The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
Review of neuroimaging in autism spectrum disorders: what have we learned and where we go from here
Open accessAbstract
Autism spectrum disorder (ASD) refers to a syndrome of social communication deficits and repetitive behaviors or restrictive interests. It remains a behaviorally defined syndrome with no reliable biological markers. The goal of this review is to summarize the available neuroimaging data and examine their implication for our understanding of the neurobiology of ASD.Although there is variability in the literature on structural magnetic resonance literature (MRI), there is evidence of volume abnormalities in both grey and white matter, with a suggestion of some region-specific differences. Early brain overgrowth is probably the most replicated finding in a subgroup of people with ASD, and new techniques, such as cortical-thickness measurements and surface morphometry have begun to elucidate in more detail the patterns of abnormalities as they evolve with age, and are implicating specific neuroanatomical or neurodevelopmental processes. Functional MRI and diffusion tensor imaging techniques suggest that such volume abnormalities are associated with atypical functional and structural connectivity in the brain, and researchers have begun to use magnetic resonance spectroscopy (MRS) techniques to explore the neurochemical substrate of such abnormalities. The data from multiple imaging methods suggests that ASD is associated with an atypically connected brain. We now need to further clarify such atypicalities, and start interpreting them in the context of what we already know about typical neurodevelopmental processes including migration and organization of the cortex. Such an approach will allow us to relate imaging findings not only to behavior, but also to genes and their expression, which may be related to such processes, and to further our understanding of the nature of neurobiologic abnormalities in ASD.
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“ASD has been associated with neurobiological differences in brain networks responsible for these functions (for example, the prefrontal cortex, the insular cortex, and the anterior cingulate cortices) [6]”
From Functional autonomic nervous system profile in children with autism spectrum disorder (2014) · cites this paper as [6]
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Citations
Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,845
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
(1982) · Trends in Neurosciences · doi:10.1016/0166-2236(82)90076-5 · Cited by 9,150
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Measuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,995
Diagnostic and Statistical Manual of Mental Disorders ( DSM ‐ IV ‐ TR )
Segal, D. L. (2010) · Wiley eBooks · doi:10.1002/9780470479216.corpsy0271 · Cited by 4,687
Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water
Song, S.-K., Sun, S.-W., Ramsbottom, M. J., Chang, C., Russell, J., & Cross, A. H. (2002) · NeuroImage · doi:10.1006/nimg.2002.1267 · Cited by 2,582
Principles of diffusion tensor imaging and its applications to basic neuroscience research
Mori, S., & Zhang, J. (2006) · Neuron · doi:10.1016/j.neuron.2006.08.012 · Cited by 1,708
Neurodevelopmental Trajectories of the Human Cerebral Cortex
Shaw, P., Kabani, N. J., Lerch, J. P., Eckstrand, K., Lenroot, R., Gogtay, N., Greenstein, D., Clasen, L., Evans, A., Rapoport, J. L., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.5309-07.2008 · Cited by 1,644
Just, M. A. (2004) · Brain · doi:10.1093/brain/awh199 · Cited by 1,589
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Self-citationRETRACTED ARTICLE: Hybrid deep learning model for autism spectrum disorder diagnosis
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Xu, M., Shayan, H., & Jo, H. (2026) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2026.112372
Metadata
- Authors
- Anagnostou, E., & Taylor, M. J.
- Affiliations
- University of Toronto (CA)
- Corresponding author
- Anagnostou, Evdokia
- Venue
- Molecular Autism
- Published
- 2011
- Volume
- 2
- Issue
- 1
- Pages
- 4
- ISSN
- 2040-2392 (ISSN-L)
- PMC
- PMC3102613
- OpenAlex
- W2101436791
- Cited by
- 321
- References
- 111
- Citations by year
- 2026: 7 · 2025: 12 · 2024: 10 · 2023: 20 · 2022: 19 · 2021: 18 · 2020: 30 · 2019: 15 · 2018: 23 · 2017: 24 · 2016: 27 · 2015: 32 · 2014: 20 · 2013: 30 · 2012: 27
- FWCI
- 13.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Molecular Autism (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- TSpace (University of Toronto) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Defense
- Canadian Institutes of Health Research (81161_1)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- autism spectrum disorder, neuroimaging, brain structure, gray matter volume, white matter volume, brain overgrowth, cortical thickness, functional connectivity, structural connectivity, diffusion tensor imaging, magnetic resonance spectroscopy, brain development, neurobiology of autism
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