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Xu Y, Yu Z, Li Y, Liu Y, Li Y, Wang Y. Autism spectrum disorder diagnosis with EEG signals using time series maps of brain functional connectivity and a combined CNN-LSTM model. Comput Methods Programs Biomed 2024; 250:108196. [PMID: 38678958 DOI: 10.1016/j.cmpb.2024.108196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 01/30/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND AND OBJECTIVE People with autism spectrum disorder (ASD) often have cognitive impairments. Effective connectivity between different areas of the brain is essential for normal cognition. Electroencephalography (EEG) has been widely used in the detection of neurological diseases. Previous studies on detecting ASD with EEG data have focused on frequency-related features. Most of these studies have augmented data by splitting the dataset into time slices or sliding windows. However, such approaches to data augmentation may cause the testing data to be contaminated by the training data. To solve this problem, this study developed a novel method for detecting ASD with EEG data. METHODS This study quantified the functional connectivity of the subject's brain from EEG signals and defined the individual to be the unit of analysis. Publicly available EEG data were gathered from 97 and 92 subjects with ASD and typical development (TD), respectively, while they were at rest or performing a task. Time-series maps of brain functional connectivity were constructed, and the data were augmented using a deep convolutional generative adversarial network. In addition, a combined network for ASD detection, based on convolutional neural network (CNN) and long short-term memory (LSTM), was designed and implemented. RESULTS Based on functional connectivity, the network achieved classification accuracies of 81.08% and 74.55% on resting state and task state data, respectively. In addition, we found that the functional connectivity of ASD differed from TD primarily in the short-distance functional connectivity of the parietal and occipital lobes and in the distant connections from the right temporoparietal junction region to the left posterior temporal lobe. CONCLUSIONS This paper provides a new perspective for better utilizing EEG to understand ASD. The method proposed in our study is expected to be a reliable tool to assist in the diagnosis of ASD.
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Affiliation(s)
- Yongjie Xu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zengjie Yu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yisheng Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuehan Liu
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ye Li
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yishan Wang
- Research Center for Biomedical Information Technology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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2
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Kember J, Patenaude P, Sweatman H, Van Schaik L, Tabuenca Z, Chai XJ. Specialization of anterior and posterior hippocampal functional connectivity differs in autism. Autism Res 2024. [PMID: 38770780 DOI: 10.1002/aur.3170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/10/2024] [Indexed: 05/22/2024]
Abstract
Structural and functional differences in the hippocampus have been related to the episodic memory and social impairments observed in autism spectrum disorder (ASD). In neurotypical individuals, hippocampal-cortical functional connectivity systematically varies between anterior and posterior hippocampus, with changes observed during typical development. It remains unknown whether this specialization of anterior-posterior hippocampal connectivity is disrupted in ASD, and whether age-related differences in this specialization exist in ASD. We examined connectivity of the anterior and posterior hippocampus in an ASD (N = 139) and non-autistic comparison group (N = 133) aged 5-21 using resting-state functional magnetic resonance imaging (MRI) data from the Healthy Brain Network (HBN). Consistent with previous results, we observed lower connectivity between the whole hippocampus and medial prefrontal cortex in ASD. Moreover, preferential connectivity of the posterior relative to the anterior hippocampus for memory-sensitive regions in posterior parietal cortex was reduced in ASD, demonstrating a weaker anterior-posterior specialization of hippocampal-cortical connectivity. Finally, connectivity between the posterior hippocampus and precuneus negatively correlated with age in the ASD group but remained stable in the comparison group, suggesting an altered developmental specialization. Together, these differences in hippocampal-cortical connectivity may help us understand the neurobiological basis of the memory and social impairments found in ASD.
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Affiliation(s)
- J Kember
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - P Patenaude
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - H Sweatman
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - L Van Schaik
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | - Z Tabuenca
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
- Department of Statistics, University of Zaragoza, Zaragoza, Spain
| | - X J Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
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3
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Hensel L, Lüdtke J, Brouzou KO, Eickhoff SB, Kamp D, Schilbach L. Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients. Cereb Cortex 2024; 34:8-18. [PMID: 38696602 DOI: 10.1093/cercor/bhae096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/13/2024] [Accepted: 02/21/2024] [Indexed: 05/04/2024] Open
Abstract
Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.
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Affiliation(s)
- Lukas Hensel
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Department of General Psychiatry 2, LVR-Klinikum Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany
| | - Jana Lüdtke
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Department of General Psychiatry 2, LVR-Klinikum Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany
| | - Katia O Brouzou
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Department of General Psychiatry 2, LVR-Klinikum Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Wilhelm-Johnen-Straße 1, 52428 Jülich, Germany
| | - Daniel Kamp
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Moorenstraße 5, 40225 Düsseldorf, Germany
- Department of General Psychiatry 2, LVR-Klinikum Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany
| | - Leonhard Schilbach
- Department of General Psychiatry 2, LVR-Klinikum Düsseldorf, Bergische Landstraße 2, 40629 Düsseldorf, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilians University Munich, Nußbaumstraße 7, 80336 Munich, Germany
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Degré-Pelletier J, Danis É, Thérien VD, Bernhardt B, Barbeau EB, Soulières I. Differential neural correlates underlying visuospatial versus semantic reasoning in autistic children. Cereb Cortex 2024; 34:19-29. [PMID: 38696600 PMCID: PMC11065103 DOI: 10.1093/cercor/bhae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/25/2024] [Accepted: 02/20/2024] [Indexed: 05/04/2024] Open
Abstract
While fronto-posterior underconnectivity has often been reported in autism, it was shown that different contexts may modulate between-group differences in functional connectivity. Here, we assessed how different task paradigms modulate functional connectivity differences in a young autistic sample relative to typically developing children. Twenty-three autistic and 23 typically developing children aged 6 to 15 years underwent functional magnetic resonance imaging (fMRI) scanning while completing a reasoning task with visuospatial versus semantic content. We observed distinct connectivity patterns in autistic versus typical children as a function of task type (visuospatial vs. semantic) and problem complexity (visual matching vs. reasoning), despite similar performance. For semantic reasoning problems, there was no significant between-group differences in connectivity. However, during visuospatial reasoning problems, we observed occipital-occipital, occipital-temporal, and occipital-frontal over-connectivity in autistic children relative to typical children. Also, increasing the complexity of visuospatial problems resulted in increased functional connectivity between occipital, posterior (temporal), and anterior (frontal) brain regions in autistic participants, more so than in typical children. Our results add to several studies now demonstrating that the connectivity alterations in autistic relative to neurotypical individuals are much more complex than previously thought and depend on both task type and task complexity and their respective underlying cognitive processes.
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Affiliation(s)
- Janie Degré-Pelletier
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Éliane Danis
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Véronique D Thérien
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801, University street, Montreal, Quebec H3A 2B4, Canada
| | - Elise B Barbeau
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
| | - Isabelle Soulières
- Laboratory on Intelligence and Development in Autism, Department of Psychology, Université du Québec à Montréal, C.P. 8888 Succursale Centre-Ville, Montreal, Quebec H3C 3P8, Canada
- Montreal Cognitive Neuroscience Autism Research Group, CIUSSS du Nord-de-l’île-de-Montreal, 7070, Boulevard Perras, Montréal, Quebec H1E 1A4, Canada
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Shan X, Uddin LQ, Ma R, Xu P, Xiao J, Li L, Huang X, Feng Y, He C, Chen H, Duan X. Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:870-880. [PMID: 37741308 DOI: 10.1016/j.biopsych.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/25/2023] [Accepted: 09/15/2023] [Indexed: 09/25/2023]
Abstract
BACKGROUND Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.
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Affiliation(s)
- Xiaolong Shan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Rui Ma
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Pengfei Xu
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinming Xiao
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Li
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Xinyue Huang
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu Feng
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Changchun He
- College of Blockchain Industry, Chengdu University of Information Technology, Chengdu, China
| | - Huafu Chen
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xujun Duan
- Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Ministry of Education Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
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6
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Tsang T, Green SA, Liu J, Lawrence K, Jeste S, Bookheimer SY, Dapretto M. Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism. Commun Biol 2024; 7:485. [PMID: 38649483 PMCID: PMC11035613 DOI: 10.1038/s42003-024-06016-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 03/06/2024] [Indexed: 04/25/2024] Open
Abstract
Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.
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Affiliation(s)
| | - Shulamite A Green
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Katherine Lawrence
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shafali Jeste
- Children's Hospital Los Angeles, USC Keck School of Medicine, Los Angeles, CA, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Center for Cognitive Neuroscience, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mirella Dapretto
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.
- Semel Institute of Neuroscience and Human Behavior, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
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7
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Newman BT, Jacokes Z, Venkadesh S, Webb SJ, Kleinhans NM, McPartland JC, Druzgal TJ, Pelphrey KA, Van Horn JD. Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder. PLoS One 2024; 19:e0301964. [PMID: 38630783 PMCID: PMC11023574 DOI: 10.1371/journal.pone.0301964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 03/26/2024] [Indexed: 04/19/2024] Open
Abstract
The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a new approach to calculating axonal conduction velocity termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel formulation for calculating aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.
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Affiliation(s)
- Benjamin T. Newman
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States of America
| | - Siva Venkadesh
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
| | - Sara J. Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle WA, United States of America
- Seattle Children’s Research Institute, Seattle WA, United States of America
| | - Natalia M. Kleinhans
- Department of Radiology, Integrated Brain Imaging Center, University of Washington, Seattle, WA, United States of America
| | - James C. McPartland
- Yale Child Study Center, New Haven, CT, United States of America
- Yale Center for Brain and Mind Health, New Haven, CT, United States of America
| | - T. Jason Druzgal
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - Kevin A. Pelphrey
- UVA School of Medicine, University of Virginia, Charlottesville, VA, United States of America
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Charlottesville, VA, United States of America
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA, United States of America
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8
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Ma L, Braun SE, Steinberg JL, Bjork JM, Martin CE, Keen Ii LD, Moeller FG. Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis. Neuroimage 2024; 292:120604. [PMID: 38604537 DOI: 10.1016/j.neuroimage.2024.120604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/31/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024] Open
Abstract
Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA.
| | | | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - Caitlin E Martin
- Institute for Drug and Alcohol Studies, USA; Department of Obstetrics and Gynecology, USA
| | - Larry D Keen Ii
- Department of Psychology, Virginia State University, Petersburg, VA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA; Department of Neurology, USA; Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA
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9
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Muta K, Haga Y, Hata J, Kaneko T, Hagiya K, Komaki Y, Seki F, Yoshimaru D, Nakae K, Woodward A, Gong R, Kishi N, Okano H. Commonality and variance of resting-state networks in common marmoset brains. Sci Rep 2024; 14:8316. [PMID: 38594386 PMCID: PMC11004137 DOI: 10.1038/s41598-024-58799-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 04/03/2024] [Indexed: 04/11/2024] Open
Abstract
Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrix jacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases.
