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Resting-state neuroimaging in social anxiety disorder: a systematic review. Mol Psychiatry 2022; 27:164-179. [PMID: 34035474 DOI: 10.1038/s41380-021-01154-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 04/21/2021] [Accepted: 04/30/2021] [Indexed: 02/04/2023]
Abstract
There has been a growing interest in resting-state brain alterations in people with social anxiety disorder. However, the evidence has been mixed and contested and further understanding of the neurobiology of this disorder may aid in informing methods to increase diagnostic accuracy and treatment targets. With this systematic review, we aimed to synthesize the findings of the neuroimaging literature on resting-state functional activity and connectivity in social anxiety disorder, and to summarize associations between brain and social anxiety symptoms to further characterize the neurobiology of the disorder. We systematically searched seven databases for empirical research studies. Thirty-five studies met the inclusion criteria, with a total of 1611 participants (795 people with social anxiety disorder and 816 controls). Studies involving resting-state seed-based functional connectivity analyses were the most common. Individuals with social anxiety disorder (vs. controls) displayed both higher and lower connectivity between frontal-amygdala and frontal-parietal regions. Frontal regions were the most consistently implicated across other analysis methods, and most associated with social anxiety symptoms. Small sample sizes and variation in the types of analyses used across studies may have contributed to the inconsistencies in the findings of this review. This review provides novel insights into established neurobiological models of social anxiety disorder and provides an update on what is known about the neurobiology of this disorder in the absence of any overt tasks (i.e., resting state). The knowledge gained from this body of research enabled us to also provide recommendations for a more standardized imaging pre-processing approach to examine resting-state brain activity and connectivity that could help advance knowledge in this field. We believe this is warranted to take the next step toward clinical translation in social anxiety disorder that may lead to better treatment outcomes by informing the identification of neurobiological targets for treatment.
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102
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Chouhan T, Black MH, Girdler S, Bölte S, Tan T, Guan C. Altered task induced functional brain networks and small-world properties in autism. Front Psychiatry 2022; 13:1039820. [PMID: 36741564 PMCID: PMC9893112 DOI: 10.3389/fpsyt.2022.1039820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/29/2022] [Indexed: 01/21/2023] Open
Abstract
INTRODUCTION Facial emotion recognition (FER) requires the integration of multi-dimensional information across various brain regions. Autistic individuals commonly experience difficulties in FER, a phenomenon often attributed to differences in brain connectivity. The nature of task-induced functional brain networks could provide insight into the neuromechanisms underlying FER difficulties in autism, however, to date, these mechanisms remain poorly understood. METHODS In this study, the task induced functional brain networks of 19 autistic and 19 gender, age, and IQ matched non-autistic individuals were examined during a complex FER task. Electroencephalogram (EEG)-based functional brain networks were examined, including the investigation of differences in the time-varying whole-brain functional networks and the exploration of the task induced small-world properties. RESULTS The results showed statistically significant differences in the task-induced functional networks between autistic and non-autistic adults. Autistic adults compared to non-autistic adults showed a significant shift in the connectivity-based FER processing from the lower to the higher EEG frequency bands. DISCUSSION These findings may provide evidence at a neural level for the notion that autistic individuals have a preference for bottom-up lower-level processing, or alterations in top-down global processing, potentially contributing to the FER difficulties observed in this population. Results also suggest that functional brain networks in autism show significantly altered task-induced whole-brain small-world properties as compared to non-autistic individuals during complex FER. This study motivates further investigation of the underlying networks-basis of altered emotion processing in autism.
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Affiliation(s)
- Tushar Chouhan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Melissa H Black
- School of Allied Health, Curtin University, Perth, WA, Australia.,Curtin Autism Research Group, Curtin University, Perth, WA, Australia.,Cooperative Research Centre for Living With Autism (Autism CRC), Brisbane, QLD, Australia.,Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Sonya Girdler
- School of Allied Health, Curtin University, Perth, WA, Australia.,Curtin Autism Research Group, Curtin University, Perth, WA, Australia.,Cooperative Research Centre for Living With Autism (Autism CRC), Brisbane, QLD, Australia.,Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,School of Allied Health, University of Western Australia, Perth, WA, Australia
| | - Sven Bölte
- School of Allied Health, Curtin University, Perth, WA, Australia.,Curtin Autism Research Group, Curtin University, Perth, WA, Australia.,Cooperative Research Centre for Living With Autism (Autism CRC), Brisbane, QLD, Australia.,Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden
| | - Tele Tan
- Curtin Autism Research Group, Curtin University, Perth, WA, Australia.,Cooperative Research Centre for Living With Autism (Autism CRC), Brisbane, QLD, Australia.,School of Mechanical Engineering, Curtin University, Perth, WA, Australia
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
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103
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Bathelt J, Koolschijn PCM, Geurts HM. Atypically slow processing of faces and non-faces in older autistic adults. AUTISM : THE INTERNATIONAL JOURNAL OF RESEARCH AND PRACTICE 2021; 26:1737-1751. [PMID: 34961340 PMCID: PMC9483195 DOI: 10.1177/13623613211065297] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Face recognition is a fundamental function that requires holistic processing. Differences in face processing have been consistently identified in autistic children, but it is unknown whether these differences persist across the adult lifespan. Using event-related functional magnetic resonance imaging, we measured holistic face processing with a rapid Mooney faces task in 50 autistic and 49 non-autistic participants (30–74 years). Behavioral tasks included a self-paced version of the same paradigm and a global–local processing task (Navon). Reduced detection rates for faces, but not non-faces, were found in autistic adults, including slower responses on all conditions. Without time constraints, differences in accuracy disappeared between groups, although reaction times in correctly identifying faces remained higher in autistic adults. The functional magnetic resonance imaging results showed lower activation in the left and right superior frontal gyrus in the autism group but no age-related differences. Overall, our findings point toward slower information processing speed rather than a face recognition deficit in autistic adults. This suggests that face-processing differences are not a core feature of autism across the adult lifespan.
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Affiliation(s)
- Joe Bathelt
- University of Amsterdam, The Netherlands.,Royal Holloway, University of London, UK
| | | | - Hilde M Geurts
- University of Amsterdam, The Netherlands.,Leo Kannerhuis, Youz/Parnassiagroup, The Netherlands
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104
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Reardon AM, Li K, Hu XP. Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning. Front Comput Neurosci 2021; 15:762781. [PMID: 34924984 PMCID: PMC8674307 DOI: 10.3389/fncom.2021.762781] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 11/04/2021] [Indexed: 11/18/2022] Open
Abstract
Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.
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Affiliation(s)
- Alexandra M Reardon
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Kaiming Li
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States
| | - Xiaoping P Hu
- Department of Bioengineering, University of California, Riverside, Riverside, CA, United States.,Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States
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105
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Okamoto N, Akama H. Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling. Front Neuroinform 2021; 15:709179. [PMID: 34924987 PMCID: PMC8671136 DOI: 10.3389/fninf.2021.709179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 10/29/2021] [Indexed: 11/13/2022] Open
Abstract
Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by transfer learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility.
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Affiliation(s)
- Naoki Okamoto
- School of Life Sciences and Technology, Tokyo Institute of Technology, Tokyo, Japan
| | - Hiroyuki Akama
- School of Life Sciences and Technology, Tokyo Institute of Technology, Tokyo, Japan.,Institute for Liberal Arts, Tokyo Institute of Technology, Tokyo, Japan
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106
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Resting state network connectivity is attenuated by fMRI acoustic noise. Neuroimage 2021; 247:118791. [PMID: 34920084 DOI: 10.1016/j.neuroimage.2021.118791] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/21/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal. METHODS Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity. RESULTS fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity. DISCUSSION rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.
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107
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Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Trans Biomed Eng 2021; 68:3628-3637. [PMID: 33989150 PMCID: PMC8696194 DOI: 10.1109/tbme.2021.3080259] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized. METHODS We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data. RESULTS Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism. CONCLUSION Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. SIGNIFICANCE ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
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108
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Zerbi V, Pagani M, Markicevic M, Matteoli M, Pozzi D, Fagiolini M, Bozzi Y, Galbusera A, Scattoni ML, Provenzano G, Banerjee A, Helmchen F, Basson MA, Ellegood J, Lerch JP, Rudin M, Gozzi A, Wenderoth N. Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes. Mol Psychiatry 2021; 26:7610-7620. [PMID: 34381171 PMCID: PMC8873017 DOI: 10.1038/s41380-021-01245-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 06/30/2021] [Accepted: 07/20/2021] [Indexed: 02/07/2023]
Abstract
Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modeled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.
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Affiliation(s)
- V Zerbi
- Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
| | - M Pagani
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - M Markicevic
- Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
| | - M Matteoli
- Laboratory of Pharmacology and Brain Pathology, Neurocenter, Humanitas Clinical and Research Center - IRCCS, Rozzano, Mi, Italy
- CNR Institute of Neuroscience, Milano, Italy
| | - D Pozzi
- Laboratory of Pharmacology and Brain Pathology, Neurocenter, Humanitas Clinical and Research Center - IRCCS, Rozzano, Mi, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - M Fagiolini
- F.M. Kirby Neurobiology Department, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Y Bozzi
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy
| | - A Galbusera
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy
| | - M L Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Rome, Italy
| | - G Provenzano
- Department of Cellular, Computational and Integrative Biology. (CIBIO), University of Trento, Trento, Italy
| | - A Banerjee
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - F Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - M A Basson
- Centre for Craniofacial and Regenerative Biology, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College, London, London, UK
| | - J Ellegood
- Mouse Imaging Ctr., Hosp. For Sick Children, Toronto, ON, Canada
| | - J P Lerch
- Mouse Imaging Ctr., Hosp. For Sick Children, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - M Rudin
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - A Gozzi
- Functional Neuroimaging Lab, Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy.
| | - N Wenderoth
- Neural Control of Movement Lab, ETH Zurich, Zurich, Switzerland
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109
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Duda M, Koutra D, Sripada C. Validating dynamicity in resting state fMRI with activation-informed temporal segmentation. Hum Brain Mapp 2021; 42:5718-5735. [PMID: 34510647 PMCID: PMC8559473 DOI: 10.1002/hbm.25649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/11/2021] [Accepted: 08/24/2021] [Indexed: 12/18/2022] Open
Abstract
Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time-varying FC in rest.
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Affiliation(s)
- Marlena Duda
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Danai Koutra
- Department of Computer Science and EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
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110
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McPartland JC, Lerner MD, Bhat A, Clarkson T, Jack A, Koohsari S, Matuskey D, McQuaid GA, Su WC, Trevisan DA. Looking Back at the Next 40 Years of ASD Neuroscience Research. J Autism Dev Disord 2021; 51:4333-4353. [PMID: 34043128 PMCID: PMC8542594 DOI: 10.1007/s10803-021-05095-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/14/2021] [Indexed: 12/18/2022]
Abstract
During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.
