Alterations in Social Brain Network Topology at Rest in Children With Autism Spectrum Disorder
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Objective Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages. Methods In this study, 31 ASD children aged 2–11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed. Results Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2–6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced. Conclusion The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.
Abstract
Objective Underconnectivity in the resting brain is not consistent in autism spectrum disorder (ASD). However, it is known that the functional connectivity of the default mode network is mainly decreased in childhood ASD. This study investigated the brain network topology as the changes in the connection strength and network efficiency in childhood ASD, including the early developmental stages. Methods In this study, 31 ASD children aged 2–11 years were compared with 31 age and sex-matched children showing typical development. We explored the functional connectivity based on graph filtration by assessing the single linkage distance and global and nodal efficiencies using resting-state functional magnetic resonance imaging. The relationship between functional connectivity and clinical scores was also analyzed. Results Underconnectivities within the posterior default mode network subregions and between the inferior parietal lobule and inferior frontal/superior temporal regions were observed in the ASD group. These areas significantly correlated with the clinical phenotypes. The global, local, and nodal network efficiencies were lower in children with ASD than in those with typical development. In the preschool-age children (2–6 years) with ASD, the anterior-posterior connectivity of the default mode network and cerebellar connectivity were reduced. Conclusion The observed topological reorganization, underconnectivity, and disrupted efficiency in the default mode network subregions and social function-related regions could be significant biomarkers of childhood ASD.
참고문헌 (88)
- DiCicco-Bloom E, "The developmental neurobiology of autism spectrum disorder", J Neurosci, 26, pp.6897~6906 (2006)
- Stigler KA, "Structural and functional magnetic resonance imaging of autism spectrum disorders", Brain Res, 1380, pp.146~161 (2011)
- Fakhoury M, "Imaging genetics in autism spectrum disorders: linking genetics and brain imaging in the pursuit of the underlying neurobiological mechanisms", Prog Neuropsychopharmacol Biol Psychiatry, 80(Pt B), pp.101~114 (2018)
- Belger A, "The neural circuitry of autism", Neurotox Res, 20, pp.201~214 (2011)
- Shafritz KM, "The neural circuitry mediating shifts in behavioral response and cognitive set in autism", Biol Psychiatry, 63, pp.974~980 (2008)
- Plitt M, "Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards", Neuroimage Clin, 7, pp.359~366 (2015)
- Holiga Š, "Patients with autism spectrum disorders display reproducible functional connectivity alterations", Sci Transl Med, 11, pp.eaat9223~ (2019)
- Hull JV, "Resting-state functional connectivity in autism spectrum disorders:a review", Front Psychiatry, 7, pp.205~ (2017)
- Rudie JD, "Altered functional and structural brain network organization in autism", Neuroimage Clin, 2, pp.79~94 (2013)
- Picci G, "A theoretical rut: revisiting and critically evaluating the generalized under", over-connectivity hypothesis of autism, 2016, pp.19, 524~549
- Lau WKW, "Resting-state abnormalities in autism spectrum disorders: a meta-analysis", Sci Rep, 9, pp.3892~ (2019)
- Haghighat H, "Functional networks abnormalities in autism spectrum disorder: age-related hypo and hyper connectivity", Brain Topogr, 34, pp.306~322 (2021)
- Harikumar A, "A review of the default mode network in autism spectrum disorders and attention deficit hyperactivity disorder", Brain Connect, 11, pp.253~263 (2021)
- Nair A, "A review of default mode network connectivity and its association with social cognition in adolescents with autism spectrum disorder and early-onset psychosis", Front Psychiatry, 11, pp.614~ (2020)
- Chen H, "Multivariate classification of autism spectrum disorder using frequency-specific restingstate functional connectivity--a multi-center study", Prog Neuropsychopharmacol Biol Psychiatry, 64, pp.1~9 (2016)
- Uddin LQ, "Salience network-based classification and prediction of symptom severity in children with autism", JAMA Psychiatry, 70, pp.869~879 (2013)
- Mars RB, "On the relationship between the “default mode network” and the “social brain”", Front Hum Neurosci, 6, pp.189~ (2012)
- Yerys BE, "Default mode network segregation and social deficits in autism spectrum disorder: evidence from non-medicated children", Neuroimage Clin, 9, pp.223~232 (2015)
