The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Cited by
Sorted by citations
Items 601–678 of 678
Shih, Y.-C., Nelson, L., Janeček, M., & Peixoto, R. T. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.23.568500 · Cited by 3
Multiscale heterogeneity of functional connectivity in autismPreprint
Ilioska, I., Oldehinkel, M., Llera, A., Rovný, M., Mei, T., Kia, S. M., Floris, D. L., Tillmann, J., Holt, R., Loth, E., et al. (2024) · medRxiv · doi:10.1101/2024.10.20.24315248 · Cited by 1
He, H., Yazdi, B. G., Sedaghat, A., Hojjati, S. H., Ozoria, S., Chernek, P., Calimag, J., Feiz, F., & Razlighi, Q. R. (2026) · NeuroImage · doi:10.1101/2025.09.19.677202 · Cited by 1
The Neurodevelopment Of Basic Sensory Processing And Integration In Autism Spectrum Disorder
Brandwein, A. B. (2014) · CUNY Academic Works (City University of New York)
Neural Correlates of Repetitive Behavior in Autism Spectrum Disorder
Abbott, A. E. (2013) · CSUN ScholarWorks (California State University, Northridge)
Murphy, E. R. (2013) · DigitalGeorgetown (Georgetown University Library)
Training der Selbstkontrolle der langsamen kortikalen Potenziale
Schneider, E. M., & Strauß, G. (2013) · Springer eBooks · doi:10.1007/978-3-642-30179-7_4
What are newborn babies thinking? Clues from MRI
Arichi, T., Doria, V., & Edwards, A. D. (2011) · Imaging in Medicine · doi:10.2217/iim.11.15
Ferreira, M. L., & Catarino, P. (2012)
Ghane, M. (2014) · CSUN ScholarWorks (California State University, Northridge)
Jackson, R. L. (2014) · Research Explorer (The University of Manchester)
Matthysen, W., Marinazzo, D., & Šiugždaitė, R. (2016) · doi:10.7287/peerj.preprints.2060v1
The role of thalamocortical networks in the symptomatology of autism spectrum disorders
Nair, A. (2015) · eScholarship (California Digital Library)
Fishburn, F. A. (2017) · DigitalGeorgetown (Georgetown University Library)
Atypical Large Scale Brain Network in Autism Spectrum Disorders
徐, 晓. (2017) · Advances in Psychology · doi:10.12677/ap.2017.712175
Venkataraman, A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813838-0.00001-7 · Cited by 1
Reyes-Vaca, J. G., Villegas-Valdez, D. M. M., Oro, A. B., & Hernández-Rodríguez, H. (2019) · Anales de radiología, México/Anales de radiología, México · doi:10.24875/arm.19000151
Internal and External Attention in Autism Spectrum Disorder
Eilam‐Stock, T. (2019) · CUNY Academic Works (City University of New York)
Youths with autism and working memory
Urbain, C. M., & Taylor, M. J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817986-4.00043-2
Vidhusha, S., & Kavitha, A. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-3038-2.ch009
Schabdach, J. (2020) · D-Scholarship@Pitt (University of Pittsburgh)
Exploration of human movement processing in autism spectrum disorderPreprint
Nijhof, D., Regener, P., Kim, N., Tohka, J., Kauppi, J., Jang, S., & Pollick, F. E. (2020) · doi:10.21203/rs.3.rs-16938/v1
Guimarães, K. D. O. P. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.10.2020.tde-22022021-084241
Advanced pCASL pediatric perfusion MRI
Kilroy, E., Shao, X., & Wang, D. J. J. (2021) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-816633-8.00013-2
Autismus: Kindheit – Krankheit – Menschheit
Spitzer, M. (2022) · Nervenheilkunde · doi:10.1055/a-1755-7539
Training der Selbstkontrolle der langsamen kortikalen Potenziale
Schneider, E. M., & Strauß, G. (2015) · Springer eBooks · doi:10.1007/978-3-662-47748-9_4
Parikh, C. (2019) · Springer eBooks · doi:10.1007/978-3-030-16558-1_4
(2016) · مجلة دراسات الطفولة · doi:10.21608/jsc.2016.93212
Can Chaos Bring Order to the Study of Functional Connectivity in Neurodevelopmental Disorders?
