Predictable information in neural signals during resting state is reduced in autism spectrum disorder
Open accessAbstract
The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals. Predictable information in neural signals measures the amount of stored information that is used for the next time step of a neural process. Thus, predictable information limits the (prior) information which might be available for other brain areas, for example, to build predictions for upcoming sensory information. We studied predictable information in neural signals based on resting-state magnetoencephalography (MEG) recordings of 19 ASD patients and 19 neurotypical controls aged between 14 and 27 years. Using whole-brain beamformer source analysis, we found reduced predictable information in ASD patients across the whole brain, but in particular in posterior regions of the default mode network. In these regions, epoch-by-epoch predictable information was positively correlated with source power in the alpha and beta frequency range as well as autocorrelation decay time. Predictable information in precuneus and cerebellum was negatively associated with nonsocial symptom severity, indicating a relevance of the analysis of predictable information for clinical research in ASD. Our findings are compatible with the assumption that use or precision of prior knowledge is reduced in ASD patients.
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A negative correlation was found between predictive abilities and the severity of restricted, repetitive, and stereotyped behaviors and interests in individuals with autism spectrum disorder.
“From a clinical point of view, a negative correlation was found be tween predictive abilities and the severity of a specific cluster of symptoms (i.e., restricted, repetitive, and stereotyped behaviors and interests) (Brodski-Guerniero et al., 2018).”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Brodski-Guerniero et al., 2018
Regardless of signal complexity, adolescents with autism spectrum disorder showed reduced activity in the posterior cingulate cortex, supramarginal gyrus, and precuneus compared to healthy controls during predictive coding tasks.
“Regardless of signal complexity, adolescents with ASD showed reduced activity in the posterior cingulate cortex, supramarginal gyrus, and precuneus compared to HC during predictive coding tasks (Brodski-Guerniero et al., 2018).”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Brodski-Guerniero et al., 2018
There is a negative correlation between predictable information and the severity of symptoms (restricted, repetitive, and stereotyped behaviors and interests) in individuals with autism spectrum disorder.
“Finally, there is a negative correlation between predictable infor mation and the severity of symptoms (i.e., restricted, repetitive, and stereotyped behaviors and interests) in individuals with ASD (Brodski-Guerniero et al., 2018).”
From Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review. (2025) · cites this paper as Brodski-Guerniero et al., 2018
Cites
Diagnostic and Statistical Manual of Mental Disorders
American Psychiatric Association (2013) · doi:10.1176/appi.books.9780890425596 · Cited by 116,369 · see citing works
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 49,574 · see citing works
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900 · see citing works
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323 · see citing works
<i>The Brain's Default Network</i>: <i>Anatomy, Function, and Relevance to Disease</i>
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 · cited 19× · see citing works
“This postulated high impact of the DMN in predictive processing is compatible with the key functions associated with the DMN like episodic memory retrieval (posterior parietal and PCC regions), future planning, self-referential thoughts (dorsomedial prefrontal cortex; PFC), and integrating sensory and interoceptive si…”
BackgroundMethodResultInfluential(1943) · The Nervous child · Cited by 8,870 · see citing works
Does the autistic child have a “theory of mind” ?
Baron-Cohen, S., Leslie, A. M., & Frith, U. (1985) · Cognition · doi:10.1016/0010-0277(85)90022-8 · Cited by 8,599 · see citing works
Nonparametric statistical testing of EEG- and MEG-data
Maris, E., & Oostenveld, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.024 · Cited by 8,496 · see citing works
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120 · see citing works
Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1038/109132a0 · Cited by 7,837 · see citing works
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 · see citing works
Whatever next? Predictive brains, situated agents, and the future of cognitive science
Clark, A. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12000477 · Cited by 5,924 · cited 1× · see citing works
“Predictive coding theory (Clark, 2013; Friston, 2005, 2010; Kiebel, Daunizeau, & Friston, 2008; Rao & Ballard, 1999) suggests that perception is a process of hierarchical probabilistic inference, in which the brain uses prior knowledge from life-long experience for building internal predictions.”
BackgroundRao, R. P. N., & Ballard, D. H. (1999) · Nature Neuroscience · doi:10.1038/4580 · Cited by 5,483 · cited 1× · see citing works
“Predictive coding theory (Clark, 2013; Friston, 2005, 2010; Kiebel, Daunizeau, & Friston, 2008; Rao & Ballard, 1999) suggests that perception is a process of hierarchical probabilistic inference, in which the brain uses prior knowledge from life-long experience for building internal predictions.”
