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Suárez V, Picotin R, Fassbender R, Gramespacher H, Haneder S, Persigehl T, Todorova P, Hackl MJ, Onur OA, Richter N, Burst V. Chronic Hyponatremia and Brain Structure and Function Before and After Treatment. Am J Kidney Dis 2024; 84:38-48.e1. [PMID: 38184092 DOI: 10.1053/j.ajkd.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 11/02/2023] [Accepted: 11/14/2023] [Indexed: 01/08/2024]
Abstract
RATIONALE & OBJECTIVE Hyponatremia is the most common electrolyte disorder and is associated with significant morbidity and mortality. This study investigated neurocognitive impairment, brain volume, and alterations in magnetic resonance imaging (MRI)-based measures of cerebral function in patients before and after treatment for hyponatremia. STUDY DESIGN Prospective cohort study. SETTING & PARTICIPANTS Patients with presumed chronic hyponatremia without signs of hypo- or hypervolemia treated in the emergency department of a German tertiary-care hospital. EXPOSURE Hyponatremia (ie, plasma sodium concentration [Na+]<125mmol/L) before and after treatment leading to [Na+]>130mmol/L. OUTCOMES Standardized neuropsychological testing (Mini-Mental State Examination, DemTect, Trail Making Test A/B, Beck Depression Inventory, Timed Up and Go) and resting-state MRI were performed before and after treatment of hyponatremia to assess total brain and white and gray matter volumes as well as neuronal activity and its synchronization. ANALYTICAL APPROACH Changes in outcomes after treatment for hyponatremia assessed using bootstrapped confidence intervals and Cohen d statistic. Associations between parameters were assessed using correlation analyses. RESULTS During a 3.7-year period, 26 patients were enrolled. Complete data were available for 21 patients. Mean [Na+]s were 118.4mmol/L before treatment and 135.5mmol/L after treatment. Most measures of cognition improved significantly. Comparison of MRI studies showed a decrease in brain tissue volumes, neuronal activity, and synchronization across all gray matter after normalization of [Na+]. Volume effects were particularly prominent in the hippocampus. During hyponatremia, synchronization of neuronal activity was negatively correlated with [Na+] (r=-0.836; 95% CI, -0.979 to-0.446) and cognitive function (Mini-Mental State Examination, r=-0.523; 95% CI, -0.805 to-0.069; DemTect, r=-0.744; 95% CI, -0.951 to-0.385; and Trail Making Test A, r=0.692; 95% CI, 0.255-0.922). LIMITATIONS Small sample size, insufficient quality of several MRI scans as a result of motion artifact. CONCLUSIONS Resolution of hyponatremia was associated with improved cognition and reductions in brain volumes and neuronal activity. Impaired cognition during hyponatremia is closely linked to increased neuronal activity rather than to tissue volumes. Furthermore, the hippocampus appears to be particularly susceptible to hyponatremia, exhibiting pronounced changes in tissue volume. PLAIN-LANGUAGE SUMMARY Hyponatremia is a common clinical problem, and patients often present with neurologic symptoms that are at least partially reversible. This study used neuropsychological testing and magnetic resonance imaging to examine patients during and after correction of hyponatremia. Treatment led to an improvement in patients' cognition as well as a decrease in their brain volumes, spontaneous neuronal activity, and synchronized neuronal activity between remote brain regions. Volume effects were particularly prominent in the hippocampus, an area of the brain that is important for the modulation of memory. During hyponatremia, patients with the lowest sodium concentrations had the highest levels of synchronized neuronal activity and the poorest cognitive test results.
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Affiliation(s)
- Victor Suárez
- Department II of Internal Medicine (Nephrology, Rheumatology, Diabetes, and General Internal Medicine) and Center for Molecular Medicine Cologne, Cologne, Germany; Emergency Department, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Rosanne Picotin
- Department of Neurology, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Ronja Fassbender
- Department of Neurology, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Hannes Gramespacher
- Department of Neurology, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Stefan Haneder
- Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Polina Todorova
- Department II of Internal Medicine (Nephrology, Rheumatology, Diabetes, and General Internal Medicine) and Center for Molecular Medicine Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Matthias Johannes Hackl
- Department II of Internal Medicine (Nephrology, Rheumatology, Diabetes, and General Internal Medicine) and Center for Molecular Medicine Cologne, Cologne, Germany; Emergency Department, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Oezguer A Onur
- Department of Neurology, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Jülich, Jülich, Germany
| | - Nils Richter
- Department of Neurology, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Volker Burst
- Department II of Internal Medicine (Nephrology, Rheumatology, Diabetes, and General Internal Medicine) and Center for Molecular Medicine Cologne, Cologne, Germany; Emergency Department, University of Cologne, Cologne, Germany; Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
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Hanson JL, Adkins DJ, Bacas E, Zhou P. Examining the reliability of brain age algorithms under varying degrees of participant motion. Brain Inform 2024; 11:9. [PMID: 38573551 PMCID: PMC10994881 DOI: 10.1186/s40708-024-00223-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024] Open
Abstract
Brain age algorithms using data science and machine learning techniques show promise as biomarkers for neurodegenerative disorders and aging. However, head motion during MRI scanning may compromise image quality and influence brain age estimates. We examined the effects of motion on brain age predictions in adult participants with low, high, and no motion MRI scans (Original N = 148; Analytic N = 138). Five popular algorithms were tested: brainageR, DeepBrainNet, XGBoost, ENIGMA, and pyment. Evaluation metrics, intraclass correlations (ICCs), and Bland-Altman analyses assessed reliability across motion conditions. Linear mixed models quantified motion effects. Results demonstrated motion significantly impacted brain age estimates for some algorithms, with ICCs dropping as low as 0.609 and errors increasing up to 11.5 years for high motion scans. DeepBrainNet and pyment showed greatest robustness and reliability (ICCs = 0.956-0.965). XGBoost and brainageR had the largest errors (up to 13.5 RMSE) and bias with motion. Findings indicate motion artifacts influence brain age estimates in significant ways. Furthermore, our results suggest certain algorithms like DeepBrainNet and pyment may be preferable for deployment in populations where motion during MRI acquisition is likely. Further optimization and validation of brain age algorithms is critical to use brain age as a biomarker relevant for clinical outcomes.
