Triple network dynamics and future alcohol consumption in adolescents
Open accessPublished version of a preprint
Abstract
Background: The human brain is a highly interconnected and dynamic system. The study of neuroimaging indicators of future teen drinking has primarily focused on the activation of individual brain regions. We applied novel methodology to identify relationships between functional brain network dynamics and future drinking outcomes in non/low drinking teens. Methods: Resting‐state functional magnetic resonance imaging (fMRI) time series from 17‐year‐old non‐/low drinking participants ( n = 295) of the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study were used to fit a Hidden semi‐Markov Model (HSMM). Regions of the default mode network (DMN), salience network (SN), and central executive network (CEN), collectively known as the Triple Network, were included in modeling. The HSMM identified each participant's most likely brain state sequence through five brain states. Poisson regression models assessed relationships between occupancy time in brain states and future drinking frequency/intensity. Sex differences were assessed with permutation testing and interaction terms in regression models. Results: No sex differences in network dynamics were observed. However, the relationship between occupancy times and future drinking frequency differed by sex for three brain states. Occupancy time in a state characterized by high activation in the DMN and SN, but low activation in the CEN, was negatively associated with future drinking in both sexes. Conclusions: Brain network dynamics may be useful neural markers of teen drinking predisposition. Brain dynamics that make teens vulnerable or resilient to drinking may differ between sexes.
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
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444 · see citing works
Fast unfolding of communities in large networks
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193 · see citing works
Finding and evaluating community structure in networks
Newman, M. E. J., & Girvan, M. (2004) · Physical Review E · doi:10.1103/physreve.69.026113 · Cited by 13,931 · see citing works
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · see citing works
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522 · see citing works
Ashburner, J., & Friston, K. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.02.018 · Cited by 7,507 · see citing works
Brain magnetic resonance imaging with contrast dependent on blood oxygenation.
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.87.24.9868 · Cited by 6,483 · see citing works
Forney, G. D. (1973) · Proceedings of the IEEE · doi:10.1109/proc.1973.9030 · Cited by 5,713 · see citing works
Multiple comparisons among means
Dunn, O. J. (1961) · Journal of the American Statistical Association · doi:10.1080/01621459.1961.10482090 · Cited by 5,006 · see citing works
Large-scale brain networks and psychopathology: a unifying triple network model
Menon, V. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.08.003 · Cited by 4,168 · 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
Multiple significance tests: the Bonferroni method
Bland, M., & Altman, D. G. (1995) · BMJ · doi:10.1136/bmj.310.6973.170 · Cited by 3,898 · see citing works
Tracking whole-brain connectivity dynamics in the resting state
Allen, E. A., Damaraju, E., Plis, S. M., Erhardt, E. B., Eichele, T., & Calhoun, V. D. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs352 · Cited by 2,992 · see citing works
Sridharan, D., Levitin, D. J., & Menon, V. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0800005105 · Cited by 2,892 · see citing works
Bassett, D. S., & Sporns, O. (2017) · Nature Neuroscience · doi:10.1038/nn.4502 · Cited by 2,398 · see citing works
Dynamic reconfiguration of human brain networks during learning
Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018985108 · Cited by 1,705 · see citing works
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.064 · Cited by 1,684 · see citing works
$I$-Divergence Geometry of Probability Distributions and Minimization Problems
Csiszar, I. (1975) · The Annals of Probability · doi:10.1214/aop/1176996454 · Cited by 1,645 · see citing works
Groupwise whole-brain parcellation from resting-state fMRI data for network node identification
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.081 · Cited by 1,000 · see citing works
Shine, J. M., Bissett, P. G., Bell, P. T., Koyejo, O., Balsters, J. H., Gorgolewski, K. J., Moodie, C. A., & Poldrack, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2016.09.018 · Cited by 886 · see citing works
Marek, S., & Dosenbach, N. U. F. (2018) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2018.20.2/smarek · Cited by 836 · see citing works
Spear, L. P. (2013) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2012.05.006 · Cited by 744 · see citing works
Developmental neurobiology of cognitive control and motivational systems.
Somerville, L. H., & Casey, B. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.01.006 · Cited by 634 · see citing works
Using alcohol expectancies to predict adolescent drinking behavior after one year.
Christiansen, B. A., Smith, G. T., Roehling, P. V., & Goldman, M. S. (1989) · Journal of Consulting and Clinical Psychology · doi:10.1037/0022-006x.57.1.93 · Cited by 545 · see citing works
Impulsivity-related personality traits and adolescent alcohol use: a meta-analytic review.
