{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:29:06Z","timestamp":1790141346567,"version":"4.0.1"},"reference-count":82,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":141,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>In neuroimaging research, understanding the intricate dynamics of brain networks over time is paramount for unraveling the complexities of brain function. One approach commonly used to explore the dynamic nature of brain networks is functional connectivity analysis. However, while functional connectivity offers valuable insights, it fails to consider the diverse timescales of coupling between different brain regions. This gap in understanding leaves a significant aspect of brain dynamics unexplored in neuroimaging research. We propose an innovative approach that delves into the dynamic coupling\/connectivity timescales of brain regions relative to one another, focusing on how brain region couplings stretch or shrink over time, rather than relying solely on functional connectivity measures. Our method introduces a novel metric called \u201cwarping elasticity,\u201d which utilizes dynamic time warping (DTW) to capture the temporal nuances of connectivity. Unlike traditional methods, our approach allows for (potentially nonlinear) dynamic compression and expansion of the time series, offering a more intricate understanding of how coupling between brain regions evolves. Through the adaptive windows employed by the DTW method, we can effectively capture transient couplings within varying connectivity timescales of brain network pairs. In extensive evaluations, our method exhibits high replicability across subjects and diverse datasets, showcasing robustness against noise. More importantly, it uncovers statistically significant distinctions between healthy control (HC) and schizophrenia (SZ) groups through the identification of warp elasticity states. These states are cluster centroids, representing the warp elasticity across subjects and time, offering a novel perspective on the dynamic nature of brain connectivity, distinct from conventional metrics focused solely on functional connectivity. For instance, controls spend more time in a warp elasticity state characterized by timescale stretching of the visual domain relative to other domains, suggesting disruptions in the visual cortex. Conversely, patients show increased time spent in a warp elasticity state with stretching timescales in higher cognitive areas relative to sensory regions, indicative of prolonged cognitive processing of sensory input. Overall, our approach presents a promising avenue for investigating the temporal dynamics of brain network interactions in functional magnetic resonance imaging (fMRI) data. By focusing on the elasticity of connectivity timescales, rather than adhering to functional connectivity metrics, we pave the way for a deeper understanding of neuropsychiatric disorders in neuroscience research.<\/jats:p>","DOI":"10.1162\/imag_a_00187","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T07:49:51Z","timestamp":1718092191000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":18,"title":["The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity"],"prefix":"10.1162","volume":"2","author":[{"given":"Sir-Lord","family":"Wiafe","sequence":"first","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashkan","family":"Faghiri","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zening","family":"Fu","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robyn","family":"Miller","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrian","family":"Preda","sequence":"additional","affiliation":[{"name":"University of California, Irvine, Irvine, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vince D.","family":"Calhoun","sequence":"additional","affiliation":[{"name":"Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"2025081117010198500_b1","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/EMBC48229.2022.9870916","article-title":"Discovery and replication of time-resolved functional network connectivity differences in adolescence and adulthood in over 50K fMRI datasets","volume-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","author":"Abrol","year":"2022"},{"key":"2025081117010198500_b2","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.neuroimage.2017.09.020","article-title":"Replicability of time-varying connectivity patterns in large resting state fMRI samples","volume":"163","author":"Abrol","year":"2017","journal-title":"NeuroImage"},{"issue":"1","key":"2025081117010198500_b3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1523\/JNEUROSCI.3874-05.2006","article-title":"A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs","volume":"26","author":"Achard","year":"2006","journal-title":"Journal