N Yakunina1, T S Kim2, W S Tae3, S S Kim4, E C Nam5. 1. From the Institute of Medical Science (N.Y.) Neuroscience Research Institute (N.Y., W.S.T., S.S.K., E.C.N.). 2. Department of Otolaryngology (T.S.K.), Kangwon National University Hospital, Chuncheon, Republic of Korea. 3. Neuroscience Research Institute (N.Y., W.S.T., S.S.K., E.C.N.). 4. Department of Radiology (S.S.K.) Neuroscience Research Institute (N.Y., W.S.T., S.S.K., E.C.N.). 5. Department of Otolaryngology (E.C.N.), Kangwon National University, School of Medicine, Chuncheon, Republic of Korea Neuroscience Research Institute (N.Y., W.S.T., S.S.K., E.C.N.) birdynec@kangwon.ac.kr.
Abstract
BACKGROUND AND PURPOSE: The ability of sparse temporal acquisition to minimize the effect of scanner background noise is of utmost importance in auditory fMRI; however, it has considerably lower temporal efficiency and resolution than the conventional continuous acquisition method. The purpose of this study was to determine whether sparse sampling could be applied to resting-state research by comparing its results with those obtained by using continuous acquisition. MATERIALS AND METHODS: We identified resting-state networks by using independent component analysis and measured their functional connectivity strength in 14 healthy subjects who underwent two 6-minute sparse (60 volumes) and continuous (360 volumes) imaging sessions. To account for the sample size difference, an additional continuous dataset was generated by temporally matching the continuous dataset to 60 volumes of the sparse dataset. RESULTS: Consistent resting-state network maps were produced through all 3 datasets. Scanner background noise did not appear to affect the spatial constitution of the networks, whereas a larger sample size influenced it substantially. The strength of the intranetwork connectivity was similar through the 3 datasets. CONCLUSIONS: Our results indicated that continuous acquisition is a recommended technique that should be applied in most of the resting-state studies due to its superior temporal efficiency and increased statistical power. The use of sparse temporal acquisition should be restricted to very particular conditions when continuous scanner noise is unacceptable.
BACKGROUND AND PURPOSE: The ability of sparse temporal acquisition to minimize the effect of scanner background noise is of utmost importance in auditory fMRI; however, it has considerably lower temporal efficiency and resolution than the conventional continuous acquisition method. The purpose of this study was to determine whether sparse sampling could be applied to resting-state research by comparing its results with those obtained by using continuous acquisition. MATERIALS AND METHODS: We identified resting-state networks by using independent component analysis and measured their functional connectivity strength in 14 healthy subjects who underwent two 6-minute sparse (60 volumes) and continuous (360 volumes) imaging sessions. To account for the sample size difference, an additional continuous dataset was generated by temporally matching the continuous dataset to 60 volumes of the sparse dataset. RESULTS: Consistent resting-state network maps were produced through all 3 datasets. Scanner background noise did not appear to affect the spatial constitution of the networks, whereas a larger sample size influenced it substantially. The strength of the intranetwork connectivity was similar through the 3 datasets. CONCLUSIONS: Our results indicated that continuous acquisition is a recommended technique that should be applied in most of the resting-state studies due to its superior temporal efficiency and increased statistical power. The use of sparse temporal acquisition should be restricted to very particular conditions when continuous scanner noise is unacceptable.
Authors: Lisa Johnson; Grigori Yourganov; Alexandra Basilakos; Roger David Newman-Norlund; Helga Thors; Lynsey Keator; Chris Rorden; Leonardo Bonilha; Julius Fridriksson Journal: Neurorehabil Neural Repair Date: 2021-12-30 Impact factor: 3.919