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Affiliation(s)
| | - Matthew D Lerner
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Anjana Bhat
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
| | - Tessa Clarkson
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Sheida Koohsari
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - David Matuskey
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Goldie A McQuaid
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Wan-Chun Su
- Department of Physical Therapy, University of Delaware, Newark, DE, USA
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111
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Wang J, Zhang F, Jia X, Wang X, Zhang H, Ying S, Wang Q, Shi J, Shen D. Multi-Class ASD Classification via Label Distribution Learning with Class-Shared and Class-Specific Decomposition. Med Image Anal 2021; 75:102294. [PMID: 34826797 DOI: 10.1016/j.media.2021.102294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 10/19/2022]
Abstract
The behavioral and cognitive deficits in autism spectrum disorder (ASD) patients are associated with abnormal brain function. The resting-state functional magnetic resonance imaging (rs-fMRI) is an effective non-invasive tool for revealing the brain dysfunction for ASD patients. However, most rs-fMRI based ASD diagnosis methods are developed for simple binary classification, instead of classification of multiple sub-types in ASD. Besides, they assume that the class boundary in ASD classification is crisp, whereas the symptoms of ASD sub-types are a continuum from mild to severe impairments in both social communication and restrictive repetitive behaviors/interests, and do not have crisp boundary between each other. To this end, we introduce label distribution learning (LDL) into multi-class ASD classification and propose LDL-CSCS under the LDL framework. Specifically, the label distribution is introduced to describe how individual disease labels correlate with the subject. In the learning crierion of LDL-CSCS, the label distribution is decomposed into the class-shared and class-specific components, in which the class-shared component records the common knowledge across all persons and the class-specific component records the specific information in each ASD sub-type. Low-rank constraint is imposed on the class-shared component whereas the group sparse constraint is imposed on the class-specific component, respectively. An Augmented Lagrange Method (ALM) is developed to find the optimal solution. The experimental results show that the proposed method for ASD diagnosis has superior classification performance, compared with some existing algorithms.
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Affiliation(s)
- Jun Wang
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Fengyexin Zhang
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China
| | - Xiuyi Jia
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, China
| | - Xin Wang
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Han Zhang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Shihui Ying
- Department of Mathematics, School of Science, Shanghai University, Shanghai 200444, China
| | - Qian Wang
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China
| | - Jun Shi
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
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112
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Abstract
The search for biomarkers for autism spectrum disorder (henceforth autism) has received a lot of attention due to their potential clinical relevance. The clinical and aetiological heterogeneity of autism suggests the presence of subgroups. The lack of identification of a valid diagnostic biomarker for autism, and the inconsistencies seen in studies assessing differences between autism and typically developing control groups, may be partially explained by the vast heterogeneity observed in autism. The focus now is to better understand the clinical and biological heterogeneity and identify stratification biomarkers, which are measures that describe subgroups of individuals with shared biology. Using stratification approaches to assess treatment within pre-defined subgroups could clarify who may benefit from different treatments and therapies, and ultimately lead to more effective individualised treatment plans.
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113
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Estimating brain effective connectivity from EEG signals of patients with autism disorder and healthy individuals by reducing volume conduction effect. Cogn Neurodyn 2021; 16:519-529. [DOI: 10.1007/s11571-021-09730-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/26/2021] [Accepted: 10/02/2021] [Indexed: 10/19/2022] Open
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114
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Liu M, Li B, Hu D. Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review. Front Neurosci 2021; 15:697870. [PMID: 34602966 PMCID: PMC8480393 DOI: 10.3389/fnins.2021.697870] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/09/2021] [Indexed: 01/01/2023] Open
Abstract
Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.
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Affiliation(s)
- Meijie Liu
- Engineering Training Center, Xi'an University of Science and Technology, Xi'an, China.,College of Missile Engineering, Rocket Force University of Engineering, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China
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115
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Mash LE, Linke AC, Gao Y, Wilkinson M, Olson MA, Jao Keehn RJ, Müller RA. Blood Oxygen Level-Dependent Lag Patterns Differ Between Rest and Task Conditions, but Are Largely Typical in Autism. Brain Connect 2021; 12:234-245. [PMID: 34102876 DOI: 10.1089/brain.2020.0910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background/Introduction: Autism spectrum disorder (ASD) is characterized by atypical functional connectivity (FC) within and between distributed brain networks. However, FC findings have often been inconsistent, possibly due to a focus on static FC rather than brain dynamics. Lagged connectivity analyses aim at evaluating temporal latency, and presumably neural propagation, between regions. This approach may, therefore, reveal a more detailed picture of network organization in ASD than traditional FC methods. Methods: The current study evaluated whole-brain lag patterns in adolescents with ASD (n = 28) and their typically developing peers (n = 22). Functional magnetic resonance imaging data were collected during rest and during a lexico-semantic decision task. Optimal lag was calculated for each pair of regions of interest by using cross-covariance, and mean latency projections were calculated for each region. Results: Latency projections did not regionally differ between groups, with the same regions emerging among the "earliest" and "latest." Although many of the longest absolute latencies were preserved across resting-state and task conditions, lag patterns overall were affected by condition, as many regions shifted toward zero-lag during task performance. Lag structure was also strongly associated with literature-derived estimates of arterial transit time. Discussion: Results suggest that lag patterns are broadly typical in ASD but undergo changes during task performance. Moreover, lag patterns appear to reflect a combination of neural and vascular sources, which should be carefully considered when interpreting lagged FC.
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Affiliation(s)
- Lisa E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California, USA
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA
| | - Yangfeifei Gao
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California, USA
| | - Molly Wilkinson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California, USA
| | - Michael A Olson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA
| | - R Joanne Jao Keehn
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, California, USA
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116
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Mo K, Sadoway T, Bonato S, Ameis SH, Anagnostou E, Lerch JP, Taylor MJ, Lai MC. Sex/gender differences in the human autistic brains: A systematic review of 20 years of neuroimaging research. Neuroimage Clin 2021; 32:102811. [PMID: 34509922 PMCID: PMC8436080 DOI: 10.1016/j.nicl.2021.102811] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 06/25/2021] [Accepted: 08/29/2021] [Indexed: 12/01/2022]
Abstract
Our current understanding of autism is largely based on clinical experiences and research involving male individuals given the male-predominance in prevalence and the under-inclusion of female individuals due to small samples, co-occurring conditions, or simply being missed for diagnosis. There is a significantly biased 'male lens' in this field with autistic females insufficiently understood. We therefore conducted a systematic review to examine how sex and gender modulate brain structure and function in autistic individuals. Findings from the past 20 years are yet to converge on specific brain regions/networks with consistent sex/gender-modulating effects. Despite at least three well-powered studies identifying specific patterns of significant sex/gender-modulation of autism-control differences, many other studies are likely underpowered, suggesting a critical need for future investigation into sex/gender-based heterogeneity with better-powered designs. Future research should also formally investigate the effects of gender, beyond biological sex, which is mostly absent in the current literature. Understanding the roles of sex and gender in the development of autism is an imperative step to extend beyond the 'male lens' in this field.
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Affiliation(s)
- Kelly Mo
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Tara Sadoway
- Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, Canada
| | - Sarah Bonato
- Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
| | - Stephanie H Ameis
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Hospital for Sick Children, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Evdokia Anagnostou
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada; Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom; Neurosciences & Mental Health Program, SickKids Research Institute, Toronto, Canada
| | - Margot J Taylor
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, SickKids Research Institute, Toronto, Canada; Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Meng-Chuan Lai
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Hospital for Sick Children, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Neurosciences & Mental Health Program, SickKids Research Institute, Toronto, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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117
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Locked-in Intact Functional Networks in Children with Autism Spectrum Disorder: A Case-Control Study. J Pers Med 2021; 11:jpm11090854. [PMID: 34575631 PMCID: PMC8465896 DOI: 10.3390/jpm11090854] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/25/2021] [Accepted: 08/25/2021] [Indexed: 11/16/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to investigate abnormalities in brain network structure and connectivity on an individual level in neurodevelopmental disorders, such as autism spectrum disorder (ASD), paving the way toward using this technology for a personalized, precision medicine approach to diagnosis and treatment. Using a case-control design, we compared five patients with severe regressive-type ASD to five patients with temporal lobe epilepsy (TLE) to examine the association between brain network characteristics and diagnosis. All children with ASD and TLE demonstrated intact motor, language, and frontoparietal (FP) networks. However, aberrant networks not usually seen in the typical brain were also found. These aberrant networks were located in the motor (40%), language (80%), and FP (100%) regions in children with ASD, while children with TLE only presented with aberrant networks in the motor (40%) and language (20%) regions, in addition to identified seizure onset zones. Fisher's exact test indicated a significant relationship between aberrant FP networks and diagnosis (p = 0.008), with ASD and atypical FP networks co-occurring more frequently than expected by chance. Despite severe cognitive delays, children with regressive-type ASD may demonstrate intact typical cortical network activation despite an inability to use these cognitive facilities. The functions of these intact cognitive networks may not be fully expressed, potentially because aberrant networks interfere with their long-range signaling, thus creating a unique "locked-in network" syndrome.
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118
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Squarcina L, Nosari G, Marin R, Castellani U, Bellani M, Bonivento C, Fabbro F, Molteni M, Brambilla P. Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine. Brain Behav 2021; 11:e2238. [PMID: 34264004 PMCID: PMC8413814 DOI: 10.1002/brb3.2238] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 05/10/2021] [Accepted: 05/23/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core symptoms. We apply support vector machine, a supervised machine learning method, in order to identify specific cortical thickness alterations in ASD subjects. METHODS A sample of 76 subjects (9.5 ± 3.4 years old) has been selected, 40 diagnosed with ASD and 36 typically developed subjects. All children underwent a magnetic resonance imaging (MRI) examination; T1-MPRAGE sequences were analyzed to extract features for the characterization and parcellation of regions of interests (ROI); average cortical thickness (CT) has been measured for each ROI. For the classification process, the extracted features were used as input for a classifier to identify ASD subjects through a "learning by example" procedure; the features with best performance was then selected by "greedy forward-feature selection." Finally, this model underwent a leave-one-out cross-validation approach. RESULTS From the training set of 68 ROIs, five ROIs reached accuracies of over 70%. After this phase, we used a recursive feature selection process in order to identify the eight features with the best accuracy (84.2%). CT resulted higher in ASD compared to controls in all the ROIs identified at the end of the process. CONCLUSION We found increased CT in various brain regions in ASD subjects, confirming their role in the pathogenesis of this condition. Considering the brain development curve during ages, these changes in CT may normalize during development. Further validation on a larger sample is required.
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Affiliation(s)
- Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Guido Nosari
- Department of Pathophysiology and Transplantation, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Riccardo Marin
- Department of Informatics, University of Verona, Verona, Italy
| | | | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Carolina Bonivento
- IRCCS "E. Medea", Polo Friuli Venezia Giulia, San Vito al Tagliamento (PN), Italy
| | - Franco Fabbro
- Department of Medicine, University of Udine, Udine, Italy
| | - Massimo Molteni
- IRCCS "E. Medea", Polo Friuli Venezia Giulia, San Vito al Tagliamento (PN), Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy.,Department of Neurosciences and Mental Health Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via Francesco Sforza 28, 20122 Milan, Italy
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119
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Siegel-Ramsay JE, Romaniuk L, Whalley HC, Roberts N, Branigan H, Stanfield AC, Lawrie SM, Dauvermann MR. Glutamate and functional connectivity - support for the excitatory-inhibitory imbalance hypothesis in autism spectrum disorders. Psychiatry Res Neuroimaging 2021; 313:111302. [PMID: 34030047 DOI: 10.1016/j.pscychresns.2021.111302] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/30/2021] [Accepted: 05/07/2021] [Indexed: 12/24/2022]
Abstract
It has been proposed that the Glutamate (Glu) system is implicated in autism spectrum disorders (ASD) via an imbalance between excitatory and inhibitory brain circuits, which impacts on brain function. Here, we investigated the excitatory-inhibitory imbalance theory by measuring Glu-concentrations and the relationship with resting-state function. Nineteen adult males with ASD and 19 age and sex-matched healthy controls (HC) (23 - 58 years) underwent Proton Magnetic Resonance Spectroscopy of the dorsal anterior cingulate cortex (dACC) and resting-state functional Magnetic Resonance Imaging (fMRI). Glu and Glx concentrations were compared between groups. Seed-based functional connectivity was analyzed with a priori seeds of the right and left dACC. Finally, metabolite concentrations were related to functional connectivity coefficients and compared between both groups. Individuals with ASD showed significantly negative associations between increased Glx concentrations and reduced functional connectivity between the dACC and insular, limbic and parietal regions. In contrast, HC displayed a positive relationship between the same metabolite and connectivity measures. We provided new evidence to support the excitatory-inhibitory imbalance theory, where excitatory Glx concentrations were related to functional dysconnectivity in ASD. Future research is needed to investigate large-scale functional networks in association with both excitatory and inhibitory metabolites in subpopulations of ASD.