- Nielsen JA, "Multisite functional connectivity MRI classification of autism:ABIDE results", Front Hum Neurosci, 7, pp.599~ (2013)
- Ypma RJ, "Default mode hypoconnectivity underlies a sex-related autism spectrum", Biol Psychiatry Cogn Neurosci Neuroimaging, 1, pp.364~371 (2016)
- Lynch CJ, "Default mode network in childhood autism: posteromedial cortex heterogeneity and relationship with social deficits", Biol Psychiatry, 74, pp.212~219 (2013)
- Yao Z, "Resting-state timevarying analysis reveals aberrant variations of functional connectivity in autism", Front Hum Neurosci, 10, pp.463~ (2016)
- Courchesne E, "Prenatal origins of ASD: the when, what, and how of ASD development", Trends Neurosci, 43, pp.326~342 (2020)
- Caputi L, "Promises and pitfalls of topological data analysis for brain connectivity analysis", Neuroimage, 238, pp.118245~ (2021)
- Lee H, "Persistent brain network homology from the perspective of dendrogram", IEEE Trans Med Imaging, 31, pp.2267~2277 (2012)
- Ha S, "Maturational delay and asymmetric information flow of brain connectivity in SHR model of ADHD revealed by topological analysis of metabolic networks", Sci Rep, 10, pp.3197~ (2020)
- Im HJ, "Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson’s disease examined using persistent homology-based analysis", Sci Rep, 6, pp.33875~ (2016)
- Lord C, "The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism", J Autism Dev Disord, 30, pp.205~223 (2000)
- Schopler E, "The childhood autism rating scale (CARS)" (1988), Western Psychological Services
- Shin MS, "Standardization study for the Korean version of the childhood autism rating scale: reliability, validity and cut-offscore", Kor J Clin Psychol, 17, pp.1~15 (1998)
- Park KS, "Korean Educational Development Institute Weschler Intelligence Scale for Children (KEDI-WISC)" (2002), Korean Educational Development Institute
- Roid GH, "Leiter international performance scale-revised (Leiter-R)", pp.10~ (1997), Stoelting
- Shin MS, "Korean Leiter international performance scale revised (K-Leiter-R)" (2010), Hakjisa
- Kwak KJ, "Korean-Wechsler intelligence scale for children" (2011), Hakjisa
- Constantino JN, "Encyclopedia of autism spectrum disorders", pp.2919~2929 (2013), Springer
- Patel AX, "A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series", Neuroimage, 95, pp.287~304 (2014)
- Ashburner J, "Unified segmentation", Neuroimage, 26, pp.839~851 (2005)
- Fonov VS, "Unbiased nonlinear average age-appropriate brain templates from birth to adulthood", NeuroImage, 47(Supplement 1), pp.S102~ (2009)
- Fonov V, "Unbiased average age-appropriate atlases for pediatric studies", Neuroimage, 54, pp.313~327 (2011)
- Fan L, "The human brainnetome atlas: a new brain atlas based on connectional architecture", Cereb Cortex, 26, pp.3508~3526 (2016)
- Diedrichsen J, "A probabilistic MR atlas of the human cerebellum", Neuroimage, 46, pp.39~46 (2009)
- Rubinov M, "Complex network measures of brain connectivity:uses and interpretations", Neuroimage, 52, pp.1059~1069 (2010)
- Achard S, "Efficiency and cost of economical brain functional networks", PLoS Comput Biol, 3, pp.e17~ (2007)
- Glerean E, "Reorganization of functionally connected brain subnetworks in highfunctioning autism", Hum Brain Mapp, 37, pp.1066~1079 (2016)
- He C, "Dynamic functional connectivity analysis reveals decreased variability of the defaultmode network in developing autistic brain", Autism Res, 11, pp.1479~1493 (2018)
- Moseley RL, "Whole-brain functional hypoconnectivity as an endophenotype of autism in adolescents", Neuroimage Clin, 9, pp.140~152 (2015)
- Uddin LQ, "The self and social cognition:the role of cortical midline structures and mirror neurons", Trends Cogn Sci, 11, pp.153~157 (2007)
- Andrews-Hanna JR, "Functional-anatomic fractionation of the brain’s default network", Neuron, 65, pp.550~562 (2010)
- Buckner RL, "The brain’s default network:anatomy, function, and relevance to disease", Ann N Y Acad Sci, 1124, pp.1~38 (2008)
- Parlatini V, "Functional segregation and integration within fronto-parietal networks", Neuroimage, 146, pp.367~375 (2017)
- Oberman LM, "The simulating social mind: the role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders", Psychol Bull, 133, pp.310~327 (2007)
- Yang J, "Action observation and imitation in autism spectrum disorders: an ALE meta-analysis of fMRI studies", Brain Imaging Behav, 10, pp.960~969 (2016)
- Wymbs NF, "Altered inferior parietal functional connectivity is correlated with praxis and social skill performance in children with autism spectrum disorder", Cereb Cortex, 31, pp.2639~2652 (2021)
- Nishitani N, "Abnormal imitation-related cortical activation sequences in Asperger’s syndrome", Ann Neurol, 55, pp.558~562 (2004)