BackgroundA theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578 · see citing works
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 4,157 · see citing works
The brain's default mode network
Raichle, M. E. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071013-014030 · Cited by 3,904 · see citing works
The weak coherence account: detail-focused cognitive style in autism spectrum disorders
Happé, F., & Frith, U. (2006) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-005-0039-0 · Cited by 2,884 · see citing works
Wandering minds: the default network and stimulus-independent thought
Mason, M. F., Norton, M. I., Van Horn, J. D., Wegner, D. M., Grafton, S. T., & Macrae, C. N. (2007) · Science · doi:10.1126/science.1131295 · Cited by 2,792 · see citing works
Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623056 · Cited by 2,561 · see citing works
Canonical Microcircuits for Predictive Coding
Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J. (2012) · Neuron · doi:10.1016/j.neuron.2012.10.038 · Cited by 2,421 · cited 1× · see citing works
“…in resting state recordings and neural activity in low frequencies adds support to the hypothesis that low frequencies are the carrier of top–down propagated information within the predictive coding framework (Bastos et al., 2012; see, e.g., BrodskiGuerniero et al., 2017 for empirical evidence).”
BackgroundElements of Information Theory
Nalewajski, R. F. (2011) · Springer eBooks · doi:10.1007/978-3-642-20180-6_8 · Cited by 2,310 · see citing works
Die „Autistischen Psychopathen” im Kindesalter
Asperger, H. (1944) · Archiv für Psychiatrie und Nervenkrankheiten · doi:10.1007/bf01837709 · Cited by 2,163 · see citing works
Spectral Analysis for Physical Applications
Percival, D. B., & Walden, A. T. (1993) · Cambridge University Press eBooks · doi:10.1017/cbo9780511622762 · Cited by 2,148 · see citing works
Using Bayes to get the most out of non-significant results
Dienes, Z. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00781 · Cited by 1,998 · see citing works
Christensen, D. L., Baio, J., Braun, K. V. N., Bilder, D., Charles, J., Constantino, J. N., Daniels, J., Durkin, M. S., Fitzgerald, R. T., Kurzius-Spencer, M., et al. (2016) · MMWR. Surveillance Summaries · doi:10.15585/mmwr.ss6503a1 · Cited by 1,964 · see citing works
Independent Component Analysis of Electroencephalographic Data
Makeig, S., Bell, A. J., Jung, T., & B, T. J. S. A. (1995) · Cited by 1,957 · see citing works
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831 · see citing works
Prolate Spheroidal Wave Functions, Fourier Analysis, and Uncertainty-V: The Discrete Case
Slepian, D. (1978) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1978.tb02104.x · Cited by 1,756 · see citing works
Autism Diagnostic Interview-Revised
Rutter, M., LeCouteur, A., & Lord, C. (2016) · PsycTESTS Dataset · doi:10.1037/t18128-000 · Cited by 1,627 · see citing works
Predictive coding under the free-energy principle
Friston, K., & Kiebel, S. (2009) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2008.0300 · Cited by 1,579 · see citing works
Parietal lobe contributions to episodic memory retrieval
Wagner, A. D., Shannon, B. J., Kahn, I., & Buckner, R. L. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.07.001 · Cited by 1,571 · see citing works
Computation at the edge of chaos: Phase transitions and emergent computation
Langton, C. G. (1990) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(90)90064-v · Cited by 1,527 · see citing works
Default Mode Network Activity and Connectivity in Psychopathology
Whitfield-Gabrieli, S., & Ford, J. M. (2012) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032511-143049 · Cited by 1,465 · see citing works
The theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw154 · Cited by 1,426 · see citing works
Mixtures of g Priors for Bayesian Variable Selection
Liang, F., Paulo, R., Molina, G., Clyde, M. A., & Berger, J. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000001337 · Cited by 1,308 · see citing works
When the world becomes ‘too real’: a Bayesian explanation of autistic perception
Pellicano, E., & Burr, D. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.08.009 · Cited by 1,156 · see citing works
The proactive brain: using analogies and associations to generate predictions
Bar, M. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.05.005 · Cited by 1,144 · see citing works
Nolte, G. (2003) · Physics in Medicine and Biology · doi:10.1088/0031-9155/48/22/002 · Cited by 1,140 · see citing works
The theory of constructed emotion: an active inference account of interoception and categorization
Barrett, L. F. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsx060 · Cited by 950 · cited 1× · see citing works
“Recently it has been argued that the DMN may play a key role in predictive coding by acting as a top level of the predictive hierarchy, being responsible for initiating predictions that cascade down to categorize sensory input and drive motor activity (Barrett, 2017; Barrett & Satpute, 2013).”
BackgroundAn islet of ability in autistic children: a research note
Shah, A., & Frith, U. (1983) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.1983.tb00137.x · Cited by 888 · see citing works
Precise minds in uncertain worlds: Predictive coding in autism.
Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014) · Psychological Review · doi:10.1037/a0037665 · Cited by 885 · see citing works
Consensus Paper: Pathological Role of the Cerebellum in Autism
Fatemi, S. H., Aldinger, K. A., Ashwood, P., Bauman, M. L., Blaha, C. D., Blatt, G. J., Chauhan, A., Chauhan, V., Dager, S. R., Dickson, P. E., et al. (2012) · The Cerebellum · doi:10.1007/s12311-012-0355-9 · Cited by 723 · see citing works
Good practice for conducting and reporting MEG research
Groß, J., Baillet, S., Barnes, G. R., Henson, R. N., Hillebrand, A., Jensen, O. B., Jerbi, K., Vladimir Litvak, A., Maeß, B., Oostenveld, R., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.001 · Cited by 717 · see citing works
Self-citationA Hierarchy of Time-Scales and the Brain
Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000209 · Cited by 694 · see citing works
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672 · see citing works
Autism as an executive disorder
Jarrold, C. (1997) · Oxford University Press eBooks · Cited by 662 · see citing works
An aberrant precision account of autism
Lawson, R. P., Rees, G., & Friston, K. J. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00302 · Cited by 641 · see citing works
Functional connectivity in a baseline resting-state network in autism
Cherkassky, V. L., Kana, R. K., Keller, T. A., & Just, M. A. (2006) · NeuroReport · doi:10.1097/01.wnr.0000239956.45448.4c · Cited by 622 · see citing works
Abnormalities of intrinsic functional connectivity in autism spectrum disorders
Monk, C. S., Peltier, S. J., Wiggins, J. L., Weng, S.-J., Carrasco, M., Risi, S., & Lord, C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.069 · Cited by 578 · see citing works
Failing to deactivate: Resting functional abnormalities in autism
Kennedy, D. P., Redcay, E., & Courchesne, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0600674103 · Cited by 543 · see citing works
Happe, F. G. E. (1996) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.1996.tb01483.x · Cited by 491 · see citing works
EEG complexity as a biomarker for autism spectrum disorder risk
Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011) · BMC Medicine · doi:10.1186/1741-7015-9-18 · Cited by 485 · see citing works
Default Bayes Factors for Model Selection in Regression
Rouder, J. N., & Morey, R. D. (2012) · Multivariate Behavioral Research · doi:10.1080/00273171.2012.734737 · Cited by 477 · see citing works
Evidence for a cerebellar role in reduced exploration and stereotyped behavior in autism
Pierce, K., & Courchesne, E. (2001) · Biological Psychiatry · doi:10.1016/s0006-3223(00)01008-8 · Cited by 464 · see citing works
Weng, S.-J., Wiggins, J. L., Peltier, S., Carrasco, M., Risi, S., Lord, C., & Monk, C. S. (2009) · Brain Research · doi:10.1016/j.brainres.2009.11.057 · Cited by 421 · see citing works
Brain anatomy and development in autism: review of structural MRI studies
Brambilla, P., Hardan, A., di Nemi, S. U., Perez, J., Soares, J. C., & Barale, F. (2003) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2003.06.001 · Cited by 418 · see citing works
JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems
Lizier, J. T. (2014) · Frontiers in Robotics and AI · doi:10.3389/frobt.2014.00011 · Cited by 391 · see citing works
Self-citationLynch, C. J., Uddin, L. Q., Supekar, K., Khouzam, A., Phillips, J. M., & Menon, V. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.12.013 · Cited by 357 · see citing works
Plaisted, K. C., O’Riordan, M., & Baron‐Cohen, S. (1998) · Journal of Child Psychology and Psychiatry · doi:10.1111/1469-7610.00375 · Cited by 356 · see citing works
Why is visual search superior in autism spectrum disorder?
