motion metrics
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2021 ◽  
Author(s):  
Carolina Badke D'Andrea ◽  
Jeanette K. Kenley ◽  
David F. Montez ◽  
Amy E. Mirro ◽  
Ryland L. Miller ◽  
...  

Imaging the infant brain with MRI has improved our understanding of early stages of neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are commonly scanned while asleep, they commonly exhibit motion during scanning, causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Hence, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.


2021 ◽  
Vol 21 (9) ◽  
pp. S163
Author(s):  
Braden McKnight ◽  
Zoë Fresquez ◽  
Paul O. Mgbam ◽  
Trevor Grieco ◽  
John A. Hipp ◽  
...  

2021 ◽  
Vol 161 ◽  
pp. S175-S176
Author(s):  
M. Dassen ◽  
D. Barten ◽  
J. Laan ◽  
H. Westerveld ◽  
A. Bel ◽  
...  

Author(s):  
Tess E. Wallace ◽  
Onur Afacan ◽  
Camilo Jaimes ◽  
Joanne Rispoli ◽  
Kristina Pelkola ◽  
...  

2020 ◽  
Vol 72 (6) ◽  
pp. 2161-2165 ◽  
Author(s):  
Viony M. Belvroy ◽  
Barathwaj Murali ◽  
Malachi G. Sheahan ◽  
Marcia K. O'Malley ◽  
Jean Bismuth

Lab Animal ◽  
2020 ◽  
Vol 49 (8) ◽  
pp. 227-232
Author(s):  
Chibueze D. Nwagwu ◽  
Erwin Defensor ◽  
Michael Y. Jiang ◽  
Danelle A. Rolle-McFarland ◽  
Anne-Marie E. Carbonell ◽  
...  

2020 ◽  
Vol 71 (3) ◽  
pp. e42-e43
Author(s):  
Viony M. Belvroy ◽  
Barathwaj Murali ◽  
Malachi G. Sheahan ◽  
Marcia K. O'Malley ◽  
Jean Bismuth

2020 ◽  
Vol 91 (4) ◽  
pp. 2010-2023 ◽  
Author(s):  
John M. Rekoske ◽  
Eric M. Thompson ◽  
Morgan P. Moschetti ◽  
Mike G. Hearne ◽  
Brad T. Aagaard ◽  
...  

Abstract Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground-motion records from multiple data centers and processed these records using newly developed ground-motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground-motion metrics. In addition, we compute station and waveform metrics such as the time-averaged shear-wave velocity to 30 m depth (VS30), finite-rupture distances, and spectral accelerations. This data set includes 22,708 records from 133 events from 4 July 2019 (UTC) to 18 October 2019 with a magnitude range from 3.6 to 7.1. We expect that the rapid collection and dissemination of this information will facilitate detailed studies of these ground motions. In this article, we describe the data selection, processing steps, and how to access the data.


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