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Affiliation(s)
- Jennifer E Siegel-Ramsay
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychiatry and Behavioral Science, University of Texas, Austin, United States
| | - Liana Romaniuk
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Neil Roberts
- Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Holly Branigan
- School of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew C Stanfield
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria R Dauvermann
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States.
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120
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Loomba N, Beckerson ME, Ammons CJ, Maximo JO, Kana RK. Corpus callosum size and homotopic connectivity in Autism spectrum disorder. Psychiatry Res Neuroimaging 2021; 313:111301. [PMID: 34022542 DOI: 10.1016/j.pscychresns.2021.111301] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 04/28/2021] [Accepted: 05/07/2021] [Indexed: 12/14/2022]
Abstract
By examining how morphology of the corpus callosum (CC) in autism spectrum disorder (ASD) may affect functional communication across hemispheres, we hope to provide new insights into the structure-function relationship in the brain. We used a sample of 94 participants from the Autism Brain Imaging Data Exchange (ABIDE) database (55 typically-developing (TD) and 39 with ASD). The CC was segmented into five sub-regions (anterior, mid-anterior, central, mid-posterior, posterior) using FreeSurfer software, which were further examined for group differences. The total volume and specific sub-region volumes of the CC, and interhemispheric (homotopic) functional connectivity were calculated, along with the relationship between volume and connectivity. These measures were correlated with social ability assessed by the Social Responsiveness Scale (SRS). The central sub-region of CC was significantly smaller in ASD, although there was no group difference in total CC volume. ASD participants also showed stronger homotopic connectivity in the superior frontal gyrus. SRS scores were negatively correlated with the CC central sub-region volumes in ASD. The findings of this study add to the body of research showing morphological differences in the CC in ASD as well as connectivity differences. The absence of a significant relationship between structure and homotopic functional connectivity aligns with previous findings.
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Affiliation(s)
- Niharika Loomba
- Interdisciplinary Graduate Program, Vanderbilt University, Nashville, TN, United States
| | - Meagan E Beckerson
- Department of Psychology, University of Alabama, Tuscaloosa, AL, United States; Center for Innovative Research in Autism, University of Alabama, Tuscaloosa, AL, United States
| | - Carla J Ammons
- Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, United States
| | - Jose O Maximo
- Department of Psychiatry & Behavior Neurobiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Rajesh K Kana
- Department of Psychology, University of Alabama, Tuscaloosa, AL, United States; Center for Innovative Research in Autism, University of Alabama, Tuscaloosa, AL, United States.
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121
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Blanken TF, Bathelt J, Deserno MK, Voge L, Borsboom D, Douw L. Connecting brain and behavior in clinical neuroscience: A network approach. Neurosci Biobehav Rev 2021; 130:81-90. [PMID: 34324918 DOI: 10.1016/j.neubiorev.2021.07.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 11/16/2022]
Abstract
In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.
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Affiliation(s)
- Tessa F Blanken
- Department of Psychological Methods, University of Amsterdam, 1018 WT, Amsterdam, the Netherlands.
| | - Joe Bathelt
- Royal Holloway, University of London, Department of Psychology, Egham, Surrey, TW20 0EX, United Kingdom
| | - Marie K Deserno
- Max Planck Institute for Human Development, 14195, Berlin, Germany
| | - Lily Voge
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HZ, Amsterdam, the Netherlands
| | - Denny Borsboom
- Department of Psychological Methods, University of Amsterdam, 1018 WT, Amsterdam, the Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam University Medical Centres, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HZ, Amsterdam, the Netherlands; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusets General Hospital, Boston, MA, 02129, USA
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122
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Sex Differences in Functional Connectivity Between Resting State Brain Networks in Autism Spectrum Disorder. J Autism Dev Disord 2021; 52:3088-3101. [PMID: 34272649 PMCID: PMC9213274 DOI: 10.1007/s10803-021-05191-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2021] [Indexed: 11/05/2022]
Abstract
Functional brain connectivity (FBC) has previously been examined in autism spectrum disorder (ASD) between-resting-state networks (RSNs) using a highly sensitive and reproducible hypothesis-free approach. However, results have been inconsistent and sex differences have only recently been taken into consideration using this approach. We estimated main effects of diagnosis and sex and a diagnosis by sex interaction on between-RSNs FBC in 83 ASD (40 females/43 males) and 85 typically developing controls (TC; 43 females/42 males). We found increased connectivity between the default mode (DM) and (a) the executive control networks in ASD (vs. TC); (b) the cerebellum networks in males (vs. females); and (c) female-specific altered connectivity involving visual, language and basal ganglia (BG) networks in ASD—in suggestive compatibility with ASD cognitive and neuroscientific theories.
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123
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Ainsworth M, Sallet J, Joly O, Kyriazis D, Kriegeskorte N, Duncan J, Schüffelgen U, Rushworth MFS, Bell AH. Viewing Ambiguous Social Interactions Increases Functional Connectivity between Frontal and Temporal Nodes of the Social Brain. J Neurosci 2021; 41:6070-6086. [PMID: 34099508 PMCID: PMC8276745 DOI: 10.1523/jneurosci.0870-20.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 04/19/2021] [Accepted: 04/28/2021] [Indexed: 11/25/2022] Open
Abstract
Social behavior is coordinated by a network of brain regions, including those involved in the perception of social stimuli and those involved in complex functions, such as inferring perceptual and mental states and controlling social interactions. The properties and function of many of these regions in isolation are relatively well understood, but less is known about how these regions interact while processing dynamic social interactions. To investigate whether the functional connectivity between brain regions is modulated by social context, we collected fMRI data from male monkeys (Macaca mulatta) viewing videos of social interactions labeled as "affiliative," "aggressive," or "ambiguous." We show activation related to the perception of social interactions along both banks of the superior temporal sulcus, parietal cortex, medial and lateral frontal cortex, and the caudate nucleus. Within this network, we show that fronto-temporal functional connectivity is significantly modulated by social context. Crucially, we link the observation of specific behaviors to changes in functional connectivity within our network. Viewing aggressive behavior was associated with a limited increase in temporo-temporal and a weak increase in cingulate-temporal connectivity. By contrast, viewing interactions where the outcome was uncertain was associated with a pronounced increase in temporo-temporal, and cingulate-temporal functional connectivity. We hypothesize that this widespread network synchronization occurs when cingulate and temporal areas coordinate their activity when more difficult social inferences are being made.SIGNIFICANCE STATEMENT Processing social information from our environment requires the activation of several brain regions, which are concentrated within the frontal and temporal lobes. However, little is known about how these areas interact to facilitate the processing of different social interactions. Here we show that functional connectivity within and between the frontal and temporal lobes is modulated by social context. Specifically, we demonstrate that viewing social interactions where the outcome was unclear is associated with increased synchrony within and between the cingulate cortex and temporal cortices. These findings suggest that the coordination between the cingulate and temporal cortices is enhanced when more difficult social inferences are being made.
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Affiliation(s)
- Matthew Ainsworth
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, CB2 7EF
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom, OX2 6GG
| | - Jérôme Sallet
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom, OX2 6GG
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, OX3 9DU
- Inserm, Stem Cell and Brain Research Institute U1208, Université Lyon 1, 69500 Bron, France
| | - Olivier Joly
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, CB2 7EF
| | - Diana Kyriazis
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, CB2 7EF
| | - Nikolaus Kriegeskorte
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, CB2 7EF
- Zuckerman Mind Brain Institute, Columbia University, New York, New York, NY 10027
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, CB2 7EF
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom, OX2 6GG
| | - Urs Schüffelgen
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom, OX2 6GG
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, OX3 9DU
| | - Matthew F S Rushworth
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom, OX2 6GG
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, OX3 9DU
| | - Andrew H Bell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, CB2 7EF
- Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom, OX2 6GG
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom, OX3 9DU
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124
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Enticott PG, Barlow K, Guastella AJ, Licari MK, Rogasch NC, Middeldorp CM, Clark SR, Vallence AM, Boulton KA, Hickie IB, Whitehouse AJO, Galletly C, Alvares GA, Fujiyama H, Heussler H, Craig JM, Kirkovski M, Mills NT, Rinehart NJ, Donaldson PH, Ford TC, Caeyenberghs K, Albein-Urios N, Bekkali S, Fitzgerald PB. Repetitive transcranial magnetic stimulation (rTMS) in autism spectrum disorder: protocol for a multicentre randomised controlled clinical trial. BMJ Open 2021; 11:e046830. [PMID: 34233985 PMCID: PMC8264904 DOI: 10.1136/bmjopen-2020-046830] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
INTRODUCTION There are no well-established biomedical treatments for the core symptoms of autism spectrum disorder (ASD). A small number of studies suggest that repetitive transcranial magnetic stimulation (rTMS), a non-invasive brain stimulation technique, may improve clinical and cognitive outcomes in ASD. We describe here the protocol for a funded multicentre randomised controlled clinical trial to investigate whether a course of rTMS to the right temporoparietal junction (rTPJ), which has demonstrated abnormal brain activation in ASD, can improve social communication in adolescents and young adults with ASD. METHODS AND ANALYSIS This study will evaluate the safety and efficacy of a 4-week course of intermittent theta burst stimulation (iTBS, a variant of rTMS) in ASD. Participants meeting criteria for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ASD (n=150, aged 14-40 years) will receive 20 sessions of either active iTBS (600 pulses) or sham iTBS (in which a sham coil mimics the sensation of iTBS, but no active stimulation is delivered) to the rTPJ. Participants will undergo a range of clinical, cognitive, epi/genetic, and neurophysiological assessments before and at multiple time points up to 6 months after iTBS. Safety will be assessed via a structured questionnaire and adverse event reporting. The study will be conducted from November 2020 to October 2024. ETHICS AND DISSEMINATION The study was approved by the Human Research Ethics Committee of Monash Health (Melbourne, Australia) under Australia's National Mutual Acceptance scheme. The trial will be conducted according to Good Clinical Practice, and findings will be written up for scholarly publication. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry (ACTRN12620000890932).
