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Marder MA, Miller GA. The future of psychophysiology, then and now. Biol Psychol 2024; 189:108792. [PMID: 38588815 DOI: 10.1016/j.biopsycho.2024.108792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 03/30/2024] [Accepted: 04/02/2024] [Indexed: 04/10/2024]
Abstract
Since its founding in 1973, Biological Psychology has showcased and provided invaluable support to psychophysiology, a field that has grown and changed enormously. This article discusses some constancies that have remained fundamental to the journal and to the field as well as some important trends. Some aspects of our science have not received due consideration, affecting not only the generalizability of our findings but the way we develop and evaluate our research questions and the potential of our field to contribute to the common good. The article offers a number of predictions and recommendations for the next period of growth of psychophysiology.
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Affiliation(s)
| | - Gregory A Miller
- University of Illinois Urbana-Champaign, USA; University of California, Los Angeles, USA
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2
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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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3
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Alho J, Samuelsson JG, Khan S, Mamashli F, Bharadwaj H, Losh A, McGuiggan NM, Graham S, Nayal Z, Perrachione TK, Joseph RM, Stoodley CJ, Hämäläinen MS, Kenet T. Both stronger and weaker cerebro-cerebellar functional connectivity patterns during processing of spoken sentences in autism spectrum disorder. Hum Brain Mapp 2023; 44:5810-5827. [PMID: 37688547 PMCID: PMC10619366 DOI: 10.1002/hbm.26478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 08/11/2023] [Accepted: 08/20/2023] [Indexed: 09/11/2023] Open
Abstract
Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age-matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left-hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro-cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus-driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI.
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Affiliation(s)
- Jussi Alho
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - John G. Samuelsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Harvard‐MIT Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridgeMassachusettsUSA
| | - Sheraz Khan
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Fahimeh Mamashli
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Hari Bharadwaj
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Speech, Language, and Hearing Sciences, and Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Ainsley Losh
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nicole M. McGuiggan
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Steven Graham
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Zein Nayal
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Tyler K. Perrachione
- Department of Speech, Language, and Hearing SciencesBoston UniversityBostonMassachusettsUSA
| | - Robert M. Joseph
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Catherine J. Stoodley
- Department of PsychologyCollege of Arts and Sciences, American UniversityWashingtonDCUSA
| | - Matti S. Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Tal Kenet
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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4
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Green HL, Shen G, Franzen RE, Mcnamee M, Berman JI, Mowad TG, Ku M, Bloy L, Liu S, Chen YH, Airey M, McBride E, Goldin S, Dipiero MA, Blaskey L, Kuschner ES, Kim M, Konka K, Roberts TPL, Edgar JC. Differential Maturation of Auditory Cortex Activity in Young Children with Autism and Typical Development. J Autism Dev Disord 2023; 53:4076-4089. [PMID: 35960416 PMCID: PMC9372967 DOI: 10.1007/s10803-022-05696-8] [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] [Accepted: 07/22/2022] [Indexed: 11/20/2022]
Abstract
Maturation of auditory cortex neural encoding processes was assessed in children with typical development (TD) and autism. Children 6-9 years old were enrolled at Time 1 (T1), with follow-up data obtained ~ 18 months later at Time 2 (T2), and ~ 36 months later at Time 3 (T3). Findings suggested an initial period of rapid auditory cortex maturation in autism, earlier than TD (prior to and surrounding the T1 exam), followed by a period of faster maturation in TD than autism (T1-T3). As a result of group maturation differences, post-stimulus group differences were observed at T1 but not T3. In contrast, stronger pre-stimulus activity in autism than TD was found at all time points, indicating this brain measure is stable across time.
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Affiliation(s)
- Heather L Green
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Guannan Shen
- 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
| | - Marybeth Mcnamee
- 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
| | - 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
| | - Matthew Ku
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 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
| | - Yu-Han Chen
- 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
| | - Emma McBride
- 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
| | - Marissa A Dipiero
- 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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5
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Wada M, Noda Y, Iwata Y, Tsugawa S, Yoshida K, Tani H, Hirano Y, Koike S, Sasabayashi D, Katayama H, Plitman E, Ohi K, Ueno F, Caravaggio F, Koizumi T, Gerretsen P, Suzuki T, Uchida H, Müller DJ, Mimura M, Remington G, Grace AA, Graff-Guerrero A, Nakajima S. Dopaminergic dysfunction and excitatory/inhibitory imbalance in treatment-resistant schizophrenia and novel neuromodulatory treatment. Mol Psychiatry 2022; 27:2950-2967. [PMID: 35444257 DOI: 10.1038/s41380-022-01572-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/31/2022] [Accepted: 04/07/2022] [Indexed: 12/13/2022]
Abstract
Antipsychotic drugs are the mainstay in the treatment of schizophrenia. However, one-third of patients do not show adequate improvement in positive symptoms with non-clozapine antipsychotics. Additionally, approximately half of them show poor response to clozapine, electroconvulsive therapy, or other augmentation strategies. However, the development of novel treatment for these conditions is difficult due to the complex and heterogenous pathophysiology of treatment-resistant schizophrenia (TRS). Therefore, this review provides key findings, potential treatments, and a roadmap for future research in this area. First, we review the neurobiological pathophysiology of TRS, particularly the dopaminergic, glutamatergic, and GABAergic pathways. Next, the limitations of existing and promising treatments are presented. Specifically, this article focuses on the therapeutic potential of neuromodulation, including electroconvulsive therapy, repetitive transcranial magnetic stimulation, transcranial direct current stimulation, and deep brain stimulation. Finally, we propose multivariate analyses that integrate various perspectives of the pathogenesis, such as dopaminergic dysfunction and excitatory/inhibitory imbalance, thereby elucidating the heterogeneity of TRS that could not be obtained by conventional statistics. These analyses can in turn lead to a precision medicine approach with closed-loop neuromodulation targeting the detected pathophysiology of TRS.
