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Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients
Open accessAbstract
Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation.
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“evidence for differences in connectivity between distant brain regions in children with ASD (Abrams et al., 2013; Assaf et al., 2010; Maximo et al., 2014; Plitt et al., 2015) would also suggest a propensity towards multisensory deficits”
From Behavioral, Perceptual, and Neural Alterations in Sensory and Multisensory Function in Autism Spectrum Disorder (2015) · cites this paper as Assaf et al., 2010
In the citing paper
“the magnitude of connection strength between DMN nodes is inversely correlated with the severity of social and communication difficulties (Assaf et al., 2010)”
From Behavioral, Perceptual, and Neural Alterations in Sensory and Multisensory Function in Autism Spectrum Disorder (2015) · cites this paper as Assaf et al., 2010
In the citing paper
“In fact, several recent imaging studies on resting-state functional connectivity and DTI confirm disruptive neural activation dynamics in ASD within the vmPFC–VS–amygdala circuitry [78-81].”
From Social “wanting” dysfunction in autism: neurobiological underpinnings and treatment implications (2012) · cites this paper as [78]
In the citing paper
“Activation in the DMN during this phase of the game is posited to occur due to mentalizing processes (e.g., trying to infer the opponent's strategy) as well as future planning (e.g., what chip to play next) and is measured by the show>no-show contrast [Assaf et al., 2010[Assaf et al., , 2009b]].”
From Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks (2015) · cites this paper as Assaf et al., 2010
In the citing paper
“This contrasts with earlier studies of resting state fMRI using low-order sICA (<= 30 components) where the DMN (excluding the temporal poles) was typically encompassed by only two to three ICNs [Assaf et al., 2010;Calhoun et al., 2008], or in some cases, by only a single ICN [Franco et al., 2009;Meindl et al., 2010;Patel et al., 2013].”
From Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks (2015) · cites this paper as Assaf et al., 2010
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Cites
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
Lord, C., Rutter, M., & Le Couteur, A. (1994) · Journal of Autism and Developmental Disorders · doi:10.1007/bf02172145 · Cited by 9,065
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565
Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., & Rutter, M. (2000) · Journal of Autism and Developmental Disorders · doi:10.1023/a:1005592401947 · Cited by 7,520
The precuneus: a review of its functional anatomy and behavioural correlates
Cavanna, A. E., & Trimble, M. R. (2006) · Brain · doi:10.1093/brain/awl004 · Cited by 5,128
Maldjian, J. A., Laurienti, P. J., Kraft, R., & Burdette, J. H. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00169-1 · Cited by 5,066
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
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Wang, Y., Xu, Q., Zuo, C., Zhao, L., & Lei, H. C. (2020) · Neuroscience Letters · doi:10.1016/j.neulet.2020.134949 · Cited by 16
Khan, N. A., Waheeb, S. A., Riaz, A., & Shang, X. (2020) · Brain Sciences · doi:10.3390/brainsci10100754 · Cited by 18
Reardon, A. M., Li, K., & Hu, X. P. (2021) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.762781 · Cited by 16
Structural and Functional MRI Studies of Autism Spectrum Disorders
Stigler, K. A., & McDougle, C. J. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-391924-3.00017-x · Cited by 18