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Affiliation(s)
- Peter G Enticott
- School of Psychology, Deakin University, Geelong, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Karen Barlow
- Child Health Research Centre, The University of Queensland, South Brisbane, Queensland, Australia
- Children's Health Queensland Hospital and Health Service, South Brisbane, Queensland, Australia
| | - Adam J Guastella
- Autism Clinic for Translational Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Melissa K Licari
- Telethon Kids Institute, Perth, Western Australia, Australia
- University of Western Australia, Crawley, Western Australia, Australia
| | - Nigel C Rogasch
- Discipline of Psychiatry, The University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Turner Institute for Brain and Mental Health, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Christel M Middeldorp
- Child Health Research Centre, The University of Queensland, South Brisbane, Queensland, Australia
- Children's Health Queensland Hospital and Health Service, South Brisbane, Queensland, Australia
| | - Scott R Clark
- Discipline of Psychiatry, The University of Adelaide, Adelaide, South Australia, Australia
| | - Ann-Maree Vallence
- Centre for Molecular Medicine and Innovative Therapeutics, Murdoch University, Murdoch, Western Australia, Australia
- Discipline of Psychology, Murdoch University, Murdoch, Western Australia, Australia
| | - Kelsie A Boulton
- Autism Clinic for Translational Research, Brain and Mind Centre, Children's Hospital Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Ian B Hickie
- Brain and Mind Centre, Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Andrew J O Whitehouse
- Telethon Kids Institute, Perth, Western Australia, Australia
- University of Western Australia, Crawley, Western Australia, Australia
| | - Cherrie Galletly
- Discipline of Psychiatry, The University of Adelaide, Adelaide, South Australia, Australia
| | - Gail A Alvares
- Telethon Kids Institute, Perth, Western Australia, Australia
- University of Western Australia, Crawley, Western Australia, Australia
| | - Hakuei Fujiyama
- Discipline of Psychology, Murdoch University, Murdoch, Western Australia, Australia
| | - Helen Heussler
- Child Health Research Centre, The University of Queensland, South Brisbane, Queensland, Australia
- Children's Health Queensland Hospital and Health Service, South Brisbane, Queensland, Australia
| | - Jeffrey M Craig
- IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - Melissa Kirkovski
- School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Natalie T Mills
- Discipline of Psychiatry, The University of Adelaide, Adelaide, South Australia, Australia
| | - Nicole J Rinehart
- School of Psychology, Deakin University, Geelong, Victoria, Australia
- Krongold Clinic, Monash Education, Monash University, Clayton, Victoria, Australia
| | - Peter H Donaldson
- School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Talitha C Ford
- School of Psychology, Deakin University, Geelong, Victoria, Australia
- Centre for Human Psychopharmacology, Faculty of Heath, Arts and Design, Swinburne University of Technology, Melbourne, Victoria, Australia
| | | | | | - Soukayna Bekkali
- School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Paul B Fitzgerald
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Epworth Centre for Innovation in Mental Health, Epworth HealthCare, Camperwell, Victoria, Australia
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Reardon AM, Hu XP, Li K, Langley J. Subtyping Autism Spectrum Disorder via Joint Modeling of Clinical and Connectomic Profiles. Brain Connect 2021; 12:193-205. [PMID: 34102874 DOI: 10.1089/brain.2020.0997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI studies, however the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes. METHODS Canonical correlation analysis was performed on two-hundred and ten ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine classifier was used to verify that subtypes were comprised of subjects with distinct clinical and connectivity features. RESULTS Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased IQ scores and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe ADI-R and SRS symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class support vector machine using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%). CONCLUSION The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.
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Affiliation(s)
- Alexandra M Reardon
- University of California Riverside, 8790, Biomedical Engineering, Riverside, California, United States;
| | - Xiaoping P Hu
- University of California Riverside, 8790, Biomedical Engineering, Riverside, California, United States.,University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| | - Kaiming Li
- University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
| | - Jason Langley
- University of California Riverside, 8790, Center for Advanced NeuroImaging, Riverside, California, United States;
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126
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Uccelli NA, Codagnone MG, Traetta ME, Levanovich N, Rosato Siri MV, Urrutia L, Falasco G, Vázquez S, Pasquini JM, Reinés AG. Neurobiological substrates underlying corpus callosum hypoconnectivity and brain metabolic patterns in the valproic acid rat model of autism spectrum disorder. J Neurochem 2021; 159:128-144. [PMID: 34081798 DOI: 10.1111/jnc.15444] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 05/26/2021] [Accepted: 05/31/2021] [Indexed: 12/26/2022]
Abstract
Atypical connectivity between brain regions and altered structure of the corpus callosum (CC) in imaging studies supports the long-distance hypoconnectivity hypothesis proposed for autism spectrum disorder (ASD). The aim of this study was to unveil the CC ultrastructural and cellular changes employing the valproic acid (VPA) rat model of ASD. Male Wistar rats were exposed to VPA (450 mg/kg i.p.) or saline (control) during gestation (embryonic day 10.5), and maturation, exploration, and social behavior were subsequently tested. Myelin content, ultrastructure, and oligodendroglial lineage were studied in the CC at post-natal days 15 (infant) and 36 (juvenile). As a functional outcome, brain metabolic activity was determined by positron emission tomography. Concomitantly with behavioral deficits in juvenile VPA rats, the CC showed reduced myelin basic protein, conserved total number of axons, reduced percentage of myelinated axons, and aberrant and less compact arrangements of myelin sheath ultrastructure. Mature oligodendrocytes decreased and oligodendrocyte precursors increased in the absence of astrogliosis or microgliosis. In medial prefrontal and somatosensory cortices of juvenile VPA rats, myelin ultrastructure and oligodendroglial lineage were preserved. VPA animals exhibited global brain hypometabolism and local hypermetabolism in brain regions relevant for ASD. In turn, the CC of infant VPA rats showed reduced myelin content but preserved oligodendroglial lineage. Our findings indicate that CC hypomyelination is established during infancy and prior to oligodendroglial pattern alterations, which suggests that axon-oligodendroglia communication could be compromised in VPA animals. Thus, CC hypomyelination may underlie white matter alterations and contribute to atypical patterns of connectivity and metabolism found in ASD.
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Affiliation(s)
- Nonthué Alejandra Uccelli
- CONICET-Universidad de Buenos Aires, Instituto de Biología Celular y Neurociencia "Prof. E. De Robertis" (IBCN) Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Martín Gabriel Codagnone
- CONICET-Universidad de Buenos Aires, Instituto de Biología Celular y Neurociencia "Prof. E. De Robertis" (IBCN) Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Farmacología, Facultad de Farmacia y Bioquímica, Cátedra de Farmacología, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Marianela Evelyn Traetta
- CONICET-Universidad de Buenos Aires, Instituto de Biología Celular y Neurociencia "Prof. E. De Robertis" (IBCN) Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Farmacología, Facultad de Farmacia y Bioquímica, Cátedra de Farmacología, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Nadia Levanovich
- Fundación para la lucha contra las enfermedades neurológicas de la infancia (FLENI), Centro de Imágenes Moleculares (CIM), Escobar, Argentina
| | - María Victoria Rosato Siri
- CONICET-Universidad de Buenos Aires, Instituto de Química y Fisicoquímica Biológica (IQUIFIB) Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Leandro Urrutia
- Fundación para la lucha contra las enfermedades neurológicas de la infancia (FLENI), Centro de Imágenes Moleculares (CIM), Escobar, Argentina
| | - Germán Falasco
- Fundación para la lucha contra las enfermedades neurológicas de la infancia (FLENI), Centro de Imágenes Moleculares (CIM), Escobar, Argentina
| | - Silvia Vázquez
- Fundación para la lucha contra las enfermedades neurológicas de la infancia (FLENI), Centro de Imágenes Moleculares (CIM), Escobar, Argentina
| | - Juana María Pasquini
- CONICET-Universidad de Buenos Aires, Instituto de Química y Fisicoquímica Biológica (IQUIFIB) Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Analía Gabriela Reinés
- CONICET-Universidad de Buenos Aires, Instituto de Biología Celular y Neurociencia "Prof. E. De Robertis" (IBCN) Universidad de Buenos Aires, Buenos Aires, Argentina.,Departamento de Farmacología, Facultad de Farmacia y Bioquímica, Cátedra de Farmacología, Universidad de Buenos Aires, Buenos Aires, Argentina
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Paakki J, Rahko JS, Kotila A, Mattila M, Miettunen H, Hurtig TM, Jussila KK, Kuusikko‐Gauffin S, Moilanen IK, Tervonen O, Kiviniemi VJ. Co-activation pattern alterations in autism spectrum disorder-A volume-wise hierarchical clustering fMRI study. Brain Behav 2021; 11:e02174. [PMID: 33998178 PMCID: PMC8213933 DOI: 10.1002/brb3.2174] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 04/05/2021] [Accepted: 04/23/2021] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION There has been a growing effort to characterize the time-varying functional connectivity of resting state (RS) fMRI brain networks (RSNs). Although voxel-wise connectivity studies have examined different sliding window lengths, nonsequential volume-wise approaches have been less common. METHODS Inspired by earlier co-activation pattern (CAP) studies, we applied hierarchical clustering (HC) to classify the image volumes of the RS-fMRI data on 28 adolescents with autism spectrum disorder (ASD) and their 27 typically developing (TD) controls. We compared the distribution of the ASD and TD groups' volumes in CAPs as well as their voxel-wise means. For simplification purposes, we conducted a group independent component analysis to extract 14 major RSNs. The RSNs' average z-scores enabled us to meaningfully regroup the RSNs and estimate the percentage of voxels within each RSN for which there was a significant group difference. These results were jointly interpreted to find global group-specific patterns. RESULTS We found similar brain state proportions in 58 CAPs (clustering interval from 2 to 30). However, in many CAPs, the voxel-wise means differed significantly within a matrix of 14 RSNs. The rest-activated default mode-positive and default mode-negative brain state properties vary considerably in both groups over time. This division was seen clearly when the volumes were partitioned into two CAPs and then further examined along the HC dendrogram of the diversifying brain CAPs. The ASD group network activations followed a more heterogeneous distribution and some networks maintained higher baselines; throughout the brain deactivation state, the ASD participants had reduced deactivation in 12/14 networks. During default mode-negative CAPs, the ASD group showed simultaneous visual network and either dorsal attention or default mode network overactivation. CONCLUSION Nonsequential volume gathering into CAPs and the comparison of voxel-wise signal changes provide a complementary perspective to connectivity and an alternative to sliding window analysis.