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Affiliation(s)
- Masataka Wada
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Yusuke Iwata
- Department of Neuropsychiatry, University of Yamanashi Faculty of Medicine, Yamanashi, Japan
| | - Sakiko Tsugawa
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Kazunari Yoshida
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan.,Tanenbaum Centre for Pharmacogenetics, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Hideaki Tani
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Kyushu University, Fukuoka, Japan.,Neural Dynamics Laboratory, Research Service, VA Boston Healthcare System, and Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Haruyuki Katayama
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Eric Plitman
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Fumihiko Ueno
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Fernando Caravaggio
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada
| | - Teruki Koizumi
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan.,Department of Psychiatry, National Hospital Organization Shimofusa Psychiatric Medical Center, Chiba, Japan
| | - Philip Gerretsen
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Takefumi Suzuki
- Department of Neuropsychiatry, University of Yamanashi Faculty of Medicine, Yamanashi, Japan
| | - Hiroyuki Uchida
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Daniel J Müller
- Tanenbaum Centre for Pharmacogenetics, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Masaru Mimura
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan
| | - Gary Remington
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Anthony A Grace
- Departments of Neuroscience, Psychiatry and Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ariel Graff-Guerrero
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University, School of Medicine, Tokyo, Japan. .,Brain Health Imaging Centre, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada.
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Zhu Y, Nakatani H, Yassin W, Maikusa N, Okada N, Kunimatsu A, Abe O, Kuwabara H, Yamasue H, Kasai K, Okanoya K, Koike S. Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study. Schizophr Bull 2022; 48:563-574. [PMID: 35352811 PMCID: PMC9077435 DOI: 10.1093/schbul/sbac030] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY DESIGN Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY RESULTS The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). CONCLUSION We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.
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Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hironori Nakatani
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- Department of Information Media Technology, School of Information and Telecommunication Engineering, Tokai University, 2-3-23, Takanawa, Minato-ku, Tokyo 108-8619, Japan
| | - Walid Yassin
- Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Naohiro Okada
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Akira Kunimatsu
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hitoshi Kuwabara
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Kiyoto Kasai
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazuo Okanoya
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Shinsuke Koike
- To whom correspondence should be addressed; Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; tel: +81-3-5454-4327, fax: +81-3-5454-4327, e-mail:
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7
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Onitsuka T, Hirano Y, Nemoto K, Hashimoto N, Kushima I, Koshiyama D, Koeda M, Takahashi T, Noda Y, Matsumoto J, Miura K, Nakazawa T, Hikida T, Kasai K, Ozaki N, Hashimoto R. Trends in big data analyses by multicenter collaborative translational research in psychiatry. Psychiatry Clin Neurosci 2022; 76:1-14. [PMID: 34716732 PMCID: PMC9306748 DOI: 10.1111/pcn.13311] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 10/01/2021] [Accepted: 10/17/2021] [Indexed: 12/01/2022]
Abstract
The underlying pathologies of psychiatric disorders, which cause substantial personal and social losses, remain unknown, and their elucidation is an urgent issue. To clarify the core pathological mechanisms underlying psychiatric disorders, in addition to laboratory-based research that incorporates the latest findings, it is necessary to conduct large-sample-size research and verify reproducibility. For this purpose, it is critical to conduct multicenter collaborative research across various fields, such as psychiatry, neuroscience, molecular biology, genomics, neuroimaging, cognitive science, neurophysiology, psychology, and pharmacology. Moreover, collaborative research plays an important role in the development of young researchers. In this respect, the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium and Cognitive Genetics Collaborative Research Organization (COCORO) have played important roles. In this review, we first overview the importance of multicenter collaborative research and our target psychiatric disorders. Then, we introduce research findings on the pathophysiology of psychiatric disorders from neurocognitive, neurophysiological, neuroimaging, genetic, and basic neuroscience perspectives, focusing mainly on the findings obtained by COCORO. It is our hope that multicenter collaborative research will contribute to the elucidation of the pathological basis of psychiatric disorders.
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Affiliation(s)
- Toshiaki Onitsuka
- Department of Neuroimaging Psychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Michihiko Koeda
- Department of Neuropsychiatry, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan.,Department of Neuropsychiatry, Nippon Medical School, Tama Nagayama Hospital, Tokyo, Japan
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Takanobu Nakazawa
- Department of Bioscience, Tokyo University of Agriculture, Tokyo, Japan
| | - Takatoshi Hikida
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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8
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Yasui-Furukori N, Muraoka H, Hasegawa N, Ochi S, Numata S, Hori H, Hishimoto A, Onitsuka T, Ohi K, Hashimoto N, Nagasawa T, Takaesu Y, Inagaki T, Tagata H, Tsuboi T, Kubota C, Furihata R, Iga JI, Iida H, Miura K, Matsumoto J, Yamada H, Watanabe K, Inada K, Shimoda K, Hashimoto R. Association between the examination rate of treatment-resistant schizophrenia and the clozapine prescription rate in a nationwide dissemination and implementation study. Neuropsychopharmacol Rep 2021; 42:3-9. [PMID: 34854260 PMCID: PMC8919118 DOI: 10.1002/npr2.12218] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/28/2021] [Accepted: 10/19/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The decision to initiate clozapine treatment should be made on an individual basis and may be closely related to the early detection of treatment-resistant schizophrenia (TRS), although there is evidence that the early use of clozapine results in a better response to treatment. Therefore, we investigated the relationship between the examination rate of TRS and the prescription rate of clozapine. METHODS After attending a 1-day educational program on schizophrenia based on the "Guidelines for the Pharmacological Treatment of Schizophrenia," we asked the participating facilities to submit records of whether or not TRS was evaluated for each patient. We calculated the clozapine prescription rate from the schizophrenic patients prescribed clozapine and all of the schizophrenic patients. Forty-nine facilities in 2017 were included in the study. RESULTS There were dichotomous distributions in the examination rate of TRS and a non-normal distribution in the prescription rate of clozapine. There was a significant correlation between the prescription rate of clozapine and the examination rate of TRS (rs = 0.531, P = 1.032 × 10-4 ). A significant difference was found in the prescription rate of clozapine between the three groups of facilities according to the examination rate of TRS. CONCLUSION As a preliminary problem for the use of clozapine, in Japan, the examination rate of TRS varies, and there are many facilities that typically do not consider the possibility of TRS; this trend leads to a low rate of clozapine use. Clearly, further clinician training is needed for the early detection and appropriate management of TRS that includes an explanation of TRS and how to introduce clozapine therapy to patients and their families.