- Pfeifer JH, "Mirroring others’emotions relates to empathy and interpersonal competence in children", Neuroimage, 39, pp.2076~2085 (2008)
- Williams JH, "Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum disorder", Neuropsychologia, 44, pp.610~621 (2006)
- Shih P, "Atypical network connectivity for imitation in autism spectrum disorder", Neuropsychologia, 48, pp.2931~2939 (2010)
- Keown CL, "Network organization is globally atypical in autism: a graph theory study of intrinsic functional connectivity", Biol Psychiatry Cogn Neurosci Neuroimaging, 2, pp.66~75 (2017)
- Itahashi T, "Altered network topologies and hub organization in adults with autism:a resting-state fMRI study", PLoS One, 9, pp.e94115~ (2014)
- Hadjikhani N, "Anatomical differences in the mirror neuron system and social cognition network in autism", Cereb Cortex, 16, pp.1276~1282 (2006)
- Odriozola P, "Insula response and connectivity during social and non-social attention in children with autism", Soc Cogn Affect Neurosci, 11, pp.433~444 (2016)
- Patriquin MA, "Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder", Hum Brain Mapp, 37, pp.3957~3978 (2016)
- Kana RK, "Inhibitory control in highfunctioning autism: decreased activation and underconnectivity in inhibition networks", Biol Psychiatry, 62, pp.198~206 (2007)
- Lukito S, "Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit", hyperactivity disorder and autism spectrum disorder, 2020, pp.50, 894~919
- Schmitz N, "Neural correlates of executive function in autistic spectrum disorders", Biol Psychiatry, 59, pp.7~16 (2006)
- Schipul SE, "Inter-regional brain communication and its disturbance in autism", Front Syst Neurosci, 5, pp.10~ (2011)
- Just MA, "Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity", Brain, 127(Pt 8), pp.1811~1821 (2004)
- Just MA, "Functional and anatomical cortical underconnectivity in autism: evidence from an FMRI study of an executive function task and corpus callosum morphometry", Cereb Cortex, 17, pp.951~961 (2007)
- Horwitz B, "The cerebral metabolic landscape in autism. Intercorrelations of regional glucose utilization", Arch Neurol, 45, pp.749~755 (1988)
- du Boisgueheneuc F, "Functions of the left superior frontal gyrus in humans: a lesion study", Brain, 129(Pt 12), pp.3315~3328 (2006)
- Fogassi L, "Motor functions of the parietal lobe", Curr Opin Neurobiol, 15, pp.626~631 (2005)
- Goodale MA, "Separate visual pathways for perception and action", Trends Neurosci, 15, pp.20~25 (1992)
- Travers BG, "Motor learning in individuals with autism spectrum disorder: activation in superior parietal lobule related to learning and repetitive behaviors", Autism Res, 8, pp.38~51 (2015)
- Baron-Cohen S, "Autism: a window onto the development of the social and the analytic brain", Annu Rev Neurosci, 28, pp.109~126 (2005)
- Beauchamp MS, "The social mysteries of the superior temporal sulcus", Trends Cogn Sci, 19, pp.489~490 (2015)
- Pelphrey KA, "Charting the typical and atypical development of the social brain", Dev Psychopathol, 20, pp.1081~1102 (2008)
- Kobayashi A, "Increased grey matter volume of the right superior temporal gyrus in healthy children with autistic cognitive style: a VBM study", Brain Cogn, 139, pp.105514~ (2020)
- Fatemi SH, "Consensus paper: pathological role of the cerebellum in autism", Cerebellum, 11, pp.777~807 (2012)
- Rogers TD, "Is autism a disease of the cerebellum? An integration of clinical and pre-clinical research", Front Syst Neurosci, 7, pp.15~ (2013)
- Courchesne E, "Hypoplasia of cerebellar vermal lobules VI and VII in autism", N Engl J Med, 318, pp.1349~1354 (1988)
- Tavano A, "Disorders of cognitive and affective development in cerebellar malformations", Brain, 130(Pt 10), pp.2646~2660 (2007)
- D’Mello AM, "Cerebro-cerebellar circuits in autism spectrum disorder", Front Neurosci, 9, pp.408~ (2015)
- Wang AT, "Reading affect in the face and voice: neural correlates of interpreting communicative intent in children and adolescents with autism spectrum disorders", Arch Gen Psychiatry, 64, pp.698~708 (2007)
- Critchley HD, "The functional neuroanatomy of social behaviour:changes in cerebral blood flow when people with autistic disorder process facial expressions", Brain, 123(Pt 11), pp.2203~2212 (2000)
- Wang SS, "The cerebellum, sensitive periods, and autism", Neuron, 83, pp.518~532 (2014)
- Scott RB, "Lateralized cognitive deficits in children following cerebellar lesions", Dev Med Child Neurol, 43, pp.685~691 (2001)
- DiFrancesco MW, "BOLD fMRI in infants under sedation: comparing the impact of pentobarbital and propofol on auditory and language activation", J Magn Reson Imaging, 38, pp.1184~1195 (2013)
- Liu X, "Propofol attenuates low-frequency fluctuations of resting-state fMRI BOLD signal in the anterior frontal cortex upon loss of consciousness", Neuroimage, 147, pp.295~301 (2017)
