{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T03:21:34Z","timestamp":1793244094056,"version":"4.5.0"},"reference-count":52,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":232,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"WU-Minn Consortium","award":["1U54MH091657"],"award-info":[{"award-number":["1U54MH091657"]}]},{"name":"16 NIH Institutes"},{"DOI":"10.13039\/100000135","name":"NIH Blueprint for Neuroscience Research","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000135","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["#197787"],"award-info":[{"award-number":["#197787"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Predicting behavioral variables from neuroimaging modalities such as magnetic resonance imaging (MRI) has the potential to allow the development of neuroimaging biomarkers of mental and neurological disorders. A crucial processing step to this aim is the extraction of suitable features. These can differ in how well they predict the target of interest, and how this prediction scales with sample size and scan time. Here, we compare nine feature subtypes extracted from resting-state functional MRI recordings for behavior prediction, ranging from regional measures of functional activity to functional connectivity (FC) and metrics derived with graph signal processing (GSP), a principled approach for the extraction of structure-informed functional features. We study 979 subjects from the Human Connectome Project Young Adult dataset, predicting summary scores for mental health, cognition, processing speed, and substance use, as well as age and sex. The scaling properties of the features are investigated for different combinations of sample size and scan time. FC comes out as the best feature for predicting cognition, age, and sex. Graph power spectral density is the second best for predicting cognition and age, while for sex, variability-based features show potential as well. When predicting sex, the low-pass graph-filtered coupled FC slightly outperforms the simple FC variant. None of the other targets were predicted significantly. The scaling results point to higher performance reserves for the better-performing features. They also indicate that it is important to balance sample size and scan time when acquiring data for prediction studies. The results confirm FC as a robust feature for behavior prediction, but also show the potential of GSP and variability-based measures. We discuss the implications for future prediction studies in terms of strategies for acquisition and sample composition.<\/jats:p>","DOI":"10.1162\/imag.a.141","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T20:02:25Z","timestamp":1755806545000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":3,"title":["Comparing and scaling fMRI features for brain-behavior\n                    prediction"],"prefix":"10.1162","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4521-9837","authenticated-orcid":false,"given":"Mikkel Sch\u00f6ttner","family":"Sieler","sequence":"first","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas A.W.","family":"Bolton","sequence":"additional","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jagruti","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patric","family":"Hagmann","sequence":"additional","affiliation":[{"name":"Department of Radiology, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2025,9,12]]},"reference":[{"issue":"1","key":"2025091216292717700_IMAG.a.141-b1","doi-asserted-by":"crossref","first-page":"170181","DOI":"10.1038\/sdata.2017.181","article-title":"An open resource for transdiagnostic research in pediatric\n                        mental health and learning disorders","volume":"4","author":"Alexander","year":"2017","journal-title":"Scientific\n                        Data"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b2","doi-asserted-by":"crossref","first-page":"17661","DOI":"10.1038\/s41598-017-17546-0","article-title":"Connectome-harmonic decomposition of human brain activity\n                        reveals dynamical repertoire re-organization under LSD","volume":"7","author":"Atasoy","year":"2017","journal-title":"Scientific Reports"},{"issue":"2","key":"2025091216292717700_IMAG.a.141-b3","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1162\/netn_a_00075","article-title":"Distance-dependent consensus thresholds\n                        for generating group-representative structural brain\n                        networks","volume":"3","author":"Betzel","year":"2019","journal-title":"Network Neuroscience"},{"key":"2025091216292717700_IMAG.a.141-b4","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neuroimage.2018.10.009","article-title":"The