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Affiliation(s)
- Norio Yasui-Furukori
- Department of Psychiatry, Dokkyo Medical University School of Medicine, Tochigi, Japan
| | - Hiroyuki Muraoka
- Department of Psychiatry, Tokyo Women's Medical University, Tokyo, Japan
| | - Naomi Hasegawa
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Shinichiro Ochi
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Shusuke Numata
- Department of Psychiatry, Graduate School of Biomedical Science, Tokushima University, Tokushima, Japan
| | - Hikaru Hori
- Department of Psychiatry, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, Yokohama City University Graduate School of Medicine, Kanagawa, Japan
| | - Toshiaki Onitsuka
- Department of Neuroimaging Psychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Tatsuya Nagasawa
- Department of Neuro-Psychiatry, Kanazawa Medical University, Kanazawa, Japan
| | - Yoshikazu Takaesu
- Department of Neuropsychiatry, Graduate School of Medicine, University of the Ryukyus, Okinawa, Japan
| | | | - Hiromi Tagata
- Department of Neuropsychiatry, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Takashi Tsuboi
- Department of Neuropsychiatry, Kyorin University School of Medicine, Tokyo, Japan
| | - Chika Kubota
- National Center of Neurology and Psychiatry Hospital, Tokyo, Japan
| | | | - Jun-Ichi Iga
- Department of Neuropsychiatry, Molecules and Function, Ehime University Graduate School of Medicine, Ehime, Japan
| | - Hitoshi Iida
- Department of Psychiatry, Graduate School of Biomedical Science, Tokushima University, Tokushima, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hisashi Yamada
- Department of Neuropsychiatry, Hyogo College of Medicine, Hyogo, Japan
| | - Koichiro Watanabe
- Department of Neuropsychiatry, Kyorin University School of Medicine, Tokyo, Japan
| | - Ken Inada
- Department of Psychiatry, Tokyo Women's Medical University, Tokyo, Japan
| | - Kazutaka Shimoda
- Department of Psychiatry, Dokkyo Medical University School of Medicine, Tochigi, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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9
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Roberts TPL, Kuschner ES, Edgar JC. Biomarkers for autism spectrum disorder: opportunities for magnetoencephalography (MEG). J Neurodev Disord 2021; 13:34. [PMID: 34525943 PMCID: PMC8442415 DOI: 10.1186/s11689-021-09385-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/03/2021] [Indexed: 11/17/2022] Open
Abstract
This paper reviews a candidate biomarker for ASD, the M50 auditory evoked response component, detected by magnetoencephalography (MEG) and presents a position on the roles and opportunities for such a biomarker, as well as converging evidence from allied imaging techniques (magnetic resonance imaging, MRI and spectroscopy, MRS). Data is presented on prolonged M50 latencies in ASD as well as extension to include children with ASD with significant language and cognitive impairments in whom M50 latency delays are exacerbated. Modeling of the M50 latency by consideration of the properties of auditory pathway white matter is shown to be successful in typical development but challenged by heterogeneity in ASD; this, however, is capitalized upon to identify a distinct subpopulation of children with ASD whose M50 latencies lie well outside the range of values predictable from the typically developing model. Interestingly, this subpopulation is characterized by low levels of the inhibitory neurotransmitter GABA. Following from this, we discuss a potential use of the M50 latency in indicating “target engagement” acutely with administration of a GABA-B agonist, potentially distinguishing “responders” from “non-responders” with the implication of optimizing inclusion for clinical trials of such agents. Implications for future application, including potential evaluation of infants with genetic risk factors, are discussed. As such, the broad scope of potential of a representative candidate biological marker, the M50 latency, is introduced along with potential future applications. This paper outlines a strategy for understanding brain dysfunction in individuals with intellectual and developmental disabilities (IDD). It is proposed that a multimodal approach (collection of brain structure, chemistry, and neuronal functional data) will identify IDD subpopulations who share a common disease pathway, and thus identify individuals with IDD who might ultimately benefit from specific treatments. After briefly demonstrating the need and potential for scope, examples from studies examining brain function and structure in children with autism spectrum disorder (ASD) illustrate how measures of brain neuronal function (from magnetoencephalography, MEG), brain structure (from magnetic resonance imaging, MRI, especially diffusion MRI), and brain chemistry (MR spectroscopy) can help us better understand the heterogeneity in ASD and form the basis of multivariate biological markers (biomarkers) useable to define clinical subpopulations. Similar approaches can be applied to understand brain dysfunction in neurodevelopmental disorders (NDD) in general. In large part, this paper represents our endeavors as part of the CHOP/Penn NICHD-funded intellectual and developmental disabilities research center (IDDRC) over the past decade.