of Neuroscience"},{"key":"2025081117010198500_b4","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1007\/s12311-016-0815-8","article-title":"Consensus paper: Cerebellum and emotion","volume":"16","author":"Adamaszek","year":"2017","journal-title":"The Cerebellum"},{"issue":"4","key":"2025081117010198500_b5","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1006\/nimg.1998.0369","article-title":"The variability of human, BOLD hemodynamic responses","volume":"8","author":"Aguirre","year":"1998","journal-title":"NeuroImage"},{"issue":"3","key":"2025081117010198500_b6","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1093\/cercor\/bhs352","article-title":"Tracking whole-brain connectivity dynamics in the resting state","volume":"24","author":"Allen","year":"2014","journal-title":"Cerebral Cortex"},{"key":"2025081117010198500_b7","volume-title":"A product theorem for Hilbert transforms","author":"Bedrosian","year":"1962"},{"issue":"1","key":"2025081117010198500_b8","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12993-022-00193-5","article-title":"The shifting role of the cerebellum in executive, emotional and social processing across the lifespan","volume":"18","author":"Beuriat","year":"2022","journal-title":"Behavioral and Brain Functions"},{"issue":"4","key":"2025081117010198500_b9","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1002\/mrm.1910340409","article-title":"Functional connectivity in the motor cortex of resting human brain using echo-planar MRI","volume":"34","author":"Biswal","year":"1995","journal-title":"Magnetic Resonance in Medicine"},{"issue":"2","key":"2025081117010198500_b10","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1016\/j.neuroimage.2011.08.044","article-title":"Test\u2013retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures","volume":"59","author":"Braun","year":"2012","journal-title":"NeuroImage"},{"key":"2025081117010198500_b11","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.neuroimage.2016.12.018","article-title":"Methods for cleaning the BOLD fMRI signal","volume":"154","author":"Caballero-Gaudes","year":"2017","journal-title":"NeuroImage"},{"issue":"3","key":"2025081117010198500_b12","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1002\/hbm.1048","article-title":"A method for making group inferences from functional MRI data using independent component analysis","volume":"14","author":"Calhoun","year":"2001","journal-title":"Human Brain Mapping"},{"issue":"2","key":"2025081117010198500_b13","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.neuron.2014.10.015","article-title":"The chronnectome: Time-varying connectivity networks as the next frontier in fMRI data discovery","volume":"84","author":"Calhoun","year":"2014","journal-title":"Neuron"},{"issue":"3","key":"2025081117010198500_b14","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1016\/j.neuroimage.2008.09.029","article-title":"Influence of heart rate on the BOLD signal: The cardiac response function","volume":"44","author":"Chang","year":"2009","journal-title":"NeuroImage"},{"issue":"1","key":"2025081117010198500_b15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.neuroimage.2009.12.011","article-title":"Time\u2013frequency dynamics of resting-state brain connectivity measured with fMRI","volume":"50","author":"Chang","year":"2010","journal-title":"NeuroImage"},{"key":"2025081117010198500_b16","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/j.neuroimage.2017.10.022","article-title":"Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis","volume":"180","author":"Du","year":"2018","journal-title":"NeuroImage"},{"key":"2025081117010198500_b17","doi-asserted-by":"crossref","first-page":"102375","DOI":"10.1016\/j.nicl.2020.102375","article-title":"NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders","volume":"28","author":"Du","year":"2020","journal-title":"NeuroImage: Clinical"},{"issue":"2","key":"2025081117010198500_b18","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1002\/hbm.22194","article-title":"A meta-analysis of cerebellar contributions to higher cognition from PET and fMRI studies","volume":"35","author":"E","year":"2014","journal-title":"Human Brain Mapping"},{"issue":"3","key":"2025081117010198500_b19","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1016\/j.neuroimage.2011.05.021","article-title":"Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation","volume":"57","author":"Eickhoff","year":"2011","journal-title":"NeuroImage"},{"issue":"1","key":"2025081117010198500_b20","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","article-title":"fMRIPrep: A robust preprocessing pipeline for functional MRI","volume":"16","author":"Esteban","year":"2019","journal-title":"Nature Methods"},{"key":"2025081117010198500_b21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ISBI52829.2022.9761427","article-title":"Single sideband modulation as a tool to improve functional connectivity estimation","volume-title":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","author":"Faghiri","year":"2022"},{"key":"2025081117010198500_b22","article-title":"Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study","author":"Faghiri","year":"2023","journal-title":"bioRxiv"},{"issue":"27","key":"2025081117010198500_b23","doi-asserted-by":"crossref","first-page":"9673","DOI":"10.1073\/pnas.0504136102","article-title":"The human brain is intrinsically organized into dynamic, anticorrelated functional networks","volume":"102","author":"Fox","year":"2005","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"2025081117010198500_b24","doi-asserted-by":"crossref","first-page":"e33","DOI":"10.1371\/journal.pbio.1000033","article-title":"Causal modelling and brain connectivity in functional magnetic resonance imaging","volume":"7","author":"Friston","year":"2009","journal-title":"PLoS Biology"},{"issue":"1\u20132","key":"2025081117010198500_b25","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1002\/hbm.460020107","article-title":"Functional and effective connectivity in neuroimaging: A synthesis","volume":"2","author":"Friston","year":"1994","journal-title":"Human Brain Mapping"},{"key":"2025081117010198500_b26","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.neuroimage.2017.09.035","article-title":"Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia","volume":"180","author":"Fu","year":"2018","journal-title":"NeuroImage"},{"key":"2025081117010198500_b27","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.neuroimage.2017.08.044","article-title":"Fluctuations between high- and low-modularity topology in time-resolved functional connectivity","volume":"180","author":"Fukushima","year":"2018","journal-title":"NeuroImage"},{"issue":"2","key":"2025081117010198500_b28","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.cogbrainres.2004.02.012","article-title":"Brain areas underlying visual mental imagery and visual perception: An fMRI study","volume":"20","author":"Ganis","year":"2004","journal-title":"Cognitive Brain Research"},{"issue":"2","key":"2025081117010198500_b29","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1089\/brain.2011.0068","article-title":"Functional magnetic resonance imaging phase synchronization as a measure of dynamic functional connectivity","volume":"2","author":"Glerean","year":"2012","journal-title":"Brain Connectivity"},{"issue":"3","key":"2025081117010198500_b30","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.neuroimage.2011.09.082","article-title":"Physiologic noise regression, motion regression, and TOAST dynamic field correction in complex-valued fMRI time series","volume":"59","author":"Hahn","year":"2012","journal-title":"NeuroImage"},{"key":"2025081117010198500_b31","doi-asserted-by":"crossref","first-page":"117704","DOI":"10.1016\/j.neuroimage.2020.117704","article-title":"Evaluating phase synchronization methods in fMRI: A comparison study and new approaches","volume":"228","author":"Honari","year":"2021","journal-title":"NeuroImage"},{"issue":"4","key":"2025081117010198500_b32","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/j.neuroimage.2012.03.011","article-title":"Medial orbitofrontal cortex is associated with shifting decision thresholds in self-serving cognition","volume":"61","author":"Hughes","year":"2012","journal-title":"NeuroImage"},{"key":"2025081117010198500_b33","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.neuroimage.2013.05.079","article-title":"Dynamic functional connectivity: Promise, issues, and interpretations","volume":"80","author":"Hutchison","year":"2013","journal-title":"NeuroImage"},{"issue":"8","key":"2025081117010198500_b34","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1093\/scan\/nsaa114","article-title":"Tools of the trade: Estimating time-varying connectivity patterns from fMRI data","volume":"16","author":"Iraji","year":"2020","journal-title":"Social Cognitive and Affective Neuroscience"},{"issue":"4","key":"2025081117010198500_b35","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.neuroimage.2007.11.001","article-title":"A method for functional network connectivity among spatially independent resting-state components in schizophrenia","volume":"39","author":"Jafri","year":"2008","journal-title":"NeuroImage"},{"key":"2025081117010198500_b36","doi-asserted-by":"crossref","first-page":"3737","DOI":"10.1109\/EMBC48229.2022.9871362","article-title":"A 