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Affiliation(s)
- Kanako Muta
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
| | - Yawara Haga
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
| | - Junichi Hata
- Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Takaaki Kaneko
- Division of Behavioral Development, Department of System Neuroscience, National Institute for Physiological Science, Aichi, Japan
| | - Kei Hagiya
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
| | - Yuji Komaki
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Fumiko Seki
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Daisuke Yoshimaru
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Live Animal Imaging Center, Central Institute for Experimental Animals, Kanagawa, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
- Division of Regenerative Medicine, The Jikei University School of Medicine, Tokyo, Japan
| | - Ken Nakae
- Exploratory Research Center on Life and Living Systems, National Institutes of Natural Sciences, Aichi, Japan
| | - Alexander Woodward
- Connectome Analysis Unit, Center for Brain Science, RIKEN, Saitama, Japan
| | - Rui Gong
- Connectome Analysis Unit, Center for Brain Science, RIKEN, Saitama, Japan
| | - Noriyuki Kishi
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Hideyuki Okano
- Laboratory for Marmoset Neural Architecture, Center for Brain Science, RIKEN, Saitama, Japan.
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan.
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10
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Jiang A, Ma X, Li S, Wang L, Yang B, Wang S, Li M, Dong G. Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach. Psychol Med 2024:1-12. [PMID: 38563297 DOI: 10.1017/s0033291724000138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
BACKGROUND Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.
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Affiliation(s)
- Anhang Jiang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Xuefeng Ma
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Shuang Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Lingxiao Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang Province, China
| | - Bo Yang
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
| | - Shizhen Wang
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Mei Li
- Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
- Center for Mental Health Education and Counselling, Hangzhou Normal University, Hangzhou, Zhejiang Province, P.R. China
| | - Guangheng Dong
- Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province, P.R. China
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11
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Yoon N, Kim S, Oh MR, Kim M, Lee JM, Kim BN. Intrinsic network abnormalities in children with autism spectrum disorder: an independent component analysis. Brain Imaging Behav 2024; 18:1-14. [PMID: 38324235 DOI: 10.1007/s11682-024-00858-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2024] [Indexed: 02/08/2024]
Abstract
Aberrant intrinsic brain networks are consistently observed in individuals with autism spectrum disorder. However, studies examining the strength of functional connectivity across brain regions have yielded conflicting results. Therefore, this study aimed to investigate the functional connectivity of the resting brain in children with low-functioning autism, including during the early developmental stages. We explored the functional connectivity of 43 children with autism spectrum disorder and 54 children with typical development aged 2 to 12 years using resting-state functional magnetic resonance imaging data. We used independent component analysis to classify the brain regions into six intrinsic networks and analyzed the functional connectivity within each network. Moreover, we analyzed the relationship between functional connectivity and clinical scores. In children with autism, the under-connectivities were observed within several brain networks, including the cognitive control, default mode, visual, and somatomotor networks. In contrast, we found over-connectivities between the subcortical, visual, and somatomotor networks in children with autism compared with children with typical development. Moderate effect sizes were observed in entire networks (Cohen's d = 0.43-0.77). These network alterations were significantly correlated with clinical scores such as the communication sub-score (r = - 0.442, p = 0.045) and the calibrated severity score (r = - 0.435, p = 0.049) of the Autism Diagnostic Observation Schedule. These opposing results observed based on the brain areas suggest that aberrant neurodevelopment proceeds in various ways depending on the functional brain regions in individuals with autism spectrum disorder.
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Affiliation(s)
- Narae Yoon
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehakno, Jongno-gu, Seoul, Korea
| | - Sohui Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Mee Rim Oh
- Biomedical Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Minji Kim
- Department of Electronic Engineering, Hanyang University, Seoul, South Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Sanhak-kisulkwan Bldg., #319, 222 Wangsipri-ro, Sungdong-gu, Seoul, 133-791, Republic of Korea.
| | - Bung-Nyun Kim
- Division of Children and Adolescent Psychiatry, Department of Psychiatry, Seoul National University College of Medicine, 101 Daehakno, Jongno-gu, Seoul, Korea.
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12
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Newman BT, Jacokes Z, Venkadesh S, Webb SJ, Kleinhans NM, McPartland JC, Druzgal TJ, Pelphrey KA, Van Horn JD. Conduction Velocity, G-ratio, and Extracellular Water as Microstructural Characteristics of Autism Spectrum Disorder. bioRxiv 2024:2023.07.23.550166. [PMID: 37546913 PMCID: PMC10402058 DOI: 10.1101/2023.07.23.550166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The neuronal differences contributing to the etiology of autism spectrum disorder (ASD) are still not well defined. Previous studies have suggested that myelin and axons are disrupted during development in ASD. By combining structural and diffusion MRI techniques, myelin and axons can be assessed using extracellular water, aggregate g-ratio, and a novel metric termed aggregate conduction velocity, which is related to the capacity of the axon to carry information. In this study, several innovative cellular microstructural methods, as measured from magnetic resonance imaging (MRI), are combined to characterize differences between ASD and typically developing adolescent participants in a large cohort. We first examine the relationship between each metric, including microstructural measurements of axonal and intracellular diffusion and the T1w/T2w ratio. We then demonstrate the sensitivity of these metrics by characterizing differences between ASD and neurotypical participants, finding widespread increases in extracellular water in the cortex and decreases in aggregate g-ratio and aggregate conduction velocity throughout the cortex, subcortex, and white matter skeleton. We finally provide evidence that these microstructural differences are associated with higher scores on the Social Communication Questionnaire (SCQ) a commonly used diagnostic tool to assess ASD. This study is the first to reveal that ASD involves MRI-measurable in vivo differences of myelin and axonal development with implications for neuronal and behavioral function. We also introduce a novel neuroimaging metric, aggregate conduction velocity, that is highly sensitive to these changes. We conclude that ASD may be characterized by otherwise intact structural connectivity but that functional connectivity may be attenuated by network properties affecting neural transmission speed. This effect may explain the putative reliance on local connectivity in contrast to more distal connectivity observed in ASD.
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Affiliation(s)
- Benjamin T. Newman
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
- UVA School of Medicine, University of Virginia, 560 Ray Hunt Drive, Charlottesville, VA 22903
| | - Zachary Jacokes
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA 22903
| | - Siva Venkadesh
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
| | - Sara J. Webb
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle WA USA 98195
- Seattle Children’s Research Institute, 1920 Terry Ave, Building Cure-03, Seattle WA 98101
| | - Natalia M. Kleinhans
- Department of Radiology, Integrated Brain Imaging Center, University of Washington, 1959 NE Pacific St Seattle, WA 98195
| | - James C. McPartland
- Yale Child Study Center, 230 South Frontage Road, New Haven, CT 06520
- Yale Center for Brain and Mind Health, 40 Temple Street, Suite 6A, New Haven, CT, 06520
| | - T. Jason Druzgal
- UVA School of Medicine, University of Virginia, 560 Ray Hunt Drive, Charlottesville, VA 22903
| | - Kevin A. Pelphrey
- UVA School of Medicine, University of Virginia, 560 Ray Hunt Drive, Charlottesville, VA 22903
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, Gilmer Hall, Charlottesville, VA 22903
- School of Data Science, University of Virginia, Elson Building, Charlottesville, VA 22903
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13
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Liloia D, Manuello J, Costa T, Keller R, Nani A, Cauda F. Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies. Eur Arch Psychiatry Clin Neurosci 2024; 274:3-18. [PMID: 36599959 PMCID: PMC10787009 DOI: 10.1007/s00406-022-01541-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/16/2022] [Indexed: 01/05/2023]
Abstract
Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy.
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
- Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital and Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
- Neuroscience Institute of Turin (NIT), Turin, Italy
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14
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Evans MM, Kim J, Abel T, Nickl-Jockschat T, Stevens HE. Developmental Disruptions of the Dorsal Striatum in Autism Spectrum Disorder. Biol Psychiatry 2024; 95:102-111. [PMID: 37652130 PMCID: PMC10841118 DOI: 10.1016/j.biopsych.2023.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 08/10/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023]
Abstract
Autism spectrum disorder (ASD) is an increasingly prevalent neurodevelopmental condition characterized by social and communication deficits as well as patterns of restricted, repetitive behavior. Abnormal brain development has long been postulated to underlie ASD, but longitudinal studies aimed at understanding the developmental course of the disorder have been limited. More recently, abnormal development of the striatum in ASD has become an area of interest in research, partially due to overlap of striatal functions and deficit areas in ASD, as well as the critical role of the striatum in early development, when ASD is first detected. Focusing on the dorsal striatum and the associated symptom domain of restricted, repetitive behavior, we review the current literature on dorsal striatal abnormalities in ASD, including studies on functional connectivity, morphometry, and cellular and molecular substrates. We highlight that observed striatal abnormalities in ASD are often dynamic across development, displaying disrupted developmental trajectories. Important findings include an abnormal trajectory of increasing corticostriatal functional connectivity with age and increased striatal growth during childhood in ASD. We end by discussing striatal findings from animal models of ASD. In sum, the studies reviewed here demonstrate a key role for developmental disruptions of the dorsal striatum in the pathogenesis of ASD. Directing attention toward these findings will improve our understanding of ASD and of how associated deficits may be better addressed.
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Affiliation(s)
- Maya M Evans
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Jaekyoon Kim
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Ted Abel
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Thomas Nickl-Jockschat
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa; Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa
| | - Hanna E Stevens
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa; Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa.
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15
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Park BY, Benkarim O, Weber CF, Kebets V, Fett S, Yoo S, Martino AD, Milham MP, Misic B, Valk SL, Hong SJ, Bernhardt BC. Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism. Neuroimage 2024; 285:120481. [PMID: 38043839 DOI: 10.1016/j.neuroimage.2023.120481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Revised: 11/29/2023] [Accepted: 12/01/2023] [Indexed: 12/05/2023] Open
Abstract
Autism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 80 ASD and 61 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction accuracy differences (∆prediction accuracy) were marked in transmodal association systems, such as the default mode network, in both neurotypical controls and ASD. Differences were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. We compared regional differences in ∆prediction accuracy between both groups to assess the impact of polysynaptic communication on structure-function coupling. This analysis revealed that between-group differences in ∆prediction accuracy followed a sensory-to-transmodal cortical hierarchy, with an increased gap between controls and ASD in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and that these can help explain its wide range of associated symptoms.
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Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Department of Data Science, Inha University, Incheon, South Korea; Department of Statistics and Data Science, Inha University, Incheon, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Clara F Weber
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Valeria Kebets
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Serena Fett
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seulki Yoo
- Convergence Research Institute, Sungkyunkwan University, Suwon, South Korea
| | - Adriana Di Martino
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, United States
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie L Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Center for the Developing Brain, Child Mind Institute, New York, United States; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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16
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Del Río M, Racey C, Ren Z, Qiu J, Wang HT, Ward J. Higher Sensory Sensitivity is Linked to Greater Expansion Amongst Functional Connectivity Gradients. J Autism Dev Disord 2024; 54:56-74. [PMID: 36227443 DOI: 10.1007/s10803-022-05772-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2022] [Indexed: 11/29/2022]
Abstract
Insofar as the autistic-like phenotype presents in the general population, it consists of partially dissociable traits, such as social and sensory issues. Here, we investigate individual differences in cortical organisation related to autistic-like traits. Connectome gradient decomposition based on resting state fMRI data reliably reveals a principal gradient spanning from unimodal to transmodal regions, reflecting the transition from perception to abstract cognition. In our non-clinical sample, this gradient's expansion, indicating less integration between visual and default mode networks, correlates with subjective sensory sensitivity (measured using the Glasgow Sensory Questionnaire, GSQ), but not other autistic-like traits (measured using the Autism Spectrum Quotient, AQ). This novel brain-based correlate of the GSQ demonstrates sensory issues can be disentangled from the wider autistic-like phenotype.
