity in preschoolers with autism spectrum disorder: a resting state MEG study. Neurol Sci 2024; 45:4549-4561. [PMID: 38639894 DOI: 10.1007/s10072-024-07528-2] [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: 12/07/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Neurophysiological studies recognized that Autism Spectrum Disorder (ASD) is associated with altered patterns of over- and under-connectivity. However, little is known about network organization in children with ASD in the early phases of development and its correlation with the severity of core autistic features. METHODS The present study aimed at investigating the association between brain connectivity derived from MEG signals and severity of ASD traits measured with different diagnostic clinical scales, in a sample of 16 children with ASD aged 2 to 6 years. RESULTS A significant correlation emerged between connectivity strength in cortical brain areas implicated in several resting state networks (Default mode, Central executive, Salience, Visual and Sensorimotor) and the severity of communication anomalies, social interaction problems, social affect problems, and repetitive behaviors. Seed analysis revealed that this pattern of correlation was mainly caused by global rather than local effects. CONCLUSIONS The present evidence suggests that altered connectivity strength in several resting state networks is related to clinical features and may contribute to neurofunctional correlates of ASD. Future studies implementing the same method on a wider and stratified sample may further support functional connectivity as a possible biomarker of the condition.
Collapse
Affiliation(s)
- Matilde Taddei
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Pablo Cuesta
- Department of Radiology, Rehabilitation, and Physiotherapy, Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain
| | - Silvia Annunziata
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
- Fondazione Don Carlo Gnocchi Onlus-IRCCS S. Maria Nascente, Via Capecelatro 66, 20148, Milan, Italy
| | - Sara Bulgheroni
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Silvia Esposito
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Elisa Visani
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Alice Granvillano
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Sara Dotta
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Davide Sebastiano Rossi
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Ferruccio Panzica
- Clinical Engineering Service, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Silvana Franceschetti
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| | - Giulia Varotto
- Neurophysiopathology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy.
- Epilepsy Unit, Bioengineering Group, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, University Politécnica de Madrid, Madrid, Spain.
| | - Daria Riva
- Unit for Neurogenetic Syndromes With Intellectual Disabilities and Autism Spectrum Disorders, Fondazione IRCCS Istituto Neurologico Carlo Besta, Via Celoria 11, 20133, Milan, Italy
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Demopoulos C, Jesson X, Gerdes MR, Jurigova BG, Hinkley LB, Ranasinghe KG, Desai S, Honma S, Mizuiri D, Findlay A, Nagarajan SS, Marco EJ. Global MEG Resting State Functional Connectivity in Children with Autism and Sensory Processing Dysfunction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.26.577499. [PMID: 38352614 PMCID: PMC10862722 DOI: 10.1101/2024.01.26.577499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Sensory processing dysfunction not only affects most individuals with autism spectrum disorder (ASD), but at least 5% of children without ASD also experience dysfunctional sensory processing. Our understanding of the relationship between sensory dysfunction and resting state brain activity is still emerging. This study compared long-range resting state functional connectivity of neural oscillatory behavior in children aged 8-12 years with autism spectrum disorder (ASD; N=18), those with sensory processing dysfunction (SPD; N=18) who do not meet ASD criteria, and typically developing control participants (TDC; N=24) using magnetoencephalography (MEG). Functional connectivity analyses were performed in the alpha and beta frequency bands, which are known to be implicated in sensory information processing. Group differences in functional connectivity and associations between sensory abilities and functional connectivity were examined. Distinct patterns of functional connectivity differences between ASD and SPD groups were found only in the beta band, but not in the alpha band. In both alpha and beta bands, ASD and SPD cohorts differed from the TDC cohort. Somatosensory cortical beta-band functional connectivity was associated with tactile processing abilities, while higher-order auditory cortical alpha-band functional connectivity was associated with auditory processing abilities. These findings demonstrate distinct long-range neural synchrony alterations in SPD and ASD that are associated with sensory processing abilities. Neural synchrony measures could serve as potential sensitive biomarkers for ASD and SPD.