5D approach to study spatio-temporal dynamism of resting-state brain networks in schizophrenia","volume-title":"2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","author":"Kazemivash","year":"2022"},{"key":"2025081117010198500_b37","doi-asserted-by":"crossref","first-page":"1097523","DOI":"10.3389\/fnimg.2023.1097523","article-title":"A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia","volume":"2","author":"Kazemivash","year":"2023","journal-title":"Front Neuroimaging"},{"issue":"11","key":"2025081117010198500_b38","doi-asserted-by":"crossref","first-page":"2955","DOI":"10.1093\/brain\/awx233","article-title":"Abnormal intrinsic brain functional network dynamics in Parkinson\u2019s disease","volume":"140","author":"Kim","year":"2017","journal-title":"Brain"},{"issue":"1","key":"2025081117010198500_b39","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s12311-013-0511-x","article-title":"Consensus paper: The cerebellum\u2019s role in movement and cognition","volume":"13","author":"Koziol","year":"2014","journal-title":"Cerebellum"},{"issue":"24","key":"2025081117010198500_b40","doi-asserted-by":"crossref","first-page":"11802","DOI":"10.1073\/pnas.90.24.11802","article-title":"Activation of human primary visual cortex during visual recall: A magnetic resonance imaging study","volume":"90","author":"Le Bihan","year":"1993","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2025081117010198500_b41","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.neuroimage.2014.09.007","article-title":"On spurious and real fluctuations of dynamic functional connectivity during rest","volume":"104","author":"Leonardi","year":"2015","journal-title":"NeuroImage"},{"issue":"1 Suppl.","key":"2025081117010198500_b42","doi-asserted-by":"crossref","first-page":"S187","DOI":"10.1016\/j.neuroimage.2008.10.065","article-title":"Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling","volume":"45","author":"Lindquist","year":"2009","journal-title":"NeuroImage"},{"key":"2025081117010198500_b43","doi-asserted-by":"crossref","first-page":"117383","DOI":"10.1016\/j.neuroimage.2020.117383","article-title":"Dynamic time warping outperforms Pearson correlation in detecting atypical functional connectivity in autism spectrum disorders","volume":"223","author":"Linke","year":"2020","journal-title":"NeuroImage"},{"key":"2025081117010198500_b44","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.neuroimage.2018.01.041","article-title":"Co-activation patterns in resting-state fMRI signals","volume":"180","author":"Liu","year":"2018","journal-title":"NeuroImage"},{"issue":"1","key":"2025081117010198500_b45","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1162\/netn_a_00116","article-title":"Questions and controversies in the study of time-varying functional connectivity in resting fMRI","volume":"4","author":"Lurie","year":"2020","journal-title":"Network Neuroscience"},{"issue":"10","key":"2025081117010198500_b46","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.tics.2011.08.003","article-title":"Large-scale brain networks and psychopathology: A unifying triple network model","volume":"15","author":"Menon","year":"2011","journal-title":"Trends in Cognitive Sciences"},{"key":"2025081117010198500_b47","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3389\/fnins.2017.00075","article-title":"Resting state fMRI functional connectivity analysis using dynamic time warping [methods]","volume":"11","author":"Meszl\u00e9nyi","year":"2017","journal-title":"Frontiers in Neuroscience"},{"key":"2025081117010198500_b48","doi-asserted-by":"crossref","first-page":"109794","DOI":"10.1016\/j.jneumeth.2023.109794","article-title":"A method for estimating and characterizing explicitly nonlinear dynamic functional network connectivity in resting-state fMRI data","volume":"389","author":"Motlaghian","year":"2023","journal-title":"Journal of Neuroscience Methods"},{"issue":"3","key":"2025081117010198500_b49","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.neuroimage.2008.09.036","article-title":"The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced","volume":"44","author":"Murphy","year":"2009","journal-title":"NeuroImage"},{"issue":"4","key":"2025081117010198500_b50","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/42.768841","article-title":"Generalized likelihood ratio detection for fMRI using complex data","volume":"18","author":"Nan","year":"1999","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2025081117010198500_b51","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.neuroimage.2017.10.003","article-title":"Dynamics of large-scale electrophysiological networks: A technical