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Affiliation(s)
- Timothy P L Roberts
- Dept. of Radiology, Lurie Family Foundations MEG Imaging Center, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA.
| | - Emily S Kuschner
- Dept. of Radiology, Lurie Family Foundations MEG Imaging Center, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA
| | - J Christopher Edgar
- Dept. of Radiology, Lurie Family Foundations MEG Imaging Center, Children's Hospital of Philadelphia, 3401 Civic Center Blvd., Philadelphia, PA, 19104, USA
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10
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Koike S, Uematsu A, Sasabayashi D, Maikusa N, Takahashi T, Ohi K, Nakajima S, Noda Y, Hirano Y. Recent Advances and Future Directions in Brain MR Imaging Studies in Schizophrenia: Toward Elucidating Brain Pathology and Developing Clinical Tools. Magn Reson Med Sci 2021; 21:539-552. [PMID: 34408115 DOI: 10.2463/mrms.rev.2021-0050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Schizophrenia is a common severe psychiatric disorder that affects approximately 1% of general population through the life course. Historically, in Kraepelin's time, schizophrenia was a disease unit conceptualized as dementia praecox; however, since then, the disease concept has changed. Recent MRI studies had shown that the neuropathology of the brain in this disorder was characterized by mild progression before and after the onset of the disease, and that the brain alterations were relatively smaller than assumed. Although genetic factors contribute to the brain alterations in schizophrenia, which are thought to be trait differences, other changes include factors that are common in psychiatric diseases. Furthermore, it has been shown that the brain differences specific to schizophrenia were relatively small compared to other changes, such as those caused by brain development, aging, and gender. In addition, compared to the disease and participant factors, machine and imaging protocol differences could affect MRI signals, which should be addressed in multi-site studies. Recent advances in MRI modalities, such as multi-shell diffusion-weighted imaging, magnetic resonance spectroscopy, and multimodal brain imaging analysis, may be candidates to sharpen the characterization of schizophrenia-specific factors and provide new insights. The Brain/MINDS Beyond Human Brain MRI (BMB-HBM) project has been launched considering the differences and noises irrespective of the disease pathologies and includes the future perspectives of MRI studies for various psychiatric and neurological disorders. The sites use restricted MRI machines and harmonized multi-modal protocols, standardized image preprocessing, and traveling subject harmonization. Data sharing to the public will be planned in FY 2024. In the future, we believe that combining a high-quality human MRI dataset with genetic data, randomized controlled trials, and MRI for non-human primates and animal models will enable us to understand schizophrenia, elucidate its neural bases and therapeutic targets, and provide tools for clinical application at bedside.
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Affiliation(s)
- Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo.,University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM).,University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB).,The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences.,Research Center for Idling Brain Science (RCIBS), University of Toyama
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences.,Research Center for Idling Brain Science (RCIBS), University of Toyama
| | - Kazutaka Ohi
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine
| | | | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University.,Institute of Industrial Science, The University of Tokyo
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11
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Edgar JC. Pediatric brain imaging research: Brain maturation constrains study design and clinical interpretation. Psychiatry Clin Neurosci 2021; 75:267-269. [PMID: 34121272 DOI: 10.1111/pcn.13278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/03/2021] [Accepted: 05/17/2021] [Indexed: 11/28/2022]
Affiliation(s)
- J Christopher Edgar
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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12
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Alho J, Bharadwaj H, Khan S, Mamashli F, Perrachione TK, Losh A, McGuiggan NM, Joseph RM, Hämäläinen MS, Kenet T. Altered maturation and atypical cortical processing of spoken sentences in autism spectrum disorder. Prog Neurobiol 2021; 203:102077. [PMID: 34033856 DOI: 10.1016/j.pneurobio.2021.102077] [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: 01/15/2021] [Revised: 04/14/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022]
Abstract
Autism spectrum disorder (ASD) is associated with widespread receptive language impairments, yet the neural mechanisms underlying these deficits are poorly understood. Neuroimaging has shown that processing of socially-relevant sounds, including speech and non-speech, is atypical in ASD. However, it is unclear how the presence of lexical-semantic meaning affects speech processing in ASD. Here, we recorded magnetoencephalography data from individuals with ASD (N = 22, ages 7-17, 4 females) and typically developing (TD) peers (N = 30, ages 7-17, 5 females) during unattended listening to meaningful auditory speech sentences and meaningless jabberwocky sentences. After adjusting for age, ASD individuals showed stronger responses to meaningless jabberwocky sentences than to meaningful speech sentences in the same left temporal and parietal language regions where TD individuals exhibited stronger responses to meaningful speech. Maturational trajectories of meaningful speech responses were atypical in temporal, but not parietal, regions in ASD. Temporal responses were associated with ASD severity, while parietal responses were associated with aberrant involuntary attentional shifting in ASD. Our findings suggest a receptive speech processing dysfunction in ASD, wherein unattended meaningful speech elicits abnormal engagement of the language system, while unattended meaningless speech, filtered out in TD individuals, engages the language system through involuntary attention capture.