Collapse
Affiliation(s)
- Carly Demopoulos
- Department of Psychiatry, University of California San Francisco, 675 18 Street, San Francisco, CA 94107
- Department of Radiology & Biomedical Imaging, University of California-San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143
| | - Xuan Jesson
- Department of Psychology, Palo Alto University, 1791 Arastradero Road, Palo Alto, CA 94304
| | - Molly Rae Gerdes
- Cortica Healthcare, Department of Neurodevelopmental Medicine, 4000 Civic Center Drive, San Rafael, CA 94903
| | - Barbora G. Jurigova
- Cortica Healthcare, Department of Neurodevelopmental Medicine, 4000 Civic Center Drive, San Rafael, CA 94903
| | - Leighton B. Hinkley
- Department of Radiology & Biomedical Imaging, University of California-San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143
| | - Kamalini G. Ranasinghe
- University of California-San Francisco, Department of Neurology, 675 Nelson Rising Lane, San Francisco, CA 94143
| | - Shivani Desai
- University of California-San Francisco, Department of Neurology, 675 Nelson Rising Lane, San Francisco, CA 94143
| | - Susanne Honma
- Department of Radiology & Biomedical Imaging, University of California-San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143
| | - Danielle Mizuiri
- Department of Radiology & Biomedical Imaging, University of California-San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143
| | - Anne Findlay
- Department of Radiology & Biomedical Imaging, University of California-San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143
| | - Srikantan S. Nagarajan
- Department of Radiology & Biomedical Imaging, University of California-San Francisco, 513 Parnassus Avenue, S362, San Francisco, CA 94143
| | - Elysa J. Marco
- Cortica Healthcare, Department of Neurodevelopmental Medicine, 4000 Civic Center Drive, San Rafael, CA 94903
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Davis P, Takach K, Maski K, Levin A. A circuit-level biomarker of Rett syndrome based on ectopic phase-amplitude coupling during slow-wave-sleep. Cereb Cortex 2023; 33:2559-2572. [PMID: 35640651 DOI: 10.1093/cercor/bhac226] [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: 01/30/2022] [Revised: 05/01/2022] [Accepted: 05/02/2022] [Indexed: 11/13/2022] Open
Abstract
Rett syndrome (RTT) is a neurodevelopmental disorder characterized by loss of purposeful hand use and spoken language following an initial period of normal development. Although much is known about the genetic and molecular underpinnings of RTT, less is known about the circuit-level etiopathology. Coupling of oscillations during slow-wave-sleep (SWS) underlies important neurocognitive processes in adulthood, yet its emergence has yet to be described in early typical development (TD) or in RTT. We therefore addressed these unknowns by describing SWS cross-frequency coupling in both RTT and early TD using a retrospective study design. We found that in TD, phase-amplitude coupling (PAC) during SWS was dominated by coupling of slow-wave (0.5-2 Hz) phase to theta amplitude (5-8 Hz, "SW:T") as well as slow-wave to spindle-range (12-15 Hz, "SW:S"). Coupling exhibited characteristic vertex-prominent spatial topography, which emerged during an early developmental window. This topography failed to develop in patients with RTT due to persistent ectopic coupling. Furthermore, we found that subtypes of RTT exhibit distinct PAC topographic profiles, and that ectopic PAC correlates with clinical severity. These findings suggest that altered PAC dynamics and spatial organization during SWS may underlie the circuit-level pathophysiology of RTT and suggest that ectopic coupling may contribute to RTT pathogenesis.