review","volume":"180","author":"O\u2019Neill","year":"2018","journal-title":"NeuroImage"},{"key":"2025081117010198500_b52","volume-title":"Discrete-time signal processing","author":"Oppenheim","year":"1999"},{"issue":"4","key":"2025081117010198500_b53","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/0165-1684(82)90009-3","article-title":"A modification over Sakoe and Chiba\u2019s dynamic time warping algorithm for isolated word recognition","volume":"4","author":"Paliwal","year":"1982","journal-title":"Signal Processing"},{"issue":"10","key":"2025081117010198500_b54","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1038\/s41583-019-0202-9","article-title":"The human imagination: The cognitive neuroscience of visual mental imagery","volume":"20","author":"Pearson","year":"2019","journal-title":"Nature Reviews Neuroscience"},{"key":"2025081117010198500_b55","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neuroimage.2018.06.020","article-title":"On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis","volume":"181","author":"Pedersen","year":"2018","journal-title":"NeuroImage"},{"key":"2025081117010198500_b56","volume-title":"Statistical parametric mapping: The analysis of functional brain images","author":"Penny","year":"2011"},{"issue":"6","key":"2025081117010198500_b57","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1093\/cercor\/bhab273","article-title":"Dynamic time warping identifies functionally distinct fMRI resting state cortical networks specific to VTA and SNc: A proof of concept","volume":"32","author":"Philips","year":"2021","journal-title":"Cerebral Cortex"},{"key":"2025081117010198500_b58","volume-title":"Digital signal processing: Principles, algorithms, and applications, 4\/E","author":"Proakis","year":"2007"},{"issue":"4","key":"2025081117010198500_b59","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1002\/mrm.27146","article-title":"Hemodynamic response function (HRF) variability confounds resting-state fMRI functional connectivity","volume":"80","author":"Rangaprakash","year":"2018","journal-title":"Magnetic Resonance in Medicine"},{"key":"2025081117010198500_b60","doi-asserted-by":"crossref","first-page":"103203","DOI":"10.1016\/j.nicl.2022.103203","article-title":"Functional connectivity dynamics reflect disability and multi-domain clinical impairment in patients with relapsing-remitting multiple sclerosis","volume":"36","author":"Romanello","year":"2022","journal-title":"NeuroImage: Clinical"},{"issue":"1","key":"2025081117010198500_b61","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1089\/brain.2012.0080","article-title":"Trouble at rest: How correlation patterns and group differences become distorted after global signal regression","volume":"2","author":"Saad","year":"2012","journal-title":"Brain Connectivity"},{"issue":"1","key":"2025081117010198500_b62","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic programming algorithm optimization for spoken word recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Transactions on Acoustics, Speech, and Signal Processing"},{"issue":"5\u20136","key":"2025081117010198500_b63","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s10334-010-0197-8","article-title":"A method for evaluating dynamic functional network connectivity and task-modulation: Application to schizophrenia","volume":"23","author":"Sako\u011flu","year":"2010","journal-title":"Magma"},{"key":"2025081117010198500_b64","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.neurobiolaging.2022.11.009","article-title":"Progressive alteration of dynamic functional connectivity patterns in subcortical ischemic vascular cognitive impairment patients","volume":"122","author":"Sang","year":"2023","journal-title":"Neurobiology of Aging"},{"issue":"2","key":"2025081117010198500_b65","doi-asserted-by":"crossref","first-page":"1517","DOI":"10.1016\/j.neuroimage.2012.01.067","article-title":"Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER","volume":"60","author":"S\u00e4rkk\u00e4","year":"2012","journal-title":"NeuroImage"},{"issue":"1\u201323","key":"2025081117010198500_b66","first-page":"40","article-title":"Dynamic time warping algorithm review","volume":"855","author":"Senin","year":"2008","journal-title":"Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA"},{"issue":"10","key":"2025081117010198500_b67","doi-asserted-by":"crossref","first-page":"2209","DOI":"10.1093\/cercor\/bhn256","article-title":"The resting brain: Unconstrained yet reliable","volume":"19","author":"Shehzad","year":"2009","journal-title":"Cerebral