Stautz, K., & Cooper, A. (2013) · Clinical Psychology Review · doi:10.1016/j.cpr.2013.03.003 · Cited by 491 · see citing works
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI
Pruim, R. H. R., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.063 · Cited by 487 · see citing works
Brown, S. A., Myers, M. G., Lippke, L., Tapert, S. F., Stewart, D. G., & Vik, P. W. (1998) · Journal of Studies on Alcohol · doi:10.15288/jsa.1998.59.427 · Cited by 466 · see citing works
Understanding brain networks and brain organization
Pessoa, L. (2014) · Physics of Life Reviews · doi:10.1016/j.plrev.2014.03.005 · Cited by 464 · see citing works
Gender differences in factors influencing alcohol use and drinking progression among adolescents
Schulte, M. T., Ramo, D. E., & Brown, S. A. (2009) · Clinical Psychology Review · doi:10.1016/j.cpr.2009.06.003 · Cited by 424 · see citing works
Sex differences in stress-related alcohol use
Peltier, M. R., Verplaetse, T. L., Mineur, Y. S., Petrakis, I. L., Cosgrove, K., Picciotto, M. R., & McKee, S. A. (2019) · Neurobiology of Stress · doi:10.1016/j.ynstr.2019.100149 · Cited by 416 · see citing works
Neurotoxic Effects of Alcohol in Adolescence
Jacobus, J., & Tapert, S. F. (2012) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185610 · Cited by 267 · see citing works
Prevalence and predictors of adolescent alcohol use and binge drinking in the United States
Patrick, M. E. (2013) · Alcohol research · doi:10.35946/arcr.v35.2.10 · Cited by 262 · see citing works
Brown, S. A., Brumback, T., Tomlinson, K., Cummins, K., Thompson, W. K., Nagel, B. J., De Bellis, M. D., Hooper, S. R., Clark, D. B., Chung, T., et al. (2015) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.2015.76.895 · Cited by 255 · see citing works
Jones, C. M., Clayton, H. B., Deputy, N. P., Roehler, D. R., Ko, J. Y., Esser, M. B., Brookmeyer, K. A., & Hertz, M. F. (2020) · MMWR Supplements · doi:10.15585/mmwr.su6901a5 · Cited by 253 · see citing works
Neural activation during inhibition predicts initiation of substance use in adolescence
Norman, A. L., Pulido, C., Squeglia, L. M., Spadoni, A. D., Paulus, M. P., & Tapert, S. F. (2011) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2011.06.019 · Cited by 244 · see citing works
Principles of dynamic network reconfiguration across diverse brain states
Shine, J. M., & Poldrack, R. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.010 · Cited by 242 · see citing works
Sex Differences in Alcohol Use Disorder
Agabio, R., Pisanu, C., Gessa, G. L., & Franconi, F. (2016) · Current Medicinal Chemistry · doi:10.2174/0929867323666161202092908 · Cited by 229 · see citing works
Sex differences in neural responses to stress and alcohol context cues
Seo, D., Jia, Z., Lacadie, C., Tsou, K. A., Bergquist, K. L., & Sinha, R. (2010) · Human Brain Mapping · doi:10.1002/hbm.21165 · Cited by 199 · see citing works
Affectivity and impulsivity: Temperament risk for adolescent alcohol involvement.
Colder, C. R., & Chassin, L. (1997) · Psychology of Addictive Behaviors · doi:10.1037/0893-164x.11.2.83 · Cited by 170 · see citing works
Neural Predictors of Initiating Alcohol Use During Adolescence
Squeglia, L. M., Ball, T. M., Jacobus, J., Brumback, T., McKenna, B. S., Nguyen‐Louie, T. T., Sorg, S. F., Paulus, M. P., & Tapert, S. F. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.15121587 · Cited by 140 · see citing works
Dynamic brain connectivity is a better predictor of PTSD than static connectivity
Jin, C., Jia, H., Lanka, P., Rangaprakash, D., Li, L., Liu, T., Hu, X. P., & Deshpande, G. (2017) · Human Brain Mapping · doi:10.1002/hbm.23676 · Cited by 135 · see citing works
Vink, M., Zandbelt, B. B., Gladwin, T., Hillegers, M., Hoogendam, J. M., van den Wildenberg, W. P. M., Du Plessis, S., & Kahn, R. S. (2014) · Human Brain Mapping · doi:10.1002/hbm.22483 · Cited by 116 · see citing works
Hammerslag, L. R., & Gulley, J. M. (2015) · Behavioural Brain Research · doi:10.1016/j.bbr.2015.04.008 · Cited by 110 · see citing works
Resting‐state functional connectivity MRI reveals active processes central to cognition
Stevens, W. D., & Spreng, R. N. (2014) · Wiley Interdisciplinary Reviews Cognitive Science · doi:10.1002/wcs.1275 · Cited by 101 · see citing works
Evolution of brain network dynamics in neurodevelopment
Chai, L. R., Khambhati, A. N., Ciric, R., Moore, T. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2017) · Network Neuroscience · doi:10.1162/netn_a_00001 · Cited by 100 · see citing works
