Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials
Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust t...
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| Published in | NeuroImage (Orlando, Fla.) Vol. 66; pp. 648 - 661 |
|---|---|
| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier Inc
01.02.2013
Elsevier Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. |
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| AbstractList | Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (=0.05,power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ...4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24 months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39 AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (I- = 0.05, power = 80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) Im4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. Various neuroimaging measures are being evaluated for tracking Alzheimer's disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer's Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. Various neuroimaging measures are being evaluated for tracking Alzheimer’s disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24 months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39 AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test ( α =0.05, power =80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI. |
| Author | Rajagopalan, Priya Ching, Christopher R.K. Boyle, Christina P. Jack, Clifford R. Harvey, Danielle Gutman, Boris A. Hibar, Derrek P. Weiner, Michael W. Thompson, Paul M. Hua, Xue Leow, Alex D. Toga, Arthur W. |
| AuthorAffiliation | b Dept. of Psychiatry, University of Illinois at Chicago, College of Medicine, Chicago, IL, USA d Mayo Clinic, Rochester, MN, USA f Dept. of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA, USA e Dept. of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA g Dept. of Medicine, UC San Francisco, San Francisco, CA, USA i Dept. of Psychiatry, Semel Institute, UCLA School of Medicine, Los Angeles, CA, USA c Dept. of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA a Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA h Dept. of Psychiatry, UC San Francisco, San Francisco, CA, USA |
| AuthorAffiliation_xml | – name: g Dept. of Medicine, UC San Francisco, San Francisco, CA, USA – name: h Dept. of Psychiatry, UC San Francisco, San Francisco, CA, USA – name: b Dept. of Psychiatry, University of Illinois at Chicago, College of Medicine, Chicago, IL, USA – name: d Mayo Clinic, Rochester, MN, USA – name: c Dept. of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA – name: a Imaging Genetics Center, Laboratory of Neuro Imaging, Dept. of Neurology, UCLA School of Medicine, Los Angeles, CA, USA – name: i Dept. of Psychiatry, Semel Institute, UCLA School of Medicine, Los Angeles, CA, USA – name: e Dept. of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA – name: f Dept. of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA, USA |
| Author_xml | – sequence: 1 givenname: Xue surname: Hua fullname: Hua, Xue – sequence: 2 givenname: Derrek P. surname: Hibar fullname: Hibar, Derrek P. – sequence: 3 givenname: Christopher R.K. surname: Ching fullname: Ching, Christopher R.K. – sequence: 4 givenname: Christina P. surname: Boyle fullname: Boyle, Christina P. – sequence: 5 givenname: Priya surname: Rajagopalan fullname: Rajagopalan, Priya – sequence: 6 givenname: Boris A. surname: Gutman fullname: Gutman, Boris A. – sequence: 7 givenname: Alex D. surname: Leow fullname: Leow, Alex D. – sequence: 8 givenname: Arthur W. surname: Toga fullname: Toga, Arthur W. – sequence: 9 givenname: Clifford R. surname: Jack fullname: Jack, Clifford R. – sequence: 10 givenname: Danielle surname: Harvey fullname: Harvey, Danielle – sequence: 11 givenname: Michael W. surname: Weiner fullname: Weiner, Michael W. – sequence: 12 givenname: Paul M. surname: Thompson fullname: Thompson, Paul M. email: thompson@loni.ucla.edu |
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| Keywords | ADNI VBM Quarc AD MRI Mild cognitive impairment BBSI stat-ROI Drug trial SPM TE MCI QC Tensor-based morphometry TI Aging ApoE MDT MI Alzheimer's disease FSL TBM TR Nervous system diseases Senescence Alzheimer disease Cerebral disorder Central nervous system disease Degenerative disease mild cognitive impairment Morphometry |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but only some participated in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. |
| OpenAccessLink | https://escholarship.org/content/qt7q20n924/qt7q20n924.pdf |
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| PublicationDate | 2013-02-01 |
| PublicationDateYYYYMMDD | 2013-02-01 |
| PublicationDate_xml | – month: 02 year: 2013 text: 2013-02-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Amsterdam |
| PublicationPlace_xml | – name: Amsterdam – name: United States |
| PublicationTitle | NeuroImage (Orlando, Fla.) |
| PublicationTitleAlternate | Neuroimage |
| PublicationYear | 2013 |
| Publisher | Elsevier Inc Elsevier Elsevier Limited |
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| Title | Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials |
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