{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:58:41Z","timestamp":1790359121067,"version":"4.1.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Schizophr"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Schizophrenia is associated with deficits in input processing, but the underlying neural dynamics of this deficit remain unclear. A recent model proposes temporal imprecision as a basic disturbance driving the input processing deficit. To address the question of whether temporal imprecision affects all three domains of the brain\u2019s signal (e.g., phase, time, and frequency) as early as first episode schizophrenia, we analyzed magnetoencephalography (MEG) data from 25 first-episode schizophrenia (FESZ) participants and 24 healthy controls (HC) during a passive auditory oddball task with standard, pitch deviant, and duration deviant tones while watching a silent movie. We quantified temporal precision via (i) intertrial phase coherence (ITPC), (ii) intrinsic neural timescale (INT), and (iii) power spectral density (PSD) heterogeneity. For all three signal domains, we also calculated their temporal variability in sliding windows over the MEG recording. FESZ showed reduced intertrial phase coherence to both deviant tones, with a broader time and frequency range for the duration deviant ITPC reduction. Intrinsic neural timescales were prolonged in FESZ subjects. PSD profiles were more heterogeneous and broader during the auditory task in the FESZ group. FESZ subjects also showed increased temporal variability in all three signal domains across sliding windows. Finally, significant associations were observed between temporal variability in all three signal domains, including their association to impaired duration deviant mismatch negativity amplitude in first episode schizophrenia, whereas these associations were largely absent for healthy participants. We conclude that first-episode schizophrenia shows convergent temporal imprecision at specific time points as well as between time points in all three signal domains including phase, time, and frequency; this is consistent with temporal imprecision as a domain-general basic disturbance of input processing in schizophrenia.<\/jats:p>","DOI":"10.1038\/s41537-026-00792-5","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T17:44:26Z","timestamp":1786556666000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Schizophrenia: temporal imprecision in phase, time, and frequency domains"],"prefix":"10.1038","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1774-1913","authenticated-orcid":false,"given":"Saeid","family":"Kholghi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5236-0951","authenticated-orcid":false,"given":"Georg","family":"Northoff","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"792_CR1","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1038\/s41380-023-02337-z","volume":"29","author":"A Wolff","year":"2024","unstructured":"Wolff, A. & Northoff, G. Temporal imprecision of phase coherence in schizophrenia and psychosis\u2014dynamic mechanisms and diagnostic marker. Mol. Psychiatry 29, 425\u2013438 (2024).","journal-title":"Mol. Psychiatry"},{"key":"792_CR2","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1038\/nrn4002","volume":"16","author":"DC Javitt","year":"2015","unstructured":"Javitt, D. C. & Sweet, R. A. Auditory dysfunction in schizophrenia: integrating clinical and basic features. Nat. Rev. Neurosci. 16, 535\u2013550 (2015).","journal-title":"Nat. Rev. Neurosci."},{"key":"792_CR3","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1176\/appi.ajp.2014.13121691","volume":"172","author":"DC Javitt","year":"2015","unstructured":"Javitt, D. C. & Freedman, R. Sensory processing dysfunction in the personal experience and neuronal machinery of schizophrenia. Am. J. Psychiatry 172, 17\u201331 (2015).","journal-title":"Am. J. Psychiatry"},{"key":"792_CR4","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.bandc.2009.02.001","volume":"70","author":"CA Carroll","year":"2009","unstructured":"Carroll, C. A., O\u2019Donnell, B. F., Shekhar, A. & Hetrick, W. P. Timing dysfunctions in schizophrenia span from millisecond to several-second durations. Brain Cogn. 70, 181\u2013190 (2009).","journal-title":"Brain Cogn."},{"key":"792_CR5","doi-asserted-by":"publisher","first-page":"3441","DOI":"10.1093\/cercor\/bhab425","volume":"32","author":"A Wolff","year":"2022","unstructured":"Wolff, A. et al. It\u2019s in the timing: reduced temporal precision in neural activity of schizophrenia. Cereb. Cortex 32, 3441\u20133456 (2022).","journal-title":"Cereb. Cortex"},{"key":"792_CR6","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s41398-025-03510-4","volume":"15","author":"S Lechner","year":"2025","unstructured":"Lechner, S. et al. Temporal imprecision and its dynamics in schizophrenia. Translat. Psychiatry 15, 279 (2025).","journal-title":"Translat. Psychiatry"},{"key":"792_CR7","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/B978-0-7020-5307-8.00006-5","volume":"62","author":"BF O\u2019Donnell","year":"2013","unstructured":"O\u2019Donnell, B. F. et al. The auditory steady-state response (ASSR): a translational biomarker for schizophrenia. Suppl. Clin. Neurophysiol. 