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Affiliation(s)
| | - Chris Racey
- School of Psychology, University of Sussex, Brighton, UK
- Sussex Neuroscience, University of Sussex, Brighton, UK
| | - Zhiting Ren
- School of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - Hao-Ting Wang
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
- Laboratory for Brain Simulation and Exploration (SIMEXP), Montreal Geriatrics Institute (CRIUGM), University of Montreal, Montreal, Canada
| | - Jamie Ward
- School of Psychology, University of Sussex, Brighton, UK
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK
- Sussex Neuroscience, University of Sussex, Brighton, UK
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17
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Chen K, Zhuang W, Zhang Y, Yin S, Liu Y, Chen Y, Kang X, Ma H, Zhang T. Alteration of the large-scale white-matter functional networks in autism spectrum disorder. Cereb Cortex 2023; 33:11582-11593. [PMID: 37851712 DOI: 10.1093/cercor/bhad392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 10/02/2023] [Accepted: 10/03/2023] [Indexed: 10/20/2023] Open
Abstract
Autism spectrum disorder is a neurodevelopmental disorder whose core deficit is social dysfunction. Previous studies have indicated that structural changes in white matter are associated with autism spectrum disorder. However, few studies have explored the alteration of the large-scale white-matter functional networks in autism spectrum disorder. Here, we identified ten white-matter functional networks on resting-state functional magnetic resonance imaging data using the K-means clustering algorithm. Compared with the white matter and white-matter functional network connectivity of the healthy controls group, we found significantly decreased white matter and white-matter functional network connectivity mainly located within the Occipital network, Middle temporo-frontal network, and Deep network in autism spectrum disorder. Compared with healthy controls, findings from white-matter gray-matter functional network connectivity showed the decreased white-matter gray-matter functional network connectivity mainly distributing in the Occipital network and Deep network. Moreover, we compared the spontaneous activity of white-matter functional networks between the two groups. We found that the spontaneous activity of Middle temporo-frontal and Deep network was significantly decreased in autism spectrum disorder. Finally, the correlation analysis showed that the white matter and white-matter functional network connectivity between the Middle temporo-frontal network and others networks and the spontaneous activity of the Deep network were significantly correlated with the Social Responsiveness Scale scores of autism spectrum disorder. Together, our findings indicate that changes in the white-matter functional networks are associated behavioral deficits in autism spectrum disorder.
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Affiliation(s)
- Kai Chen
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yanfang Zhang
- Department of Ultrasonic Medicine, Baiyun Branch, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou City, Guangdong Province, China
| | - Shunjie Yin
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yinghua Liu
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Yuan Chen
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No. 81 Bayi Road, Yongning Street, Wenjiang District, Chengdu City 610075, China
| | - Hailin Ma
- Plateau Brain Science Research Center, Tibet University, 10 Zangda East Road, Lhasa City 510631, China
| | - Tao Zhang
- Mental Health Education Center and School of Big Health Management, Xihua University, Jinniu District, Chengdu, Sichuan, China
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18
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Millevert C, Vidas-Guscic N, Vanherp L, Jonckers E, Verhoye M, Staelens S, Bertoglio D, Weckhuysen S. Resting-State Functional MRI and PET Imaging as Noninvasive Tools to Study (Ab)Normal Neurodevelopment in Humans and Rodents. J Neurosci 2023; 43:8275-8293. [PMID: 38073598 PMCID: PMC10711730 DOI: 10.1523/jneurosci.1043-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 06/09/2023] [Accepted: 09/13/2023] [Indexed: 12/18/2023] Open
Abstract
Neurodevelopmental disorders (NDDs) are a group of complex neurologic and psychiatric disorders. Functional and molecular imaging techniques, such as resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET), can be used to measure network activity noninvasively and longitudinally during maturation in both humans and rodent models. Here, we review the current knowledge on rs-fMRI and PET biomarkers in the study of normal and abnormal neurodevelopment, including intellectual disability (ID; with/without epilepsy), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), in humans and rodent models from birth until adulthood, and evaluate the cross-species translational value of the imaging biomarkers. To date, only a few isolated studies have used rs-fMRI or PET to study (abnormal) neurodevelopment in rodents during infancy, the critical period of neurodevelopment. Further work to explore the feasibility of performing functional imaging studies in infant rodent models is essential, as rs-fMRI and PET imaging in transgenic rodent models of NDDs are powerful techniques for studying disease pathogenesis, developing noninvasive preclinical imaging biomarkers of neurodevelopmental dysfunction, and evaluating treatment-response in disease-specific models.
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Affiliation(s)
- Charissa Millevert
- Applied & Translational Neurogenomics Group, Vlaams Instituut voor Biotechnology (VIB) Center for Molecular Neurology, VIB, Antwerp 2610, Belgium
- Department of Neurology, University Hospital of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Nicholas Vidas-Guscic
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Liesbeth Vanherp
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Elisabeth Jonckers
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Marleen Verhoye
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp (MICA), University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Daniele Bertoglio
- Bio-Imaging Lab, University of Antwerp, Antwerp 2610, Belgium
- Molecular Imaging Center Antwerp (MICA), University of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
| | - Sarah Weckhuysen
- Applied & Translational Neurogenomics Group, Vlaams Instituut voor Biotechnology (VIB) Center for Molecular Neurology, VIB, Antwerp 2610, Belgium
- Department of Neurology, University Hospital of Antwerp, Antwerp 2610, Belgium
- µNEURO Research Centre of Excellence, University of Antwerp, Antwerp 2610, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Science, University of Antwerp, Antwerp 2610, Belgium
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19
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Zhang S, Chen X, Shen X, Ren B, Yu Z, Yang H, Jiang X, Shen D, Zhou Y, Zhang XY. A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders. Med Image Anal 2023; 90:102932. [PMID: 37657365 DOI: 10.1016/j.media.2023.102932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 07/06/2023] [Accepted: 08/08/2023] [Indexed: 09/03/2023]
Abstract
Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-consuming cognitive tests and potential human bias in clinics. To address this challenge, we propose a novel adversarial self-supervised graph neural network (GNN) based on graph contrastive learning, named A-GCL, for diagnosing neurodevelopmental disorders using functional magnetic resonance imaging (fMRI) data. Taking advantage of the success of GNNs in psychiatric disease diagnosis using fMRI, our proposed A-GCL model is expected to improve the performance of diagnosis and provide more robust results. A-GCL takes graphs constructed from the fMRI images as input and uses contrastive learning to extract features for classification. The graphs are constructed with 3 bands of the amplitude of low-frequency fluctuation (ALFF) as node features and Pearson's correlation coefficients (PCC) of the average fMRI time series in different brain regions as edge weights. The contrastive learning creates an edge-dropped graph from a trainable Bernoulli mask to extract features that are invariant to small variations of the graph. Experiment results on three datasets - Autism Brain Imaging Data Exchange (ABIDE) I, ABIDE II, and attention deficit hyperactivity disorder (ADHD) - with 3 atlases - AAL1, AAL3, Shen268 - demonstrate the superiority and generalizability of A-GCL compared to the other GNN-based models. Extensive ablation studies verify the robustness of the proposed approach to atlas selection and model variation. Explanatory results reveal key functional connections and brain regions associated with neurodevelopmental disorders.
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Affiliation(s)
- Shengjie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Xiang Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Xin Shen
- Department of Mathematics, Beijing Normal University, Beijing, 100032, China
| | - Bohan Ren
- Department of School of Cyber Science and Technology, Beihang University, Beijing, 100191, China
| | - Ziqi Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Haibo Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Xi Jiang
- Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, 201210, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, 200030, China; Shanghai Clinical Research and Trial Center, Shanghai, 201210, China
| | - Yuan Zhou
- School of Data Science, Fudan University, Shanghai, 200433, China.
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
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20
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Ghosal N, Basu S, Bhaumik D. Detection of sparse differential dependent functional brain connectivity. Stat Med 2023; 42:4664-4680. [PMID: 37647942 DOI: 10.1002/sim.9882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 06/26/2023] [Accepted: 08/14/2023] [Indexed: 09/01/2023]
Abstract
Functional brain connectivity analysis is an increasingly important technique in neuroscience, psychiatry, and autism research. Functional connectivity can be measured by considering co-activation of brain regions in resting-state functional magnetic resonance imaging (rs-fMRI). We propose a novel Bayesian model to detect differential connections in cross-correlated functional connectivity between region of interest (ROI) pairs. The proposed sparse clustered neighborhood model induces a lower-dimensional sparsity and clustering based on a nonparametric Bayesian approach to model sparse differentially connected ROI pairs. Second, it induces a structured dependence model for modeling potential dependence among ROI pairs. We demonstrate Bayesian inference and performance of the proposed model in simulation studies and compare with a standard model. We utilize the proposed model to contrast functional connectivities between participants with autism spectrum disorder and neurotypical participants using cross-correlated rs-fMRI data from four sites of the Autism Brain Image Data Exchange.
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Affiliation(s)
- Nairita Ghosal
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Sanjb Basu
- Division of Epidemiology and Biostatistics, University of Illinois Chicago, Chicago, Illinois, USA
| | - Dulal Bhaumik
- Division of Epidemiology and Biostatistics, University of Illinois Chicago, Chicago, Illinois, USA
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois, USA
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21
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Razzak R, Li J, He S, Sokhadze E. Investigating Sex-Based Neural Differences in Autism and Their Extended Reality Intervention Implications. Brain Sci 2023; 13:1571. [PMID: 38002531 PMCID: PMC10670246 DOI: 10.3390/brainsci13111571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
Autism Spectrum Disorder (ASD) affects millions of individuals worldwide, and there is growing interest in the use of extended reality (XR) technologies for intervention. Despite the promising potential of XR interventions, there remain gaps in our understanding of the neurobiological mechanisms underlying ASD, particularly in relation to sex-based differences. This scoping review synthesizes the current research on brain activity patterns in ASD, emphasizing the implications for XR interventions and neurofeedback therapy. We examine the brain regions commonly affected by ASD, the potential benefits and drawbacks of XR technologies, and the implications of sex-specific differences for designing effective interventions. Our findings underscore the need for ongoing research into the neurobiological underpinnings of ASD and sex-based differences, as well as the importance of developing tailored interventions that consider the unique needs and experiences of autistic individuals.