Collapse
Affiliation(s)
- Patrick Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Kyle Takach
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
- Division of Developmental Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Kiran Maski
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - April Levin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, United States
| |
Collapse
|
6
|
Alho J, Khan S, Mamashli F, Perrachione TK, Losh A, McGuiggan NM, Graham S, Nayal Z, Joseph RM, Hämäläinen MS, Bharadwaj H, Kenet T. Atypical cortical processing of bottom-up speech binding cues in children with autism spectrum disorders. Neuroimage Clin 2023; 37:103336. [PMID: 36724734 PMCID: PMC9898310 DOI: 10.1016/j.nicl.2023.103336] [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: 06/13/2022] [Revised: 01/10/2023] [Accepted: 01/20/2023] [Indexed: 01/23/2023]
Abstract
Individuals with autism spectrum disorder (ASD) commonly display speech processing abnormalities. Binding of acoustic features of speech distributed across different frequencies into coherent speech objects is fundamental in speech perception. Here, we tested the hypothesis that the cortical processing of bottom-up acoustic cues for speech binding may be anomalous in ASD. We recorded magnetoencephalography while ASD children (ages 7-17) and typically developing peers heard sentences of sine-wave speech (SWS) and modulated SWS (MSS) where binding cues were restored through increased temporal coherence of the acoustic components and the introduction of harmonicity. The ASD group showed increased long-range feedforward functional connectivity from left auditory to parietal cortex with concurrent decreased local functional connectivity within the parietal region during MSS relative to SWS. As the parietal region has been implicated in auditory object binding, our findings support our hypothesis of atypical bottom-up speech binding in ASD. Furthermore, the long-range functional connectivity correlated with behaviorally measured auditory processing abnormalities, confirming the relevance of these atypical cortical signatures to the ASD phenotype. Lastly, the group difference in the local functional connectivity was driven by the youngest participants, suggesting that impaired speech binding in ASD might be ameliorated upon entering adolescence.
Collapse
Affiliation(s)
- Jussi Alho
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA.
| | - Sheraz Khan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Fahimeh Mamashli
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Tyler K Perrachione
- Department of Speech, Language, and Hearing Sciences, Boston University, 635 Commonwealth Ave, Boston, MA 02215, USA
| | - Ainsley Losh
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Nicole M McGuiggan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Steven Graham
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Zein Nayal
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Robert M Joseph
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 72 East Concord St, Boston, MA 02118, USA
| | - Matti S Hämäläinen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA
| | - Hari Bharadwaj
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Department of Speech, Language, and Hearing Sciences, and Weldon School of Biomedical Engineering, Purdue University, 715 Clinic Drive, West Lafayette, IN 47907, USA
| | - Tal Kenet
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, 149 13th St, Boston, MA 02129, USA.
| |
Collapse
|
7
|
Korisky A, Gordon I, Goldstein A. Oxytocin impacts top-down and bottom-up social perception in adolescents with ASD: a MEG study of neural connectivity. Mol Autism 2022; 13:36. [PMID: 36064612 PMCID: PMC9446859 DOI: 10.1186/s13229-022-00513-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 07/18/2022] [Indexed: 11/24/2022] Open
Abstract
Background In the last decade, accumulative evidence has shown that oxytocin can modulate social perception in typically developed individuals and individuals diagnosed with autism. While several studies show that oxytocin (OT) modulates neural activation in social-related neural regions, the mechanism that underlies OT effects in ASD is not fully known yet. Despite evidence from animal studies on connections between the oxytocinergic system and excitation/inhibition neural balance, the influence of OT on oscillatory responses among individuals with ASD has been rarely examined. To bridge these gaps in knowledge, we investigated the effects of OT on both social and non-social stimuli while focusing on its specific influence on the neural connectivity between three socially related neural regions—the left and right fusiform and the medial frontal cortex.
Methods Twenty-five adolescents with ASD participated in a wall-established social task during a randomized, double-blind placebo-controlled MEG and OT administration study. Our main task was a social-related task that required the identification of social and non-social-related pictures. We hypothesized that OT would modulate the oscillatory connectivity between three pre-selected regions of interest to be more adaptive to social processing. Specifically, we focused on alpha and gamma bands which are known to play an important role in face processing and top-down/bottom-up balance.