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Affiliation(s)
- Jussi Alho
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Hari Bharadwaj
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Speech, Language, and Hearing Sciences, and Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Sheraz Khan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fahimeh Mamashli
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tyler K Perrachione
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA, USA
| | - Ainsley Losh
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Graduate School of Education, University of California, Riverside, CA, USA
| | - Nicole M McGuiggan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert M Joseph
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, USA
| | - Matti S Hämäläinen
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tal Kenet
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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13
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Wolf A, Ueda K, Hirano Y. Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review. Psychiatry Clin Neurosci 2021; 75:82-100. [PMID: 33314465 PMCID: PMC7986125 DOI: 10.1111/pcn.13188] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 11/27/2020] [Accepted: 12/09/2020] [Indexed: 12/15/2022]
Abstract
AIM Although eye-tracking technology expands beyond capturing eye data just for the sole purpose of ensuring participants maintain their gaze at the presented fixation cross, gaze technology remains of less importance in clinical research. Recently, impairments in visual information encoding processes indexed by novel gaze metrics have been frequently reported in patients with schizophrenia. This work undertakes a scoping review of research on saccadic dysfunctions and exploratory eye movement deficits among patients with schizophrenia. It gathers promising pieces of evidence of eye movement abnormalities in attention-demanding tasks on the schizophrenia spectrum that have mounted in recent years and their outcomes as potential biological markers. METHODS The protocol was drafted based on PRISMA for scoping review guidelines. Electronic databases were systematically searched to identify articles published between 2010 and 2020 that examined visual processing in patients with schizophrenia and reported eye movement characteristics as potential biomarkers for this mental illness. RESULTS The use of modern eye-tracking instrumentation has been reported by numerous neuroscientific studies to successfully and non-invasively improve the detection of visual information processing impairments among the screened population at risk of and identified with schizophrenia. CONCLUSIONS Eye-tracking technology has the potential to contribute to the process of early intervention and more apparent separation of the diagnostic entities, being put together by the syndrome-based approach to the diagnosis of schizophrenia. However, context-processing paradigms should be conducted and reported in equally accessible publications to build comprehensive models.
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Affiliation(s)
- Alexandra Wolf
- International Research Fellow of Japan Society for the Promotion of Science, Fukuoka, Japan.,Department of Human Science, Research Center for Applied Perceptual Science, Kyushu University, Fukuoka, Japan
| | - Kazuo Ueda
- Department of Human Science, Research Center for Applied Perceptual Science, Kyushu University, Fukuoka, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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14
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Kuschner ES, Kim M, Bloy L, Dipiero M, Edgar JC, Roberts TPL. MEG-PLAN: a clinical and technical protocol for obtaining magnetoencephalography data in minimally verbal or nonverbal children who have autism spectrum disorder. J Neurodev Disord 2021; 13:8. [PMID: 33485311 PMCID: PMC7827989 DOI: 10.1186/s11689-020-09350-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 12/10/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Neuroimaging research on individuals who have autism spectrum disorder (ASD) has historically been limited primarily to those with age-appropriate cognitive and language performance. Children with limited abilities are frequently excluded from such neuroscience research given anticipated barriers like tolerating the loud sounds associated with magnetic resonance imaging and remaining still during data collection. To better understand brain function across the full range of ASD there is a need to (1) include individuals with limited cognitive and language performance in neuroimaging research (non-sedated, awake) and (2) improve data quality across the performance range. The purpose of this study was to develop, implement, and test the feasibility of a clinical/behavioral and technical protocol for obtaining magnetoencephalography (MEG) data. Participants were 38 children with ASD (8-12 years) meeting the study definition of minimally verbal/nonverbal language. MEG data were obtained during a passive pure-tone auditory task. RESULTS Based on stakeholder feedback, the MEG Protocol for Low-language/cognitive Ability Neuroimaging (MEG-PLAN) was developed, integrating clinical/behavioral and technical components to be implemented by an interdisciplinary team (clinicians, behavior specialists, scientists, and technologists). Using MEG-PLAN, a 74% success rate was achieved for acquiring MEG data, with a 71% success rate for evaluable and analyzable data. Exploratory analyses suggested nonverbal IQ and adaptive skills were related to reaching the point of acquirable data. No differences in group characteristics were observed between those with acquirable versus evaluable/analyzable data. Examination of data quality (evaluable trial count) was acceptable. Moreover, results were reproducible, with high intraclass correlation coefficients for pure-tone auditory latency. CONCLUSIONS Children who have ASD who are minimally verbal/nonverbal, and often have co-occurring cognitive impairments, can be effectively and comfortably supported to complete an electrophysiological exam that yields valid and reproducible results. MEG-PLAN is a protocol that can be disseminated and implemented across research teams and adapted across technologies and neurodevelopmental disorders to collect electrophysiology and neuroimaging data in previously understudied groups of individuals.
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Affiliation(s)
- Emily S Kuschner
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 2716 South Street, 5th Floor, Room 5251, Philadelphia, PA, 19146, 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, 2716 South Street, 5th Floor, Room 5251, Philadelphia, PA, 19146, USA
| | - Luke Bloy
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 2716 South Street, 5th Floor, Room 5251, Philadelphia, PA, 19146, USA
| | - Marissa Dipiero
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 2716 South Street, 5th Floor, Room 5251, Philadelphia, PA, 19146, USA
| | - J Christopher Edgar
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 2716 South Street, 5th Floor, Room 5251, Philadelphia, PA, 19146, USA
| | - Timothy P L Roberts
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 2716 South Street, 5th Floor, Room 5251, Philadelphia, PA, 19146, USA
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15
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Beppi C, Ribeiro Violante I, Scott G, Sandrone S. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn 2021; 148:105677. [PMID: 33486194 DOI: 10.1016/j.bandc.2020.105677] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 12/26/2020] [Accepted: 12/27/2020] [Indexed: 01/04/2023]
Abstract
Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.
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Affiliation(s)
- Carolina Beppi
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland; Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
| | - Inês Ribeiro Violante
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom; School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
| | - Stefano Sandrone
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
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16
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Chen Y, Slinger M, Edgar JC, Bloy L, Kuschner ES, Kim M, Green HL, Chiang T, Yount T, Liu S, Lebus J, Lam S, Stephen JM, Huang H, Roberts TPL. Maturation of hemispheric specialization for face encoding during infancy and toddlerhood. Dev Cogn Neurosci 2021; 48:100918. [PMID: 33571846 PMCID: PMC7876542 DOI: 10.1016/j.dcn.2021.100918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 12/28/2020] [Accepted: 01/08/2021] [Indexed: 11/30/2022] Open
Abstract
Using infant magnetoencephalography (MEG), study findings show maturational changes to fusiform gyrus (FFG) activity when viewing faces. Earlier right FFG activity to face stimuli is associated with better social and cognitive ability. Stronger right- than left-hemisphere FFG responses to face stimuli are most evident after 1 year of age.