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Affiliation(s)
- Rehma Razzak
- Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA; (R.R.); (S.H.)
| | - Joy Li
- Department of Software Engineering and Game Development, Kennesaw State University, Marietta, GA 30060, USA;
| | - Selena He
- Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA; (R.R.); (S.H.)
| | - Estate Sokhadze
- Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA
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22
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Sun S, Yu H, Yu R, Wang S. Functional connectivity between the amygdala and prefrontal cortex underlies processing of emotion ambiguity. Transl Psychiatry 2023; 13:334. [PMID: 37898626 PMCID: PMC10613296 DOI: 10.1038/s41398-023-02625-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 09/27/2023] [Accepted: 10/06/2023] [Indexed: 10/30/2023] Open
Abstract
Processing facial expressions of emotion draws on a distributed brain network. In particular, judging ambiguous facial emotions involves coordination between multiple brain areas. Here, we applied multimodal functional connectivity analysis to achieve network-level understanding of the neural mechanisms underlying perceptual ambiguity in facial expressions. We found directional effective connectivity between the amygdala, dorsomedial prefrontal cortex (dmPFC), and ventromedial PFC, supporting both bottom-up affective processes for ambiguity representation/perception and top-down cognitive processes for ambiguity resolution/decision. Direct recordings from the human neurosurgical patients showed that the responses of amygdala and dmPFC neurons were modulated by the level of emotion ambiguity, and amygdala neurons responded earlier than dmPFC neurons, reflecting the bottom-up process for ambiguity processing. We further found parietal-frontal coherence and delta-alpha cross-frequency coupling involved in encoding emotion ambiguity. We replicated the EEG coherence result using independent experiments and further showed modulation of the coherence. EEG source connectivity revealed that the dmPFC top-down regulated the activities in other brain regions. Lastly, we showed altered behavioral responses in neuropsychiatric patients who may have dysfunctions in amygdala-PFC functional connectivity. Together, using multimodal experimental and analytical approaches, we have delineated a neural network that underlies processing of emotion ambiguity.
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Affiliation(s)
- Sai Sun
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University 6-3 Aramaki aza Aoba, Aoba-ku, Sendai, 980-8578, Japan.
- Research Institute of Electrical Communication, Tohoku University 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan.
| | - Hongbo Yu
- Department of Psychological & Brain Sciences, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Rongjun Yu
- Department of Management, Marketing, and Information Systems, Hong Kong Baptist University, Hong Kong, China
| | - Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
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23
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Sidulova M, Park CH. Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study. Bioengineering (Basel) 2023; 10:1209. [PMID: 37892939 PMCID: PMC10604768 DOI: 10.3390/bioengineering10101209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/30/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for atypical pattern detection in brain imaging. During training, these models learn to capture the underlying patterns within "normal" brain images and generate new samples from those patterns. Neurodivergent states can be observed by measuring the dissimilarity between the generated/reconstructed images and the input images. This paper leverages VAEs to conduct Functional Connectivity (FC) analysis from functional Magnetic Resonance Imaging (fMRI) scans of individuals with Autism Spectrum Disorder (ASD), aiming to uncover atypical interconnectivity between brain regions. In the first part of our study, we compare multiple VAE architectures-Conditional VAE, Recurrent VAE, and a hybrid of CNN parallel with RNN VAE-aiming to establish the effectiveness of VAEs in application FC analysis. Given the nature of the disorder, ASD exhibits a higher prevalence among males than females. Therefore, in the second part of this paper, we investigate if introducing phenotypic data could improve the performance of VAEs and, consequently, FC analysis. We compare our results with the findings from previous studies in the literature. The results showed that CNN-based VAE architecture is more effective for this application than the other models.
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Affiliation(s)
- Mariia Sidulova
- Department of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA;
| | - Chung Hyuk Park
- Department of Biomedical Engineering, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA;
- Department of Computer Science, School of Engineering and Applied Science, The George Washington University, Washington, DC 20052, USA
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24
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Sun B, Wang B, Wei Z, Feng Z, Wu ZL, Yassin W, Stone WS, Lin Y, Kong XJ. Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking. Front Neurosci 2023; 17:1236637. [PMID: 37886678 PMCID: PMC10598595 DOI: 10.3389/fnins.2023.1236637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 09/12/2023] [Indexed: 10/28/2023] Open
Abstract
Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.
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Affiliation(s)
- Binbin Sun
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Bryan Wang
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Department of English and Creative Writing, Brandeis University, Waltham, MA, United States
| | - Zhen Wei
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhe Feng
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Zhi-Liu Wu
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Walid Yassin
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- McLean Hospital, Harvard Medical School, Belmont, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - William S. Stone
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yan Lin
- Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Xue-Jun Kong
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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25
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Márquez-García AV, Ng BK, Iarocci G, Moreno S, Vakorin VA, Doesburg SM. Atypical Associations between Functional Connectivity during Pragmatic and Semantic Language Processing and Cognitive Abilities in Children with Autism. Brain Sci 2023; 13:1448. [PMID: 37891816 PMCID: PMC10605927 DOI: 10.3390/brainsci13101448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/26/2023] [Accepted: 10/01/2023] [Indexed: 10/29/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized by both atypical functional brain connectivity and cognitive challenges across multiple cognitive domains. The relationship between task-dependent brain connectivity and cognitive abilities, however, remains poorly understood. In this study, children with ASD and their typically developing (TD) peers engaged in semantic and pragmatic language tasks while their task-dependent brain connectivity was mapped and compared. A multivariate statistical approach revealed associations between connectivity and psychometric assessments of relevant cognitive abilities. While both groups exhibited brain-behavior correlations, the nature of these associations diverged, particularly in the directionality of overall correlations across various psychometric categories. Specifically, greater disparities in functional connectivity between the groups were linked to larger differences in Autism Questionnaire, BRIEF, MSCS, and SRS-2 scores but smaller differences in WASI, pragmatic language, and Theory of Mind scores. Our findings suggest that children with ASD utilize distinct neural communication patterns for language processing. Although networks recruited by children with ASD may appear less efficient than those typically engaged, they could serve as compensatory mechanisms for potential disruptions in conventional brain networks.
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Affiliation(s)
- Amparo V. Márquez-García
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Bonnie K. Ng
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
- School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Grace Iarocci
- Department of Psychology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
| | - Sylvain Moreno
- School of Interactive Arts and Technology, Simon Fraser University, Surrey, BC V3T 0A3, Canada
| | - Vasily A. Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Sam M. Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
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26
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Ruiz M, Groessing A, Guran A, Koçan AU, Mikus N, Nater UM, Kouwer K, Posserud MB, Salomon-Gimmon M, Todorova B, Wagner IC, Gold C, Silani G, Specht K. Music for autism: a protocol for an international randomized crossover trial on music therapy for children with autism. Front Psychiatry 2023; 14:1256771. [PMID: 37886114 PMCID: PMC10598663 DOI: 10.3389/fpsyt.2023.1256771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/18/2023] [Indexed: 10/28/2023] Open
Abstract
The notion of a connection between autism and music is as old as the first reported cases of autism, and music has been used as a therapeutic tool for many decades. Music therapy holds promise as an intervention for individuals with autism, harnessing their strengths in music processing to enhance communication and expression. While previous randomized controlled trials have demonstrated positive outcomes in terms of global improvement and quality of life, their reliance on psychological outcomes restricts our understanding of underlying mechanisms. This paper introduces the protocol for the Music for Autism study, a randomized crossover trial designed to investigate the effects of a 12-week music therapy intervention on a range of psychometric, neuroimaging, and biological outcomes in school-aged children with autism. The protocol builds upon previous research and aims to both replicate and expand upon findings that demonstrated improvements in social communication and functional brain connectivity following a music intervention. The primary objective of this trial is to determine whether music therapy leads to improvements in social communication and functional brain connectivity as compared to play-based therapy. In addition, secondary aims include exploring various relevant psychometric, neuroimaging, and biological outcomes. To achieve these objectives, we will enroll 80 participants aged 6-12 years in this international, assessor-blinded, crossover randomized controlled trial. Each participant will be randomly assigned to receive either music therapy or play-based therapy for a period of 12 weeks, followed by a 12-week washout period, after which they will receive the alternate intervention. Assessments will be conducted four times, before and after each intervention period. The protocol of the Music for Autism trial provides a comprehensive framework for studying the effects of music therapy on a range of multidimensional outcomes in children with autism. The findings from this trial have the potential to contribute to the development of evidence-based interventions that leverage strengths in music processing to address the complex challenges faced by individuals with autism. Clinical Trial Registration: Clinicaltrials.gov identifier NCT04936048.
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Affiliation(s)
- Marianna Ruiz
- Department of Health and Social Sciences, Norwegian Research Centre (NORCE), Bergen, Norway
- Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Alexander Groessing
- Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Alexandrina Guran
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
| | - Asena U. Koçan
- Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Nace Mikus
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- School of Culture and Society, Interacting Minds Centre, Aarhus University, Aarhus, Denmark
| | - Urs M. Nater
- Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Karlijn Kouwer
- Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
| | - Maj-Britt Posserud
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- Department of Clinical Medicine, Faculty of Medicine, University of Bergen, Bergen, Norway
| | - Maayan Salomon-Gimmon
- Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- The School of Creative Arts Therapies, Faculty of Social Welfare and Health Sciences, University of Haifa, Haifa, Israel
| | - Boryana Todorova
- Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Isabella C. Wagner
- Social, Cognitive and Affective Neuroscience Unit, Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- Vienna Cognitive Science Hub, University of Vienna, Vienna, Austria
- Centre for Microbiology and Environmental Systems Science, University of Vienna, Vienna, Austria
| | - Christian Gold
- Department of Health and Social Sciences, Norwegian Research Centre (NORCE), Bergen, Norway
- Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Giorgia Silani
- Department of Clinical and Health Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
| | - Karsten Specht
- Department of Biological and Medical Psychology, Faculty of Psychology, University of Bergen, Bergen, Norway
- Department of Radiology, Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
- Department of Education, Faculty of Humanities, Social Sciences and Education, UiT-The Arctic University of Norway, Tromsø, Norway
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27
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Deserno MK, Bathelt J, Groenman AP, Geurts HM. Probing the overarching continuum theory: data-driven phenotypic clustering of children with ASD or ADHD. Eur Child Adolesc Psychiatry 2023; 32:1909-1923. [PMID: 35687205 PMCID: PMC10533623 DOI: 10.1007/s00787-022-01986-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 04/06/2022] [Indexed: 11/03/2022]
Abstract
The clinical validity of the distinction between ADHD and ASD is a longstanding discussion. Recent advances in the realm of data-driven analytic techniques now enable us to formally investigate theories aiming to explain the frequent co-occurrence of these neurodevelopmental conditions. In this study, we probe different theoretical positions by means of a pre-registered integrative approach of novel classification, subgrouping, and taxometric techniques in a representative sample (N = 434), and replicate the results in an independent sample (N = 219) of children (ADHD, ASD, and typically developing) aged 7-14 years. First, Random Forest Classification could predict diagnostic groups based on questionnaire data with limited accuracy-suggesting some remaining overlap in behavioral symptoms between them. Second, community detection identified four distinct groups, but none of them showed a symptom profile clearly related to either ADHD or ASD in neither the original sample nor the replication sample. Third, taxometric analyses showed evidence for a categorical distinction between ASD and typically developing children, a dimensional characterization of the difference between ADHD and typically developing children, and mixed results for the distinction between the diagnostic groups. We present a novel framework of cutting-edge statistical techniques which represent recent advances in both the models and the data used for research in psychiatric nosology. Our results suggest that ASD and ADHD cannot be unambiguously characterized as either two separate clinical entities or opposite ends of a spectrum, and highlight the need to study ADHD and ASD traits in tandem.