Harlalka, V., Bapi, R. S., Vinod, P. K. U., & Roy, D. (2018) · Brain Connectivity · doi:10.1089/brain.2018.0616 · Cited by 14
Xiao, Z., Wu, J., Wang, C., Jia, N., & Yang, X. (2019) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2019.7448 · Cited by 15
Zhang, L., Gan, J. Q., & Wang, H. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00430 · Cited by 38
Intrinsic brain activity associated with eye gaze during mother–child interaction
Kuboshita, R., Fujisawa, T. X., Makita, K., Kasaba, R., Okazawa, H., & Tomoda, A. (2020) · Scientific Reports · doi:10.1038/s41598-020-76044-y · Cited by 18
Wu, X., Liang, C., Bustillo, J., Kochunov, P., Wen, X., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Jiang, R., Yang, X., Fu, Z., Zhang, D., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70206 · Cited by 16
Self-citationMantra Meditation Suppression of Default Mode Beyond an Active Task: a Pilot Study
Simon, R. A., Pihlsgård, J., Berglind, U., Söderfeldt, B. A., & Engström, M. (2017) · Journal of Cognitive Enhancement · doi:10.1007/s41465-017-0028-1 · Cited by 16
The effect of seed location on functional connectivity: evidence from an image-based meta-analysis
Li, M.-T., Sun, J.-W., Zhan, L., Antwi, C. O., Lv, Y., Jia, X., & Ren, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1120741 · Cited by 14
Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism
Park, B., Benkarim, O. M., Weber, C. F., Kebets, V., Fett, S., Yoo, S., Martino, A. D., Milham, M. P., Mišić, B., Valk, S. L., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120481 · Cited by 15
Intranasal oxytocin modulates the salience network in aging
Liu, P., Lin, T., Feifel, D., & Ebner, N. C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119045 · Cited by 13
Lou, H. C. (2011) · Acta Paediatrica · doi:10.1111/j.1651-2227.2011.02456.x · Cited by 14
Lubrini, G., Martín-Montes, A., Díez-Ascaso, O., & Díez‐Tejedor, E. (2017) · Neurología · doi:10.1016/j.nrl.2017.02.005 · Cited by 14
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Won, D., Manzour, H., & Chaovalitwongse, W. A. (2019) · INFORMS journal on computing · doi:10.1287/ijoc.2018.0868 · Cited by 14
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2018) · doi:10.1101/473603 · Cited by 16
Zhao, H., Lou, H., Yao, L., & Zhang, Y. (2025) · Journal of Neural Engineering · doi:10.1088/1741-2552/adb07a · Cited by 15
ASD-GraphNet: A novel graph learning approach for Autism Spectrum Disorder diagnosis using fMRI data
Zeraati, M., & Davoodi, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110723 · Cited by 14
Cognitive theories of autism based on the interactions between brain functional networks
Alamdari, S. B., Sadeghi, M., Zarei, M., & Khosrowabadi, R. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.828985 · Cited by 13
Altered intra- and inter-network connectivity in autism spectrum disorder
Zhou, R., Sun, C., Sun, M., Ruan, Y., Li, W., & Gao, X. (2024) · Aging · doi:10.18632/aging.205913 · Cited by 13
Abnormal static and dynamic functional connectivity of resting-state fMRI in multiple system atrophy
Zheng, W., Ge, Y., Ren, S., Ran, W., Zhang, X., Tian, W., Chen, Z., Dou, W., & Wang, Z. (2020) · Aging · doi:10.18632/aging.103676 · Cited by 12
Alteration of Effective Connectivity in the Default Mode Network of Autism After an Intervention
Han, Y., Qu, H., Chen, A., Du, Y., Liu, Z., & Wang, W. J. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.796437 · Cited by 13
Zhao, F., Han, Z., Cheng, D., Mao, N., Chen, X., Li, Y., Fan, D., & Liu, P. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.810431 · Cited by 14