lifespan human connectome project in\n                        aging: An overview","volume":"185","author":"Bookheimer","year":"2019","journal-title":"NeuroImage"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b5","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1093\/cercor\/bhaa243","article-title":"Greater BOLD variability is associated with poorer cognitive\n                        function in an adult lifespan sample","volume":"31","author":"Boylan","year":"2021","journal-title":"Cerebral\n                        Cortex"},{"key":"2025091216292717700_IMAG.a.141-b6","doi-asserted-by":"crossref","first-page":"101870","DOI":"10.1016\/j.artmed.2020.101870","article-title":"Graph Fourier transform of fMRI temporal\n                        signals based on an averaged structural connectome for the classification of\n                        neuroimaging","volume":"106","author":"Brahim","year":"2020","journal-title":"Artificial Intelligence in\n                        Medicine"},{"issue":"3","key":"2025091216292717700_IMAG.a.141-b7","first-page":"223","article-title":"Machine learning for precision\n                        psychiatry: Opportunities and challenges","volume":"3","author":"Bzdok","year":"2018","journal-title":"Biological\n                        Psychiatry: Cognitive Neuroscience and Neuroimaging"},{"issue":"2","key":"2025091216292717700_IMAG.a.141-b8","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.jneumeth.2011.09.031","article-title":"Mapping the human connectome at multiple\n                        scales with diffusion spectrum MRI","volume":"203","author":"Cammoun","year":"2012","journal-title":"Journal of\n                        Neuroscience Methods"},{"issue":"3","key":"2025091216292717700_IMAG.a.141-b9","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","article-title":"An automated labeling system for subdividing the human\n                        cerebral cortex on MRI scans into gyral based regions of\n                        interest","volume":"31","author":"Desikan","year":"2006","journal-title":"NeuroImage"},{"issue":"8","key":"2025091216292717700_IMAG.a.141-b10","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.biopsych.2022.09.024","article-title":"One size does not fit all: Methodological considerations for\n                        brain-based predictive modeling in psychiatry","volume":"93","author":"Dhamala","year":"2023","journal-title":"Biological Psychiatry"},{"issue":"6","key":"2025091216292717700_IMAG.a.141-b11","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.tics.2016.03.014","article-title":"Building a science of individual\n                        differences from fMRI","volume":"20","author":"Dubois","year":"2016","journal-title":"Trends in Cognitive\n                        Sciences"},{"issue":"3","key":"2025091216292717700_IMAG.a.141-b12","doi-asserted-by":"crossref","first-page":"277","DOI":"10.31887\/DCNS.2016.18.3\/efinn","article-title":"Individual variation in functional brain\n                        connectivity: Implications for personalized approaches to psychiatric\n                        disease","volume":"18","author":"Finn","year":"2016","journal-title":"Dialogues in Clinical\n                    Neuroscience"},{"issue":"1\u20132","key":"2025091216292717700_IMAG.a.141-b13","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1002\/hbm.460020107","article-title":"Functional and effective connectivity in neuroimaging: A\n                        synthesis","volume":"2","author":"Friston","year":"1994","journal-title":"Human Brain Mapping"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neuron.2014.10.047","article-title":"Prediction as a humanitarian and\n                        pragmatic contribution from human cognitive neuroscience","volume":"85","author":"Gabrieli","year":"2015","journal-title":"Neuron"},{"issue":"4","key":"2025091216292717700_IMAG.a.141-b15","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.neubiorev.2013.02.015","article-title":"Moment-to-moment brain signal variability: A next frontier in\n                        human brain mapping?","volume":"37","author":"Garrett","year":"2013","journal-title":"Neuroscience & Biobehavioral Reviews"},{"key":"2025091216292717700_IMAG.a.141-b16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127","article-title":"The minimal preprocessing pipelines for\n                        the Human Connectome Project","volume":"80","author":"Glasser","year":"2013","journal-title":"NeuroImage"},{"key":"2025091216292717700_IMAG.a.141-b17","doi-asserted-by":"crossref","first-page":"118970","DOI":"10.1016\/j.neuroimage.2022.118970","article-title":"Brain structure-function