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Affiliation(s)
- M K Deserno
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.
- Max Planck Institute for Human Development, Berlin, Germany.
| | - J Bathelt
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Royal Holloway, University of London, Egham, UK
| | - A P Groenman
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - H M Geurts
- Dutch Autism and ADHD Research Centre (d'Arc), Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Leo Kannerhuis, Amsterdam (Youz, Parnassiagroep), Amsterdam, The Netherlands
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Javaheripour N, Wagner G, de la Cruz F, Walter M, Szycik GR, Tietze FA. Altered brain network organization in adults with Asperger's syndrome: decreased connectome transitivity and assortativity with increased global efficiency. Front Psychiatry 2023; 14:1223147. [PMID: 37701094 PMCID: PMC10494541 DOI: 10.3389/fpsyt.2023.1223147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 07/26/2023] [Indexed: 09/14/2023] Open
Abstract
Introduction Autism spectrum disorder (ASD) is a neurodevelopmental disorder that persists into adulthood with both social and cognitive disturbances. Asperger's syndrome (AS) was a distinguished subcategory of autism in the DSM-IV-TR defined by specific symptoms including difficulties in social interactions, inflexible thinking patterns, and repetitive behaviour without any delay in language or cognitive development. Studying the functional brain organization of individuals with these specific symptoms may help to better understand Autism spectrum symptoms. Methods The aim of this study is therefore to investigate functional connectivity as well as functional network organization characteristics using graph-theory measures of the whole brain in male adults with AS compared to healthy controls (HC) (AS: n = 15, age range 21-55 (mean ± sd: 39.5 ± 11.6), HC: n = 15, age range 22-57 [mean ± sd: 33.5 ± 8.5]). Results No significant differences were found when comparing the region-by-region connectivity at the whole-brain level between the AS group and HC. However, measures of "transitivity," which reflect local information processing and functional segregation, and "assortativity," indicating network resilience, were reduced in the AS group compared to HC. On the other hand, global efficiency, which represents the overall effectiveness and speed of information transfer across the entire brain network, was increased in the AS group. Discussion Our findings suggest that individuals with AS may have alterations in the organization and functioning of brain networks, which could contribute to the distinctive cognitive and behavioural features associated with this condition. We suggest further research to explore the association between these altered functional patterns in brain networks and specific behavioral traits observed in individuals with AS, which could provide valuable insights into the underlying mechanisms of its symptomatology.
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Affiliation(s)
- Nooshin Javaheripour
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Gerd Wagner
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena, Germany
| | - Feliberto de la Cruz
- Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Center for Intervention and Research on Adaptive and Maladaptive Brain Circuits Underlying Mental Health (C-I-R-C), Jena, Germany
- Clinical Affective Neuroimaging Laboratory (CANLAB), Magdeburg, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
- Leibniz Institute for Neurobiology, Magdeburg, Germany
- Center for Behavioral Brain Sciences, Magdeburg, Germany
- German Center for Mental Health (DZPG), Jena, Germany
| | - Gregor R. Szycik
- Department of Psychiatry and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Fabian-Alexander Tietze
- Department of Psychiatry and Psychotherapy, Jüdisches Krankenhaus Berlin—Berlin Jewish Hospital, Academic Teaching Hospital of the Charité—Universitätsmedizin Berlin, Berlin, Germany
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Banks MI, Krause BM, Berger DG, Campbell DI, Boes AD, Bruss JE, Kovach CK, Kawasaki H, Steinschneider M, Nourski KV. Functional geometry of auditory cortical resting state networks derived from intracranial electrophysiology. PLoS Biol 2023; 21:e3002239. [PMID: 37651504 PMCID: PMC10499207 DOI: 10.1371/journal.pbio.3002239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 09/13/2023] [Accepted: 07/07/2023] [Indexed: 09/02/2023] Open
Abstract
Understanding central auditory processing critically depends on defining underlying auditory cortical networks and their relationship to the rest of the brain. We addressed these questions using resting state functional connectivity derived from human intracranial electroencephalography. Mapping recording sites into a low-dimensional space where proximity represents functional similarity revealed a hierarchical organization. At a fine scale, a group of auditory cortical regions excluded several higher-order auditory areas and segregated maximally from the prefrontal cortex. On mesoscale, the proximity of limbic structures to the auditory cortex suggested a limbic stream that parallels the classically described ventral and dorsal auditory processing streams. Identities of global hubs in anterior temporal and cingulate cortex depended on frequency band, consistent with diverse roles in semantic and cognitive processing. On a macroscale, observed hemispheric asymmetries were not specific for speech and language networks. This approach can be applied to multivariate brain data with respect to development, behavior, and disorders.
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Affiliation(s)
- Matthew I. Banks
- Department of Anesthesiology, University of Wisconsin, Madison, Wisconsin, United States of America
- Department of Neuroscience, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Bryan M. Krause
- Department of Anesthesiology, University of Wisconsin, Madison, Wisconsin, United States of America
| | - D. Graham Berger
- Department of Anesthesiology, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Declan I. Campbell
- Department of Anesthesiology, University of Wisconsin, Madison, Wisconsin, United States of America
| | - Aaron D. Boes
- Department of Neurology, The University of Iowa, Iowa City, Iowa, United States of America
| | - Joel E. Bruss
- Department of Neurology, The University of Iowa, Iowa City, Iowa, United States of America
| | - Christopher K. Kovach
- Department of Neurosurgery, The University of Iowa, Iowa City, Iowa, United States of America
| | - Hiroto Kawasaki
- Department of Neurosurgery, The University of Iowa, Iowa City, Iowa, United States of America
| | - Mitchell Steinschneider
- Department of Neurology, Albert Einstein College of Medicine, New York, New York, United States of America
- Department of Neuroscience, Albert Einstein College of Medicine, New York, New York, United States of America
| | - Kirill V. Nourski
- Department of Neurosurgery, The University of Iowa, Iowa City, Iowa, United States of America
- Iowa Neuroscience Institute, The University of Iowa, Iowa City, Iowa, United States of America
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30
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Cho A, Wood JJ, Ferrer E, Rosenau K, Storch EA, Kendall PC. Empirically-identified subgroups of children with autism spectrum disorder and their response to two types of cognitive behavioral therapy. Dev Psychopathol 2023; 35:1188-1202. [PMID: 34866567 DOI: 10.1017/s0954579421001115] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Autism spectrum disorder (ASD) is heterogeneous and likely entails distinct phenotypes with varying etiologies. Identifying these subgroups may contribute to hypotheses about differential treatment responses. The present study aimed to discern subgroups among children with ASD and anxiety in context of the five-factor model of personality (FFM) and evaluate treatment response differences to two cognitive-behavioral therapy treatments. The present study is a secondary data analysis of children with ASD and anxiety (N=202; ages 7-13; 20.8% female) in a cognitive behavioral therapy (CBT) randomized controlled trial (Wood et al., 2020). Subgroups were identified via latent profile analysis of parent-reported FFM data. Treatment groups included standard-of-practice CBT (CC), designed for children with anxiety, and adapted CBT (BIACA), designed for children with ASD and comorbid anxiety. Five subgroups with distinct profiles were extracted. Analysis of covariance revealed CBT response was contingent on subgroup membership. Two subgroups responded better to BIACA on the primary outcome measure and a third responded better to BIACA on a peer-social adaptation measure, while a fourth subgroup responded better to CC on a school-related adaptation measure. These findings suggest that the FFM may be useful in empirically identifying subgroups of children with ASD, which could inform intervention selection decisions for children with ASD and anxiety.
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Affiliation(s)
- Anchuen Cho
- University of California, Los Angeles, 405 Hilgard Ave., 3132A Moore Hall, Los Angeles, CA90095, USA
| | - Jeffrey J Wood
- University of California, Los Angeles, 405 Hilgard Ave., 3132A Moore Hall, Los Angeles, CA90095, USA
| | - Emilio Ferrer
- University of California, Davis, 1 Shields Ave., 135 Young Hall, Davis, CA95616, USA
| | - Kashia Rosenau
- University of California, Los Angeles, 405 Hilgard Ave., 3132A Moore Hall, Los Angeles, CA90095, USA
| | - Eric A Storch
- Baylor College of Medicine, 7200 Cambridge St, Houston, TX77030, USA
| | - Philip C Kendall
- Temple University, 1701 North 13th St., Weiss Hall, Philadelphia, PA19122, USA
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31
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Wen G, Cao P, Liu L, Yang J, Zhang X, Wang F, Zaiane OR. Graph Self-Supervised Learning With Application to Brain Networks Analysis. IEEE J Biomed Health Inform 2023; 27:4154-4165. [PMID: 37159311 DOI: 10.1109/jbhi.2023.3274531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The less training data and insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is significant to construct a learning framework that can capture more information in limited data and insufficient supervision. To address these issues, we focus on self-supervised learning and aim to generalize the self-supervised learning to the brain networks, which are non-Euclidean graph data. More specifically, we propose an ensemble masked graph self-supervised framework named BrainGSLs, which incorporates 1) a local topological-aware encoder that takes the partially visible nodes as input and learns these latent representations, 2) a node-edge bi-decoder that reconstructs the masked edges by the representations of both the masked and visible nodes, 3) a signal representation learning module for capturing temporal representations from BOLD signals and 4) a classifier used for the classification. We evaluate our model on three real medical clinical applications: diagnosis of Autism Spectrum Disorder (ASD), diagnosis of Bipolar Disorder (BD) and diagnosis of Major Depressive Disorder (MDD). The results suggest that the proposed self-supervised training has led to remarkable improvement and outperforms state-of-the-art methods. Moreover, our method is able to identify the biomarkers associated with the diseases, which is consistent with the previous studies. We also explore the correlation of these three diseases and find the strong association between ASD and BD. To the best of our knowledge, our work is the first attempt of applying the idea of self-supervised learning with masked autoencoder on the brain network analysis.