Kwon, H., Kim, J. I., Son, S., Jang, Y. H., Kim, B.-N., Lee, H. J., & Lee, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.935431 · Cited by 12
Electroencephalographic studies in children with autism spectrum disorders
Strzelecka, J. (2014) · Research in Autism · doi:10.1016/j.rasd.2013.11.010 · Cited by 16
Predictive connectome subnetwork extraction with anatomical and connectivity priors
Brown, C. J., Miller, S. P., Booth, B. G., Zwicker, J. G., GRUNAU, R. E., Synnes, A. R., Chau, V., & Hamarneh, G. (2018) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2018.08.009 · Cited by 14
Álvarez, D. M. H., Pacheco, L., Velasco-Segura, R., de la Mora, M. P., Romero, C. T., & García, N. G. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.00209 · Cited by 13
Wu, X., Wen, X., Sun, J., Dong, Y., Wu, C., Wang, B., & Xiang, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106944 · Cited by 14
Default mode network activation at task switches reflects mental task-set structure
Zhou, A. X., Duncan, J., & Mitchell, D. J. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00515 · Cited by 11
Jeong, B., Choi, J., & Kim, J.-W. (2012) · Korean Journal of Radiology · doi:10.3348/kjr.2012.13.3.265 · Cited by 13
Vidal, M. C., Sato, J. R., Balardin, J. B., Takahashi, D. Y., & Fujita, A. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00016 · Cited by 12
Bhinge, S., Long, Q., Calhoun, V. D., & Adalı, T. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.3003891 · Cited by 12
Self-citationNiego, A., & Benítez‐Burraco, A. (2020) · Autism · doi:10.1177/1362361320965074 · Cited by 12
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Zhang, X., Li, X., Jin, C., Chen, H., Li, K., Zhu, D., Jiang, X., Zhang, T., Lv, J., Hu, X., et al. (2014) · Brain Topography · doi:10.1007/s10548-014-0357-7 · Cited by 12
Solomon, M., Hogeveen, J., Libero, L. E., & Nordahl, C. W. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.06.002 · Cited by 12
Abnormal Brain Regions in Two-Group Cross-Location Dynamics Model of Autism
Fan, G., Chen, Y., Chen, Y., Yang, M., Wang, J., Li, C., Li, Y., & Liu, T. (2020) · IEEE Access · doi:10.1109/access.2020.2995209 · Cited by 12
Anatomical and Functional Connectivity in Autism Spectrum Disorders
Müller, R. (2013) · Springer eBooks · doi:10.1007/978-1-4614-4788-7_192 · Cited by 13
The Cost of Untracked Diversity in Brain-Imaging PredictionPreprint
Benkarim, O. M., Paquola, C., Park, B., Kebets, V., Hong, S., de Wael, R. V., Zhang, S., Yeo, B. T. T., Eickenberg, M., Ge, T., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.16.448764 · Cited by 12
Maskrey, M. K., & Wang, W. (2018) · Apress eBooks · doi:10.1007/978-1-4842-3381-8_8 · Cited by 11
Bednarz, H. M., & Kana, R. K. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-019-04168-w · Cited by 11
Valenza, M., Steardo, L., Steardo, L., Verkhratsky, A., & Scuderi, C. (2021) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2021.748136 · Cited by 12
Clawson, A., Clayson, P. E., Worsham, W., Johnston, O., South, M., & Larson, M. J. (2014) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2014.01.009 · Cited by 11
Dong, M., Qin, W., Sun, J., Liu, P., Yuan, K., Liu, J., Zhou, G., von Deneen, K. M., & Tian, J. (2011) · Brain Research · doi:10.1016/j.brainres.2011.11.050 · Cited by 11
Developmental implications of children’s brain networks and learning
Chan, J. S. Y., Wang, Y., Yan, J. H., & Chen, H. (2016) · Reviews in the Neurosciences · doi:10.1515/revneuro-2016-0007 · Cited by 12
Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder
Andrews, D. S., Marquand, A. F., Ecker, C., & McAlonan, G. M. (2018) · Current topics in behavioral neurosciences · doi:10.1007/7854_2018_47 · Cited by 14