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Affiliation(s)
- Jamie L Hanson
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA.
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA.
| | - Dorthea J Adkins
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Eva Bacas
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
| | - Peiran Zhou
- Learning, Research & Development Center, University of Pittsburgh, Murdoch Building 3420 Forbes Ave. Rm. 639, Pittsburgh, PA, 15260, USA
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Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers. Commun Biol 2022; 5:913. [PMID: 36068295 PMCID: PMC9448776 DOI: 10.1038/s42003-022-03880-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 08/23/2022] [Indexed: 11/12/2022] Open
Abstract
Fundamental and clinical neuroscience has benefited tremendously from the development of automated computational analyses. In excess of 600 human neuroimaging papers using Voxel-based Morphometry (VBM) are now published every year and a number of different automated processing pipelines are used, although it remains to be systematically assessed whether they come up with the same answers. Here we examined variability between four commonly used VBM pipelines in two large brain structural datasets. Spatial similarity and between-pipeline reproducibility of the processed gray matter brain maps were generally low between pipelines. Examination of sex-differences and age-related changes revealed considerable differences between the pipelines in terms of the specific regions identified. Machine learning-based multivariate analyses allowed accurate predictions of sex and age, however accuracy differed between pipelines. Our findings suggest that the choice of pipeline alone leads to considerable variability in brain structural markers which poses a serious challenge for reproducibility and interpretation. Four common processing pipelines tested on two Voxel-based Morphometry (VBM) datasets yield considerable variations in results, raising issues on the interpretability and robustness of VBM results.
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Hobday H, Cole JH, Stanyard RA, Daws RE, Giampietro V, O'Daly O, Leech R, Váša F. Tissue volume estimation and age prediction using rapid structural brain scans. Sci Rep 2022; 12:12005. [PMID: 35835813 PMCID: PMC9283414 DOI: 10.1038/s41598-022-14904-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
The multicontrast EPImix sequence generates six contrasts, including a T1-weighted scan, in ~1 min. EPImix shows comparable diagnostic performance to conventional scans under qualitative clinical evaluation, and similarities in simple quantitative measures including contrast intensity. However, EPImix scans have not yet been compared to standard MRI scans using established quantitative measures. In this study, we compared conventional and EPImix-derived T1-weighted scans of 64 healthy participants using tissue volume estimates and predicted brain-age. All scans were pre-processed using the SPM12 DARTEL pipeline, generating measures of grey matter, white matter and cerebrospinal fluid volume. Brain-age was predicted using brainageR, a Gaussian Processes Regression model previously trained on a large sample of standard T1-weighted scans. Estimates of both global and voxel-wise tissue volume showed significantly similar results between standard and EPImix-derived T1-weighted scans. Brain-age estimates from both sequences were significantly correlated, although EPImix T1-weighted scans showed a systematic offset in predictions of chronological age. Supplementary analyses suggest that this is likely caused by the reduced field of view of EPImix scans, and the use of a brain-age model trained using conventional T1-weighted scans. However, this systematic error can be corrected using additional regression of T1-predicted brain-age onto EPImix-predicted brain-age. Finally, retest EPImix scans acquired for 10 participants demonstrated high test-retest reliability in all evaluated quantitative measurements. Quantitative analysis of EPImix scans has potential to reduce scanning time, increasing participant comfort and reducing cost, as well as to support automation of scanning, utilising active learning for faster and individually-tailored (neuro)imaging.