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32
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Kilpatrick S, Irwin C, Singh KK. Human pluripotent stem cell (hPSC) and organoid models of autism: opportunities and limitations. Transl Psychiatry 2023; 13:217. [PMID: 37344450 DOI: 10.1038/s41398-023-02510-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/09/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by genetic or environmental perturbations during early development. Diagnoses are dependent on the identification of behavioral abnormalities that likely emerge well after the disorder is established, leaving critical developmental windows uncharacterized. This is further complicated by the incredible clinical and genetic heterogeneity of the disorder that is not captured in most mammalian models. In recent years, advancements in stem cell technology have created the opportunity to model ASD in a human context through the use of pluripotent stem cells (hPSCs), which can be used to generate 2D cellular models as well as 3D unguided- and region-specific neural organoids. These models produce profoundly intricate systems, capable of modeling the developing brain spatiotemporally to reproduce key developmental milestones throughout early development. When complemented with multi-omics, genome editing, and electrophysiology analysis, they can be used as a powerful tool to profile the neurobiological mechanisms underlying this complex disorder. In this review, we will explore the recent advancements in hPSC-based modeling, discuss present and future applications of the model to ASD research, and finally consider the limitations and future directions within the field to make this system more robust and broadly applicable.
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Affiliation(s)
- Savannah Kilpatrick
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Biochemistry and Biomedical Science, McMaster University, Hamilton, ON, Canada
| | - Courtney Irwin
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Karun K Singh
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada.
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Rahn RM, Yen A, Chen S, Gaines SH, Bice AR, Brier LM, Swift RG, Lee L, Maloney SE, Culver JP, Dougherty JD. Mecp2 deletion results in profound alterations of developmental and adult functional connectivity. Cereb Cortex 2023; 33:7436-7453. [PMID: 36897048 PMCID: PMC10267622 DOI: 10.1093/cercor/bhad050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 03/11/2023] Open
Abstract
As a regressive neurodevelopmental disorder with a well-established genetic cause, Rett syndrome and its Mecp2 loss-of-function mouse model provide an excellent opportunity to define potentially translatable functional signatures of disease progression, as well as offer insight into the role of Mecp2 in functional circuit development. Thus, we applied widefield optical fluorescence imaging to assess mesoscale calcium functional connectivity (FC) in the Mecp2 cortex both at postnatal day (P)35 in development and during the disease-related decline. We found that FC between numerous cortical regions was disrupted in Mecp2 mutant males both in juvenile development and early adulthood. Female Mecp2 mice displayed an increase in homotopic contralateral FC in the motor cortex at P35 but not in adulthood, where instead more posterior parietal regions were implicated. An increase in the amplitude of connection strength, both with more positive correlations and more negative anticorrelations, was observed across the male cortex in numerous functional regions. Widespread rescue of MeCP2 protein in GABAergic neurons rescued none of these functional deficits, nor, surprisingly, the expected male lifespan. Altogether, the female results identify early signs of disease progression, while the results in males indicate MeCP2 protein is required for typical FC in the brain.
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Affiliation(s)
- Rachel M Rahn
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Allen Yen
- Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Siyu Chen
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Seana H Gaines
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Annie R Bice
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Lindsey M Brier
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Raylynn G Swift
- Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - LeiLani Lee
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
| | - Susan E Maloney
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Intellectual and Developmental Disabilities Research Center, 660 South Euclid Avenue, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Joseph P Culver
- Department of Radiology, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Physics, Washington University School of Arts and Sciences, 1 Brookings Drive, St. Louis, MO 63130, United States
- Department of Biomedical Engineering, Washington University School of Engineering, 1 Brookings Drive, St. Louis, MO 63130, United States
| | - Joseph D Dougherty
- Department of Genetics, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Department of Psychiatry, Washington University School of Medicine, 660 South Euclid Avenue, St. Louis, MO 63110, United States
- Intellectual and Developmental Disabilities Research Center, 660 South Euclid Avenue, Washington University School of Medicine, St. Louis, MO 63110, United States
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Xie H, Moraczewski D, McNaughton KA, Warnell KR, Alkire D, Merchant JS, Kirby LA, Yarger HA, Redcay E. Social reward network connectivity differs between autistic and neurotypical youth during social interaction. bioRxiv 2023:2023.06.05.543807. [PMID: 37333161 PMCID: PMC10274709 DOI: 10.1101/2023.06.05.543807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
A core feature of autism is difficulties with social interaction. Atypical social motivation is proposed to underlie these difficulties. However, prior work testing this hypothesis has shown mixed support and has been limited in its ability to understand real-world social-interactive processes in autism. We attempted to address these limitations by scanning neurotypical and autistic youth (n = 86) during a text-based reciprocal social interaction that mimics a "live" chat and elicits social reward processes. We focused on task-evoked functional connectivity (FC) of regions responsible for motivational-reward and mentalizing processes within the broader social reward circuitry. We found that task-evoked FC between these regions was significantly modulated by social interaction and receipt of social-interactive reward. Compared to neurotypical peers, autistic youth showed significantly greater task-evoked connectivity of core regions in the mentalizing network (e.g., posterior superior temporal sulcus) and the amygdala, a key node in the reward network. Furthermore, across groups, the connectivity strength between these mentalizing and reward regions was negatively correlated with self-reported social motivation and social reward during the scanner task. Our results highlight an important role of FC within the broader social reward circuitry for social-interactive reward. Specifically, greater context-dependent FC (i.e., differences between social engagement and non-social engagement) may indicate an increased "neural effort" during social reward and relate to differences in social motivation within autistic and neurotypical populations.
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Affiliation(s)
- Hua Xie
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
- Center for Neuroscience Research, Children’s National Hospital, Washington, D.C., USA
- The George Washington University School of Medicine, Washington, D.C., USA
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Kathryn A. McNaughton
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | | | - Diana Alkire
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Junaid S. Merchant
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Laura A. Kirby
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Heather A. Yarger
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Elizabeth Redcay
- Neuroscience and Cognitive Science Program, University of Maryland, College Park, Maryland, USA
- Department of Psychology, University of Maryland, College Park, Maryland, USA
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Abi-Dargham A, Moeller SJ, Ali F, DeLorenzo C, Domschke K, Horga G, Jutla A, Kotov R, Paulus MP, Rubio JM, Sanacora G, Veenstra-VanderWeele J, Krystal JH. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023; 22:236-262. [PMID: 37159365 PMCID: PMC10168176 DOI: 10.1002/wps.21078] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/08/2023] [Indexed: 05/11/2023] Open
Abstract
The field of psychiatry is hampered by a lack of robust, reliable and valid biomarkers that can aid in objectively diagnosing patients and providing individualized treatment recommendations. Here we review and critically evaluate the evidence for the most promising biomarkers in the psychiatric neuroscience literature for autism spectrum disorder, schizophrenia, anxiety disorders and post-traumatic stress disorder, major depression and bipolar disorder, and substance use disorders. Candidate biomarkers reviewed include various neuroimaging, genetic, molecular and peripheral assays, for the purposes of determining susceptibility or presence of illness, and predicting treatment response or safety. This review highlights a critical gap in the biomarker validation process. An enormous societal investment over the past 50 years has identified numerous candidate biomarkers. However, to date, the overwhelming majority of these measures have not been proven sufficiently reliable, valid and useful to be adopted clinically. It is time to consider whether strategic investments might break this impasse, focusing on a limited number of promising candidates to advance through a process of definitive testing for a specific indication. Some promising candidates for definitive testing include the N170 signal, an event-related brain potential measured using electroencephalography, for subgroup identification within autism spectrum disorder; striatal resting-state functional magnetic resonance imaging (fMRI) measures, such as the striatal connectivity index (SCI) and the functional striatal abnormalities (FSA) index, for prediction of treatment response in schizophrenia; error-related negativity (ERN), an electrophysiological index, for prediction of first onset of generalized anxiety disorder, and resting-state and structural brain connectomic measures for prediction of treatment response in social anxiety disorder. Alternate forms of classification may be useful for conceptualizing and testing potential biomarkers. Collaborative efforts allowing the inclusion of biosystems beyond genetics and neuroimaging are needed, and online remote acquisition of selected measures in a naturalistic setting using mobile health tools may significantly advance the field. Setting specific benchmarks for well-defined target application, along with development of appropriate funding and partnership mechanisms, would also be crucial. Finally, it should never be forgotten that, for a biomarker to be actionable, it will need to be clinically predictive at the individual level and viable in clinical settings.
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Affiliation(s)
- Anissa Abi-Dargham
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Scott J Moeller
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Farzana Ali
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | - Katharina Domschke
- Department of Psychiatry and Psychotherapy, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Centre for Basics in Neuromodulation, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Guillermo Horga
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Amandeep Jutla
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - Roman Kotov
- Renaissance School of Medicine at Stony Brook University, Stony Brook, NY, USA
| | | | - Jose M Rubio
- Zucker School of Medicine at Hofstra-Northwell, Hempstead, NY, USA
- Feinstein Institute for Medical Research - Northwell, Manhasset, NY, USA
- Zucker Hillside Hospital - Northwell Health, Glen Oaks, NY, USA
| | - Gerard Sanacora
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jeremy Veenstra-VanderWeele
- Department of Psychiatry, Columbia University, New York, NY, USA
- New York State Psychiatric Institute, New York, NY, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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36
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Zhang J, Fang S, Yao Y, Li F, Luo Q. Parsing the heterogeneity of brain-symptom associations in autism spectrum disorder via random forest with homogeneous canonical correlation. J Affect Disord 2023; 335:36-43. [PMID: 37156272 DOI: 10.1016/j.jad.2023.04.102] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/16/2023] [Accepted: 04/28/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a highly heterogeneous developmental disorder, but the neuroimaging substrates of its heterogeneity remain unknown. The difficulty lies mainly on the significant individual variability in the brain-symptom association. METHODS T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Database Exchange (ABIDE) (NTDC = 1146) were used to generate a normative model to map brain structure deviations of cases (NASD = 571). Voxel-based morphometry (VBM) was used to compute gray matter volume (GMV). Singular Value Decomposition (SVD) was employed to perform dimensionality reduction. A tree-based algorithm was proposed to identify the ASD subtypes according to the pattern of brain-symptom association as assessed by a homogeneous canonical correlation. RESULTS We identified 4 ASD subtypes with distinct association patterns between residual volumes and a social symptom score. More severe the social symptom was associated with greater GMVs in both the frontoparietal regions for the subtype1 (r = 0.29-0.44) and the ventral visual pathway for the subtype3 (r = 0.19-0.23), but lower GMVs in both the right anterior cingulate cortex for the subtype4 (r = -0.25) and a few subcortical regions for the subtype2 (r = -0.31-0.20). The subtyping significantly improved the classification accuracy between cases and controls from 0.64 to 0.75 (p < 0.05, permutation test), which was also better than the accuracy of 0.68 achieved by the k-means-based subtyping (p < 0.01). LIMITATIONS Sample size limited the study due to the missing data. CONCLUSIONS These findings suggest that the heterogeneity of ASD might reflect changes in different subsystems of the social brain, especially including social attention, motivation, perceiving and evaluation.
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Affiliation(s)
- Jiajun Zhang
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, School of Life Sciences, Fudan University, Shanghai 200433, PR China
| | - Shuanfeng Fang
- Department of Children Health Care, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou 450007, PR China
| | - Yin Yao
- Department of Computational Biology, School of Life Sciences, Fudan University, PR China
| | - Fei Li
- Developmental and Behavioral Pediatric Department & Child Primary Care Department, Ministry of Education Key Laboratory for Children's Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, PR China
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, School of Life Sciences, Fudan University, Shanghai 200433, PR China; Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Human Phenome Institute, Fudan University, Shanghai 200032, China.