Results Compared to placebo, OT reduced the connectivity between the medial frontal cortex and the fusiform in the low gamma more for social stimuli than for non-social ones, a reduction that was correlated with individuals’ performance in the task. Additionally, for both social and non-social stimuli, OT increased the connectivity in the alpha and beta bands. Limitations Sample size was determined based on sample sizes previously reported in MEG in clinical populations, especially OT administration studies in combination with neuroimaging in ASD. We were limited in our capability to recruit for such a study, and as such, the sample size was not based on a priori power analysis. Additionally, we limited our analyses to specific neural bands and regions. To validate the current results, future studies may be needed to explore other parameters using whole-brain approaches in larger samples. Conclusion These results suggest that OT influenced social perception by modifying the communication between frontal and posterior regions, an attenuation that potentially impacts both social and non-social early perception. We also show that OT influences differ between top-down and bottom-up processes, depending on the social context. Overall, by showing that OT influences both social-related perception and overall attention during early processing stages, we add new information to the existing understanding of the impact of OT on neural processing in ASD. Furthermore, by highlighting the influence of OT on early perception, we provide new directions for treatments for difficulties in early attentional phases in this population. Trial registration Registered on October 27, 2021—Retrospectively registered, https://clinicaltrials.gov/ct2/show/record/NCT05096676 (details on clinical registration can be found in www.clinicalTrial.gov, unique identifier: NCT05096676). Supplementary Information The online version contains supplementary material available at 10.1186/s13229-022-00513-6.
Collapse
Affiliation(s)
- Adi Korisky
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, 5290002, Ramat Gan, Israel
| | - Ilanit Gordon
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, 5290002, Ramat Gan, Israel. .,Department of Psychology, Bar-Ilan University, 5290002, Ramat Gan, Israel.
| | - Abraham Goldstein
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, 5290002, Ramat Gan, Israel.,Department of Psychology, Bar-Ilan University, 5290002, Ramat Gan, Israel
| |
Collapse
|
8
|
Cross-frequency coupling in psychiatric disorders: A systematic review. Neurosci Biobehav Rev 2022; 138:104690. [PMID: 35569580 DOI: 10.1016/j.neubiorev.2022.104690] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 05/02/2022] [Accepted: 05/08/2022] [Indexed: 11/21/2022]
Abstract
Cross-frequency coupling (CFC), an electrophysiologically derived measure of oscillatory coupling in the brain, is believed to play a critical role in neuronal computation, learning and communication. It has received much recent attention in the study of both health and disease. We searched for literature that studied CFC during resting state and task-related activities during electroencephalography and magnetoencephalography in psychiatric disorders. Thirty-eight studies were identified, which included attention-deficit hyperactivity disorder, Alzheimer's dementia, autism spectrum disorder, bipolar disorder, depression, obsessive compulsive disorder, social anxiety disorder and schizophrenia. The systematic review was registered with PROSPERO (ID#CRD42021224188). The current review indicates measurable differences exist between CFC in disease states vs. healthy controls. There was variance in CFC at different regions of the brain within the same psychiatric disorders, perhaps this could be explained by the mechanisms and functionality of CFC. There was heterogeneity in methodologies used, which may lead to spurious CFC analyses. Going forward, standardized methodologies need to be established and utilized in further research to understand the neuropathophysiology associated with psychiatric disorders.
Collapse
|
9
|
Assessment of Dynamic Phase Amplitude Coupling Using Matching Pursuit. J Neurosci Methods 2022; 376:109610. [DOI: 10.1016/j.jneumeth.2022.109610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 02/26/2022] [Accepted: 04/21/2022] [Indexed: 11/22/2022]
|
10
|
Green HL, Dipiero M, Koppers S, Berman JI, Bloy L, Liu S, McBride E, Ku M, Blaskey L, Kuschner E, Airey M, Kim M, Konka K, Roberts TP, Edgar JC. Peak Alpha Frequency and Thalamic Structure in Children with Typical Development and Autism Spectrum Disorder. J Autism Dev Disord 2022; 52:103-112. [PMID: 33629214 PMCID: PMC8384980 DOI: 10.1007/s10803-021-04926-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/10/2021] [Indexed: 01/03/2023]
Abstract
Associations between age, resting-state (RS) peak-alpha-frequency (PAF = frequency showing largest amplitude alpha activity), and thalamic volume (thalamus thought to modulate alpha activity) were examined to understand differences in RS alpha activity between children with autism spectrum disorder (ASD) and typically-developing children (TDC) noted in prior studies. RS MEG and structural-MRI data were obtained from 51 ASD and 70 TDC 6- to 18-year-old males. PAF and thalamic volume maturation were observed in TDC but not ASD. Although PAF was associated with right thalamic volume in TDC (R2 = 0.12, p = 0.01) but not ASD (R2 = 0.01, p = 0.35), this group difference was not large enough to reach significance. Findings thus showed unusual maturation of brain function and structure in ASD as well as an across-group thalamic contribution to alpha rhythms.