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Affiliation(s)
- Harriet Hobday
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ryan A Stanyard
- Department of Forensic and Developmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Richard E Daws
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Vincent Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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Takao H, Amemiya S, Abe O. Reproducibility of Longitudinal Changes in Cortical Thickness Determined by Surface-Based Morphometry Between Non-Accelerated and Accelerated MR Imaging. J Magn Reson Imaging 2021; 55:1151-1160. [PMID: 34555231 DOI: 10.1002/jmri.27929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/07/2021] [Accepted: 09/09/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Scan acceleration such as parallel imaging reduces scan time, but shorter scan time may reduce the signal-to-noise ratio and affect image quality. The reproducibility of longitudinal changes in the brain structure between non-accelerated and accelerated imaging by surface-based analysis is unclear. PURPOSE To determine the reproducibility of longitudinal changes in cortical thickness, measured by surface-based morphometry, between non-accelerated and accelerated structural T1 -weighted imaging in the healthy elderly and those with mild cognitive impairment (MCI) and Alzheimer's disease (AD). STUDY TYPE Retrospective. SUBJECTS Fifty healthy elderly subjects (age = 73 ± 5 years, 29 females, 21 males), 54 MCI patients (age = 71 ± 7 years, 23 females, 31 males), and 8 AD patients (age = 78 ± 6 years, 6 females, 2 males). FIELD STRENGTH/SEQUENCE 3 T, magnetization-prepared rapid gradient-echo. ASSESSMENT Longitudinal changes in cortical thickness estimated by the longitudinal stream in FreeSurfer from 2-year interval data, and visual assessment of image quality by three radiologists. STATISTICAL TESTS Intraclass correlation coefficient (ICC) and Kruskal-Wallis test. A P value <0.05 was considered significant. RESULTS Healthy elderly subjects, MCI patients, and AD patients showed different patterns in the ICC maps. For the smoothing of 20 mm full width at half maximum, the mean ICC was 0.45 overall (healthy elderly, 0.33; MCI patients, 0.49; AD patients, 0.31). The within-subject SDs of the symmetrized percent changes were similar between healthy elderly subjects (mean, 1.3%/year) and MCI patients (mean, 1.3%/year) but larger in AD patients (mean, 1.7%/year). Image quality did not significantly differ per group (P = 0.18). DATA CONCLUSION The results of this study indicate the reproducibility of longitudinal changes in cortical thickness measured by surface-based morphometry between non-accelerated and accelerated imaging, and that the reproducibility varies by disease and region. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
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Takao H, Amemiya S, Abe O. Reproducibility of Brain Volume Changes in Longitudinal Voxel-Based Morphometry Between Non-Accelerated and Accelerated Magnetic Resonance Imaging. J Alzheimers Dis 2021; 83:281-290. [DOI: 10.3233/jad-210596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background: Scan acceleration techniques, such as parallel imaging, can reduce scan times, but reliability is essential to implement these techniques in neuroimaging. Objective: To evaluate the reproducibility of the longitudinal changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) between non-accelerated and accelerated magnetic resonance images (MRI) in normal aging, mild cognitive impairment (MCI), and Alzheimer’s disease (AD). Methods: Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 2 database, comprising subjects who underwent non-accelerated and accelerated structural T1-weighted MRI at screening and at a 2-year follow-up on 3.0 T Philips scanners, we examined the reproducibility of longitudinal gray matter volume changes determined by longitudinal VBM processing between non-accelerated and accelerated imaging in 50 healthy elderly subjects, 54 MCI patients, and eight AD patients. Results: The intraclass correlation coefficient (ICC) maps differed among the three groups. The mean ICC was 0.72 overall (healthy elderly, 0.63; MCI, 0.75; AD, 0.63), and the ICC was good to excellent (0.6–1.0) for 81.4%of voxels (healthy elderly, 64.8%; MCI, 85.0%; AD, 65.0%). The differences in image quality (head motion) were not significant (Kruskal–Wallis test, p = 0.18) and the within-subject standard deviations of longitudinal gray matter volume changes were similar among the groups. Conclusion: The results indicate that the reproducibility of longitudinal gray matter volume changes determined by VBM between non-accelerated and accelerated MRI is good to excellent for many regions but may vary between diseases and regions.
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Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Bunkyo-ku, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Bunkyo-ku, Tokyo, Japan
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Takao H, Amemiya S, Abe O. Longitudinal stability of resting-state networks in normal aging, mild cognitive impairment, and Alzheimer's disease. Magn Reson Imaging 2021; 82:55-73. [PMID: 34153437 DOI: 10.1016/j.mri.2021.06.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 11/18/2022]
Abstract
Test-retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer's disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test-retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test-retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from -0.06 to 0.44 in patients with AD. The overall test-retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test-retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test-retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test-retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.
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Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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Gabr RE. Editorial for "Reliability of Changes in Brain Volume Determined by Longitudinal Voxel-Based Morphometry". J Magn Reson Imaging 2021; 54:617. [PMID: 33974335 DOI: 10.1002/jmri.27683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 04/19/2021] [Indexed: 11/10/2022] Open
Affiliation(s)
- Refaat E Gabr
- Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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