Little is known about the neural processes associated with attending to social stimuli during infancy and toddlerhood. Using infant magnetoencephalography (MEG), fusiform gyrus (FFG) activity while processing Face and Non-Face stimuli was examined in 46 typically developing infants 3 to 24 months old (28 males). Several findings indicated FFG maturation throughout the first two years of life. First, right FFG responses to Face stimuli decreased as a function of age. Second, hemispheric specialization to the face stimuli developed somewhat slowly, with earlier right than left FFG peak activity most evident after 1 year of age. Right FFG activity to Face stimuli was of clinical interest, with an earlier right FFG response associated with better performance on tests assessing social and cognitive ability. Building on the above, clinical studies examining maturational change in FFG activity (e.g., lateralization and speed) in infants at-risk for childhood disorders associated with social deficits are of interest to identify atypical FFG maturation before a formal diagnosis is possible.
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Affiliation(s)
- Yuhan Chen
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
| | - Michelle Slinger
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - J Christopher Edgar
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Luke Bloy
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Emily S Kuschner
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mina Kim
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Heather L Green
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Taylor Chiang
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Tess Yount
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Song Liu
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Jill Lebus
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Samantha Lam
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Julia M Stephen
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Hao Huang
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Timothy P L Roberts
- Lurie Family Foundations MEG Imaging Center, Dept. of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
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17
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Hirano Y, Uhlhaas PJ. Editorial: Current MEG Research in Psychiatry. Front Psychiatry 2021; 12:647085. [PMID: 33603691 PMCID: PMC7884450 DOI: 10.3389/fpsyt.2021.647085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 01/08/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Psychiatry, Harvard Medical School, VA Boston Healthcare System, Boston, MA, United States
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité - Universitätsmedizin, Berlin, Germany.,Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
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18
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Wong IA, Yang FX. A quarantined lodging stay: The buffering effect of service quality. INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT 2020; 91:102655. [PMID: 32868959 PMCID: PMC7449669 DOI: 10.1016/j.ijhm.2020.102655] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 08/08/2020] [Accepted: 08/16/2020] [Indexed: 05/05/2023]
Abstract
How do guests feel during their stay at quarantine lodging? This study draws on terror management theory and social exclusion theory to synthesize a model that highlights guests' perceptions about their experience under enforced isolation. The model articulates guests' feeling of anxiety and loneliness, whereas quality of service presents warmth and care that activates an anxiety buffer mechanism that mitigates the effect of anxiety. In turn guests' level of anxiety is further explained by an interaction between their health status and the length of stay. Results point to a conduit for studying the dark side of hospitality, opening up research avenues that could help assess broader social behavioral changes during the global pandemic, while offering operators revelations for lodging management during a crisis.
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Affiliation(s)
- IpKin Anthony Wong
- Institute for Research on Portuguese-Speaking Country, City University of Macau, China
| | - Fiona X Yang
- Faculty of Business Administration, University of Macau, Macau, China
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19
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Edgar JC, Blaskey L, Green HL, Konka K, Shen G, Dipiero MA, Berman JI, Bloy L, Liu S, McBride E, Ku M, Kuschner ES, Airey M, Kim M, Franzen RE, Miller GA, Roberts TPL. Maturation of Auditory Cortex Neural Activity in Children and Implications for Auditory Clinical Markers in Diagnosis. Front Psychiatry 2020; 11:584557. [PMID: 33329127 PMCID: PMC7717950 DOI: 10.3389/fpsyt.2020.584557] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 10/15/2020] [Indexed: 01/14/2023] Open
Abstract
Functional brain markers that can inform research on brain abnormalities, and especially those ready to facilitate clinical work on such abnormalities, will need to show not only considerable sensitivity and specificity but enough consistency with respect to developmental course that their validity in individual cases can be trusted. A challenge to establishing such markers may be individual differences in developmental course. The present study examined auditory cortex activity in children at an age when developmental changes to the auditory cortex 50 ms (M50) and 100 ms (M100) components are prominent to better understand the use of auditory markers in pediatric clinical research. MEG auditory encoding measures (auditory evoked fields in response to pure tone stimuli) were obtained from 15 typically developing children 6-8 years old, with measures repeated 18 and 36 months after the initial exam. MEG analyses were conducted in source space (i.e., brain location), with M50 and M100 sources identified in left and right primary/secondary auditory cortex (Heschl's gyrus). A left and right M50 response was observed at all times (Time 1, Time 2, Time 3), with M50 latency (collapsing across hemisphere) at Time 3 (77 ms) 10 ms earlier than Time 1 (87 ms; p < 0.001) and with M50 responses on average (collapsing across time) 5 ms earlier in the right (80 ms) than left hemisphere (85 ms; p < 0.05). In the majority of children, however, M50 latency changes were not constant across the three-year period; for example, whereas in some children a ~10 ms latency reduction was observed from Time 1 to Time 2, in other children a ~10 ms latency reduction was observed from Time 2 to Time 3. M100 responses were defined by a significant "peak" of detected power with magnetic field topography opposite M50 and occurring 50-100 ms later than the M50. Although M100s were observed in a few children at Time 1 and Time 2 (and more often in the right than left hemisphere), M100s were not observed in the majority of children except in the right hemisphere at Time 3. In sum, longitudinal findings showed large between- and within-subject variability in rate of change as well as time to reach neural developmental milestones (e.g., presence of a detectable M100 response). Findings also demonstrated the need to examine whole-brain activity, given hemisphere differences in the rate of auditory cortex maturation. Pediatric research will need to take such normal variability into account when seeking clinical auditory markers.