Collapse
Affiliation(s)
- Heather L. Green
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA,Corresponding Author: Heather Green, PhD, Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, Tel: 267-425-2464, Fax: 215-590-1345,
| | - Marissa Dipiero
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Simon Koppers
- Institute of Imaging and Computer Vision, RWTH Aachen University, Aachen, Germany
| | - 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
| | - 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
| | - Emma McBride
- 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
| | - 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.,Center for Autism Research, Department of Pediatrics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Emily 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
| | - Megan Airey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children’s Hospital of Philadelphia, 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
| | - 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
| |
Collapse
|
11
|
Mariscal MG, Berry-Kravis E, Buxbaum JD, Ethridge LE, Filip-Dhima R, Foss-Feig JH, Kolevzon A, Modi ME, Mosconi MW, Nelson CA, Powell CM, Siper PM, Soorya L, Thaliath A, Thurm A, Zhang B, Sahin M, Levin AR. Shifted phase of EEG cross-frequency coupling in individuals with Phelan-McDermid syndrome. Mol Autism 2021; 12:29. [PMID: 33910615 PMCID: PMC8082621 DOI: 10.1186/s13229-020-00411-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 12/28/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Phelan-McDermid Syndrome (PMS) is a rare condition caused by deletion or mutation of the SHANK3 gene. Individuals with PMS frequently present with intellectual disability, autism spectrum disorder, and other neurodevelopmental challenges. Electroencephalography (EEG) can provide a window into network-level function in PMS. METHODS Here, we analyze EEG data collected across multiple sites in individuals with PMS (n = 26) and typically developing individuals (n = 15). We quantify oscillatory power, alpha-gamma phase-amplitude coupling strength, and phase bias, a measure of the phase of cross frequency coupling thought to reflect the balance of feedforward (bottom-up) and feedback (top-down) activity. RESULTS We find individuals with PMS display increased alpha-gamma phase bias (U = 3.841, p < 0.0005), predominantly over posterior electrodes. Most individuals with PMS demonstrate positive overall phase bias while most typically developing individuals demonstrate negative overall phase bias. Among individuals with PMS, strength of alpha-gamma phase-amplitude coupling was associated with Sameness, Ritualistic, and Compulsive behaviors as measured by the Repetitive Behavior Scales-Revised (Beta = 0.545, p = 0.011). CONCLUSIONS Increased phase bias suggests potential circuit-level mechanisms underlying phenotype in PMS, offering opportunities for back-translation of findings into animal models and targeting in clinical trials.
Collapse
Affiliation(s)
| | - Elizabeth Berry-Kravis
- Department of Pediatrics, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
- Department of Biochemistry, Rush University Medical Center, Chicago, IL, USA
| | - Joseph D Buxbaum
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY, USA
- Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA
| | - Lauren E Ethridge
- Department of Pediatrics, University of Oklahoma Health Science Center, Oklahoma City, OK, USA
| | - Rajna Filip-Dhima
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - Jennifer H Foss-Feig
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Alexander Kolevzon
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Meera E Modi
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - Matthew W Mosconi
- Clinical Child Psychology Program, Schiefelbusch Institute for Life Span Studies, University of Kansas, Lawrence, KS, USA
| | - Charles A Nelson
- Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA
| | - Craig M Powell
- Department of Neurobiology, UAB School of Medicine, Birmingham, AL, USA
| | - Paige M Siper
- Seaver Autism Center, Icahn School of Medicine at Mount Sinai Hospital, New York, NY, USA
| | - Latha Soorya
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA
| | - Andrew Thaliath
- Department of Pediatrics, Rush University Medical Center, Chicago, IL, USA
| | - Audrey Thurm
- Intramural Research Program, National Institute of Mental Health, Bethesda, USA
| | - Bo Zhang
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - April R Levin
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA.
