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Halliday AR, Vucic SN, Georges B, LaRoche M, Mendoza Pardo MA, Swiggard LO, McDonald K, Olofsson M, Menon SN, Francis SM, Oberman LM, White T, van der Velpen IF. Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature. Front Psychiatry 2024; 15:1474003. [PMID: 39479591 PMCID: PMC11521827 DOI: 10.3389/fpsyt.2024.1474003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2024] [Accepted: 09/30/2024] [Indexed: 11/02/2024] Open
Abstract
Background A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities. Methods Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023. Results In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRI-based papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis. Conclusions Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD.
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Affiliation(s)
- Amanda R. Halliday
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Samuel N. Vucic
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Brianna Georges
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Madison LaRoche
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - María Alejandra Mendoza Pardo
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Liam O. Swiggard
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Kaylee McDonald
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Michelle Olofsson
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Sahit N. Menon
- Noninvasive Neuromodulation Unit, Experimental Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
- School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Sunday M. Francis
- Noninvasive Neuromodulation Unit, Experimental Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Lindsay M. Oberman
- Noninvasive Neuromodulation Unit, Experimental Pathophysiology Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Tonya White
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Isabelle F. van der Velpen
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
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Siegel JS, Subramanian S, Perry D, Kay BP, Gordon EM, Laumann TO, Reneau TR, Metcalf NV, Chacko RV, Gratton C, Horan C, Krimmel SR, Shimony JS, Schweiger JA, Wong DF, Bender DA, Scheidter KM, Whiting FI, Padawer-Curry JA, Shinohara RT, Chen Y, Moser J, Yacoub E, Nelson SM, Vizioli L, Fair DA, Lenze EJ, Carhart-Harris R, Raison CL, Raichle ME, Snyder AZ, Nicol GE, Dosenbach NUF. Psilocybin desynchronizes the human brain. Nature 2024; 632:131-138. [PMID: 39020167 PMCID: PMC11291293 DOI: 10.1038/s41586-024-07624-5] [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: 10/24/2023] [Accepted: 05/29/2024] [Indexed: 07/19/2024]
Abstract
A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.
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Affiliation(s)
- Joshua S Siegel
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA.
| | - Subha Subramanian
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Demetrius Perry
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - T Rick Reneau
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Nicholas V Metcalf
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Ravi V Chacko
- Department of Emergency Medicine, Advocate Christ Health Care, Oak Lawn, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | | | - Samuel R Krimmel
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Julie A Schweiger
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Dean F Wong
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - David A Bender
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Kristen M Scheidter
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Forrest I Whiting
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | - Jonah A Padawer-Curry
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
| | - Russell T Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Endeavor, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Moser
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Luca Vizioli
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric J Lenze
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Robin Carhart-Harris
- Department of Neurology, University of California, San Francisco, CA, USA
- Centre for Psychedelic Research, Imperial College London, London, UK
| | - Charles L Raison
- Usona Institute, Fitchburg, WI, USA
- Department of Psychiatry, University of Wisconsin School of Medicine & Public Health, Madison, WI, USA
| | - Marcus E Raichle
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO, USA
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - Ginger E Nicol
- Department of Psychiatry, Washington University School of Medicine, St Louis, MO, USA
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, USA
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO, USA
- Department of Pediatrics, Washington University School of Medicine, St Louis, MO, USA
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3
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Siegel JS, Subramanian S, Perry D, Kay B, Gordon E, Laumann T, Reneau R, Gratton C, Horan C, Metcalf N, Chacko R, Schweiger J, Wong D, Bender D, Padawer-Curry J, Raison C, Raichle M, Lenze EJ, Snyder AZ, Dosenbach NUF, Nicol G. Psilocybin desynchronizes brain networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.22.23294131. [PMID: 37701731 PMCID: PMC10493007 DOI: 10.1101/2023.08.22.23294131] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
1The relationship between the acute effects of psychedelics and their persisting neurobiological and psychological effects is poorly understood. Here, we tracked brain changes with longitudinal precision functional mapping in healthy adults before, during, and for up to 3 weeks after oral psilocybin and methylphenidate (17 MRI visits per participant) and again 6+ months later. Psilocybin disrupted connectivity across cortical networks and subcortical structures, producing more than 3-fold greater acute changes in functional networks than methylphenidate. These changes were driven by desynchronization of brain activity across spatial scales (area, network, whole brain). Psilocybin-driven desynchronization was observed across association cortex but strongest in the default mode network (DMN), which is connected to the anterior hippocampus and thought to create our sense of self. Performing a perceptual task reduced psilocybin-induced network changes, suggesting a neurobiological basis for grounding, connecting with physical reality during psychedelic therapy. The acute brain effects of psilocybin are consistent with distortions of space-time and the self. Psilocybin induced persistent decrease in functional connectivity between the anterior hippocampus and cortex (and DMN in particular), lasting for weeks but normalizing after 6 months. Persistent suppression of hippocampal-DMN connectivity represents a candidate neuroanatomical and mechanistic correlate for psilocybin's pro-plasticity and anti-depressant effects.
