Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: The spatial coefficient of variation (sCoV) of arterial spin-labeled (ASL) MRI can index cerebral blood flow spatial heterogeneity. This metric reflects delayed blood delivery—seen as a hyperintense ASL signal juxtaposed by hypointense regions. Purpose: To investigate the use of ASL-sCoV in the classification of cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts. Study Type: Prospective/cohort. Population: Baseline ASL images from AD neuroimaging initiative dataset in three groups of CU, MCI, and AD (N = 258). Field Strength/Sequence: Pulsed ASL (PICORE QT2) images were acquired on 3 T Siemens systems (TE/TR = 12/3400 msec, TI1/2 = 700/1900 msec). Assessment: ASL-sCoV was calculated in temporal, parietal, occipital, and frontal lobes as well as whole gray matter. Statistical Tests: The primary analysis used an analysis of covariance to investigate sCoV and cognitive group (CU, MCI, AD) associations. We also evaluated the repeatability of sCoV by calculating within-subject agreement in a subgroup of CU participants with a repeat ASL. The secondary analyses assessed ventricular volume, amyloid burden, glucose uptake, ASL-sCoV, and regional CBF as cognitive group classifiers using logistic regression models and receiver operating characteristic analyses. Results: We found that global and temporal lobe sCoV differed between cognitive groups (P = 0.006). Post-hoc tests showed that temporal lobe sCoV was lower in CU than in MCI (Cohen's d = –0.36) or AD (Cohen's d = –1.36). We found that sCoV was moderately repeatable in CU (intersession intraclass correlation = 0.50; intrasession intraclass correlation = 0.88). Subsequent logistic regression analyses revealed that temporal lobe sCoV and amyloid uptake classified CU vs. MCI (P < 0.01; accuracy = 78%). Temporal lobe sCoV, amyloid, and glucose uptake classified CU vs. AD (P < 0.01; accuracy = 97%); glucose uptake significantly classified MCI vs. AD (P < 0.01; accuracy = 85%). Data Conclusion: We showed that ASL spatial heterogeneity can be used alongside AD neuroimaging markers to distinguish cognitive groups, in particular, cognitively unimpaired from cognitively impaired individuals. Level of Evidence: 2. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2019.
Original languageEnglish
JournalJournal of Magnetic Resonance Imaging
DOIs
Publication statusE-pub ahead of print - 2019

Cite this

@article{350cab205d7142f7bb7fbbb7c6490970,
title = "Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images",
abstract = "Background: The spatial coefficient of variation (sCoV) of arterial spin-labeled (ASL) MRI can index cerebral blood flow spatial heterogeneity. This metric reflects delayed blood delivery—seen as a hyperintense ASL signal juxtaposed by hypointense regions. Purpose: To investigate the use of ASL-sCoV in the classification of cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts. Study Type: Prospective/cohort. Population: Baseline ASL images from AD neuroimaging initiative dataset in three groups of CU, MCI, and AD (N = 258). Field Strength/Sequence: Pulsed ASL (PICORE QT2) images were acquired on 3 T Siemens systems (TE/TR = 12/3400 msec, TI1/2 = 700/1900 msec). Assessment: ASL-sCoV was calculated in temporal, parietal, occipital, and frontal lobes as well as whole gray matter. Statistical Tests: The primary analysis used an analysis of covariance to investigate sCoV and cognitive group (CU, MCI, AD) associations. We also evaluated the repeatability of sCoV by calculating within-subject agreement in a subgroup of CU participants with a repeat ASL. The secondary analyses assessed ventricular volume, amyloid burden, glucose uptake, ASL-sCoV, and regional CBF as cognitive group classifiers using logistic regression models and receiver operating characteristic analyses. Results: We found that global and temporal lobe sCoV differed between cognitive groups (P = 0.006). Post-hoc tests showed that temporal lobe sCoV was lower in CU than in MCI (Cohen's d = –0.36) or AD (Cohen's d = –1.36). We found that sCoV was moderately repeatable in CU (intersession intraclass correlation = 0.50; intrasession intraclass correlation = 0.88). Subsequent logistic regression analyses revealed that temporal lobe sCoV and amyloid uptake classified CU vs. MCI (P < 0.01; accuracy = 78{\%}). Temporal lobe sCoV, amyloid, and glucose uptake classified CU vs. AD (P < 0.01; accuracy = 97{\%}); glucose uptake significantly classified MCI vs. AD (P < 0.01; accuracy = 85{\%}). Data Conclusion: We showed that ASL spatial heterogeneity can be used alongside AD neuroimaging markers to distinguish cognitive groups, in particular, cognitively unimpaired from cognitively impaired individuals. Level of Evidence: 2. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2019.",
author = "{Alzheimer's Disease Neuroimaging Initiative} and Zahra Shirzadi and Bojana Stefanovic and Mutsaerts, {Henri J. M. M.} and Mario Masellis and MacIntosh, {Bradley J.}",
year = "2019",
doi = "10.1002/jmri.26650",
language = "English",
journal = "Journal of Magnetic Resonance Imaging",
issn = "1053-1807",
publisher = "John Wiley and Sons Inc.",

}

Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images. / Alzheimer's Disease Neuroimaging Initiative.