Zhang, L., Ma, R., Yuan, Y., Lian, D., Qi, X., Zheng, N., & Li, K. (2019) · Medicine · doi:10.1097/md.0000000000015058 · Cited by 11
A network clustering based feature selection strategy for classifying autism spectrum disorder
Tang, L., Mostafa, S., Liao, B., & Wu, F. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0598-0 · Cited by 11
Blume, J., Kahathuduwa, C. N., & Mastergeorge, A. M. (2022) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-022-05460-y · Cited by 14
Zhang, H., Ji, S., Ren, S., Liu, M., Ran, W., Zhang, X., Tian, W., Chen, Z., & Wang, Z. (2020) · The Cerebellum · doi:10.1007/s12311-020-01144-4 · Cited by 11
Jeong, J., Chugani, H. T., Behen, M. E., Guy, W., & Juhász, C. (2012) · Journal of Child Neurology · doi:10.1177/0883073812469296 · Cited by 10
Li, B., Li, X., Pan, Y., Qiu, J., & Zhang, D. (2018) · Scientific Reports · doi:10.1038/s41598-018-21890-0 · Cited by 10
Lefebvre, E., & D’Angiulli, A. (2019) · Brain Sciences · doi:10.3390/brainsci9060143 · Cited by 13
Maneshi, M., Vahdat, S., Gotman, J., & Grova, C. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00417 · Cited by 10
Towards robust and replicable sex differences in the intrinsic brain function of autismPreprint
Floris, D. L., Filho, J. O. A., Lai, M., Giavasis, S., Oldehinkel, M., Mennes, M., Charman, T., Tillmann, J., Dumas, G., Ecker, C., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.09.142471 · Cited by 10
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Padole, H. P., Joshi, S. D., & Gandhi, T. K. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.021310 · Cited by 9
Hu, L., Katz, E. S., & Stamoulis, C. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120459 · Cited by 11
Wylie, K. P., Tregellas, J. R., Bear, J. J., & Legget, K. T. (2020) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-020-04383-w · Cited by 9
Kahathuduwa, C. N., West, B. D., & Mastergeorge, A. M. (2019) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-019-04187-7 · Cited by 9
Response: Infant EEG activity as a biomarker for autism: A promising approach or a false promise?
Bosl, W. J., Tierney, A. L., Tager-Flusberg, H. B., & Nelson, C. A. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-60 · Cited by 9
Tang, S., Sun, N., Floris, D. L., Zhang, X., Martino, A. D., & Yeo, B. T. T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/692772 · Cited by 9
Wang, H., Liu, Y., & Ding, Y. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09662-w · Cited by 8
Similarity-guided multi-view functional brain network fusion
Li, Z., Liu, J., Sun, M., Zhang, F., Hu, B., & Dong, Q. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103632 · Cited by 10
Inter-hemispherical Investigations on the Functional Connectivity of Autistic Resting State fMRI
Vidhusha, S., & Kavitha, A. (2016) · International Journal of Cognitive Informatics and Natural Intelligence · doi:10.4018/ijcini.2016040105 · Cited by 8
Computational model for detection of abnormal brain connections in children with autism
Askari, E., Setarehdan, S. K., Sheikhani, A., Mohammadi, M. R., & Teshnehlab, M. (2018) · Journal of Integrative Neuroscience · doi:10.3233/jin-180075 · Cited by 11
Cook, K. M., You, X., Cherry, J. B. C., Merchant, J. S., Skapek, M., Powers, M. D., Pugliese, C. E., Kenworthy, L. E., & Vaidya, C. J. (2021) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-021-09388-9 · Cited by 8
Assessment of functional connectivity in autistic brain fMRI
Chengaiyan, S., Vidhusha, S., S, G. R., Sneha Priya, B., & Kavitha, A. (2015) · IEEE International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) · doi:10.1109/icci-cc.2015.7259382 · Cited by 8