coupling provides signatures for\n                        task decoding and individual fingerprinting","volume":"250","author":"Griffa","year":"2022","journal-title":"NeuroImage"},{"key":"2025091216292717700_IMAG.a.141-b18","article-title":"Challenges in multi-task learning for\n                        fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would\n                        likely require thousands of patients","volume":"2","author":"Harvey","year":"2023","journal-title":"Imaging\n                        Neuroscience"},{"key":"2025091216292717700_IMAG.a.141-b19","doi-asserted-by":"crossref","first-page":"116276","DOI":"10.1016\/j.neuroimage.2019.116276","article-title":"Deep neural networks and kernel regression achieve comparable\n                        accuracies for functional connectivity prediction of behavior and\n                        demographics","volume":"206","author":"He","year":"2020","journal-title":"NeuroImage"},{"key":"2025091216292717700_IMAG.a.141-b20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neuroimage.2015.04.042","article-title":"A computational atlas of the hippocampal\n                        formation using ex vivo, ultra-high resolution MRI:\n                        Application to adaptive segmentation of in vivo\n                        MRI","volume":"115","author":"Iglesias","year":"2015","journal-title":"NeuroImage"},{"key":"2025091216292717700_IMAG.a.141-b21","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neuroimage.2015.02.065","article-title":"Bayesian segmentation of brainstem\n                        structures in MRI","volume":"113","author":"Iglesias","year":"2015","journal-title":"NeuroImage"},{"issue":"6","key":"2025091216292717700_IMAG.a.141-b22","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1001\/archpsyc.62.6.593","article-title":"Lifetime prevalence and age-of-onset distributions of DSM-IV\n                        disorders in the national comorbidity survey replication","volume":"62","author":"Kessler","year":"2005","journal-title":"Archives of General Psychiatry"},{"key":"2025091216292717700_IMAG.a.141-b23","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.jad.2015.01.054","article-title":"The patterns of fractional amplitude of\n                        low-frequency fluctuations in depression patients: The dissociation between\n                        temporal regions and fronto-parietal regions","volume":"175","author":"Lai","year":"2015","journal-title":"Journal of Affective Disorders"},{"issue":"2","key":"2025091216292717700_IMAG.a.141-b24","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1162\/netn_a_00183","article-title":"Gradients of connectivity as graph\n                        Fourier bases of brain activity","volume":"5","author":"Lioi","year":"2021","journal-title":"Network\n                        Neuroscience"},{"issue":"2","key":"2025091216292717700_IMAG.a.141-b25","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1148\/radiol.2016152149","article-title":"Psychoradiology: The Frontier of\n                        Neuroimaging in Psychiatry","volume":"281","author":"Lui","year":"2016","journal-title":"Radiology"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b26","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1038\/s41386-024-01893-4","article-title":"Quality over quantity: Powering neuroimaging samples in\n                        psychiatry","volume":"50","author":"Makowski","year":"2024","journal-title":"Neuropsychopharmacology"},{"issue":"7","key":"2025091216292717700_IMAG.a.141-b27","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1016\/j.biopsych.2021.09.026","article-title":"Moment-to-moment brain signal variability reliably predicts\n                        psychiatric treatment outcome","volume":"91","author":"M\u00e5nsson","year":"2022","journal-title":"Biological\n                        Psychiatry"},{"issue":"7902","key":"2025091216292717700_IMAG.a.141-b28","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41586-022-04492-9","article-title":"Reproducible brain-wide association studies require thousands\n                        of individuals","volume":"603","author":"Marek","year":"2022","journal-title":"Nature"},{"key":"2025091216292717700_IMAG.a.141-b29","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/GlobalSIP.2017.8309033","article-title":"Evaluating graph signal processing for\n                        neuroimaging through classification and dimensionality\n                        reduction","author":"M\u00e9noret","year":"2017","journal-title":"2017 IEEE Global Conference on Signal and\n                        Information Processing (GlobalSIP)"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b30","doi-asserted-by":"crossref","first-page":"180270","DOI":"10.1038\/sdata.2018.270","article-title":"In-vivo probabilistic atlas of human\n                        thalamic nuclei based on diffusion- weighted magnetic resonance\n                        imaging","volume":"5","author":"Najdenovska","year":"2018","journal-title":"Scientific Data"},{"issue":"11","key":"2025091216292717700_IMAG.a.141-b31","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1093\/cercor\/bhx230","article-title":"Influences on the test\u2013retest\n                        reliability of functional connectivity MRI and its relationship with\n                        behavioral utility","volume":"27","author":"Noble","year":"2017","journal-title":"Cerebral Cortex"},{"issue":"22","key":"2025091216292717700_IMAG.a.141-b32","doi-asserted-by":"crossref","first-page":"5539","DOI":"10.1523\/JNEUROSCI.3408-16.2017","article-title":"Moment-to-moment BOLD signal variability reflects regional\n                        changes in neural flexibility across the lifespan","volume":"37","author":"Nomi","year":"2017","journal-title":"Journal of Neuroscience"},{"key":"2025091216292717700_IMAG.a.141-b33","doi-asserted-by":"crossref","first-page":"119636","DOI":"10.1016\/j.neuroimage.2022.119636","article-title":"Comparison of individualized behavioral predictions across\n                        anatomical, diffusion and functional connectivity MRI","volume":"263","author":"Ooi","year":"2022","journal-title":"NeuroImage"},{"key":"2025091216292717700_IMAG.a.141-b34","article-title":"MRI economics: Balancing sample size and scan duration in\n                        brain wide association studies (p. 2024.02.16.580448)","author":"Ooi","year":"2024","journal-title":"bioRxiv"},{"key":"2025091216292717700_IMAG.a.141-b35","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1109\/ICASSP.2019.8683901","article-title":"Spectral graph wavelet transform as\n                        feature extractor for machine learning in neuroimaging","author":"Pilavci","year":"2019","journal-title":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics,\n                        Speech and Signal Processing (ICASSP)"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b36","doi-asserted-by":"crossref","first-page":"4747","DOI":"10.1038\/s41467-019-12765-7","article-title":"Decoupling of brain function from\n                        structure reveals regional behavioral specialization in\n                        humans","volume":"10","author":"Preti","year":"2019","journal-title":"Nature Communications"},{"issue":"3","key":"2025091216292717700_IMAG.a.141-b37","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1037\/0033-295X.103.3.403","article-title":"The processing-speed theory of adult age differences in\n                        cognition","volume":"103","author":"Salthouse","year":"1996","journal-title":"Psychological Review"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b38","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/S0301-0511(00)00052-1","article-title":"Aging and measures of processing speed","volume":"54","author":"Salthouse","year":"2000","journal-title":"Biological Psychology"},{"key":"2025091216292717700_IMAG.a.141-b39","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.neucom.2015.01.079","article-title":"Local activity features for computer\n                        aided diagnosis of schizophrenia on resting-state fMRI","volume":"164","author":"Savio","year":"2015","journal-title":"Neurocomputing"},{"key":"2025091216292717700_IMAG.a.141-b40","article-title":"Predicting cognitive abilities\n                            with graph signal processing metrics","author":"Sch\u00f6ttner","year":"2024","journal-title":"Zenodo"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b41","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1038\/s41598-022-27101-1","article-title":"Exploring the latent structure of\n                        behavior using the Human Connectome Project\u2019s data","volume":"13","author":"Sch\u00f6ttner","year":"2023","journal-title":"Scientific Reports"},{"key":"2025091216292717700_IMAG.a.141-b42","unstructured":"Tourbier,\n                                S.,\n                                Aleman-Gomez,\n                                Y.,\n                                Mullier,\n                                E.,\n                                Griffa,\n                                A.,\n                                Bach Cuadra,\n                                M., &\n                                Hagmann,\n                                P.