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Affiliation(s)
- J Christopher Edgar
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Lisa Blaskey
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Heather L Green
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Kimberly Konka
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Guannan Shen
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Marissa A Dipiero
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Jeffrey I Berman
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Luke Bloy
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Song Liu
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Emma McBride
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Matt Ku
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Emily S Kuschner
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Center for Autism Research, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Megan Airey
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Mina Kim
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Rose E Franzen
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Gregory A Miller
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Timothy P L Roberts
- Department of Radiology, Lurie Family Foundations Magnetoencephalography Imaging Center, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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20
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Ohara N, Hirano Y, Oribe N, Tamura S, Nakamura I, Hirano S, Tsuchimoto R, Ueno T, Togao O, Hiwatashi A, Nakao T, Onitsuka T. Neurophysiological Face Processing Deficits in Patients With Chronic Schizophrenia: An MEG Study. Front Psychiatry 2020; 11:554844. [PMID: 33101080 PMCID: PMC7495506 DOI: 10.3389/fpsyt.2020.554844] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 08/19/2020] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Neuropsychological studies have revealed that patients with schizophrenia (SZ) have facial recognition difficulties and a reduced visual evoked N170 response to human faces. However, detailed neurophysiological evidence of this face processing deficit in SZ with a higher spatial resolution has yet to be acquired. In this study, we recorded visual evoked magnetoencephalography (MEG) and examined whether M170 (a magnetic counterpart of the N170) activity deficits are specific to faces in patients with chronic SZ. METHODS Participants were 26 patients with SZ and 26 healthy controls (HC). The M170 responses to faces and cars were recorded from whole-head MEG, and global field power over each temporal cortex was analyzed. The distributed M170 sources were also localized using a minimum-norm estimation (MNE) method. Correlational analyses between M170 responses and demographics/symptoms were performed. RESULTS As expected, the M170 was significantly smaller in the SZ compared with the HC group in response to faces, but not to cars (faces: p = 0.01; cars: p = 0.55). The MNE analysis demonstrated that while the M170 was localized over the fusiform face area (FFA) in the HC group, visual-related brain regions other than the FFA were strongly activated in the SZ group in both stimulus conditions. The severity of negative symptoms was negatively correlated with M170 power (rho = -0.47, p = 0.01) in SZ. Within HC, there was a significant correlation between age and the M170 responses to faces averaged for both hemispheres (rho = 0.60, p = 0.001), while such a relationship was not observed in patients with SZ (rho = 0.09, p = 0.67). CONCLUSION The present study showed specific reductions in the M170 response to human faces in patients with SZ. Our findings could suggest that SZ is characterized by face processing deficits that are associated with the severity of negative symptoms. Thus, we suggest that social cognition impairments in SZ might, at least in part, be caused by this functional face processing deficit.
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Affiliation(s)
- Naotoshi Ohara
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Medical Corporation Seiryokai, Mimamigaoka Hospital, Fukuoka, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Naoya Oribe
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Division of Clinical Research, National Hospital Organization, Hizen Psychiatric Medical Center, Saga, Japan
| | - Shunsuke Tamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Itta Nakamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shogo Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Rikako Tsuchimoto
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Center for Health Sciences and Counseling, Kyushu University, Fukuoka, Japan
| | - Takefumi Ueno
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Division of Clinical Research, National Hospital Organization, Hizen Psychiatric Medical Center, Saga, Japan
| | - Osamu Togao
- Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Akio Hiwatashi
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Tomohiro Nakao
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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21
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Hirano Y, Nakamura I, Tamura S, Onitsuka T. Long-Term Test-Retest Reliability of Auditory Gamma Oscillations Between Different Clinical EEG Systems. Front Psychiatry 2020; 11:876. [PMID: 32982810 PMCID: PMC7492637 DOI: 10.3389/fpsyt.2020.00876] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/11/2020] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE There is increasing interest in the utility of gamma-band activity for assessing various brain functions, including perception, language, memory, and cognition. The auditory steady-state response (ASSR) involves neural activity in the brain elicited by trains of a click sound, and its maximum response is obtained at 40 Hz (40-Hz ASSR). Abnormalities of the 40-Hz ASSR are also widely reported in patients with schizophrenia. Thus, the test-retest reliability of the ASSR is important for its clinical and translational application. However, there are only limited studies reporting the short-term reliability between acquisitions at two time points made using the same electroencephalogram (EEG) system. Furthermore, the long-term reliability between multiple EEG systems and the reliability of spontaneous gamma activity are unknown but are crucial for multicenter collaborative research. METHODS We examined the long-term test-retest reliability of 40-Hz ASSR oscillatory activities indexed by the phase locking factor (PLF), evoked power, and (non-phase-locked) induced power between two clinical 19-electrode EEG systems [recorded twice for EEG-1 (time1 and time2) and EEG-2 (time3 and time4)] at four time points from 14 healthy controls over a duration of 5 months. Test-retest reliability was examined using intraclass correlation coefficients (ICCs). RESULTS Both PLF and evoked power showed good to excellent ICCs (>0.60), mainly in the Fz-electrode, both within each EEG system-EEG-1 [(time1 vs. time2) PLF: ICC = 0.66, evoked power: ICC = 0.88] and EEG-2 [(time3 vs. time4) PLF: ICC = 0.82, evoked power: ICC = 0.77]-and between the two EEG systems [(EEG-1 vs. EEG-2) PLF: ICC = 0.73, evoked power: ICC = 0.84]. In contrast, induced power showed the highest (excellent) ICC between the two EEG systems (ICC = 0.95) mainly in the Cz-electrode. For PLF, the Fz-electrode showed better test-retest reliability across all EEG recordings than the Cz-electrode (Fz: ICC = 0.67, Cz: ICC = 0.63), whereas we found similar excellent reproducibility across all EEG recordings from both electrodes for evoked power (Fz: ICC = 0.79, Cz: ICC = 0.77) and induced power (Fz: ICC = 0.79, Cz: ICC = 0.80). CONCLUSION The 40-Hz ASSR oscillatory activities, including induced power, showed excellent test-retest reliability, even when using different EEG systems over a duration of 5 months. These findings confirm the utility of the 40-Hz ASSR as a reliable clinical and translatable biomarker for multicenter collaborative research.