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4
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Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome. J Clin Med 2023; 12:jcm12020634. [PMID: 36675563 PMCID: PMC9864739 DOI: 10.3390/jcm12020634] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/23/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023] Open
Abstract
Obstructive sleep apnea syndrome (OSAS) causes intermittent hypoxia and sleep disruption in the brain, resulting in cognitive dysfunction, but its pathogenesis is unclear. The sleep spindle wave is a transient neural event involved in sleep memory consolidation and synaptic plasticity. This study aimed to investigate the characteristics of sleep spindle activity and its relationship with memory ability in patients with OSAS. A total of 119 patients, who were divided into the OSAS group (n = 59, AHI ≥ 15) and control group (n = 60, AHI < 15) according to the Apnea Hypopnea Index (AHI), were enrolled and underwent polysomnography. Power spectral density (PSD) and omega complexity were used to analyze the characteristics of single and different brain regions of sleep spindles. Memory-related cognitive functions were assessed in all subjects, including logical memory, digit ordering, pattern recognition, spatial recognition and spatial working memory. The spindle PSD of the OSAS group was significantly slower than the control group, regardless of the slow, fast, or total spindle. The complexity of the spindles in the prefrontal and central region decreased significantly, whereas it increased in the occipital region. Sleep spindle PSD was positively correlated with logical memory and working memory. Spindle complexity was positively correlated with immediate logical and visual memory in the prefrontal region and positively correlated with immediate/delayed logical and working memory in the central region. In contrast, spindle complexity in the occipital region negatively correlated with delayed logical memory. Spindle hyperconnectivity in the prefrontal and central regions underlies declines in logical, visual and working memory and weak connections in the occipital spindles underlie the decline in delayed logical memory.
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5
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Zhang C, Han S, Li Z, Wang X, Lv C, Zou X, Zhu F, Zhang K, Lu S, Bie L, Lv G, Guo Y. Multidimensional Assessment of Electroencephalography in the Neuromodulation of Disorders of Consciousness. Front Neurosci 2022; 16:903703. [PMID: 35812212 PMCID: PMC9260110 DOI: 10.3389/fnins.2022.903703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
In the present study, we aimed to elucidate changes in electroencephalography (EEG) metrics during recovery of consciousness and to identify possible clinical markers thereof. More specifically, in order to assess changes in multidimensional EEG metrics during neuromodulation, we performed repeated stimulation using a high-density transcranial direct current stimulation (HD-tDCS) protocol in 42 patients with disorders of consciousness (DOC). Coma Recovery Scale-Revised (CRS-R) scores and EEG metrics [brain network indicators, spectral energy, and normalized spatial complexity (NSC)] were obtained before as well as fourteen days after undergoing HD-tDCS stimulation. CRS-R scores increased in the responders (R +) group after HD-tDCS stimulation. The R + group also showed increased spectral energy in the alpha2 and beta1 bands, mainly at the frontal and parietal electrodes. Increased graphical metrics in the alpha1, alpha2, and beta1 bands combined with increased NSC in the beta2 band in the R + group suggested that improved consciousness was associated with a tendency toward stronger integration in the alpha1 band and greater isolation in the beta2 band. Following this, using NSC as a feature to predict responsiveness through machine learning, which yielded a prediction accuracy of 0.929, demonstrated that the NSC of the alpha and gamma bands at baseline successfully predicted improvement in consciousness. According to our findings reported herein, we conclude that neuromodulation of the posterior lobe can lead to an EEG response related to consciousness in DOC, and that the posterior cortex may be one of the key brain areas involved in the formation or maintenance of consciousness.