62, 101\u2013112 (2013).","journal-title":"Suppl. Clin. Neurophysiol."},{"key":"792_CR8","doi-asserted-by":"publisher","first-page":"R890","DOI":"10.1016\/j.cub.2019.07.075","volume":"29","author":"P Lakatos","year":"2019","unstructured":"Lakatos, P., Gross, J. & Thut, G. A new unifying account of the roles of neuronal entrainment. Curr. Biol. 29, R890\u2013R905 (2019).","journal-title":"Curr. Biol."},{"key":"792_CR9","doi-asserted-by":"publisher","unstructured":"Cohen, M. X. Intertrial phase clustering. Anal. Neural Time Ser. Data 261\u2013278 https:\/\/doi.org\/10.7551\/MITPRESS\/9609.003.0024 (2014).","DOI":"10.7551\/MITPRESS\/9609.003.0024"},{"key":"792_CR10","doi-asserted-by":"publisher","first-page":"695","DOI":"10.3390\/brainsci13040695","volume":"13","author":"S Lechner","year":"2023","unstructured":"Lechner, S. & Northoff, G. Prolonged intrinsic neural timescales dissociate from phase coherence in schizophrenia. Brain Sci. 13, 695 (2023).","journal-title":"Brain Sci."},{"key":"792_CR11","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.tics.2021.11.007","volume":"26","author":"A Wolff","year":"2022","unstructured":"Wolff, A. et al. Intrinsic neural timescales: temporal integration and segregation. Trends Cogn. Sci. 26, 159\u2013173 (2022).","journal-title":"Trends Cogn. Sci."},{"key":"792_CR12","doi-asserted-by":"crossref","unstructured":"Golesorkhi, M. et al. The brain and its time: intrinsic neural timescales are key for input processing. Commun. Biol. 4, 970 (2021).","DOI":"10.1038\/s42003-021-02483-6"},{"key":"792_CR13","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.tics.2015.04.006","volume":"19","author":"U Hasson","year":"2015","unstructured":"Hasson, U., Chen, J. & Honey, C. J. Hierarchical process memory: memory as an integral component of information processing. Trends Cogn. Sci. 19, 304\u2013313 (2015).","journal-title":"Trends Cogn. Sci."},{"key":"792_CR14","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1038\/nn.3862","volume":"17","author":"JD Murray","year":"2014","unstructured":"Murray, J. D. et al. A hierarchy of intrinsic timescales across primate cortex. Nat. Neurosci. 17, 1661\u20131663 (2014).","journal-title":"Nat. Neurosci."},{"key":"792_CR15","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.neuron.2012.08.011","volume":"76","author":"CJ Honey","year":"2012","unstructured":"Honey, C. J. et al. Slow cortical dynamics and the accumulation of information over long timescales. Neuron 76, 423\u2013434 (2012).","journal-title":"Neuron"},{"key":"792_CR16","doi-asserted-by":"publisher","first-page":"8261","DOI":"10.21105\/joss.08261","volume":"10","author":"Y \u00c7atal","year":"2025","unstructured":"\u00c7atal, Y. & Northoff, G. IntrinsicTimescales.jl: A Julia package to estimate intrinsic (neural) timescales (INTs) from time-series data. J. Open Source Softw. 10, 8261 (2025).","journal-title":"J. Open Source Softw."},{"key":"792_CR17","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1038\/s42003-021-01785-z","volume":"4","author":"M Golesorkhi","year":"2021","unstructured":"Golesorkhi, M., Gomez-Pilar, J., Tumati, S., Fraser, M. & Northoff, G. Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization. Commun. Biol. 4, 277 (2021).","journal-title":"Commun. Biol."},{"key":"792_CR18","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neuroscience.2018.04.030","volume":"389","author":"KD Himberger","year":"2018","unstructured":"Himberger, K. D., Chien, H. Y. & Honey, C. J. Principles of temporal processing across the cortical hierarchy. Neuroscience 389, 161\u2013174 (2018).","journal-title":"Neuroscience"},{"key":"792_CR19","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nn.4450","volume":"20","author":"J Chen","year":"2016","unstructured":"Chen, J. et al. Shared memories reveal shared structure in neural activity across individuals. Nat. Neurosci. 20, 115\u2013125 (2016).","journal-title":"Nat. Neurosci."},{"key":"792_CR20","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1093\/schbul\/sbaa083","volume":"47","author":"G Northoff","year":"2021","unstructured":"Northoff, G., Sandsten, K. E., Nordgaard, J., Kjaer, T. W. & Parnas, J. The self and its prolonged intrinsic neural timescale in schizophrenia. Schizophr. Bull. 47, 170\u2013179 (2021).","journal-title":"Schizophr. Bull."