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Affiliation(s)
- Jyri‐Johan Paakki
- Faculty of Medicine, Health and Biosciences Doctoral ProgrammeUniversity of Oulu Graduate SchoolUniversity of OuluOuluFinland
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
| | - Jukka S. Rahko
- Faculty of Medicine, Health and Biosciences Doctoral ProgrammeUniversity of Oulu Graduate SchoolUniversity of OuluOuluFinland
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Aija Kotila
- Faculty of HumanitiesResearch Unit of LogopedicsUniversity of OuluOuluFinland
| | - Marja‐Leena Mattila
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Helena Miettunen
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Tuula M. Hurtig
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
- Research Unit of Clinical Neuroscience, PsychiatryUniversity of OuluOuluFinland
| | - Katja K. Jussila
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Sanna Kuusikko‐Gauffin
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Irma K. Moilanen
- PEDEGO Research UnitFaculty of MedicineChild PsychiatryUniversity of OuluOuluFinland
- Institute of Clinical MedicineClinic of Child PsychiatryUniversity and University Hospital of OuluOuluFinland
| | - Osmo Tervonen
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
| | - Vesa J. Kiviniemi
- The Faculty of MedicineResearch Unit of Medical Imaging, Physics and TechnologyOulu Functional NeuroImaging GroupUniversity of OuluOuluFinland
- Department of Diagnostic RadiologyMedical Research CenterOulu University HospitalOuluFinland
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128
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Nassar MR, Troiani V. The stability flexibility tradeoff and the dark side of detail. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:607-623. [PMID: 33236296 PMCID: PMC8141540 DOI: 10.3758/s13415-020-00848-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/04/2020] [Indexed: 12/26/2022]
Abstract
Learning in dynamic environments requires integrating over stable fluctuations to minimize the impact of noise (stability) but rapidly responding in the face of fundamental changes (flexibility). Achieving one of these goals often requires sacrificing the other to some degree, producing a stability-flexibility tradeoff. Individuals navigate this tradeoff in different ways; some people learn rapidly (emphasizing flexibility) and others rely more heavily on historical information (emphasizing stability). Despite the prominence of such individual differences in learning tasks, the degree to which they relate to broader characteristics of real-world behavior or pathologies has not been well explored. We relate individual differences in learning behavior to self-report measures thought to capture collectively the characteristics of the Autism spectrum. We show that young adults who learn most slowly tend to integrate more effective samples into their beliefs about the world making them more robust to noise (more stability) but are more likely to integrate information from previous contexts (less flexibility). We show that individuals who report paying more attention to detail tend to use high flexibility and low stability information processing strategies. We demonstrate the robustness of this inverse relationship between attention to detail and formation of stable beliefs in a heterogeneous population of children that includes a high proportion of Autism diagnoses. Together, our results highlight that attention to detail reflects an information processing policy that comes with a substantial downside, namely the ability to integrate data to overcome environmental noise.
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Affiliation(s)
- Matthew R Nassar
- Department of Neuroscience; Carney Institute for Brain Science, Brown University, Providence, RI, 02912-1821, USA.
| | - Vanessa Troiani
- Geisinger-Bucknell Autism & Developmental Medicine Institute, Lewisburg, PA, USA
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129
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Lawrence KE, Hernandez LM, Fuster E, Padgaonkar NT, Patterson G, Jung J, Okada NJ, Lowe JK, Hoekstra JN, Jack A, Aylward E, Gaab N, Van Horn JD, Bernier RA, McPartland JC, Webb SJ, Pelphrey KA, Green SA, Bookheimer SY, Geschwind DH, Dapretto M. Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect. Brain 2021; 145:378-387. [PMID: 34050743 PMCID: PMC8967090 DOI: 10.1093/brain/awab204] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 04/23/2021] [Accepted: 05/11/2021] [Indexed: 01/27/2023] Open
Abstract
The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.
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Affiliation(s)
- Katherine E Lawrence
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA,Correspondence to: Mirella Dapretto Ahmanson-Lovelace Brain Mapping Center 660 Charles E. Young Drive South Los Angeles, CA 90095, USA E-mail:
| | - Leanna M Hernandez
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Emily Fuster
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Namita T Padgaonkar
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Genevieve Patterson
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jiwon Jung
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Nana J Okada
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jennifer K Lowe
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Jackson N Hoekstra
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Allison Jack
- Department of Psychology, George Mason University, Fairfax, VA 22030, USA
| | - Elizabeth Aylward
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Nadine Gaab
- Harvard Graduate School of Education, Cambridge, MA 02138, USA
| | - John D Van Horn
- Department of Psychology and School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA
| | | | - Sara J Webb
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA,Center on Child Health, Behavior, and Development, Seattle Children’s Research Institute, Seattle, WA 98101, USA
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA 22904, USA
| | - Shulamite A Green
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel H Geschwind
- Department of Neurology, University of California Los Angeles, Los Angeles, CA 90095, USA,Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, CA, 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA 90095, USA
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Daedelow LS, Beck A, Romund L, Mascarell-Maricic L, Dziobek I, Romanczuk-Seiferth N, Wüstenberg T, Heinz A. Neural correlates of RDoC-specific cognitive processes in a high-functional autistic patient: a statistically validated case report. J Neural Transm (Vienna) 2021; 128:845-859. [PMID: 34003357 PMCID: PMC8205905 DOI: 10.1007/s00702-021-02352-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/08/2021] [Indexed: 11/29/2022]
Abstract
The level of functioning of individuals with autism spectrum disorder (ASD) varies widely. To better understand the neurobiological mechanism associated with high-functioning ASD, we studied the rare case of a female patient with an exceptional professional career in the highly competitive academic field of Mathematics. According to the Research Domain Criteria (RDoC) approach, which proposes to describe the basic dimensions of functioning by integrating different levels of information, we conducted four fMRI experiments targeting the (1) social processes domain (Theory of mind (ToM) and face matching), (2) positive valence domain (reward processing), and (3) cognitive domain (N-back). Patient’s data were compared to data of 14 healthy controls (HC). Additionally, we assessed the subjective experience of our case during the experiments. The patient showed increased response times during face matching and achieved a higher total gain in the Reward task, whereas her performance in N-back and ToM was similar to HC. Her brain function differed mainly in the positive valence and cognitive domains. During reward processing, she showed reduced activity in a left-hemispheric frontal network and cortical midline structures but increased connectivity within this network. During the working memory task patients’ brain activity and connectivity in left-hemispheric temporo-frontal regions were elevated. In the ToM task, activity in posterior cingulate cortex and temporo-parietal junction was reduced. We suggest that the high level of functioning in our patient is rather related to the effects in brain connectivity than to local cortical information processing and that subjective report provides a fruitful framework for interpretation.
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Affiliation(s)
- Laura S Daedelow
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Anne Beck
- Health and Medical University Potsdam, Potsdam, Germany
| | - Lydia Romund
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Lea Mascarell-Maricic
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Isabel Dziobek
- Berlin School of Mind and Brain, Berlin, Germany.,Department of Psychology, Humboldt-University of Berlin, Berlin, Germany
| | - Nina Romanczuk-Seiferth
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Torsten Wüstenberg
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany. .,Department of Clinical Psychology and Psychotherapy, Psychological Institute, Ruprecht-Karls-University Heidelberg, Hauptstr. 47-51, 69117, Heidelberg, Germany.
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin, and Berlin Institute of Health, Berlin, Germany
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Leming MJ, Baron-Cohen S, Suckling J. Single-participant structural similarity matrices lead to greater accuracy in classification of participants than function in autism in MRI. Mol Autism 2021; 12:34. [PMID: 33971956 PMCID: PMC8112019 DOI: 10.1186/s13229-021-00439-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 04/16/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Autism has previously been characterized by both structural and functional differences in brain connectivity. However, while the literature on single-subject derivations of functional connectivity is extensively developed, similar methods of structural connectivity or similarity derivation from T1 MRI are less studied. METHODS We introduce a technique of deriving symmetric similarity matrices from regional histograms of grey matter volumes estimated from T1-weighted MRIs. We then validated the technique by inputting the similarity matrices into a convolutional neural network (CNN) to classify between participants with autism and age-, motion-, and intracranial-volume-matched controls from six different databases (29,288 total connectomes, mean age = 30.72, range 0.42-78.00, including 1555 subjects with autism). We compared this method to similar classifications of the same participants using fMRI connectivity matrices as well as univariate estimates of grey matter volumes. We further applied graph-theoretical metrics on output class activation maps to identify areas of the matrices that the CNN preferentially used to make the classification, focusing particularly on hubs. LIMITATIONS While this study used a large sample size, the majority of data was from a young age group; furthermore, to make a viable machine learning study, we treated autism, a highly heterogeneous condition, as a binary label. Thus, these results are not necessarily generalizable to all subtypes and age groups in autism. RESULTS Our models gave AUROCs of 0.7298 (69.71% accuracy) when classifying by only structural similarity, 0.6964 (67.72% accuracy) when classifying by only functional connectivity, and 0.7037 (66.43% accuracy) when classifying by univariate grey matter volumes. Combining structural similarity and functional connectivity gave an AUROC of 0.7354 (69.40% accuracy). Analysis of classification performance across age revealed the greatest accuracy in adolescents, in which most data were present. Graph analysis of class activation maps revealed no distinguishable network patterns for functional inputs, but did reveal localized differences between groups in bilateral Heschl's gyrus and upper vermis for structural similarity. CONCLUSION This study provides a simple means of feature extraction for inputting large numbers of structural MRIs into machine learning models. Our methods revealed a unique emphasis of the deep learning model on the structure of the bilateral Heschl's gyrus when characterizing autism.
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Affiliation(s)
- Matthew J Leming
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK.
- Center for Systems Biology, Massachusetts General Hospital, 149 13th Street, Boston, MA, 02129, USA.
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Robinson Way, Cambridge, Cambridgeshire, CB2 0SZ, UK
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Yao S, Zhou M, Zhang Y, Zhou F, Zhang Q, Zhao Z, Jiang X, Xu X, Becker B, Kendrick KM. Decreased homotopic interhemispheric functional connectivity in children with autism spectrum disorder. Autism Res 2021; 14:1609-1620. [PMID: 33908177 DOI: 10.1002/aur.2523] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 11/11/2022]
Abstract
While several functional and structural changes occur in large-scale brain networks in autism spectrum disorder (ASD), reduced interhemispheric resting-state functional connectivity (rsFC) between homotopic regions may be of particular importance as a biomarker. ASD is an early-onset developmental disorder and neural alterations are often age-dependent. Although there is some evidence for homotopic interhemispheric rsFC alterations in language processing regions in ASD children, wider analyses using large data sets have not been performed. The present study, therefore, conducted a voxel-based homotopic interhemispheric rsFC analysis in 146 ASD and 175 typically developing children under-age 10 and examined associations with symptom severity in the autism brain imaging data exchange data sets. Given the role of corpus callosum (CC) in interhemispheric connectivity and reported CC volume changes in ASD we additionally examined whether there were parallel volumetric changes. Results demonstrated decreased homotopic rsFC in ASD children in the posterior cingulate cortex (PCC) and precuneus of the default mode network, the precentral gyrus of the mirror neuron system, and the caudate of the reward system. Homotopic rsFC of the PCC was associated with symptom severity. Furthermore, although no significant CC volume changes were found in ASD children, there was a significant negative correlation between the anterior CC volumes and homotopic rsFC strengths in the caudate. The present study shows that a reduced pattern of homotopic interhemispheric rsFC in ASD adults/adolescents is already present in children of 5-10 years old and further supports their potential use as a general ASD biomarker. LAY SUMMARY: Homotopic interhemispheric functional connectivity plays an important role in synchronizing activity between the two hemispheres and is altered in adults and adolescents with autism spectrum disorder (ASD). In the present study focused on children with ASD, we have observed a similar pattern of decreased homotopic connectivity, suggesting that alterations in homotopic interhemispheric connectivity may occur early in ASD and be a useful general biomarker across ages.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Menghan Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yuan Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Feng Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Qianqian Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Zhongbo Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaolei Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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133
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Haghighat H, Mirzarezaee M, Araabi BN, Khadem A. Functional Networks Abnormalities in Autism Spectrum Disorder: Age-Related Hypo and Hyper Connectivity. Brain Topogr 2021; 34:306-322. [PMID: 33905003 DOI: 10.1007/s10548-021-00831-7] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 03/03/2021] [Indexed: 11/30/2022]
Abstract
Autism spectrum disorder (ASD) is a developmental disorder characterized by defects in social interaction. The past functional connectivity studies using resting-state fMRI have found both patterns of hypo-connectivity and hyper-connectivity in ASD and proposed the age as an important factor on functional connectivity disorders. However, this influence is not clearly characterized yet. Previous studies have often examined the functional connectivity disorders in particular brain regions in an age group or a mixture of age groups. The present study compares whole-brain within-connectivity and between-connectivity between ASD individuals and typically developing (TD) controls in three age groups including children (< 11 years), adolescents (11-18 years), and adults (> 18 years), each comprising 21 ASD individuals and 21 TD controls. The age groups were matched for age, Full IQ, and gender. Independent component analysis and dual regression were used to investigate within-connectivity. The full and partial correlations between ICs were used to investigate between-connectivity. Examination of the within-connectivity showed hyper-connectivity, especially in cerebellum and brainstem in ASD children but both hyper/hypo connectivity in adolescents and ASD adults. In ASD children, difference in the between-connectivity among default mode network (DMN), salience-executive network and fronto-parietal network were observed. There was also a negative correlation between DMN and temporal network. Full correlation comparison between ASD adolescents and TD individuals showed significant differences between cerebellum and DMN. Our results supported just the hyper-connectivity in childhood, but both hypo and hyper-connectivity after childhood and hypothesized that abnormal resting connections in ASD exist in the regions of the brain known to be involved in social cognition.