Kuntsche, E. N., & Müller, S. (2011) · European Addiction Research · doi:10.1159/000333036 · Cited by 97 · see citing works
Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R
O’Connell, J., & Højsgaard, S. (2011) · Journal of Statistical Software · doi:10.18637/jss.v039.i04 · Cited by 87 · see citing works
Petit, G., Kornreich, C., Verbanck, P., & Campanella, S. (2013) · Psychiatry Research · doi:10.1016/j.psychres.2013.04.005 · Cited by 81 · see citing works
Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models
Shappell, H. M., Caffo, B. S., Pekar, J. J., & Lindquist, M. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.013 · Cited by 77 · see citing works
Self-citationCurrent and Binge Drinking Among High School Students — United States, 1991–2015
Esser, M. B., Clayton, H. B., Demissie, Z., Kanny, D., & Brewer, R. D. (2017) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6618a4 · Cited by 69 · see citing works
Silveira, S., Shah, R., Nooner, K. B., Nagel, B. J., Tapert, S. F., de Bellis, M. D., & Mishra, J. (2020) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.01.011 · Cited by 68 · see citing works
Predictors of Substance Use Initiation by Early Adolescence
Green, R., Wolf, B. J., Chen, A., Kirkland, A. E., Ferguson, P. L., Browning, B. D., Bryant, B. E., Tomko, R. L., Gray, K. M., Mewton, L. R., et al. (2024) · American Journal of Psychiatry · doi:10.1176/appi.ajp.20230882 · Cited by 66 · see citing works
Shappell, H. M., Duffy, K. A., Rosch, K. S., Pekar, J. J., Mostofsky, S. H., Lindquist, M. A., & Cohen, J. R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117753 · Cited by 60 · see citing works
Self-citationLees, B., Garcia, A. M., Debenham, J., Kirkland, A. E., Bryant, B. E., Mewton, L. R., & Squeglia, L. M. (2021) · Neuropharmacology · doi:10.1016/j.neuropharm.2021.108500 · Cited by 58 · see citing works
Functional brain network reconfiguration during learning in a dynamic environment
Kao, C.-H., Khambhati, A. N., Bassett, D. S., Nassar, M. R., McGuire, J. T., Gold, J. I., & Kable, J. W. (2020) · Nature Communications · doi:10.1038/s41467-020-15442-2 · Cited by 57 · see citing works
Predicting Risk-Taking Behavior from Prefrontal Resting-State Activity and Personality
Studer, B., Pedroni, A., & Rieskamp, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076861 · Cited by 52 · see citing works
Müller‐Oehring, E. M., Kwon, D., Nagel, B. J., Sullivan, E. V., Chu, W., Torsten Rohlfing, B., Prouty, D., Nichols, B. N., Poline, J., Tapert, S. F., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx014 · Cited by 50 · see citing works
Trends in adolescent alcohol use: effects of age, sex and cohort on prevalence and heritability
Geels, L. M., Bartels, M., van Beijsterveldt, T. C. E. M., Willemsen, G. A. H. M., van der Aa, N., Boomsma, D. I., & Vink, J. M. (2011) · Addiction · doi:10.1111/j.1360-0443.2011.03612.x · Cited by 45 · see citing works
Striatal alcohol cue‐reactivity is stronger in male than female problem drinkers
Kaag, A. M., Wiers, R. W. H. J., Vries, T. J. D., Pattij, T., & Goudriaan, A. E. (2018) · European Journal of Neuroscience · doi:10.1111/ejn.13991 · Cited by 44 · see citing works
Vaghi, M. M. S., Moutoussis, M., Váša, F., Kievit, R., Hauser, T. U., Vértes, P. E., Shahar, N., Romero-García, R., Kitzbichler, M. G., Bullmore, E. T., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1922273117 · Cited by 41 · see citing works
Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates
de Lacy, N., McCauley, E., Kutz, J., & Calhoun, V. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116116 · Cited by 39 · see citing works
Dynamic fMRI networks predict success in a behavioral weight loss program among older adults
Mokhtari, F., Rejeski, W. J., Zhu, Y., Guorong Wu, A., Simpson, S. L., Burdette, J. H., & Laurienti, P. J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.025 · Cited by 36 · see citing works
Self-citationMorris, T. P., Kucyi, A., Anteraper, S. A., Geddes, M. R., Nieto-Castañón, A., Burzynska, A. Z., Gothe, N. P., Fanning, J. T., Salerno, E. A., Whitfield, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-021-04738-y · Cited by 27 · see citing works
Corr, R., Glier, S., Bizzell, J., Pelletier‐Baldelli, A., Campbell, A., Killian-Farrell, C., & Belger, A. (2022) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2022.03.003 · Cited by 23 · see citing works