},{"key":"792_CR21","doi-asserted-by":"publisher","first-page":"15154","DOI":"10.1038\/s41598-024-66110-0","volume":"14","author":"SM Redwan","year":"2024","unstructured":"Redwan, S. M., Uddin, M. P., Ulhaq, A., Sharif, M. I. & Krishnamoorthy, G. Power spectral density-based resting-state EEG classification of first-episode psychosis. Sci. Rep. 14, 15154 (2024).","journal-title":"Sci. Rep."},{"key":"792_CR22","doi-asserted-by":"publisher","first-page":"e70263","DOI":"10.1111\/ejn.70263","volume":"62","author":"G Hasanaj","year":"2025","unstructured":"Hasanaj, G. et al. Periodic and aperiodic alterations of resting-state EEG in schizophrenia spectrum disorders: cognitive and clinical insights. Eur. J. Neurosci. 62, e70263 (2025).","journal-title":"Eur. J. Neurosci."},{"key":"792_CR23","doi-asserted-by":"publisher","first-page":"102485","DOI":"10.1016\/j.nicl.2020.102485","volume":"28","author":"G Alamian","year":"2020","unstructured":"Alamian, G. et al. Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia. Neuroimage Clin. 28, 102485 (2020).","journal-title":"Neuroimage Clin."},{"key":"792_CR24","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.neuroimage.2012.03.008","volume":"61","author":"VV Nikulin","year":"2012","unstructured":"Nikulin, V. V., J\u00f6nsson, E. G. & Brismar, T. Attenuation of long-range temporal correlations in the amplitude dynamics of alpha and beta neuronal oscillations in patients with schizophrenia. Neuroimage 61, 162\u2013169 (2012).","journal-title":"Neuroimage"},{"key":"792_CR25","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1016\/j.schres.2022.10.006","volume":"263","author":"G Northoff","year":"2024","unstructured":"Northoff, G. & Hirjak, D. Spatiotemporal psychopathology \u2013 an integrated brain-mind approach and catatonia. Schizophr. Res. 263, 151\u2013159 (2024).","journal-title":"Schizophr. Res."},{"key":"792_CR26","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1111\/ejn.16107","volume":"59","author":"F L\u00f3pez-Caballero","year":"2024","unstructured":"L\u00f3pez-Caballero, F., Curtis, M., Coffman, B. A. & Salisbury, D. F. Is source-resolved magnetoencephalographic mismatch negativity a viable biomarker for early psychosis?. Eur. J. Neurosci. 59, 1889\u20131906 (2024).","journal-title":"Eur. J. Neurosci."},{"key":"792_CR27","doi-asserted-by":"publisher","first-page":"1655","DOI":"10.1038\/s41593-020-00744-x","volume":"23","author":"T Donoghue","year":"2020","unstructured":"Donoghue, T. et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat. Neurosci. 23, 1655\u20131665 (2020).","journal-title":"Nat. Neurosci."},{"key":"792_CR28","doi-asserted-by":"publisher","unstructured":"Larson, E. et al. MNE-Python. https:\/\/doi.org\/10.5281\/ZENODO.14519545.","DOI":"10.5281\/ZENODO.14519545"},{"key":"792_CR29","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.ics.2004.05.004","volume":"1270","author":"S Taulu","year":"2004","unstructured":"Taulu, S., Simola, J. & Kajola, M. Clinical applications of the signal space separation method. Int. Congr. Ser. 1270, 32\u201337 (2004).","journal-title":"Int. Congr. Ser."},{"key":"792_CR30","doi-asserted-by":"crossref","unstructured":"Taulu, S. & Simola, J. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Phys. Med. Biol. 51, 1759\u201368 (2006).","DOI":"10.1088\/0031-9155\/51\/7\/008"},{"key":"792_CR31","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.neuroimage.2017.06.030","volume":"159","author":"M Jas","year":"2017","unstructured":"Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F. & Gramfort, A. Autoreject: automated artifact rejection for MEG and EEG data. Neuroimage 159, 417\u2013429 (2017).","journal-title":"Neuroimage"},{"key":"792_CR32","doi-asserted-by":"publisher","first-page":"3610","DOI":"10.1093\/cercor\/bhx233","volume":"28","author":"R Chaudhuri","year":"2018","unstructured":"Chaudhuri, R., He, B. J. & Wang, X. J. Random recurrent networks near criticality capture the broadband power distribution of human ECoG dynamics. Cereb. Cortex 28, 3610\u20133622 (2018).","journal-title":"Cereb. Cortex"},{"key":"792_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7554\/eLife.61277","volume":"9","author":"R Gao","year":"2020","unstructured":"Gao, R., Van den Brink, R. L., Pfeffer, T. & Voytek, B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 9, 1\u201344 (2020).","journal-title":"eLife"},{"key":"792_CR34","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/TAU.1967.1161901","volume":"15","author":"PD Welch","year":"1967","unstructured":"Welch, P. D. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15, 70\u201373 (1967).","journal-title":"IEEE Trans. Audio Electroacoust."