| |
Collapse
|
12
|
Mamashli F, Kozhemiako N, Khan S, Nunes AS, McGuiggan NM, Losh A, Joseph RM, Ahveninen J, Doesburg SM, Hämäläinen MS, Kenet T. Children with autism spectrum disorder show altered functional connectivity and abnormal maturation trajectories in response to inverted faces. Autism Res 2021; 14:1101-1114. [PMID: 33709531 DOI: 10.1002/aur.2497] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/08/2021] [Indexed: 12/21/2022]
Abstract
The processing of information conveyed by faces is a critical component of social communication. While the neurophysiology of processing upright faces has been studied extensively in autism spectrum disorder (ASD), less is known about the neurophysiological abnormalities associated with processing inverted faces in ASD. We used magnetoencephalography (MEG) to study both long-range and local functional connectivity, with the latter assessed using local cross-frequency coupling, in response to inverted faces stimuli, in 7-18 years old individuals with ASD and age and IQ matched typically developing (TD) individuals. We found abnormally reduced coupling between the phase of the alpha rhythm and the amplitude of the gamma rhythm in the fusiform face area (FFA) in response to inverted faces, as well as reduced long-range functional connectivity between the FFA and the inferior frontal gyrus (IFG) in response to inverted faces in the ASD group. These group differences were absent in response to upright faces. The magnitude of functional connectivity between the FFA and the IFG was significantly correlated with the severity of ASD, and FFA-IFG long-range functional connectivity increased with age in TD group, but not in the ASD group. Our findings suggest that both local and long-range functional connectivity are abnormally reduced in children with ASD when processing inverted faces, and that the pattern of abnormalities associated with the processing of inverted faces differs from the pattern of upright faces in ASD, likely due to the presumed greater reliance on top-down regulations necessary for efficient processing of inverted faces. LAY SUMMARY: We found alterations in the neurophysiological responses to inverted faces in children with ASD, that were not reflected in the evoked responses, and were not observed in the responses to upright faces. These alterations included reduced local functional connectivity in the fusiform face area (FFA), and decreased long-range alpha-band modulated functional connectivity between the FFA and the left IFG. The magnitude of long-range functional connectivity between the FFA and the inferior frontal gyrus was correlated with the severity of ASD.
Collapse
Affiliation(s)
- Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Nataliia Kozhemiako
- Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Adonay S Nunes
- Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Nicole M McGuiggan
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA
| | - Ainsley Losh
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert M Joseph
- Department of Anatomy and Neurobiology, Boston University, Boston, Massachusetts, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada.,Behavioral and Cognitive Neuroscience Institute, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Tal Kenet
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
13
|
Alakuş C, Larocque D, Jacquemont S, Barlaam F, Martin CO, Agbogba K, Lippé S, Labbe A. Conditional canonical correlation estimation based on covariates with random forests. Bioinformatics 2021; 37:2714-2721. [PMID: 33693547 DOI: 10.1093/bioinformatics/btab158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/03/2021] [Accepted: 03/03/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender, or other clinical measures. In this case, applying CCA to the whole population is not optimal and methods to estimate conditional CCA, given the covariates, can be useful. RESULTS We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates. The individual trees in the forest are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. We also propose a significance test to detect the global effect of the covariates on the relationship between two sets of variables. The performance of the proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error. We also show an application of the proposed method with EEG data. AVAILABILITY RFCCA is implemented in a freely available R package on CRAN (https://CRAN.R-project.org/package=RFCCA). SUPPLEMENTARY INFORMATION Supplementary material are available at Bioinformatics online.
Collapse
Affiliation(s)
- Cansu Alakuş
- Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada
| | - Denis Larocque
- Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada
| | - Sébastien Jacquemont
- Department of Pediatrics, Université de Montréal, Montréal, QC H3T 1C5, Canada.,CHU Sainte-Justine Research Center, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Fanny Barlaam
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Charles-Olivier Martin
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Kristian Agbogba
- CHU Sainte-Justine Research Center, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Sarah Lippé
- Department of Psychology, Université de Montréal, Montréal, QC H3T 1J4, Canada.,CHU Sainte-Justine Research Center, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Aurélie Labbe
- Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada
| |
Collapse
|