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Wang S, Li X. A revisit of the amygdala theory of autism: Twenty years after. Neuropsychologia 2023; 183:108519. [PMID: 36803966 PMCID: PMC10824605 DOI: 10.1016/j.neuropsychologia.2023.108519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 01/23/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.
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Affiliation(s)
- Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
| | - Xin Li
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
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Nakai N, Sato M, Yamashita O, Sekine Y, Fu X, Nakai J, Zalesky A, Takumi T. Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism. Cell Rep 2023; 42:112258. [PMID: 36990094 DOI: 10.1016/j.celrep.2023.112258] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/16/2023] [Accepted: 02/28/2023] [Indexed: 03/30/2023] Open
Abstract
Functional connectivity (FC) can provide insight into cortical circuit dysfunction in neuropsychiatric disorders. However, dynamic changes in FC related to locomotion with sensory feedback remain to be elucidated. To investigate FC dynamics in locomoting mice, we develop mesoscopic Ca2+ imaging with a virtual reality (VR) environment. We find rapid reorganization of cortical FC in response to changing behavioral states. By using machine learning classification, behavioral states are accurately decoded. We then use our VR-based imaging system to study cortical FC in a mouse model of autism and find that locomotion states are associated with altered FC dynamics. Furthermore, we identify FC patterns involving the motor area as the most distinguishing features of the autism mice from wild-type mice during behavioral transitions, which might correlate with motor clumsiness in individuals with autism. Our VR-based real-time imaging system provides crucial information to understand FC dynamics linked to behavioral abnormality of neuropsychiatric disorders.
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Affiliation(s)
- Nobuhiro Nakai
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, Kobe 650-0017, Japan
| | - Masaaki Sato
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Neuropharmacology, Hokkaido University Graduate School of Medicine, Kita, Sapporo 060-8638, Japan.
| | - Okito Yamashita
- RIKEN Center for Advanced Intelligence Project, Chuo, Tokyo 103-0027, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, Seika, Kyoto 619-0288, Japan
| | - Yukiko Sekine
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Xiaochen Fu
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan
| | - Junichi Nakai
- Division of Oral Physiology, Department of Disease Management Dentistry, Tohoku University Graduate School of Dentistry, Aoba, Sendai 980-8575, Japan
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Toru Takumi
- RIKEN Brain Science Institute, Wako, Saitama 351-0198, Japan; Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, Kobe 650-0017, Japan; RIKEN Center for Biosystems Dynamics Research, Chuo, Kobe 650-0047, Japan.
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Cong J, Zhuang W, Liu Y, Yin S, Jia H, Yi C, Chen K, Xue K, Li F, Yao D, Xu P, Zhang T. Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis. Hum Brain Mapp 2023; 44:2279-2293. [PMID: 36661190 PMCID: PMC10028659 DOI: 10.1002/hbm.26209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/26/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo-parietal junction showed more inter-regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In-Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter-regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.
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Affiliation(s)
- Jing Cong
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Yunhong Liu
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Shunjie Yin
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Hai Jia
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Kai Chen
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, Chengdu, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, Chengdu, China
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Duroux D, Van Steen K. netANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA. Brief Bioinform 2023; 24:bbad029. [PMID: 36738256 PMCID: PMC10025436 DOI: 10.1093/bib/bbad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 01/02/2023] [Accepted: 01/12/2023] [Indexed: 02/05/2023] Open
Abstract
Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these issues, we developed an unsupervised workflow to identify groups of graphs from reliable network-based statistics. In particular, we first compute the similarity between networks via appropriate distance measures between graphs and use them in an unsupervised hierarchical algorithm to identify classes of similar networks. Then, to determine the optimal number of clusters, we recursively test for distances between two groups of networks. The test itself finds its inspiration in distance-wise ANOVA algorithms. Finally, we assess significance via the permutation of between-object distance matrices. Notably, the approach, which we will call netANOVA, is flexible since users can choose multiple options to adapt to specific contexts and network types. We demonstrate the benefits and pitfalls of our approach via extensive simulations and an application to two real-life datasets. NetANOVA achieved high performance in many simulation scenarios while controlling type I error. On non-synthetic data, comparison against state-of-the-art methods showed that netANOVA is often among the top performers. There are many application fields, including precision medicine, for which identifying disease subtypes via individual-level biological networks improves prevention programs, diagnosis and disease monitoring.
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Affiliation(s)
- Diane Duroux
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, Belgium
| | - Kristel Van Steen
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, Belgium
- BIO3 - Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, Belgium
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Vandewouw MM, Brian J, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data. JAMA Netw Open 2023; 6:e232066. [PMID: 36912839 PMCID: PMC10011941 DOI: 10.1001/jamanetworkopen.2023.2066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/14/2023] Open
Abstract
IMPORTANCE Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings. OBJECTIVE To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets. DESIGN, SETTING, AND PARTICIPANTS This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study. MAIN OUTCOMES AND MEASURES The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested. RESULTS Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set. CONCLUSIONS AND RELEVANCE The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.
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Affiliation(s)
- Marlee M. Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Brian
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell J. Schachar
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert Nicolson
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Muhammad Ayub
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Margot J. Taylor
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Jason P. Lerch
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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Donovan K, Tustison NJ, Linn KA, Shinohara RT. Multivariate Residualization in Medical Imaging Analysis. bioRxiv 2023:2023.02.15.528657. [PMID: 36824801 PMCID: PMC9949070 DOI: 10.1101/2023.02.15.528657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Nuisance variables in medical imaging research are common, complicating association and prediction studies based on image data. Medical image data are typically high dimensional, often consisting of many highly correlated features. As a result, computationally efficient and robust methods to address nuisance variables are difficult to implement. By-region univariate residualization is commonly used to remove the influence of nuisance variables, as are various extensions. However, these methods neglect multivariate properties and may fail to fully remove influence related to the joint distribution of these regions. Some methods, such as functional regression and others, do consider multivariate properties when controlling for nuisance variables. However, the utility of these methods is limited for data with many image regions due to computational and model complexity. We develop a multivariate residualization method to estimate the association between the image and nuisance variable using a machine learning algorithm and then compute the orthogonal projection of each subject's image data onto this space. We illustrate this method's performance in a set of simulation studies and apply it to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
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Hong J, Hwang J, Lee JH. General psychopathology factor (p-factor) prediction using resting-state functional connectivity and a scanner-generalization neural network. J Psychiatr Res 2023; 158:114-125. [PMID: 36580867 DOI: 10.1016/j.jpsychires.2022.12.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/09/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022]
Abstract
The general psychopathology factor (p-factor) represents shared variance across mental disorders based on psychopathologic symptoms. The Adolescent Brain Cognitive Development (ABCD) Study offers an unprecedented opportunity to investigate functional networks (FNs) from functional magnetic resonance imaging (fMRI) associated with the psychopathology of an adolescent cohort (n > 10,000). However, the heterogeneities associated with the use of multiple sites and multiple scanners in the ABCD Study need to be overcome to improve the prediction of the p-factor using fMRI. We proposed a scanner-generalization neural network (SGNN) to predict the individual p-factor by systematically reducing the scanner effect for resting-state functional connectivity (RSFC). We included 6905 adolescents from 18 sites whose fMRI data were collected using either Siemens or GE scanners. The p-factor was estimated based on the Child Behavior Checklist (CBCL) scores available in the ABCD study using exploratory factor analysis. We evaluated the Pearson's correlation coefficients (CCs) for p-factor prediction via leave-one/two-site-out cross-validation (LOSOCV/LTSOCV) and identified important FNs from the weight features (WFs) of the SGNN. The CCs were higher for the SGNN than for alternative models when using both LOSOCV (0.1631 ± 0.0673 for the SGNN vs. 0.1497 ± 0.0710 for kernel ridge regression [KRR]; p < 0.05 from a two-tailed paired t-test) and LTSOCV (0.1469 ± 0.0381 for the SGNN vs. 0.1394 ± 0.0359 for KRR; p = 0.01). It was found that (a) the default-mode and dorsal attention FNs were important for p-factor prediction, and (b) the intra-visual FN was important for scanner generalization. We demonstrated the efficacy of our novel SGNN model for p-factor prediction while simultaneously eliminating scanner-related confounding effects for RSFC.
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Affiliation(s)
- Jinwoo Hong
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Jundong Hwang
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
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Fittipaldi S, Armony JL, García AM, Migeot J, Cadaveira M, Ibáñez A, Baez S. Emotional descriptions increase accidental harm punishment and its cortico-limbic signatures during moral judgment in autism. Sci Rep 2023; 13:1745. [PMID: 36720905 DOI: 10.1038/s41598-023-27709-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 01/06/2023] [Indexed: 02/01/2023] Open
Abstract
Individuals with autism spectrum disorder (ASD) present difficulties in integrating mental state information in complex moral tasks. Yet, ASD research has not examined whether this process is influenced by emotions, let alone while capturing its neural bases. We investigated how language-induced emotions modulate intent-based moral judgment in ASD. In a fMRI task, 30 adults with ASD and 27 neurotypical controls read vignettes whose protagonists commit harm either accidentally or intentionally, and then decided how much punishment the protagonist deserved. Emotional content was manipulated across scenarios through the use of graphic language (designed to trigger arousing negative responses) vs. plain (just-the-facts, emotionless) language. Off-line functional connectivity correlates of task performance were also analyzed. In ASD, emotional (graphic) descriptions amplified punishment ratings of accidental harms, associated with increased activity in fronto-temporo-limbic, precentral, and postcentral/supramarginal regions (critical for emotional and empathic processes), and reduced connectivity among the orbitofrontal cortex and the angular gyrus (involved in mentalizing). Language manipulation did not influence intentional harm processing in ASD. In conclusion, in arousing and ambiguous social situations that lack intentionality clues (i.e. graphic accidental harm scenarios), individuals with ASD would misuse their emotional responses as the main source of information to guide their moral decisions. Conversely, in face of explicit harmful intentions, they would be able to compensate their socioemotional alterations and assign punishment through non-emotional pathways. Despite limitations, such as the small sample size and low ecological validity of the task, results of the present study proved reliable and have relevant theoretical and translational implications.