\n                        (2021). connectomicslab\/connectomemapper3: Connectome\n                        Mapper v3.0.0-RC4 (Version v3.0.0-RC4) [Computer software]. Zenodo.\n                        https:\/\/doi.org\/10.5281\/zenodo.4587906"},{"issue":"74","key":"2025091216292717700_IMAG.a.141-b43","doi-asserted-by":"crossref","first-page":"4248","DOI":"10.21105\/joss.04248","article-title":"Connectome mapper 3: A flexible and\n                        open-source pipeline software for multiscale multimodal human connectome\n                        mapping","volume":"7","author":"Tourbier","year":"2022","journal-title":"Journal of Open Source Software"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b44","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1002\/ima.22005","article-title":"MRtrix: Diffusion tractography in\n                        crossing fiber regions","volume":"22","author":"Tournier","year":"2012","journal-title":"International Journal of\n                        Imaging Systems and Technology"},{"issue":"4","key":"2025091216292717700_IMAG.a.141-b45","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The Human\n                        Connectome Project: A data acquisition perspective","volume":"62","author":"Van Essen","year":"2012","journal-title":"NeuroImage"},{"key":"2025091216292717700_IMAG.a.141-b46","doi-asserted-by":"crossref","first-page":"101654","DOI":"10.1016\/j.intell.2022.101654","article-title":"On the prediction of human intelligence from neuroimaging: A\n                        systematic review of methods and reporting","volume":"93","author":"Vieira","year":"2022","journal-title":"Intelligence"},{"issue":"23","key":"2025091216292717700_IMAG.a.141-b47","doi-asserted-by":"crossref","first-page":"e2022288118","DOI":"10.1073\/pnas.2022288118","article-title":"Segregation, integration, and balance of\n                        large-scale resting brain networks configure different cognitive\n                        abilities","volume":"118","author":"Wang","year":"2021","journal-title":"Proceedings of the National Academy of\n                        Sciences"},{"key":"2025091216292717700_IMAG.a.141-b48","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/978-3-030-32248-9_85","article-title":"A novel graph wavelet model for brain\n                        multi-scale activational-connectional feature fusion","volume-title":"Medical image computing and computer assisted intervention \u2013\n                        MICCAI 2019","author":"Xu","year":"2019"},{"key":"2025091216292717700_IMAG.a.141-b49","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1007\/s11682-019-00169-6","article-title":"Frequency-dependent changes in\n                        fractional amplitude of low-frequency oscillations in Alzheimer\u2019s\n                        disease: A resting-state fMRI study","volume":"14","author":"Yang","year":"2019","journal-title":"Brain Imaging\n                        and Behavior"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b50","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1007\/s11682-019-00233-1","article-title":"Frequency-specific alternations in the\n                        moment-to-moment BOLD signals variability in schizophrenia","volume":"15","author":"Zhang","year":"2021","journal-title":"Brain Imaging and Behavior"},{"issue":"2","key":"2025091216292717700_IMAG.a.141-b51","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection\n                        via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"Journal of the Royal\n                        Statistical Society Series B: Statistical Methodology"},{"issue":"1","key":"2025091216292717700_IMAG.a.141-b52","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jneumeth.2008.04.012","article-title":"An improved approach to detection of\n                        amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI:\n                        Fractional ALFF","volume":"172","author":"Zou","year":"2008","journal-title":"Journal of Neuroscience\n                        Methods"}],"container-title":["Imaging Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/IMAG.a.141\/2546814\/imag.a.141.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/IMAG.a.141\/2546814\/imag.a.141.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T20:29:38Z","timestamp":1757708978000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/imag\/article\/doi\/10.1162\/IMAG.a.141\/132743\/Comparing-and-scaling-fMRI-features-for-brain"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1162\/imag.a.141","relation":{},"ISSN":["2837-6056"],"issn-type":[{"value":"2837-6056","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025]]},"published":{"date-parts":[[2025]]},"article-number":"IMAG.a.141"}}