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Affiliation(s)
- Chunyun Zhang
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Shuai Han
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Zean Li
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - XinJun Wang
- Department of Neurosurgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Chuanxiang Lv
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Xiangyun Zou
- Department of Pediatrics, Qilu Hospital of Shandong University, Qingdao, China
| | - Fulei Zhu
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Kang Zhang
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Shouyong Lu
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Li Bie
- Department of Neurosurgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Yongkun Guo
- Department of Neurosurgery, Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Engineering Research Center for Prevention and Treatment of Brain Injury, Zhengzhou, China
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6
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Liang Z, Wang Y, Tian H, Gu Y, Arimitsu T, Takahashi T, Minagawa Y, Niu H, Tong Y. Spatial complexity method for tracking brain development and degeneration using functional near-infrared spectroscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:1718-1736. [PMID: 35414994 PMCID: PMC8973163 DOI: 10.1364/boe.449341] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 02/07/2022] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Abstract
Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (Δ[HbO]) or two deoxy-hemoglobin (Δ[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE's performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDEHbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.
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Affiliation(s)
- Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yuxi Wang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Hao Tian
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
- Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Qinhuangdao 066004, China
| | - Yue Gu
- Key Laboratory of Computer Vision and System (Ministry of Education), School of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Takeshi Arimitsu
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Takao Takahashi
- Department of Pediatrics, Keio University School of Medicine, Tokyo, Japan
| | - Yasuyo Minagawa
- Department of Psychology, Faculty of Letters, Keio University, Tokyo, Japan
| | - Haijing Niu
- Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Yunjie Tong
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Ding K, Wang H, Li C, Liu F, Yu D. Decreased Right Prefrontal Synchronization Strength and Asymmetry During Joint Attention in the Left-Behind Children: A Functional Near-Infrared Spectroscopy Study. Front Physiol 2021; 12:759788. [PMID: 34867465 PMCID: PMC8634881 DOI: 10.3389/fphys.2021.759788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/11/2021] [Indexed: 11/24/2022] Open
Abstract
Although there are millions of left-behind children in China, the researches on brain structure and functions in left-behind children are not sufficient at the brain imaging level. This study aimed to explore whether there is decreased prefrontal synchronization during joint attention in left-behind children. Sixty children (65.12 ± 6.54 months, 29 males) with 34 left-behind children were recruited. The functional near-infrared spectroscopy (fNIRS) imaging data from the prefrontal cortex during joint attention, as well as behavioral measures (associated with family income, intelligence, language, and social-emotional abilities), were collected. Results verified that brain imaging data and behavioral measures are correlative and support that left-behind children have deficits in social-emotional abilities. More importantly, left-behind children showed decreased synchronization strength and asymmetry in the right middle frontal gyrus during joint attention. The findings suggest that decreased right prefrontal synchronization strength and asymmetry during joint attention might be vulnerability factors in the development of left-behind children.
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Affiliation(s)
- Keya Ding
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Hongan Wang
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Chuanjiang Li
- Hangzhou College of Early Childhood Teachers' Education, Zhejiang Normal University, Hangzhou, China
| | - Fulin Liu
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Dongchuan Yu
- Key Laboratory of Child Development and Learning Science, Research Center for Learning Science, Southeast University, Nanjing, China.,School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.,Department of Child Development and Behavior, Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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8
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Zhang T, Huang W, Wu X, Sun W, Lin F, Sun H, Li J. Altered complexity in resting-state fNIRS signal in autism: a multiscale entropy approach. Physiol Meas 2021; 42. [PMID: 34315139 DOI: 10.1088/1361-6579/ac184d] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/27/2021] [Indexed: 11/12/2022]
Abstract
Objective.Feature extraction and recognition in brain signal processing is of great significance for understanding the neurological mechanism of autism spectrum disorder (ASD). Resting-state (RS) functional near-infrared spectroscopy measurement provides a way to investigate the possible alteration in ASD-related complexity of resting-state (RS) functional near-infrared spectroscopy (fNIRS) signals and to explore the relationship between brain functional connectivity and complexity.Approach.Using the multiscale entropy (MSE) of fNIRS signals recorded from the bilateral temporal lobes (TLs) on 25 children with ASD and 22 typical development (TD) children, the pattern of brain complexity was assessed for both the ASD and TD groups.Main results.The quantitative analysis of MSE revealed the increased complexity in RS-fNIRS in children with ASD, particularly in the left temporal lobe. The complexity in the RS signal and resting state functional connectivity (RSFC) were also observed to exhibit negative correlation in the medium magnitude.Significance.These results indicated that the MSE might serve as a novel measure for RS-fNIRS signals in characterizing and understanding ASD.
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Affiliation(s)
- Tingzhen Zhang
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Wen Huang
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Xiaoyin Wu
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Weiting Sun
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Fang Lin
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Huiwen Sun
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China
| | - Jun Li
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, People's Republic of China.,Key Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou 510006, People's Republic of China
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