Adolescent alcohol use disrupts functional neurodevelopment in sensation seeking girls
Zhao, Q., Sullivan, E. V., Müller‐Oehring, E. M., Honnorat, N., Adeli, E., Podhajsky, S., Baker, F. C., Colrain, I. M., Prouty, D., Tapert, S. F., et al. (2020) · Addiction Biology · doi:10.1111/adb.12914 · Cited by 22 · see citing works
Sex-specific resting state brain network dynamics in patients with major depressive disorder
Dong, D., Pizzagalli, D. A., Bolton, T. A. W., Ironside, M., Zhang, X., Li, C., Sun, X., Xiong, G., Cheng, C., Wang, X., et al. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01799-1 · Cited by 19 · see citing works
Testing the triple network model of psychopathology in a transdiagnostic neurodevelopmental cohort
Jones, J. S., Monaghan, A., Leyland‐Craggs, A., & Astle, D. E. (2023) · NeuroImage Clinical · doi:10.1016/j.nicl.2023.103539 · Cited by 17 · see citing works
Neural cue reactivity is not stronger in male than in female patients with alcohol use disorder
Gerhardt, S., Hoffmann, S., Tan, H., Gerchen, M. F., Kirsch, P., Vollstädt‐Klein, S., Kiefer, F., Bach, P., & Lenz, B. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.1039917 · Cited by 16 · see citing works
MIND-Map; A Comprehensive Toolbox for Estimating Brain Dynamic StatesPreprint
Khodaei, M., Laurienti, P. J., Simpson, S. L., & Shappell, H. M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.13.628351 · Cited by 6 · see citing works
Self-citationDifferences in Brain Network Topology Based on Alcohol Use History in Adolescents
Kirse, H. A., Bahrami, M., Lyday, R. G., Simpson, S. L., Peterson-Sockwell, H., Burdette, J. H., & Laurienti, P. J. (2023) · Brain Sciences · doi:10.3390/brainsci13121676 · Cited by 5 · see citing works
Self-citationCao, W., Liu, Y., Zhong, M., Liao, H., Cai, S., Chu, J., Zheng, S., Tan, C., & Yi, J. (2023) · Borderline Personality Disorder and Emotion Dysregulation · doi:10.1186/s40479-023-00227-y · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Fractal Analysis of Brain Activity During Risky Drinking in Adolescents and Young Adults
Madden, D., Lyday, R. G., Bahrami, M., Shappell, H. M., Burdette, J. H., & Laurienti, P. J. (2025) · Brain Sciences · doi:10.3390/brainsci15121256 · Cited by 1 · see citing works
Self-citationSilveri, M. M. (2025) · Alcoholism Clinical and Experimental Research · doi:10.1111/acer.70104
Abstinence Alters Triple Network Dynamics in Moderate‐to‐Heavy Drinkers
Khodaei, M., Peterson, H., McIntyre, C. C., Lyday, R. G., Simpson, S. L., Laurienti, P. J., & Shappell, H. M. (2025) · Human Brain Mapping · doi:10.1002/hbm.70432
Self-citationO’Donnell, S. M., Rejeski, W. J., Khodaei, M., Lyday, R. G., Burdette, J. H., Laurienti, P. J., & Shappell, H. M. (2026) · Brain Sciences · doi:10.3390/brainsci16030312
Self-citationThe individuality of single-frame functional brain connectivity
McIntyre, C. C., Shappell, H. M., Bahrami, M., Lyday, R. G., & Laurienti, P. J. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1254
Self-citation
- Authors
- McIntyre, C. C., Khodaei, M., Lyday, R. G., Weiner, J. L., Laurienti, P. J., & Shappell, H. M.
- Affiliations
- Wake Forest University (US)
- Virginia Tech - Wake Forest University School of Biomedical Engineering & Sciences (US)
- Wake Forest University Health Sciences (US)
- Corresponding author
- Laurienti, Paul J.
- ORCID
- Venue
- Alcohol, Clinical and Experimental Research
- Publisher
- Wiley
- Published
- 2025-05-30
- Volume
- 49
- Issue
- 6
- Pages
- 1206-1220
- ISSN
- 0145-6008 (ISSN-L)
- 2993-7175
- PMC
- PMC12173777
- OpenAlex
- W4410892031
- Cited by
- 5
- References
- 72
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Alcoholism Clinical and Experimental Research (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (K25EB032903)
- National Institute on Alcohol Abuse and Alcoholism (P50AA026117)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Epidemiology › Substance Abuse Treatment and Outcomes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- adolescent alcohol use, brain network dynamics, triple network, default mode network, salience network, frontoparietal network, hidden semi-Markov model, resting-state fMRI, NCANDA, sex differences, neural markers
- MeSH terms
- Underage Drinking, Adolescent, Alcohol Drinking, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Nerve Net
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