Washington, S. D. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.03.014
Yang, P., Phipps, M. A., Newton, A. T., Jonathan, S. V., Manuel, T. J., Gore, J. C., Grissom, W. A., Caskey, C. F., & Chen, L. M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4131980
Network changes underlying neural disorders: Relation to the MMN networks
Eggermont, J. J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15548-2.00012-0
Intrinsische Bereitschaftsnetzwerke
Sidiropoulos, K. (2023) · Springer eBooks · doi:10.1007/978-3-662-65726-3_10
Tang, X., Ma, Z., Kat, S., Xu, L., Liu, Q., Yang, L., Wang, Y., Cao, Q., Li, X., & Liu, J. (2024) · Journal of Attention Disorders · doi:10.1177/10870547241233207
Lakshmipriya, K., Harini, R., -, A. J. E., & Maheswari, O. U. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i02.18204
An Explainable Multi-atlas Fusion Model based on Spatial Overlap for ASD Diagnosis
Ma, Y., Mu, X., & Zhang, T. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679873
Wu, X. (2024) · IEEE Smart World Congress (SWC) · doi:10.1109/swc62898.2024.00040
A causal adversarial graph neural network for multi-center autism spectrum disorder identification
Zhang, Z., Qijian, C. H. E. N., Wang, L., Jian, C., Zhu, Y., Wei, H., & Wang, L. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116036
Challenges and hopes for treatment of anxiety disorder in the autistic population
Dulski, T., Tolak, S., & Zmorzyńska, J. (2025) · Brain Research · doi:10.1016/j.brainres.2025.149675 · Cited by 2
Guo, X., Xing, Y., Cui, D., Zhou, R., & Gao, L. (2025) · Brain Connectivity · doi:10.1089/brain.2024.0033
Su, L., Liu, T., & Wei, L. (2025) · Acta Psychologica · doi:10.1016/j.actpsy.2025.105365
Flippable Siamese Differential Neural Network for Differential Graph InferencePreprint
Leng, J., Yu, J., & Wu, L. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.31.673390
Bruno, J. L., Plank, J. R., Leder, S., Lake, E. M. R., Finn, E. S., & Green, T. (2025) · medRxiv · doi:10.1101/2025.09.17.25336005
Lim, J., Mitrai, I., Daoutidis, P., & Stamoulis, C. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121562 · Cited by 4
Roseby, W., & Moar, C. O. (2025) · Psychoactives · doi:10.3390/psychoactives4040041
STGNet: Spatio-Temporal aware GCN-Based Model for Autism Prediction in Brain fMRI Data
Li, H., Jin, T., & Li, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227250
Williams, D. K. A., Biswas, T. K., Brusco, C., Molholm, S., & Coen-Cagli, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.17.695033
Floris, D. L., Filho, J. O. A., Lai, M.-C., Giavasis, S., Oldehinkel, M., Mennes, M., Charman, T., Tillmann, J., Dumas, G., Ecker, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14140167
Floris, D. L., Filho, J. O. A., Lai, M.-C., Giavasis, S., Oldehinkel, M., Mennes, M., Charman, T., Tillmann, J., Dumas, G., Ecker, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14140161
Floris, D. L., Filho, J. O. A., Lai, M.-C., Giavasis, S., Oldehinkel, M., Mennes, M., Charman, T., Tillmann, J., Dumas, G., Ecker, C., et al. (2021) · Figshare · doi:10.6084/m9.figshare.14140164
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) · Figshare · doi:10.6084/m9.figshare.16625372
Lesion Network Mapping of Cognitive Decline in Three Cases of Autism Spectrum Disorder
Ranjbar, A., Zeynali, S., & Ghanbari, E. (2025) · Neurology Letters · doi:10.61882/nl.4.2.81
