Day2day: investigating daily variability of magnetic resonance imaging measures over half a year

Filevich, Elisa; Lisofsky, Nina; Becker, Maxi; Butler, Oisín; Lochstet, Martyna; Mårtensson, Johan; Wenger, Elisabeth; Lindenberger, Ulman; Kühn, Simone

doi:10.1186/s12868-017-0383-y

“…3, Supplementary Figs S8, S9, and S11). Given the increasing availability of multi-session rs-fMRI from many different research groups (Zuo et al 2014;Holmes et al 2015;Poldrack et al 2015;Filevich et al 2017;Gordon, Laumann, Gilmore, Newbold, et al 2017c), it might be possible to add another layer to the hierarchical model to account for intersite variability, in addition to intra-subject and inter-subject variabilities. Furthermore, our experiments did not differentiate between rs-fMRI runs collected within the same session versus rs-fMRI runs collected from different sessions.…”

Section: Methodological Considerationsmentioning

confidence: 99%

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Kong

¹

,

Li

²

,

Orban

³

et al. 2018

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Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

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“…Although the MS-HBM approach did not account for inter-site variability, we demonstrated that model parameters estimated from one site can generalize to another site with a different acquisition protocol and scanner (Figures 3, S8, S9, S11). Given the increasing availability of multi-session rs-fMRI from many different research groups (Zuo et al, 2014;Holmes et al, 2015;Poldrack et al, 2015;Filevich et al, 2017;Gordon et al, 2017c), it might be possible to add another layer to the hierarchical model to account for inter-site variability, in addition to intra-subject and inter-subject variability. Furthermore, our experiments did not differentiate between rs-fMRI runs collected within the same session versus rs-fMRI runs collected from different sessions.…”

Section: Methodological Considerationsmentioning

confidence: 99%

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion

Kong

¹

,

Li

²

,

Orban

³

et al. 2017

Preprint

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Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individualspecific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects.More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 minutes) showed comparable generalizability as parcellations estimated by two state-of-theart methods using five sessions (50 minutes). We also showed that behavioral phenotypes across cognition, personality and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individualspecific network topography might also serve as a fingerprint of human behavior. substantially across participants (Harrison et al., 2015;Laumann et al., 2015;Wang et al., 2015;Glasser et al., 2016;Gordon et al., 2017aGordon et al., , 2017bGordon et al., , 2017cBraga and Buckner, 2017).Yet, the possible behavioral relevance of individual differences in network size and network topography (location and spatial arrangement) remains unknown.We proposed a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific network parcellations of the cerebral cortex and investigated whether individual-specific network topography and size are associated with human behavior. The multiple layers of the MS-HBM allowed explicit separation of inter-subject (betweensubject) and intra-subject (within-session) functional connectivity variability. Previous individual-specific network mappings only accounted for inter-subject variability, but not intra-subject variability. However, inter-subject and intra-subject RSFC variability can be markedly different across regions (Mueller et al., 2013;Chen et al., 2015;Laumann et al., 2015). For example, the motor cortex exhibits high intra-subject functional connectivity variability, but low inter-subject functional connectivity variability .Therefore, observed RSFC variability in the motor cortex might be incorrectly attributed to inter-subject spatial variability of brain networks, rather than just intra-...

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“…We performed a retrospective analysis of data of HP collected from the Day2day daily variability study, 18 and an ongoing observational single-center cohort (EA1/163/12). Inclusion criteria were as follows: a minimum of 18 years of age and no presence of any neurologic/psychiatric disorders, contraindications for an MR imaging examination, or incidental MR imaging findings.…”

Section: Cohortmentioning

confidence: 99%

Considerations for Mean Upper Cervical Cord Area Implementation in a Longitudinal MRI Setting: Methods, Interrater Reliability, and MRI Quality Control

Chien

¹

,

Juenger

²

,

Scheel³

et al. 2020

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Spinal cord atrophy is commonly measured from cerebral MRIs, including the upper cervical cord. However, rescan intraparticipant measures have not been investigated in healthy cohorts. This study investigated technical and rescan variability in the mean upper cervical cord area calculated from T1-weighted cerebral MRIs. MATERIALS AND METHODS: In this retrospective study, 8 healthy participants were scanned and rescanned with non-distortionand distortion-corrected MPRAGE sequences (11-50 sessions in 6-8 months), and 50 participants were scanned once with distortion-corrected MPRAGE sequences in the Day2day daily variability study. From another real-world observational cohort, we collected non-distortion-corrected MPRAGE scans from 27 healthy participants (annually for 2-4 years) and cross-sectionally from 77 participants. Statistical analyses included coefficient of variation, smallest real difference, intraclass correlation coefficient, Bland-Altman limits of agreement, and paired t tests.

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Day2day: investigating daily variability of magnetic resonance imaging measures over half a year

Cited by 39 publications

References 29 publications

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion

Considerations for Mean Upper Cervical Cord Area Implementation in a Longitudinal MRI Setting: Methods, Interrater Reliability, and MRI Quality Control

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