TY - JOUR
T1 - Associations Between the Digital Clock Drawing Test and Brain Volume
T2 - Large Community-Based Prospective Cohort (Framingham Heart Study)
AU - Yuan, Jing
AU - Au, Rhoda
AU - Karjadi, Cody
AU - Ang, Ting Fang
AU - Devine, Sherral
AU - Auerbach, Sanford
AU - DeCarli, Charles
AU - Libon, David J.
AU - Mez, Jesse
AU - Lin, Honghuang
N1 - Funding Information:
We thank the Framingham Heart Study participants for their decades of dedication and the staff for their hard work in collecting and preparing the data. This work was supported by the Framingham Heart Study?s National Heart, Lung, and Blood Institute contract (N01-HC-25195; HHSN268201500001I), and National Institutes of Health grants from the National Institute on Aging (AG008122, AG016495, AG033040, AG054156, AG049810, AG062109, and U01AG068221) and Pfizer. This work was also supported by the Alzheimer?s Association Grant (AARG-NTF-20-643020) and American Heart Association Grant (20SFRN35360180). Support for JY was provided by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2016I2M1004). The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Institutes of Health or the US Department of Health and Human Services.
Funding Information:
We thank the Framingham Heart Study participants for their decades of dedication and the staff for their hard work in collecting and preparing the data. This work was supported by the Framingham Heart Study’s National Heart, Lung, and Blood Institute contract (N01-HC-25195; HHSN268201500001I), and National Institutes of Health grants from the National Institute on Aging (AG008122, AG016495, AG033040, AG054156, AG049810, AG062109, and U01AG068221) and Pfizer. This work was also supported by the Alzheimer’s Association Grant (AARG-NTF-20-643020) and American Heart Association Grant (20SFRN35360180). Support for JY was provided by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2016I2M1004). The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Institutes of Health or the US Department of Health and Human Services.
Publisher Copyright:
© Jing Yuan, Rhoda Au, Cody Karjadi, Ting Fang Ang, Sherral Devine, Sanford Auerbach, Charles DeCarli, David J Libon, Jesse Mez, Honghuang Lin.
PY - 2022/4
Y1 - 2022/4
N2 - Background: The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. Objective: We aimed to investigate the association between dCDT features and brain volume. Methods: This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. Results: A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. Conclusions: dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.
AB - Background: The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. Objective: We aimed to investigate the association between dCDT features and brain volume. Methods: This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. Results: A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. Conclusions: dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.
KW - Alzheimer
KW - Boston Process Approach
KW - Clock Drawing Test
KW - cognitive
KW - dementia
KW - digital
KW - Framingham Heart Study
KW - neurology
KW - neuropsychological test
KW - technology
UR - http://www.scopus.com/inward/record.url?scp=85128433093&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85128433093&partnerID=8YFLogxK
U2 - 10.2196/34513
DO - 10.2196/34513
M3 - Article
C2 - 35436225
AN - SCOPUS:85128433093
VL - 24
JO - Journal of Medical Internet Research
JF - Journal of Medical Internet Research
SN - 1439-4456
IS - 4
M1 - e34513
ER -