دورية أكاديمية
أعرض في EDS

Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data.

التفاصيل البيبلوغرافية
العنوان: Double-wavelet transform for multi-subject resting state functional magnetic resonance imaging data.
المؤلفون: Zhou M; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee., Boyd BD; The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee., Taylor WD; The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee.; The Geriatric Research, Education, and Clinical Center (GRECC), Department of Veterans Affairs Medical Center, Tennessee Valley Healthcare System, Nashville, Tennessee., Kang H; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.; Center for Quantitative Science, Vanderbilt University Medical Center, Nashville, Tennessee.
المصدر: Statistics in medicine [Stat Med] 2021 Dec 30; Vol. 40 (30), pp. 6762-6776. Date of Electronic Publication: 2021 Oct 01.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Chichester ; New York : Wiley, c1982-
مواضيع طبية MeSH: Depressive Disorder, Major* , Wavelet Analysis*, Brain/diagnostic imaging ; Brain/physiology ; Brain Mapping/methods ; Humans ; Magnetic Resonance Imaging
مستخلص: Conventional regions of interest (ROIs)-level resting state fMRI (functional magnetic resonance imaging) response analyses do not rigorously model the underlying spatial correlation within each ROI. This can result in misleading inference. Moreover, they tend to estimate the temporal covariance matrix with the assumption of stationary time series, which may not always be valid. To overcome these limitations, we propose a double-wavelet approach that simplifies temporal and spatial covariance structure because wavelet coefficients are approximately uncorrelated under mild regularity conditions. This property allows us to analyze much larger dimensions of spatial and temporal resting-state fMRI data with reasonable computational burden. Another advantage of our double-wavelet approach is that it does not require the stationarity assumption. Simulation studies show that our method reduced false positive and false negative rates by properly taking into account spatial and temporal correlations in data. We also demonstrate advantages of our method by using resting-state fMRI data to study the difference in resting-state functional connectivity between healthy subjects and patients with major depressive disorder.
(© 2021 John Wiley & Sons Ltd.)
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معلومات مُعتمدة: K24 MH110598 United States MH NIMH NIH HHS; R01 MH077745 United States MH NIMH NIH HHS
فهرسة مساهمة: Keywords: double-wavelet transform; functional magnetic resonance imaging; multi-subject; resting state; spatio-temporal model
تواريخ الأحداث: Date Created: 20211001 Date Completed: 20220309 Latest Revision: 20220309
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC8753629
DOI: 10.1002/sim.9209
PMID: 34596260
قاعدة البيانات: MEDLINE
الوصف
تدمد:1097-0258
DOI:10.1002/sim.9209