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Affiliation(s)
- Hossein Haghighat
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Mitra Mirzarezaee
- Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
| | - Babak Nadjar Araabi
- Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
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134
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Nair A, Jalal R, Liu J, Tsang T, McDonald NM, Jackson L, Ponting C, Jeste SS, Bookheimer SY, Dapretto M. Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder. Cereb Cortex 2021; 31:4191-4205. [PMID: 33866373 DOI: 10.1093/cercor/bhab078] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 03/11/2021] [Accepted: 03/12/2021] [Indexed: 12/14/2022] Open
Abstract
Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6 weeks predicted atypical social development between 9 and 36 months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.
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Affiliation(s)
- Aarti Nair
- Department of Psychology, School of Behavioral Health, Loma Linda University, Loma Linda, CA 92354, USA
| | - Rhideeta Jalal
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Janelle Liu
- Interdepartmental Neuroscience Program, University of California, Los Angeles, CA 90095, USA
| | - Tawny Tsang
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Nicole M McDonald
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Lisa Jackson
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Carolyn Ponting
- Department of Psychology, University of California, Los Angeles, CA 90095, USA
| | - Shafali S Jeste
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA 90095, USA
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135
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Bernas A, Breuer LEM, Aldenkamp AP, Zinger S. Emulative, coherent, and causal dynamics between large-scale brain networks are neurobiomarkers of Accelerated Cognitive Ageing in epilepsy. PLoS One 2021; 16:e0250222. [PMID: 33861794 PMCID: PMC8051821 DOI: 10.1371/journal.pone.0250222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 04/03/2021] [Indexed: 11/25/2022] Open
Abstract
Accelerated cognitive ageing (ACA) is an ageing co-morbidity in epilepsy that is diagnosed through the observation of an evident IQ decline of more than 1 standard deviation (15 points) around the age of 50 years old. To understand the mechanism of action of this pathology, we assessed brain dynamics with the use of resting-state fMRI data. In this paper, we present novel and promising methods to extract brain dynamics between large-scale resting-state networks: the emulative power, wavelet coherence, and granger causality between the networks were extracted in two resting-state sessions of 24 participants (10 ACA, 14 controls). We also calculated the widely used static functional connectivity to compare the methods. To find the best biomarkers of ACA, and have a better understanding of this epilepsy co-morbidity we compared the aforementioned between-network neurodynamics using classifiers and known machine learning algorithms; and assessed their performance. Results show that features based on the evolutionary game theory on networks approach, the emulative powers, are the best descriptors of the co-morbidity, using dynamics associated with the default mode and dorsal attention networks. With these dynamic markers, linear discriminant analysis could identify ACA patients at 82.9% accuracy. Using wavelet coherence features with decision-tree algorithm, and static functional connectivity features with support vector machine, ACA could be identified at 77.1% and 77.9% accuracy respectively. Granger causality fell short of being a relevant biomarker with best classifiers having an average accuracy of 67.9%. Combining the features based on the game theory, wavelet coherence, Granger-causality, and static functional connectivity- approaches increased the classification performance up to 90.0% average accuracy using support vector machine with a peak accuracy of 95.8%. The dynamics of the networks that lead to the best classifier performances are known to be challenged in elderly. Since our groups were age-matched, the results are in line with the idea of ACA patients having an accelerated cognitive decline. This classification pipeline is promising and could help to diagnose other neuropsychiatric disorders, and contribute to the field of psychoradiology.
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Affiliation(s)
- Antoine Bernas
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Cognitive Neuropsychiatry and Clinical Neurosciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Lisanne E. M. Breuer
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Albert P. Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Cognitive Neuropsychiatry and Clinical Neurosciences, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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136
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Reiter MA, Jahedi A, Jac Fredo A, Fishman I, Bailey B, Müller RA. Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity. Neural Comput Appl 2021; 33:3299-3310. [PMID: 34149191 PMCID: PMC8210842 DOI: 10.1007/s00521-020-05193-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to distinguish ASD from typical developing (TD) samples, cohorts differing in gender and symptom severity composition are often treated statistically as one "ASD group". Using resting-state functional connectivity (FC) data, we implemented random forest to build diagnostic classifiers in 4 ASD samples including a total of 656 participants (NASD = 306, NTD = 350, ages 6-18). Groups were manipulated to titrate heterogeneity of gender and symptom severity and partially overlapped. Each sample differed on inclusionary criteria: (1) all genders, unrestricted severity range; (2) only male participants, unrestricted severity; (3) all genders, higher severity only; (4) only male participants, higher severity. Each set consisted of 200 participants per group (ASD, TD; matched on age and head motion), 160 for training and 40 for validation. FMRI time series from 237 regions of interest (ROIs) were Pearson correlated in a 237×237 FC matrix and classifiers were built using random forest in training samples. Classification accuracies in validation samples were 62.5%, 65%, 70% and 73.75%, respectively for samples 1-4. Connectivity within cingulo-opercular task control (COTC) network, and between COTC ROIs and default mode and dorsal attention network contributed overall most informative features, but features differed across sets. Findings suggest that diagnostic classifiers vary depending on ASD sample composition. Specifically, greater homogeneity of samples regarding gender and symptom severity enhances classifier performance. However, given the true heterogeneity of ASDs, performance metrics alone may not adequately reflect classifier utility.
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Affiliation(s)
- Maya A. Reiter
- Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA,Joint Doctoral Program in Clinical Psychology, San Diego State University/UC San Diego, San Diego, CA, USA
| | - Afrooz Jahedi
- Computational Science, San Diego State University/ Claremont Graduate University’s Joint Doctoral Program, San Diego, CA, USA
| | - A.R. Jac Fredo
- Computational Science, San Diego State University/ Claremont Graduate University’s Joint Doctoral Program, San Diego, CA, USA
| | - Inna Fishman
- Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA
| | - Barbara Bailey
- Department of Mathematics and Statistics, San Diego State University, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Lab (BDIL), Psychology, San Diego State University (SDSU), 6363 Alvarado Ct. Suite 200, San Diego, CA 92120, USA,Joint Doctoral Program in Clinical Psychology, San Diego State University/UC San Diego, San Diego, CA, USA
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137
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Hammill C, Lerch JP, Taylor MJ, Ameis SH, Chakravarty MM, Szatmari P, Anagnostou E, Lai MC. Quantitative and Qualitative Sex Modulations in the Brain Anatomy of Autism. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:898-909. [PMID: 33713843 DOI: 10.1016/j.bpsc.2021.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Sex-based neurobiological heterogeneity in autism is poorly understood. Research is disproportionately biased to males, leading to an unwarranted presumption that autism neurobiology is the same across sexes. Previous neuroimaging studies using amalgamated multicenter datasets to increase autistic female samples are characterized by large statistical noise. METHODS We used a better-powered dataset of 1183 scans of 839 individuals-299 (467 scans) autistic males, 74 (102 scans) autistic females, 240 (334 scans) control males, and 226 (280 scans) control females-to test two whole-brain models of overall/global sex modulations on autism neuroanatomy, by summary measures computed across the brain: the local magnitude model, in which the same brain regions/circuitries are involved across sexes but effect sizes are larger in females, indicating quantitative sex modulation; and spatial dissimilarity model, in which the neuroanatomy differs spatially between sexes, indicating qualitative sex modulation. The male and female autism groups were matched on age, IQ, and autism symptoms. Autism brain features were defined by comparisons with same-sex control individuals. RESULTS Across five metrics (cortical thickness, surface area, volume, mean absolute curvature, and subcortical volume), we found no evidence supporting the local magnitude model. We found indicators supporting the spatial dissimilarity model on cortical mean absolute curvature and subcortical volume, but not on other metrics. CONCLUSIONS The overall/global autism neuroanatomy in females and males does not simply differ quantitatively in the same brain regions/circuitries. They may differ qualitatively in spatial involvement in cortical curvature and subcortical volume. The neuroanatomy of autism may be partly sex specific. Sex stratification to inform autism preclinical/clinical research is needed to identify sex-informed neurodevelopmental targets.
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Affiliation(s)
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Peter Szatmari
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital and Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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138
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Yao S, Becker B, Kendrick KM. Reduced Inter-hemispheric Resting State Functional Connectivity and Its Association With Social Deficits in Autism. Front Psychiatry 2021; 12:629870. [PMID: 33746796 PMCID: PMC7969641 DOI: 10.3389/fpsyt.2021.629870] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Autism spectrum disorder (ASD) is an early onset developmental disorder which persists throughout life and is increasing in prevalence over the last few decades. Given its early onset and variable cognitive and emotional functional impairments, it is generally challenging to assess ASD individuals using task-based behavioral and functional MRI paradigms. Consequently, resting state functional MRI (rs-fMRI) has become a key approach for examining ASD-associated neural alterations and revealed functional alterations in large-scale brain networks relative to typically developing (TD) individuals, particularly those involved in social-cognitive and affective processes. Recent progress suggests that alterations in inter-hemispheric resting state functional connectivity (rsFC) between regions in the 2 brain hemispheres, particularly homotopic ones, may be of great importance. Here we have reviewed neuroimaging studies examining inter-hemispheric rsFC abnormities in ASD and its associations with symptom severity. As an index of inter-hemispheric functional connectivity, we have additionally reviewed previous studies on corpus callosum (CC) volumetric and fiber changes in ASD. There are converging findings on reduced inter-hemispheric (including homotopic) rsFC in large-scale brain networks particularly in posterior hubs of the default mode network, reduced volumes in the anterior and posterior CC, and on decreased FA and increased MD or RD across CC subregions. Associations between the strength of inter-hemispheric rsFC and social impairments in ASD together with their classification performance in distinguishing ASD subjects from TD controls across ages suggest that the strength of inter-hemispheric rsFC may be a more promising biomarker for assisting in ASD diagnosis than abnormalities in either brain wide rsFC or brain structure.