Connectivity abnormalities in autism spectrum disorder patients: A resting state fMRI study
Ahmadi, S. M. M., Mohajeri, N., & Hamid Soltanian-zadeh, A. (2014) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2014.6999846 · Cited by 8
Shared and Specific Independent Components Analysis for Between-Group Comparison
Vahdat, S., Maneshi, M., Grova, C., Gotman, J., & Milner, T. E. (2012) · Neural Computation · doi:10.1162/neco_a_00355 · Cited by 8
MAFT-SO: A novel multi-atlas fusion template based on spatial overlap for ASD diagnosis
Ma, Y., Mu, X., Zhang, T., & Zhao, Y. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104714 · Cited by 7
Sex heterogeneity of dynamic brain activity and functional connectivity in autism spectrum disorder
Lu, H., Dong, Q., Gao, L., Xue, Z., Niu, X., Zhou, R., & Guo, X. (2024) · Autism Research · doi:10.1002/aur.3227 · Cited by 7
Chan, S. Y., Chuah, J. S. M., Huang, P., & Tan, A. P. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101483 · Cited by 7
Zhang, J., Fang, S., Yao, Y., Li, F., & Luo, Q. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.04.102 · Cited by 7
Behboodi, B., Lim, S., Luna, M., Jeon, H., & Choi, J.-W. (2019) · Journal of Near Infrared Spectroscopy · doi:10.1177/0967033519836623 · Cited by 7
Machinskaya, R. I., Kurgansky, А. V., & Ломакин, Д. И. (2019) · Human Physiology · doi:10.1134/s0362119719050098 · Cited by 7
Long, Q., Bhinge, S., Calhoun, V. D., & Adalı, T. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0815 · Cited by 7
Self-citationA Multi-Site Anti-Interference Neural Network for ASD Classification
Lv, W., Li, F., Luo, S., & Xiang, J. (2023) · Algorithms · doi:10.3390/a16070315 · Cited by 7
Molnar-Szakacs, I., & Uddin, L. Q. (2016) · Cambridge University Press eBooks · doi:10.1017/cbo9781139941297.016 · Cited by 7
Kotila, A., Järvelä, M., Korhonen, V. O., Loukusa, S., Hurtig, T. M., Ebeling, H. E., Kiviniemi, V. J., & Raatikainen, V. (2020) · Autism Research · doi:10.1002/aur.2433 · Cited by 6
Discrete Cortical Neuropathology in Autism Spectrum Disorders
Uppal, N., & Hof, P. R. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-391924-3.00022-3 · Cited by 7
Functional idiosyncrasy has a shared topography with group-level connectivity alterations in autismPreprint
Benkarim, O. M., Paquola, C., Park, B., Hong, S., Royer, J., de Wael, R. V., Larivière, S., Valk, S. L., Bzdok, D., Mottron, L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.18.423291 · Cited by 7
Elandaloussi, Y., Dufrenne, O., Lefebvre, A., Houenou, J., Senova, S., & Laidi, C. (2025) · The Cerebellum · doi:10.1007/s12311-025-01801-6 · Cited by 6
Kim, Y.-W., Kim, S., Jin, M. J., Im, C., & Lee, S. (2023) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.22.1035 · Cited by 7
Linke, A. C., Chen, B., Olson, L., Cordova, M., Wilkinson, M. K., Wang, T., Herrera, M., Salmina, M., Ríos, A., Mahmalji, J., et al. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.09.003 · Cited by 6
Xu, J., He, K., Lan, M., Bian, Q., Li, W., Li, T., Ke, Y., & Qiao, M. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679560 · Cited by 6
Sun, H., Lin, F., Wu, X., Zhang, T., & Li, J. (2023) · Journal of Biophotonics · doi:10.1002/jbio.202200369 · Cited by 7
Neural correlates of socio-emotional perception in 22q11.2 deletion syndrome
Dubourg, L., Vrtička, P., Debbané, M., Chambaz, L., Eliez, S., & Schneider, M. (2018) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-018-9232-2 · Cited by 7
Classification of Autism Based on fMRI Data with Feature-Fused Convolutional Neural Network
You, Y., Liu, H., Zhang, S., & Shao, L. (2020) · Communications in computer and information science · doi:10.1007/978-981-33-4336-8_7 · Cited by 6