BrainGraphDiff: A framework for enhanced brain network analysis via adaptive subgraph generation
Tang, J., Zhu, T., Zhou, W., & Zhao, W. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.108326 · Cited by 1
Leisman, G. (2026) · Informatics and Health · doi:10.1016/j.infoh.2025.11.001 · Cited by 1
Zhou, L., Han, J., Wu, H., Bao, H., Wang, J., & Qin, P. (2026) · Psychoradiology · doi:10.1093/psyrad/kkag011
Etude de la réorganisation de la connectivité cérébrale au repos dans la sclérose en plaques
Faivre, A. (2014) · theses.fr (ABES) · doi:10.70675/e50f72d6z6aaaz4457za932z19affe0fe4dc
Bouamrane, L. (2015) · theses.fr (ABES) · doi:10.70675/00597159za953z4221z95b8z57a6c1d48c8e
La mémoire chez l’adolescent avec autisme : électrophysiologie, cognition et prise en charge
Wantzen, P. (2019) · theses.fr (ABES) · doi:10.70675/14f5cf3dz8c44z4e1ezb486z5ecae3619a17
d’Albis, M.-A. (2020) · theses.fr (ABES) · doi:10.70675/206f79f9z2fd3z499fz9abdz096c72713a99
Laurén, K., Järvelä, M., Huotari, N., Raitamaa, L., Kananen, J., Tuunanen, J., Väyrynen, T., Korhonen, V. O., Hautamäki, K., Hurtig, T. M., et al. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121908
Grassia, M., d’Andrea, V., Finc, K., Domenico, M. D., & Mangioni, G. (2026) · Scientific Reports · doi:10.1038/s41598-026-47391-z
Simultaneous Inference for Covariance and Precision Matrices of Long-Range Dependent Time Series
Zhai, P. S., Kolar, M., & Wu, W. B. (2026) · IEEE Transactions on Information Theory · doi:10.1109/tit.2026.3685246
Low, Z. X. B. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111717 · Cited by 2
Kang, J., Zhang, L., Yang, X., Xiaoli Li, A., Fan, X., & Zheng, S. (2026) · ALPHA PSYCHIATRY · doi:10.31083/ap45250
Dynamic mode decomposition analysis of brain dynamics in autism spectrum disorder patients
Jeong, M., Song, Y., & Jeong, J. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.122044
Zhang, Q. H., Qiao, Y., Li, H., Zeng, J., Chen, J., Zhou, H. J., Hu, Y., & Luo, J. (2026) · Neuroscience Bulletin · doi:10.1007/s12264-026-01653-y
Naturalistic stimuli in neuroimaging studies of autism spectrum disorder: a systematic review
Ntoumanis, I., & Papadelis, C. L. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1887914
Collus, J., & Fontes, R. M. (2024) · EdUFERSA eBooks · doi:10.7476/9788557571570.0013
Wang, Y., Zx, M., Wang, Z., Xu, X., Xia, Z., Zhang, H., Zhang, J., Zong, Y., Ke, X., & Li, Y. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1743755
Bazay, F. E.-Z., Gueddou, D., Oussalah, M., Jennane, R., & Maliani, A. D. E. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-7425-4_59
Chen, L., Riggs, A., & Luckenbach, N. (2026) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2026.1894578
Li, A., Zhang, J., Yu, C., Tan, L., Yi, A., & Xiao, Y. (2026) · Psychiatry Research · doi:10.1016/j.psychres.2026.117529
Graph Construction Dominates Model Choice in GCN-Based Autism Classification from Resting-State fMRI
Николаевна, П. Е., & Antipova, A. Y. (2026) · Scientific School Dynamics of Complex Networks and their Applications (DCNA) · doi:10.1109/dcna72004.2026.11733421
Rusadze, K. Z., Burjanadze, M. A., Beselia, G. V., Chkhikvishvili, N. C., Chighladze, M. R., Nozadze, B. G., Naneishvili, T. L., & Dashniani, M. G. (2026) · Neuroreport · doi:10.1097/wnr.0000000000002308
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