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Affiliation(s)
- Shuxia Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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139
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Floris DL, Filho JOA, Lai MC, Giavasis S, Oldehinkel M, Mennes M, Charman T, Tillmann J, Dumas G, Ecker C, Dell'Acqua F, Banaschewski T, Moessnang C, Baron-Cohen S, Durston S, Loth E, Murphy DGM, Buitelaar JK, Beckmann CF, Milham MP, Di Martino A. Towards robust and replicable sex differences in the intrinsic brain function of autism. Mol Autism 2021; 12:19. [PMID: 33648569 PMCID: PMC7923310 DOI: 10.1186/s13229-021-00415-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/18/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Marked sex differences in autism prevalence accentuate the need to understand the role of biological sex-related factors in autism. Efforts to unravel sex differences in the brain organization of autism have, however, been challenged by the limited availability of female data. METHODS We addressed this gap by using a large sample of males and females with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics (voxel-level Z > 3.1, cluster-level P < 0.01, gaussian random field corrected). Secondary analyses assessed the robustness of the results to different pre-processing approaches and their replicability in two independent samples: the EU-AIMS Longitudinal European Autism Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance Autism Research. RESULTS Discovery analyses in ABIDE revealed significant main effects of diagnosis and sex across the intrinsic functional connectivity of the posterior cingulate cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several cortical regions, largely converging in the default network midline. Sex-by-diagnosis interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in females with autism. All findings were robust to different pre-processing steps. Replicability in independent samples varied by R-fMRI measures and effects with the targeted sex-by-diagnosis interaction being replicated in the larger of the two replication samples-EU-AIMS LEAP. LIMITATIONS Given the lack of a priori harmonization among the discovery and replication datasets available to date, sample-related variation remained and may have affected replicability. CONCLUSIONS Atypical cross-hemispheric interactions are neurobiologically relevant to autism. They likely result from the combination of sex-dependent and sex-independent factors with a differential effect across functional cortical networks. Systematic assessments of the factors contributing to replicability are needed and necessitate coordinated large-scale data collection across studies.
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Affiliation(s)
- Dorothea L Floris
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - José O A Filho
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA
| | - Meng-Chuan Lai
- The Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health, Azrieli Adult Neurodevelopmental Centre, and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Steve Giavasis
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA
| | - Marianne Oldehinkel
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Maarten Mennes
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Tony Charman
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Julian Tillmann
- Department of Psychology, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Applied Psychology: Health, Development, Enhancement, and Intervention, University of Vienna, Vienna, Austria
| | - Guillaume Dumas
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, Paris, France
- CHU Sainte-Justine Research Center, Department of Psychiatry, Université de Montréal, Montreal, QC, Canada
| | - Christine Ecker
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Frankfurt am Main, Goethe University, Frankfurt, Germany
| | - Flavio Dell'Acqua
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Carolin Moessnang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, University of Heidelberg, Mannheim, Germany
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sarah Durston
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Eva Loth
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Declan G M Murphy
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Jan K Buitelaar
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, the Netherlands
| | - Christian F Beckmann
- Donders Center for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Centre for Functional MRI of the Brain, University of Oxford, Oxford, UK
| | - Michael P Milham
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Adriana Di Martino
- Autism Center, The Child Mind Institute, 101 E 56 Street, New York City, New York, 10026, USA.
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140
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Zink N, Lenartowicz A, Markett S. A new era for executive function research: On the transition from centralized to distributed executive functioning. Neurosci Biobehav Rev 2021; 124:235-244. [PMID: 33582233 DOI: 10.1016/j.neubiorev.2021.02.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/04/2021] [Indexed: 02/07/2023]
Abstract
"Executive functions" (EFs) is an umbrella term for higher cognitive control functions such as working memory, inhibition, and cognitive flexibility. One of the most challenging problems in this field of research has been to explain how the wide range of cognitive processes subsumed as EFs are controlled without an all-powerful but ill-defined central executive in the brain. Efforts to localize control mechanisms in circumscribed brain regions have not led to a breakthrough in understanding how the brain controls and regulates itself. We propose to re-conceptualize EFs as emergent consequences of highly distributed brain processes that communicate with a pool of highly connected hub regions, thus precluding the need for a central executive. We further discuss how graph-theory driven analysis of brain networks offers a unique lens on this problem by providing a reference frame to study brain connectivity in EFs in a holistic way and helps to refine our understanding of the mechanisms underlying EFs by providing new, testable hypotheses and resolves empirical and theoretical inconsistencies in the EF literature.
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Affiliation(s)
- Nicolas Zink
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States.
| | - Agatha Lenartowicz
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, United States
| | - Sebastian Markett
- Department of Psychology, Humboldt University Berlin, Berlin, Germany
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141
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Ayub R, Sun KL, Flores RE, Lam VT, Jo B, Saggar M, Fung LK. Thalamocortical connectivity is associated with autism symptoms in high-functioning adults with autism and typically developing adults. Transl Psychiatry 2021; 11:93. [PMID: 33536431 PMCID: PMC7859407 DOI: 10.1038/s41398-021-01221-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 01/07/2021] [Accepted: 01/15/2021] [Indexed: 01/30/2023] Open
Abstract
Alterations in sensorimotor functions are common in individuals with autism spectrum disorder (ASD). Such aberrations suggest the involvement of the thalamus due to its key role in modulating sensorimotor signaling in the cortex. Although previous research has linked atypical thalamocortical connectivity with ASD, investigations of this association in high-functioning adults with autism spectrum disorder (HFASD) are lacking. Here, for the first time, we investigated the resting-state functional connectivity of the thalamus, medial prefrontal, posterior cingulate, and left dorsolateral prefrontal cortices and its association with symptom severity in two matched cohorts of HFASD. The principal cohort consisted of 23 HFASD (mean[SD] 27.1[8.9] years, 39.1% female) and 20 age- and sex-matched typically developing controls (25.1[7.2] years, 30.0% female). The secondary cohort was a subset of the ABIDE database consisting of 58 HFASD (25.4[7.8] years, 37.9% female) and 51 typically developing controls (24.4[6.7] years, 39.2% female). Using seed-based connectivity analysis, between-group differences were revealed as hyperconnectivity in HFASD in the principal cohort between the right thalamus and bilateral precentral/postcentral gyri and between the right thalamus and the right superior parietal lobule. The former was associated with autism-spectrum quotient in a sex-specific manner, and was further validated in the secondary ABIDE cohort. Altogether, we present converging evidence for thalamocortical hyperconnectivity in HFASD that is associated with symptom severity. Our results fill an important knowledge gap regarding atypical thalamocortical connectivity in HFASD, previously only reported in younger cohorts.
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Affiliation(s)
- Rafi Ayub
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Kevin L Sun
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- School of Medicine, Stanford University, Stanford, CA, USA
| | - Ryan E Flores
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vicky T Lam
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Booil Jo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Lawrence K Fung
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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142
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Chen B, Linke A, Olson L, Ibarra C, Reynolds S, Müller RA, Kinnear M, Fishman I. Greater functional connectivity between sensory networks is related to symptom severity in toddlers with autism spectrum disorder. J Child Psychol Psychiatry 2021; 62:160-170. [PMID: 32452051 PMCID: PMC7688487 DOI: 10.1111/jcpp.13268] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2020] [Indexed: 01/21/2023]
Abstract
BACKGROUND Symptoms of autism spectrum disorder (ASD) emerge in the first years of life. Yet, little is known about the organization and development of functional brain networks in ASD proximally to the symptom onset. Further, the relationship between brain network connectivity and emerging ASD symptoms and overall functioning in early childhood is not well understood. METHODS Resting-state fMRI data were acquired during natural sleep from 24 young children with ASD and 23 typically developing (TD) children, aged 17-45 months. Intrinsic functional connectivity (iFC) within and between resting-state functional networks was derived with independent component analysis (ICA). RESULTS Increased iFC between visual and sensorimotor networks was found in young children with ASD compared to TD participants. Within the ASD group, the degree of overconnectivity between visual and sensorimotor networks was associated with greater autism symptoms. Age-related weakening of the visual-auditory between-network connectivity was observed in the ASD but not the TD group. CONCLUSIONS Taken together, these results provide evidence for disrupted functional network maturation and differentiation, particularly involving visual and sensorimotor networks, during the first years of life in ASD. The observed pattern of greater visual-sensorimotor between-network connectivity associated with poorer clinical outcomes suggests that disruptions in multisensory brain circuitry may play a critical role for early development of behavioral skills and autism symptomatology in young children with ASD.
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Affiliation(s)
- Bosi Chen
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Annika Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Lindsay Olson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Cynthia Ibarra
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Sarah Reynolds
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
| | - Mikaela Kinnear
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Inna Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA.,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, USA
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143
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Soma D, Hirosawa T, Hasegawa C, An KM, Kameya M, Hino S, Yoshimura Y, Nobukawa S, Iwasaki S, Tanaka S, Yaoi K, Sano M, Shiota Y, Naito N, Kikuchi M. Atypical Resting State Functional Neural Network in Children With Autism Spectrum Disorder: Graph Theory Approach. Front Psychiatry 2021; 12:790234. [PMID: 34970170 PMCID: PMC8712628 DOI: 10.3389/fpsyt.2021.790234] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/19/2021] [Indexed: 12/17/2022] Open
Abstract
Measuring whole brain networks is a promising approach to extract features of autism spectrum disorder (ASD), a brain disorder of widespread regions. Objectives of this study were to evaluate properties of resting-state functional brain networks in children with and without ASD and to evaluate their relation with social impairment severity. Magnetoencephalographic (MEG) data were recorded for 21 children with ASD (7 girls, 60-89 months old) and for 25 typically developing (TD) control children (10 girls, 60-91 months old) in a resting state while gazing at a fixation cross. After signal sources were localized onto the Desikan-Killiany brain atlas, statistical relations between localized activities were found and evaluated in terms of the phase lag index. After brain networks were constructed and after matching with intelligence using a coarsened exact matching algorithm, ASD and TD graph theoretical measures were compared. We measured autism symptoms severity using the Social Responsiveness Scale and investigated its relation with altered small-worldness using linear regression models. Children with ASD were found to have significantly lower small-worldness in the beta band (p = 0.007) than TD children had. Lower small-worldness in the beta band of children with ASD was associated with higher Social Responsiveness Scale total t-scores (p = 0.047). Significant relations were also inferred for the Social Awareness (p = 0.008) and Social Cognition (p = 0.015) sub-scales. Results obtained using graph theory demonstrate a difference between children with and without ASD in MEG-derived resting-state functional brain networks, and the relation of that difference with social impairment. Combining graph theory and MEG might be a promising approach to establish a biological marker for ASD.