Comparative study of various machine learning methods on ASD classification
Rimal, R., Brannon, M., Wang, Y., & Yang, X. (2023) · International Journal of Data Science and Analytics · doi:10.1007/s41060-023-00408-6 · Cited by 8
Lake, E. M. R., Finn, E. S., Noble, S., Vanderwal, T., Shen, X., Rosenberg, M. D., Spann, M. N., Chun, M. M., Scheinost, D., & Constable, R. T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/290320 · Cited by 6
Iraji, A., Fu, Z., Damaraju, E., DeRamus, T. P., Lewis, N., Bustillo, J., Lenroot, R., Belger, A., Ford, J. M., McEwen, S. C., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/391094 · Cited by 7
Self-citationSaitovitch, A., Rechtman, E., Lemaître, H., Tacchella, J.-M., Vinçon-Leite, A., Douard, E. A., Calmon, R., Grévent, D., Philippe, A., Chabane, N., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/771584 · Cited by 6
Li, Y., Li, R., Wang, N., Gu, J., & Gao, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1203690 · Cited by 5
Weber, C. F., Lake, E. M. R., Haider, S. P., Mozayan, A., Bobba, P. S., Mukherjee, P., Scheinost, D., Constable, R. T., Ment, L. R., & Payabvash, S. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1285396 · Cited by 4
Qiu, L., Liang, C., Kochunov, P. V., Hutchison, K. E., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Jiang, R., Zhi, D., Vergara, V. M., Yang, X., Zhang, D., et al. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-03035-2 · Cited by 5
Self-citationPatwardhan, A., & Choe, K. Y. (2025) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2025.102787 · Cited by 7
Modelling and analysis of brain functional network
Huang, W., Cao, L., Qing-jian, C., Su, C.-W., Li, Y., & Huang, Z. (2019) · Zhongguo kexue. Wulixue Lixue Tianwenxue · doi:10.1360/sspma2019-0149 · Cited by 5
Schabdach, J., Ceschin, R. C., Schmithorst, V. J., Tisdall, M. D., Alexander‐Bloch, A. F., & Panigrahy, A. (2022) · Diagnostics · doi:10.3390/diagnostics12051032 · Cited by 5
Fu, Y., Yan, X., Mao, J., Su, H., & Cao, F. (2024) · npj Science of Learning · doi:10.1038/s41539-024-00266-2 · Cited by 6
Gao, L., Zhang, T., Zhang, Y., Liu, J., & Guo, X. (2024) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-024-06696-6 · Cited by 6
Metadata
- Authors
- Assaf, M., Jagannathan, K., Calhoun, V. D., Miller, L., Stevens, M. C., Sahl, R., O'Boyle, J. G., Schultz, R. T., & Pearlson, G. D.
- Affiliations
- Hartford Hospital (US)
- Children's Hospital of Philadelphia (US)
- Corresponding author
- Assaf, Michal
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-10
- Volume
- 53
- Issue
- 1
- Pages
- 247-256
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3058935
- OpenAlex
- W2063167566
- Cited by
- 680
- References
- 71
- Citations by year
- 2026: 28 · 2025: 37 · 2024: 33 · 2023: 28 · 2022: 39 · 2021: 52 · 2020: 52 · 2019: 60 · 2018: 53 · 2017: 39 · 2016: 41 · 2015: 47 · 2014: 47 · 2013: 46 · 2012: 43
- FWCI
- 23.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Autism Speaks
- National Institute of Biomedical Imaging and Bioengineering (R01EB000840, R01EB020407, R01EB006841)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Keywords
- autism spectrum disorder, default mode network, functional connectivity, resting-state fMRI, independent component analysis, precuneus, medial prefrontal cortex, anterior cingulate cortex, theory of mind, high-functioning autism
- MeSH terms
- Adolescent, Brain, Child, Child Development Disorders, Pervasive, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Sensitivity and Specificity, Reproducibility of Results, Young Adult
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