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Sun S, Yu H, Yu R, Wang S. Functional connectivity between the amygdala and prefrontal cortex underlies processing of emotion ambiguity. bioRxiv 2023:2023.01.24.525116. [PMID: 36747862 PMCID: PMC9900805 DOI: 10.1101/2023.01.24.525116] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Processing facial expressions of emotion draws on a distributed brain network. In particular, judging ambiguous facial emotions involves coordination between multiple brain areas. Here, we applied multimodal functional connectivity analysis to achieve network-level understanding of the neural mechanisms underlying perceptual ambiguity in facial expressions. We found directional effective connectivity between the amygdala, dorsomedial prefrontal cortex (dmPFC), and ventromedial PFC, supporting both bottom-up affective processes for ambiguity representation/perception and top-down cognitive processes for ambiguity resolution/decision. Direct recordings from the human neurosurgical patients showed that the responses of amygdala and dmPFC neurons were modulated by the level of emotion ambiguity, and amygdala neurons responded earlier than dmPFC neurons, reflecting the bottom-up process for ambiguity processing. We further found parietal-frontal coherence and delta-alpha cross-frequency coupling involved in encoding emotion ambiguity. We replicated the EEG coherence result using independent experiments and further showed modulation of the coherence. EEG source connectivity revealed that the dmPFC top-down regulated the activities in other brain regions. Lastly, we showed altered behavioral responses in neuropsychiatric patients who may have dysfunctions in amygdala-PFC functional connectivity. Together, using multimodal experimental and analytical approaches, we have delineated a neural network that underlies processing of emotion ambiguity. Significance Statement A large number of different brain regions participate in emotion processing. However, it remains elusive how these brain regions interact and coordinate with each other and collectively encode emotions, especially when the task requires orchestration between different brain areas. In this study, we employed multimodal approaches that well complemented each other to comprehensively study the neural mechanisms of emotion ambiguity. Our results provided a systematic understanding of the amygdala-PFC network underlying emotion ambiguity with fMRI-based connectivity, EEG coordination of cortical regions, synchronization of brain rhythms, directed information flow of the source signals, and latency of single-neuron responses. Our results further shed light on neuropsychiatric patients who have abnormal amygdala-PFC connectivity.
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Desaunay P, Guillery B, Moussaoui E, Eustache F, Bowler DM, Guénolé F. Brain correlates of declarative memory atypicalities in autism: a systematic review of functional neuroimaging findings. Mol Autism 2023; 14:2. [PMID: 36627713 PMCID: PMC9832704 DOI: 10.1186/s13229-022-00525-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 11/29/2022] [Indexed: 01/11/2023] Open
Abstract
The long-described atypicalities of memory functioning experienced by people with autism have major implications for daily living, academic learning, as well as cognitive remediation. Though behavioral studies have identified a robust profile of memory strengths and weaknesses in autism spectrum disorder (ASD), few works have attempted to establish a synthesis concerning their neural bases. In this systematic review of functional neuroimaging studies, we highlight functional brain asymmetries in three anatomical planes during memory processing between individuals with ASD and typical development. These asymmetries consist of greater activity of the left hemisphere than the right in ASD participants, of posterior brain regions-including hippocampus-rather than anterior ones, and presumably of the ventral (occipito-temporal) streams rather than the dorsal (occipito-parietal) ones. These functional alterations may be linked to atypical memory processes in ASD, including the pre-eminence of verbal over spatial information, impaired active maintenance in working memory, and preserved relational memory despite poor context processing in episodic memory.
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Affiliation(s)
- Pierre Desaunay
- grid.411149.80000 0004 0472 0160Service de Psychiatrie de l’Enfant et de l’Adolescent, CHU de Caen Normandie, 27 rue des compagnons, 14000 Caen, France ,grid.412043.00000 0001 2186 4076EPHE, INSERM, U1077, Pôle des Formations et de Recherche en Santé, CHU de Caen Normandie, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Univ, UNICAEN, PSL Research University, 2 rue des Rochambelles, 14032 Caen Cedex CS, France
| | - Bérengère Guillery
- grid.412043.00000 0001 2186 4076EPHE, INSERM, U1077, Pôle des Formations et de Recherche en Santé, CHU de Caen Normandie, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Univ, UNICAEN, PSL Research University, 2 rue des Rochambelles, 14032 Caen Cedex CS, France
| | - Edgar Moussaoui
- grid.411149.80000 0004 0472 0160Service de Psychiatrie de l’Enfant et de l’Adolescent, CHU de Caen Normandie, 27 rue des compagnons, 14000 Caen, France
| | - Francis Eustache
- grid.412043.00000 0001 2186 4076EPHE, INSERM, U1077, Pôle des Formations et de Recherche en Santé, CHU de Caen Normandie, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Univ, UNICAEN, PSL Research University, 2 rue des Rochambelles, 14032 Caen Cedex CS, France
| | - Dermot M. Bowler
- grid.28577.3f0000 0004 1936 8497Autism Research Group, City University of London, DG04 Rhind Building, Northampton Square, EC1V 0HB London, UK
| | - Fabian Guénolé
- grid.411149.80000 0004 0472 0160Service de Psychiatrie de l’Enfant et de l’Adolescent, CHU de Caen Normandie, 27 rue des compagnons, 14000 Caen, France ,grid.412043.00000 0001 2186 4076EPHE, INSERM, U1077, Pôle des Formations et de Recherche en Santé, CHU de Caen Normandie, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Normandie Univ, UNICAEN, PSL Research University, 2 rue des Rochambelles, 14032 Caen Cedex CS, France ,grid.412043.00000 0001 2186 4076Faculté de Médecine, Pôle des Formation et de Recherche en Santé, Université de Caen Normandie, 2 rue des Rochambelles, 14032 Caen cedex CS, France
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ElNakieb Y, Ali MT, Elnakib A, Shalaby A, Mahmoud A, Soliman A, Barnes GN, El-Baz A. Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study. Bioengineering (Basel) 2023; 10:bioengineering10010056. [PMID: 36671628 PMCID: PMC9855190 DOI: 10.3390/bioengineering10010056] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 12/22/2022] [Accepted: 12/27/2022] [Indexed: 01/04/2023]
Abstract
In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.
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Affiliation(s)
- Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Mohamed T. Ali
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Soliman
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Gregory Neal Barnes
- Department of Neurology, Pediatric Research Institute, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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48
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Linke AC, Chen B, Olson L, Ibarra C, Fong C, Reynolds S, Apostol M, Kinnear M, Müller RA, Fishman I. Sleep Problems in Preschoolers With Autism Spectrum Disorder Are Associated With Sensory Sensitivities and Thalamocortical Overconnectivity. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:21-31. [PMID: 34343726 PMCID: PMC9826645 DOI: 10.1016/j.bpsc.2021.07.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/08/2021] [Accepted: 07/21/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND Projections between the thalamus and sensory cortices are established early in development and play an important role in regulating sleep as well as in relaying sensory information to the cortex. Atypical thalamocortical functional connectivity frequently observed in children with autism spectrum disorder (ASD) might therefore be linked to sensory and sleep problems common in ASD. METHODS Here, we investigated the relationship between auditory-thalamic functional connectivity measured during natural sleep functional magnetic resonance imaging, sleep problems, and sound sensitivities in 70 toddlers and preschoolers (1.5-5 years old) with ASD compared with a matched group of 46 typically developing children. RESULTS In children with ASD, sleep problems and sensory sensitivities were positively correlated, and increased sleep latency was associated with overconnectivity between the thalamus and auditory cortex in a subsample with high-quality magnetic resonance imaging data (n = 29). In addition, auditory cortex blood oxygen level-dependent signal amplitude was elevated in children with ASD, potentially reflecting reduced sensory gating or a lack of auditory habituation during natural sleep. CONCLUSIONS These findings indicate that atypical thalamocortical functional connectivity can be detected early in development and may play a crucial role in sleep problems and sensory sensitivities in ASD.
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Affiliation(s)
- Annika Carola Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.
| | - Bosi Chen
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Lindsay Olson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Cynthia Ibarra
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Chris Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California
| | - Sarah Reynolds
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Michael Apostol
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Mikaela Kinnear
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; SDSU Center for Autism and Developmental Disorders, San Diego, California
| | - Inna Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California; San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California; SDSU Center for Autism and Developmental Disorders, San Diego, California
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49
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Randeniya R, Vilares I, Mattingley JB, Garrido MI. Increased functional activity, bottom-up and intrinsic effective connectivity in autism. Neuroimage Clin 2023; 37:103293. [PMID: 36527995 PMCID: PMC9791168 DOI: 10.1016/j.nicl.2022.103293] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/17/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022]
Abstract
Sensory perceptual alterations such as sensory sensitivities in autism have been proposed to be caused by differences in sensory observation (Likelihood) or in forming models of the environment (Prior), which result in an increase in bottom-up information flow relative to top-down control. To investigate this conjecture, we had autistic individuals (AS) and neurotypicals (NT) perform a decision-under-uncertainty paradigm while undergoing functional magnetic resonance imaging (fMRI). There were no group differences in task performance and in Prior and Likelihood representations in brain activity. However, there were significant group differences in overall task activity, with the AS group showing significantly greater activation in the bilateral precuneus, mid-occipital gyrus, cuneus, superior frontal gyrus (SFG) and left putamen relative to the NT group. Further, when pooling the data across both groups, we found that those with higher AQ scores showed greater activity in the left cuneus and precuneus. Effective connectivity analysis using dynamic causal modelling (DCM) revealed that group differences in BOLD signals were underpinned by increased activity within sensory regions and a net increase in bottom-up connectivity from the occipital region to the precuneus and the left SFG. These findings support the hypothesis of increased bottom-up information flow in autism during sensory learning tasks.
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Affiliation(s)
- R Randeniya
- Queensland Brain Institute, The University of Queensland, Australia.
| | - I Vilares
- Department of Psychology, University of Minnesota, USA
| | - J B Mattingley
- Queensland Brain Institute, The University of Queensland, Australia; School of Psychology, The University of Queensland, Australia; Canadian Institute for Advanced Research (CIFAR), Canada; Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
| | - M I Garrido
- Melbourne School of Psychological Sciences, University of Melbourne, Australia; Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
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50
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Lu Z, Wang J, Mao R, Lu M, Shi J. Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems. IEEE/ACM Trans Comput Biol Bioinform 2023; 20:476-488. [PMID: 35349448 DOI: 10.1109/tcbb.2022.3163140] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Autism spectrum disorder (ASD) is characterized by poor social communication abilities and repetitive behaviors or restrictive interests, which has brought a heavy burden to families and society. In many attempts to understand ASD neurobiology, resting-state functional magnetic resonance imaging (rs-fMRI) has been an effective tool. However, current ASD diagnosis methods based on rs-fMRI have two major defects. First, the instability of rs-fMRI leads to functional connectivity (FC) uncertainty, affecting the performance of ASD diagnosis. Second, many FCs are involved in brain activity, making it difficult to determine effective features in ASD classification. In this study, we propose an interpretable ASD classifier DeepTSK, which combines a multi-output Takagi-Sugeno-Kang (MO-TSK) fuzzy inference system (FIS) for composite feature learning and a deep belief network (DBN) for ASD classification in a unified network. To avoid the suboptimal solution of DeepTSK, a joint optimization procedure is employed to simultaneously learn the parameters of MO-TSK and DBN. The proposed DeepTSK was evaluated on datasets collected from three sites of the Autism Brain Imaging Data Exchange (ABIDE) database. The experimental results showed the effectiveness of the proposed method, and the discriminant FCs are presented by analyzing the consequent parameters of Deep MO-TSK.
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