Joseph, R. M., Keehn, B., Connolly, C., Wolfe, J. M., & Horowitz, T. S. (2009) · Developmental Science · doi:10.1111/j.1467-7687.2009.00855.x · Cited by 313 · see citing works
Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis
Catarino, A., Churches, O., Baron-Cohen, S., Andrade, A., & Ring, H. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2011.05.004 · Cited by 271 · see citing works
Dysmaturation of the default mode network in autism
Washington, S. D., Gordon, E. M., Brar, J., Warburton, S., Sawyer, A. T., Wolfe, A., Mease‐Ference, E. R., Girton, L., Hailu, A., Mbwana, J., et al. (2014) · Human Brain Mapping · doi:10.1002/hbm.22252 · Cited by 269 · see citing works
A spiking network model of short-term active memory
Zipser, D., Kehoe, B., Littlewort, G. C., & Fuster, J. M. (1993) · Journal of Neuroscience · doi:10.1523/jneurosci.13-08-03406.1993 · Cited by 256 · see citing works
Local active information storage as a tool to understand distributed neural information processing
Wibral, M., Lizier, J. T., Vögler, S., Priesemann, V., & Galuske, R. A. W. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00001 · Cited by 239 · see citing works
Self-citationLindner, M., Vicente, R., Priesemann, V., & Wibral, M. (2011) · BMC Neuroscience · doi:10.1186/1471-2202-12-119 · Cited by 231 · see citing works
Self-citationA voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder
Waiter, G. D., Williams, J. H. G., Murray, A. D., Gilchrist, A., Perrett, D. I., & Whiten, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.02.029 · Cited by 230 · see citing works
The relation of ongoing brain activity, evoked neural responses, and cognition
Sadaghiani, S. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00020 · Cited by 225 · see citing works
Sampling the form of inner experience in three adults with Asperger syndrome
Hurlburt, R. T., Happé, F., & Frith, U. (1994) · Psychological Medicine · doi:10.1017/s0033291700027367 · Cited by 215 · see citing works
Grandin, T. (1992) · Springer eBooks · doi:10.1007/978-1-4899-2456-8_6 · Cited by 189 · see citing works
Local measures of information storage in complex distributed computation
Lizier, J. T., Prokopenko, M., & Zomaya, A. Y. (2012) · Information Sciences · doi:10.1016/j.ins.2012.04.016 · Cited by 180 · see citing works
Self-citationAlternative Bayesian accounts of autistic perception: Comment on Pellicano and Burr
Brock, J. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.10.005 · Cited by 173 · see citing works
On hyperpriors and hypopriors: comment on Pellicano and Burr
Friston, K. J., Lawson, R., & Frith, C. D. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.11.003 · Cited by 151 · see citing works
Markov models from data by simple nonlinear time series predictors in delay embedding spaces
Ragwitz, M., & Kantz, H. (2002) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.65.056201 · Cited by 136 · see citing works
Bits from Brains for Biologically Inspired Computing
Wibral, M., Lizier, J. T., & Priesemann, V. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00005 · Cited by 131 · see citing works
Self-citationQualitative Representations for Recognition
Sinha, P. (2002) · Lecture notes in computer science · doi:10.1007/3-540-36181-2_25 · Cited by 94 · see citing works
Reduced predictable information in brain signals in autism spectrum disorder
Gómez, C., Lizier, J. T., Schaum, M., Wollstadt, P., Grützner, C., Uhlhaas, P., Freitag, C. M., Schlitt, S., Bölte, S., Hornero, R., et al. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00009 · Cited by 59 · see citing works
Self-citationBarrett, L. F., & Satpute, A. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.12.012 · Cited by 58 · see citing works
Breakdown of local information processing may underlie isoflurane anesthesia effects
Wollstadt, P., Sellers, K. K., Rudelt, L., Priesemann, V., Hutt, A., Fröhlich, F., & Wibral, M. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005511 · Cited by 49 · see citing works
Self-citationInformation-Theoretic Evidence for Predictive Coding in the Face-Processing System
Brodski‐Guerniero, A., Paasch, G.-F., Wollstadt, P., Özdemir, İ., Lizier, J. T., & Wibral, M. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0614-17.2017 · Cited by 44 · see citing works
Self-citation
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10 years of Bayesian theories of autism: A comprehensive review
Angeletos Chrysaitis, N., & Seriès, P. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.105022 · Cited by 92 · see citing works
Predictive coding in neuropsychiatric disorders: a systematic transdiagnostic review.
Qela, B., Damiani, S., De Santis, S., Groppi, F., Pichiecchio, A., Asteggiano, C., Brondino, N., Monteleone, A. M., Grassi, L., Politi, P., et al. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106020 · Cited by 26 · see citing works
- Authors
- Brodski‐Guerniero, A., Naumer, M. J., Moliadze, V., Chan, J., Althen, H., Ferreira‐Santos, F., Lizier, J. T., Schlitt, S., Kitzerow, J., Schütz, M., Langer, A., Kaiser, J., Freitag, C. M., & Wibral, M.
- Affiliations
- Goethe University Frankfurt (DE)
- Universidade do Porto (PT)
- The University of Sydney (AU)
- Corresponding author
- Brodski‐Guerniero, Alla
- ORCID
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2018-04-04
- Volume
- 39
- Issue
- 8
- Pages
- 3227-3240
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC6866422
- OpenAlex
- W2796202756
- Cited by
- 23
- References
- 79
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 2 · 2020: 3 · 2019: 1 · 2018: 1
- FWCI
- 1.04
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Human Brain Mapping (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Funders
- EU Framework Programme for Research and Innovation H2020 (720270 (HBP SGA1))
- German Academic Exchange Service
- Australian Research Council (DE160100630)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- autism spectrum disorder, predictive coding, default mode network, precuneus, cerebellum, prior knowledge, top-down and bottom-up processing, alpha and beta oscillations, autocorrelation decay, source power
- MeSH terms
- Autism Spectrum Disorder, Adolescent, Adult, Brain, Brain Mapping, Humans, Male, Rest, Signal Processing, Computer-Assisted, Magnetoencephalography, Young Adult
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