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Affiliation(s)
- Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Neural Dynamics Laboratory, Research Service, VA Boston Healthcare System, Boston, MA, United States.,Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Itta Nakamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shunsuke Tamura
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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22
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Hironaga N, Takei Y, Mitsudo T, Kimura T, Hirano Y. Prospects for Future Methodological Development and Application of Magnetoencephalography Devices in Psychiatry. Front Psychiatry 2020; 11:863. [PMID: 32973591 PMCID: PMC7472776 DOI: 10.3389/fpsyt.2020.00863] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
Magnetoencephalography (MEG) is a functional neuroimaging tool that can record activity from the entire cortex on the order of milliseconds. MEG has been used to investigate numerous psychiatric disorders, such as schizophrenia, bipolar disorder, major depression, dementia, and autism spectrum disorder. Although several review papers on the subject have been published, perspectives and opinions regarding the use of MEG in psychiatric research have primarily been discussed from a psychiatric research point of view. Owing to a newly developed MEG sensor, the use of MEG devices will soon enter a critical period, and now is a good time to discuss the future of MEG use in psychiatric research. In this paper, we will discuss MEG devices from a methodological point of view. We will first introduce the utilization of MEG in psychiatric research and the development of its technology. Then, we will describe the principle theory of MEG and common algorithms, which are useful for applying MEG tools to psychiatric research. Next, we will consider three topics-child psychiatry, resting-state networks, and cortico-subcortical networks-and address the future use of MEG in psychiatry from a broader perspective. Finally, we will introduce the newly developed device, the optically-pumped magnetometer, and discuss its future use in MEG systems in psychiatric research from a methodological point of view. We believe that state-of-the-art electrophysiological tools, such as this new MEG system, will further contribute to our understanding of the core pathology in various psychiatric disorders and translational research.
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Affiliation(s)
- Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Takako Mitsudo
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Takahiro Kimura
- Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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23
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Qu X, Liukasemsarn S, Tu J, Higgins A, Hickey TJ, Hall MH. Identifying Clinically and Functionally Distinct Groups Among Healthy Controls and First Episode Psychosis Patients by Clustering on EEG Patterns. Front Psychiatry 2020; 11:541659. [PMID: 33061914 PMCID: PMC7530247 DOI: 10.3389/fpsyt.2020.541659] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 08/21/2020] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE The mismatch negativity (MMN) is considered as a promising biomarker that can inform future therapeutic studies. However, there is a large variability among patients with first episode psychosis (FEP). Also, most studies report a single electrode site and on comparing case-control group differences. Few have taken advantage of the full wealth of multi-channel EEG signals to examine observable patterns. None, to our knowledge, have used machine learning (ML) approaches to investigate neurophysiological derived subgroups with distinct cognitive and functional outcome characteristics. In this study, we applied ML to empirically stratify individuals into homogeneous subgroups based on multi-channel MMN data. We then characterized the functional, cognitive, and clinical profiles of these neurobiologically derived subgroups. We also explored the underlying low frequency range responses (delta, theta, alpha) during MMN. METHODS Clinical, neurocognitive, functioning data of 33 healthy controls and 20 FEP patients were collected. 90% of the patients had 6-month follow-up data. Neurocognition, social cognition, and functioning measures were assessed using the NCCB Cognitive Battery, the Awareness of Social Inference Test, UCSD Performance-Based Skills Assessment, and Multnomah Community Ability Scale. Symptom severity was collected using the PANSS. MMN amplitude and single-trial derived low frequency activity across 24 frontocentral channels were used as main variables in the ML k-means clustering analyses. RESULTS We found a consistent pattern of two distinctive subgroups. We labeled them as "better functioning" and "poorer functioning" clusters, respectively. Each subgroup can be mapped onto either better or poorer clinical, cognitive, and functioning profiles. Also, we identified two subgroups of patients: one showed improved MMN and one showed worsening of MMN over time. Patients with improved MMN had better follow-up clinical, cognitive, and functioning profile than those with worsening MMN. Among the low frequency bands, delta frequency appeared to be the most relevant to the observed MMN responses in all individuals. However, higher delta responses were not necessarily associated with a better functioning profile, suggesting that delta frequency alone may not be useful in clinical characterization. CONCLUSIONS The ML approach could be a robust tool to explore heterogeneity and facilitate the identification of neurobiological homogeneous subgroups in FEP.
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Affiliation(s)
- Xiaodong Qu
- Department of Computer Science, Brandeis University, Waltham, MA, United States
| | - Saran Liukasemsarn
- Psychosis Neurobiology Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, United States.,Schizophrenia and Bipolar Disorders Program, McLean Hospital, Belmont, MA, United States
| | - Jingxuan Tu
- Department of Computer Science, Brandeis University, Waltham, MA, United States
| | - Amy Higgins
- Psychosis Neurobiology Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, United States.,Schizophrenia and Bipolar Disorders Program, McLean Hospital, Belmont, MA, United States
| | - Timothy J Hickey
- Department of Computer Science, Brandeis University, Waltham, MA, United States
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, Harvard Medical School, Belmont, MA, United States.,Schizophrenia and Bipolar Disorders Program, McLean Hospital, Belmont, MA, United States
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