Cortex"},{"issue":"2","key":"2025081117010198500_b68","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.neuroimage.2007.07.037","article-title":"Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal","volume":"38","author":"Shmueli","year":"2007","journal-title":"NeuroImage"},{"key":"2025081117010198500_b69","volume-title":"Networks of the brain","author":"Sporns","year":"2016"},{"key":"2025081117010198500_b70","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jpsychires.2021.05.010","article-title":"Brain state-dependent dynamic functional connectivity patterns in attention-deficit\/hyperactivity disorder","volume":"138","author":"Sun","year":"2021","journal-title":"Journal of Psychiatric Research"},{"issue":"1","key":"2025081117010198500_b71","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1175\/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","article-title":"A practical guide to wavelet analysis","volume":"79","author":"Torrence","year":"1998","journal-title":"Bulletin of the American Meteorological Society"},{"issue":"1","key":"2025081117010198500_b72","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.neuroimage.2005.01.007","article-title":"Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters","volume":"26","author":"Triantafyllou","year":"2005","journal-title":"NeuroImage"},{"issue":"1\u20132","key":"2025081117010198500_b73","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s002210050538","article-title":"Functional magnetic resonance imaging of the human brain: Data acquisition and analysis","volume":"123","author":"Turner","year":"1998","journal-title":"Experimental Brain Research"},{"issue":"12","key":"2025081117010198500_b74","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.tics.2013.09.012","article-title":"Network hubs in the human brain","volume":"17","author":"Van den Heuvel","year":"2013","journal-title":"Trends in Cognitive Sciences"},{"key":"2025081117010198500_b75","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-minn human connectome project: An overview","volume":"80","author":"Van Essen","year":"2013","journal-title":"NeuroImage"},{"issue":"4","key":"2025081117010198500_b76","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The Human Connectome Project: A data acquisition perspective","volume":"62","author":"Van Essen","year":"2012","journal-title":"NeuroImage"},{"key":"2025081117010198500_b77","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s12311-020-01155-1","article-title":"Consensus paper: Cerebellum and social cognition","volume":"19","author":"Van Overwalle","year":"2020","journal-title":"The Cerebellum"},{"issue":"11","key":"2025081117010198500_b78","doi-asserted-by":"crossref","first-page":"e77089","DOI":"10.1371\/journal.pone.0077089","article-title":"On the definition of signal-to-noise ratio and contrast-to-noise ratio for FMRI data","volume":"8","author":"Welvaert","year":"2013","journal-title":"PLoS One"},{"key":"2025081117010198500_b79","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/EMBC40787.2023.10341073","article-title":"Phase and amplitude, two sides of functional connectivity","volume-title":"2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","author":"Wiafe","year":"2023"},{"issue":"4","key":"2025081117010198500_b80","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1016\/j.neuroimage.2003.11.025","article-title":"Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal","volume":"21","author":"Wise","year":"2004","journal-title":"NeuroImage"},{"issue":"9","key":"2025081117010198500_b81","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1090\/S0002-9939-06-08315-8","article-title":"The Bedrosian identity for the Hilbert transform of product functions","volume":"134","author":"Xu","year":"2006","journal-title":"Proceedings of the American Mathematical Society"},{"key":"2025081117010198500_b82","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neuroimage.2015.07.002","article-title":"Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information","volume":"120","author":"Yaesoubi","year":"2015","journal-title":"NeuroImage"}],"container-title":["Imaging Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/imag_a_00187\/2380311\/imag_a_00187.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/imag_a_00187\/2380311\/imag_a_00187.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T21:01:16Z","timestamp":1754946076000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/imag\/article\/doi\/10.1162\/imag_a_00187\/121174\/The-dynamics-of-dynamic-time-warping-in-fMRI-data"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":82,"URL":"https:\/\/doi.org\/10.1162\/imag_a_00187","relation":{},"ISSN":["2837-6056"],"issn-type":[{"value":"2837-6056","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024]]},"published":{"date-parts":[[2024]]},"article-number":"imag-2-00187"}}