In: Journal of Magnetic Resonance Imaging, 2019.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Classifying cognitive impairment based on the spatial heterogeneity of cerebral blood flow images

AU - Alzheimer's Disease Neuroimaging Initiative

AU - Shirzadi, Zahra

AU - Stefanovic, Bojana

AU - Mutsaerts, Henri J. M. M.

AU - Masellis, Mario

AU - MacIntosh, Bradley J.

PY - 2019

Y1 - 2019

N2 - Background: The spatial coefficient of variation (sCoV) of arterial spin-labeled (ASL) MRI can index cerebral blood flow spatial heterogeneity. This metric reflects delayed blood delivery—seen as a hyperintense ASL signal juxtaposed by hypointense regions. Purpose: To investigate the use of ASL-sCoV in the classification of cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts. Study Type: Prospective/cohort. Population: Baseline ASL images from AD neuroimaging initiative dataset in three groups of CU, MCI, and AD (N = 258). Field Strength/Sequence: Pulsed ASL (PICORE QT2) images were acquired on 3 T Siemens systems (TE/TR = 12/3400 msec, TI1/2 = 700/1900 msec). Assessment: ASL-sCoV was calculated in temporal, parietal, occipital, and frontal lobes as well as whole gray matter. Statistical Tests: The primary analysis used an analysis of covariance to investigate sCoV and cognitive group (CU, MCI, AD) associations. We also evaluated the repeatability of sCoV by calculating within-subject agreement in a subgroup of CU participants with a repeat ASL. The secondary analyses assessed ventricular volume, amyloid burden, glucose uptake, ASL-sCoV, and regional CBF as cognitive group classifiers using logistic regression models and receiver operating characteristic analyses. Results: We found that global and temporal lobe sCoV differed between cognitive groups (P = 0.006). Post-hoc tests showed that temporal lobe sCoV was lower in CU than in MCI (Cohen's d = –0.36) or AD (Cohen's d = –1.36). We found that sCoV was moderately repeatable in CU (intersession intraclass correlation = 0.50; intrasession intraclass correlation = 0.88). Subsequent logistic regression analyses revealed that temporal lobe sCoV and amyloid uptake classified CU vs. MCI (P < 0.01; accuracy = 78%). Temporal lobe sCoV, amyloid, and glucose uptake classified CU vs. AD (P < 0.01; accuracy = 97%); glucose uptake significantly classified MCI vs. AD (P < 0.01; accuracy = 85%). Data Conclusion: We showed that ASL spatial heterogeneity can be used alongside AD neuroimaging markers to distinguish cognitive groups, in particular, cognitively unimpaired from cognitively impaired individuals. Level of Evidence: 2. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2019.

AB - Background: The spatial coefficient of variation (sCoV) of arterial spin-labeled (ASL) MRI can index cerebral blood flow spatial heterogeneity. This metric reflects delayed blood delivery—seen as a hyperintense ASL signal juxtaposed by hypointense regions. Purpose: To investigate the use of ASL-sCoV in the classification of cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer's disease (AD) cohorts. Study Type: Prospective/cohort. Population: Baseline ASL images from AD neuroimaging initiative dataset in three groups of CU, MCI, and AD (N = 258). Field Strength/Sequence: Pulsed ASL (PICORE QT2) images were acquired on 3 T Siemens systems (TE/TR = 12/3400 msec, TI1/2 = 700/1900 msec). Assessment: ASL-sCoV was calculated in temporal, parietal, occipital, and frontal lobes as well as whole gray matter. Statistical Tests: The primary analysis used an analysis of covariance to investigate sCoV and cognitive group (CU, MCI, AD) associations. We also evaluated the repeatability of sCoV by calculating within-subject agreement in a subgroup of CU participants with a repeat ASL. The secondary analyses assessed ventricular volume, amyloid burden, glucose uptake, ASL-sCoV, and regional CBF as cognitive group classifiers using logistic regression models and receiver operating characteristic analyses. Results: We found that global and temporal lobe sCoV differed between cognitive groups (P = 0.006). Post-hoc tests showed that temporal lobe sCoV was lower in CU than in MCI (Cohen's d = –0.36) or AD (Cohen's d = –1.36). We found that sCoV was moderately repeatable in CU (intersession intraclass correlation = 0.50; intrasession intraclass correlation = 0.88). Subsequent logistic regression analyses revealed that temporal lobe sCoV and amyloid uptake classified CU vs. MCI (P < 0.01; accuracy = 78%). Temporal lobe sCoV, amyloid, and glucose uptake classified CU vs. AD (P < 0.01; accuracy = 97%); glucose uptake significantly classified MCI vs. AD (P < 0.01; accuracy = 85%). Data Conclusion: We showed that ASL spatial heterogeneity can be used alongside AD neuroimaging markers to distinguish cognitive groups, in particular, cognitively unimpaired from cognitively impaired individuals. Level of Evidence: 2. Technical Efficacy: Stage 3. J. Magn. Reson. Imaging 2019.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/30666734

U2 - 10.1002/jmri.26650

DO - 10.1002/jmri.26650

M3 - Article

JO - Journal of Magnetic Resonance Imaging

JF - Journal of Magnetic Resonance Imaging

SN - 1053-1807

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