Brain volume loss in individuals over time: Source of variance and limits of detectability
Background: Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to he...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-07-01
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| Series: | NeuroImage |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S105381192030224X |
| _version_ | 1818840680432140288 |
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| author | Sridar Narayanan Kunio Nakamura Vladimir S. Fonov Josefina Maranzano Zografos Caramanos Paul S. Giacomini D. Louis Collins Douglas L. Arnold |
| author_facet | Sridar Narayanan Kunio Nakamura Vladimir S. Fonov Josefina Maranzano Zografos Caramanos Paul S. Giacomini D. Louis Collins Douglas L. Arnold |
| author_sort | Sridar Narayanan |
| collection | DOAJ |
| description | Background: Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability. Goal: To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals. Material and methods: Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 h for 12 h), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 Siemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference. Results: The RMSE of PBVC over 12 h, for both scanners combined, (“Hours”, 0.15%), was similar to the day-to-day variation over 1 week (“Days”, 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for S1, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on S1 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p < 0.01). Conclusion: Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6–0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long. |
| first_indexed | 2024-12-19T04:14:02Z |
| format | Article |
| id | doaj.art-246595c565924914ba964300257e7e08 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-19T04:14:02Z |
| publishDate | 2020-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-246595c565924914ba964300257e7e082022-12-21T20:36:20ZengElsevierNeuroImage1095-95722020-07-01214116737Brain volume loss in individuals over time: Source of variance and limits of detectabilitySridar Narayanan0Kunio Nakamura1Vladimir S. Fonov2Josefina Maranzano3Zografos Caramanos4Paul S. Giacomini5D. Louis Collins6Douglas L. Arnold7McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, Canada; Corresponding author. 3801 University Street, WB321, Montreal Neurological Institute, Montreal, Quebec, H3A 2B4, Canada.McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, Canada; Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, Cleveland, OH, 44122, USAMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CanadaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CanadaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CanadaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CanadaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CanadaMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, H3A 2B4, CanadaBackground: Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability. Goal: To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals. Material and methods: Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 h for 12 h), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 Siemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference. Results: The RMSE of PBVC over 12 h, for both scanners combined, (“Hours”, 0.15%), was similar to the day-to-day variation over 1 week (“Days”, 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for S1, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on S1 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p < 0.01). Conclusion: Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6–0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long.http://www.sciencedirect.com/science/article/pii/S105381192030224X |
| spellingShingle | Sridar Narayanan Kunio Nakamura Vladimir S. Fonov Josefina Maranzano Zografos Caramanos Paul S. Giacomini D. Louis Collins Douglas L. Arnold Brain volume loss in individuals over time: Source of variance and limits of detectability NeuroImage |
| title | Brain volume loss in individuals over time: Source of variance and limits of detectability |
| title_full | Brain volume loss in individuals over time: Source of variance and limits of detectability |
| title_fullStr | Brain volume loss in individuals over time: Source of variance and limits of detectability |
| title_full_unstemmed | Brain volume loss in individuals over time: Source of variance and limits of detectability |
| title_short | Brain volume loss in individuals over time: Source of variance and limits of detectability |
| title_sort | brain volume loss in individuals over time source of variance and limits of detectability |
| url | http://www.sciencedirect.com/science/article/pii/S105381192030224X |
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