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Affiliation(s)
- Daiki Soma
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Chiaki Hasegawa
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Kyung-Min An
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Shoryoku Hino
- Department of Neuropsychiatry, Ishikawa Prefectural Takamatsu Hospital, Kahoku, Japan
| | - Yuko Yoshimura
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan.,Faculty of Education, Institute of Human and Social Sciences, Kanazawa University, Kanazawa, Japan
| | - Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, Narashino, Japan
| | - Sumie Iwasaki
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Sanae Tanaka
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Ken Yaoi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Masuhiko Sano
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuka Shiota
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Nobushige Naito
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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144
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Wang K, Li K, Niu X. Altered Functional Connectivity in a Triple-Network Model in Autism With Co-occurring Attention Deficit Hyperactivity Disorder. Front Psychiatry 2021; 12:736755. [PMID: 34925086 PMCID: PMC8674431 DOI: 10.3389/fpsyt.2021.736755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: This study aimed to explore alterations in functional connectivity (FC) within and between default mode network (DMN), central executive network, and salience network in autism spectrum disorder (ASD) with co-occurring attention deficit hyperactivity disorder (ADHD). Method: A total of 135 individuals' date of the Autism Brain Imaging Data Exchange II was used to compare the ASD+ADHD group with the ASD group in relation to the abnormal within-network and between-network connectivity of the ASD group relative to the TD group; consequently, the correlation analysis between abnormal FC and behavior was performed. Results: The ASD+ADHD group exhibited decreased within-network connectivity in the precuneus of the ventral DMN compared with the ASD group. Among the three groups, the ASD+ADHD group showed lower connectivity, whereas the ASD group had higher connectivity than the TD group, although the effect of the separate post hoc test was not significant. Meanwhile, the ASD+ADHD group showed increased between-network connectivity between the ventral DMN and dorsal DMN and between the ventral DMN and left executive control network, compared with the ASD and TD groups. Conclusion: Dysfunction of DMN in the "triple-network model" is the core evidence for ASD with co-occurring ADHD.
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Affiliation(s)
- Kai Wang
- Department of Pediatrics, First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Ke Li
- Department of Child Healthcare, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China
| | - Xiaoyu Niu
- Department of Pediatrics, First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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145
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Tokuda T, Yamashita O, Sakai Y, Yoshimoto J. Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork. Front Psychiatry 2021; 12:683280. [PMID: 34483983 PMCID: PMC8416352 DOI: 10.3389/fpsyt.2021.683280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 07/26/2021] [Indexed: 12/04/2022] Open
Abstract
Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.
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Affiliation(s)
- Tomoki Tokuda
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Junichiro Yoshimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Nara, Japan
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146
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Pua EPK, Thomson P, Yang JYM, Craig JM, Ball G, Seal M. Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders. Cereb Cortex 2021; 31:681-693. [PMID: 32959054 DOI: 10.1093/cercor/bhaa252] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 08/06/2020] [Accepted: 08/07/2020] [Indexed: 12/18/2022] Open
Abstract
The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43 years, IQ = 110.58; controls: n = 100, mean age = 11.43 years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.
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Affiliation(s)
- Emmanuel Peng Kiat Pua
- Melbourne School of Psychological Sciences, University of Melbourne, Parkville VIC 3010, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Medicine, Austin Health, University of Melbourne, Parkville VIC 3010, Australia
| | - Phoebe Thomson
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
| | - Joseph Yuan-Mou Yang
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Suite (NACIS), The Royal Children's Hospital, Parkville VIC 3052, Australia
| | - Jeffrey M Craig
- Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia.,Molecular Epidemiology, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Centre for Molecular and Medical Research, School of Medicine, Deakin University, Geelong VIC 3220, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
| | - Marc Seal
- Developmental Imaging, Murdoch Children's Research Institute, Parkville VIC 3052, Australia.,Department of Paediatrics, University of Melbourne, Parkville VIC 3010, Australia
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147
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Liu J, Tsang T, Ponting C, Jackson L, Jeste SS, Bookheimer SY, Dapretto M. Lack of neural evidence for implicit language learning in 9-month-old infants at high risk for autism. Dev Sci 2020; 24:e13078. [PMID: 33368921 DOI: 10.1111/desc.13078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 12/18/2020] [Accepted: 12/21/2020] [Indexed: 11/30/2022]
Abstract
Word segmentation is a fundamental aspect of language learning, since identification of word boundaries in continuous speech must occur before the acquisition of word meanings can take place. We previously used functional magnetic resonance imaging (fMRI) to show that youth with autism spectrum disorder (ASD) are less sensitive to statistical and speech cues that guide implicit word segmentation. However, little is known about the neural mechanisms underlying this process during infancy and how this may be associated with ASD risk. Here, we examined early neural signatures of language-related learning in 9-month-old infants at high (HR) and low familial risk (LR) for ASD. During natural sleep, infants underwent fMRI while passively listening to three speech streams containing strong statistical and prosodic cues, strong statistical cues only, or minimal statistical cues to word boundaries. Compared to HR infants, LR infants showed greater activity in the left amygdala for the speech stream containing statistical and prosodic cues. While listening to this same speech stream, LR infants also showed more learning-related signal increases in left temporal regions as well as increasing functional connectivity between bilateral primary auditory cortex and right anterior insula. Importantly, learning-related signal increases at 9 months positively correlated with expressive language outcome at 36 months in both groups. In the HR group, greater signal increases were additionally associated with less severe ASD symptomatology at 36 months. These findings suggest that early differences in the neural networks underlying language learning may predict subsequent language development and altered trajectories associated with ASD risk.
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Affiliation(s)
- Janelle Liu
- Interdepartmental Neuroscience Program, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.,Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA
| | - Tawny Tsang
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.,Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Carolyn Ponting
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA.,Department of Psychology, 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
| | - Lisa Jackson
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.,Ahmanson-Lovelace Brain Mapping Center, 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
| | - Shafali S Jeste
- 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
| | - 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
| | - Mirella Dapretto
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA.,Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, USA
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148
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Hendriks MHA, Dillen C, Vettori S, Vercammen L, Daniels N, Steyaert J, Op de Beeck H, Boets B. Neural processing of facial identity and expression in adults with and without autism: A multi-method approach. NEUROIMAGE-CLINICAL 2020; 29:102520. [PMID: 33338966 PMCID: PMC7750419 DOI: 10.1016/j.nicl.2020.102520] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 10/23/2020] [Accepted: 11/30/2020] [Indexed: 11/28/2022]
Abstract
The ability to recognize faces and facial expressions is a common human talent. It has, however, been suggested to be impaired in individuals with autism spectrum disorder (ASD). The goal of this study was to compare the processing of facial identity and emotion between individuals with ASD and neurotypicals (NTs). Behavioural and functional magnetic resonance imaging (fMRI) data from 46 young adults (aged 17-23 years, NASD = 22, NNT = 24) was analysed. During fMRI data acquisition, participants discriminated between short clips of a face transitioning from a neutral to an emotional expression. Stimuli included four identities and six emotions. We performed behavioural, univariate, multi-voxel, adaptation and functional connectivity analyses to investigate potential group differences. The ASD-group did not differ from the NT-group on behavioural identity and expression processing tasks. At the neural level, we found no differences in average neural activation, neural activation patterns and neural adaptation to faces in face-related brain regions. In terms of functional connectivity, we found that amygdala seems to be more strongly connected to inferior occipital cortex and V1 in individuals with ASD. Overall, the findings indicate that neural representations of facial identity and expression have a similar quality in individuals with and without ASD, but some regions containing these representations are connected differently in the extended face processing network.
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Affiliation(s)
- Michelle H A Hendriks
- Department of Brain and Cognition, KU Leuven, Tiensestraat 102 - bus 3714, Leuven, Belgium; Leuven Autism Research Consortium, KU Leuven, Leuven, Belgium
| | - Claudia Dillen
- Department of Brain and Cognition, KU Leuven, Tiensestraat 102 - bus 3714, Leuven, Belgium; Leuven Autism Research Consortium, KU Leuven, Leuven, Belgium
| | - Sofie Vettori
- Centre for Developmental Psychiatry, KU Leuven, Kapucijnenvoer 7 blok h - bus 7001, Leuven, Belgium; Leuven Autism Research Consortium, KU Leuven, Leuven, Belgium
| | - Laura Vercammen
- Department of Brain and Cognition, KU Leuven, Tiensestraat 102 - bus 3714, Leuven, Belgium
| | - Nicky Daniels
- Department of Brain and Cognition, KU Leuven, Tiensestraat 102 - bus 3714, Leuven, Belgium; Centre for Developmental Psychiatry, KU Leuven, Kapucijnenvoer 7 blok h - bus 7001, Leuven, Belgium; Leuven Autism Research Consortium, KU Leuven, Leuven, Belgium
| | - Jean Steyaert
- Centre for Developmental Psychiatry, KU Leuven, Kapucijnenvoer 7 blok h - bus 7001, Leuven, Belgium; Leuven Autism Research Consortium, KU Leuven, Leuven, Belgium
| | - Hans Op de Beeck
- Department of Brain and Cognition, KU Leuven, Tiensestraat 102 - bus 3714, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Bart Boets
- Centre for Developmental Psychiatry, KU Leuven, Kapucijnenvoer 7 blok h - bus 7001, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium; Leuven Autism Research Consortium, KU Leuven, Leuven, Belgium.
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149
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Lynch CJ, Power JD, Scult MA, Dubin M, Gunning FM, Liston C. Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI. Cell Rep 2020; 33:108540. [PMID: 33357444 PMCID: PMC7792478 DOI: 10.1016/j.celrep.2020.108540] [Citation(s) in RCA: 76] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 10/15/2020] [Accepted: 11/25/2020] [Indexed: 12/20/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T2* decay, and S0-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations. Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.
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Affiliation(s)
- Charles J Lynch
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jonathan D Power
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Matthew A Scult
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Marc Dubin
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Faith M Gunning
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA
| | - Conor Liston
- Department of Psychiatry, Weill Cornell Medicine, New York, NY 10021, USA.
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150
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Linke A, Mash L, Fong C, Kinnear M, Kohli J, Wilkinson M, Tung R, Keehn RJ, Carper R, Fishman I, Müller R.A. Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders. Neuroimage 2020; 223:117383. [PMID: 32949710 PMCID: PMC9851773 DOI: 10.1016/j.neuroimage.2020.117383] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/12/2020] [Indexed: 01/21/2023] Open
Abstract
Resting state fMRI (rsfMRI) is frequently used to study brain function, including in clinical populations. Similarity of blood-oxygen-level-dependent (BOLD) fluctuations during rsfMRI between brain regions is thought to reflect intrinsic functional connectivity (FC), potentially due to history of coactivation. To quantify similarity, studies have almost exclusively relied on Pearson correlation, which assumes linearity and can therefore underestimate FC if the hemodynamic response function differs regionally or there is BOLD signal lag between timeseries. Here we show in three cohorts of children, adolescents and adults, with and without autism spectrum disorders (ASDs), that measuring the similarity of BOLD signal fluctuations using non-linear dynamic time warping (DTW) is more robust to global signal regression (GSR), has higher test-retest reliability and is more sensitive to task-related changes in FC. Additionally, when comparing FC between individuals with ASDs and typical controls, more group differences are detected using DTW. DTW estimates are also more related to ASD symptom severity and executive function, while Pearson correlation estimates of FC are more strongly associated with respiration during rsfMRI. Together these findings suggest that non-linear methods such as DTW improve estimation of resting state FC, particularly when studying clinical populations whose hemodynamics or neurovascular coupling may be altered compared to typical controls.
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Affiliation(s)
- A.C. Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,Corresponding author. (A.C. Linke)
| | - L.E. Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - C.H. Fong
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - M.K. Kinnear
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States
| | - J.S. Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - M. Wilkinson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - R. Tung
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States
| | - R.J. Jao Keehn
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States
| | - R.A. Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - I. Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
| | - R.-.A. Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, 6363 Alvarado Ct., Suite 200, San Diego, CA 92120, United States,San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology, San Diego, CA, United States
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