},{"key":"792_CR35","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1111\/j.1541-0420.2005.00440.x","volume":"62","author":"MJ Anderson","year":"2006","unstructured":"Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245\u2013253 (2006).","journal-title":"Biometrics"},{"key":"792_CR36","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/biomet\/53.3-4.325","volume":"53","author":"JC Gower","year":"1966","unstructured":"Gower, J. C. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325 (1966).","journal-title":"Biometrika"},{"key":"792_CR37","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1001\/jamapsychiatry.2016.2619","volume":"73","author":"H Thun","year":"2016","unstructured":"Thun, H., Recasens, M. & Uhlhaas, P. J. The 40-Hz auditory steady-state response in patients with schizophrenia: a meta-analysis. JAMA Psychiatry 73, 1145\u20131153 (2016).","journal-title":"JAMA Psychiatry"},{"key":"792_CR38","doi-asserted-by":"publisher","first-page":"818711","DOI":"10.3389\/fnhum.2022.818711","volume":"16","author":"PF Liddle","year":"2022","unstructured":"Liddle, P. F. & Liddle, E. B. Imprecise predictive coding is at the core of classical schizophrenia. Front. Hum. Neurosci. 16, 818711 (2022).","journal-title":"Front. Hum. Neurosci."},{"key":"792_CR39","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1038\/s41537-022-00239-7","volume":"8","author":"M Karanikolaou","year":"2022","unstructured":"Karanikolaou, M., Limanowski, J. & Northoff, G. Does temporal irregularity drive prediction failure in schizophrenia? Temporal modelling of ERPs. Schizophrenia 8, 23 (2022).","journal-title":"Schizophrenia"},{"key":"792_CR40","doi-asserted-by":"publisher","unstructured":"Byrne, \u00c1., Coombes, S. & Liddle, P. F. A neural mass model for abnormal beta-rebound in schizophrenia. 21\u201327 https:\/\/doi.org\/10.1007\/978-3-030-18830-6_3 (2019).","DOI":"10.1007\/978-3-030-18830-6_3"},{"key":"792_CR41","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1159\/000519500","volume":"55","author":"F Arantes-Gonc\u0105lves","year":"2022","unstructured":"Arantes-Gonc\u0105lves, F., Wolman, A., Bastos-Leite, A. J. & Northoff, G. Scale for space and time experience in psychosis: converging phenomenological and psychopathological perspectives. Psychopathology 55, 132\u2013142 (2022).","journal-title":"Psychopathology"},{"key":"792_CR42","doi-asserted-by":"publisher","DOI":"10.1159\/000547153\/929646","author":"S Lechner","year":"2025","unstructured":"Lechner, S. et al. From experience to symptoms: a multilayer hierarchy of psychopathological dimensions in schizophrenia. Psychopathology https:\/\/doi.org\/10.1159\/000547153\/929646 (2025).","journal-title":"Psychopathology"},{"key":"792_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7554\/eLife.56151","volume":"9","author":"K Wengler","year":"2020","unstructured":"Wengler, K., Goldberg, A. T., Chahine, G. & Horga, G. Distinct hierarchical alterations of intrinsic neural timescales account for different manifestations of psychosis. eLife 9, 1\u201327 (2020).","journal-title":"eLife"},{"key":"792_CR44","doi-asserted-by":"crossref","unstructured":"Usc\u0103tescu, L. C. et al. Reduced intrinsic neural timescales in schizophrenia along posterior parietal and occipital areas. npj Schizophr. 7, 55 (2021).","DOI":"10.1038\/s41537-021-00184-x"},{"key":"792_CR45","doi-asserted-by":"publisher","first-page":"e1007055","DOI":"10.1371\/journal.pcbi.1007055","volume":"15","author":"N Schaworonkow","year":"2019","unstructured":"Schaworonkow, N. & Nikulin, V. V. Spatial neuronal synchronization and the waveform of oscillations: implications for EEG and MEG. PLoS Comput. Biol. 15, e1007055 (2019).","journal-title":"PLoS Comput. Biol."},{"key":"792_CR46","doi-asserted-by":"publisher","first-page":"119093","DOI":"10.1016\/j.neuroimage.2022.119093","volume":"253","author":"N Schaworonkow","year":"2022","unstructured":"Schaworonkow, N. & Nikulin, V. V. Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG\/MEG rhythms. Neuroimage 253, 119093 (2022).","journal-title":"Neuroimage"}],"container-title":["Schizophrenia"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41537-026-00792-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41537-026-00792-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41537-026-00792-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:03:27Z","timestamp":1790355807000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41537-026-00792-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":46,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["792"],"URL":"https:\/\/doi.org\/10.1038\/s41537-026-00792-5","relation":{},"ISSN":["2754-6993"],"issn-type":[{"value":"2754-6993","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]},"assertion":[{"value":"20 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"77"}}