TY - JOUR
T1 - Developing Topics
AU - Sudhakar, Tejaswi D.
AU - Levendovszky, Swati Rane
AU - Zabetian, Cyrus P.
AU - Tsuang, Debby W.
AU - Pillai, Jagan A.
AU - Rao, Stephen M.
AU - Oguh, Odinachi
AU - Lippa, Carol
AU - Lopez, Oscar L.
AU - Berman, Sarah
AU - Irwin, David J.
AU - Galasko, Douglas R.
AU - Litvan, Irene
AU - Marder, Karen
AU - Honig, Lawrence S.
AU - Fleisher, Jori E.
AU - Galvin, James E.
AU - Bozoki, Andrea
AU - Taylor, Angela
AU - Sabbagh, Marwan
AU - Wint, Dylan
AU - Cholerton, Brenna
AU - Leverenz, James B.
AU - Olson, Valerie
N1 - Publisher Copyright:
© 2023 the Alzheimer's Association.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - BACKGROUND: Hippocampal and amygdala subfields variably affect cognitive impairment in neurodegenerative diseases. Subfields of these regions can be well segmented using modern neuroimaging tools but their role in neurodegenerative disease is under active investigation. In this study, we identified hippocampal and amygdala subregions predictive of cognitive performance and motor symptoms severity measured by MoCA (Montreal Cognitive Assessment) and MDS-UPDRS III, respectively, in patients with dementia with Lewy Bodies (DLB). METHOD: We selected all participants with probable DLB (N = 48, mean age = 71±7 years, 15% female) enrolled in the Dementia with Lewy Bodies Consortium (DLBC) as of July 2022, with concurrent measures of 3D T1 MRI sequence, MoCA, and MDS-UPDRS III scores. We performed cortical reconstruction and volumetric segmentation of hippocampal subfields and nuclei of the amygdala using FreeSurfer (v 7.2). We used combat harmonization to account for site and scanner differences. We trained and applied a bootstrapped, bidirectional stepwise regression model of 29 predictor variables comprised of sub-fields and mean cortical thickness against MoCA and MDS-UPDRS III, respectively, with an 80-20 train-test split ratio, and 5000 repetitions, corrected for age and sex. RESULT: Subfield segmentation is shown in Figure 1A. The best fitting model for MoCA included mean cortical thickness, parasubiculum, hippocampal and amygdala transition area, corticoamygdaloid transition area, and CA3 body (Figure 1B, adjusted R2 = 0.51). The best fitting model for MDS-UPDRS III included the cortical nucleus of the amygdala and CA1 body (Figure 1C, adjusted R2 = 0.22). This model was considered a poor fit. We considered MoCA for further analysis and closely predicted scores in our 20% partitioned test sample (Figure 2A, R2 = 0.38). CONCLUSION: We report model-based selection of hippocampal and amygdala subfields to predict MoCA scores in DLB. Atrophy in these regions has been associated with global cognitive deficit in mild cognitive impairment and Alzheimer disease cohorts. The model fit for MDS-UPDRS III scores was poor, providing evidence that these brain regions do not serve a role in motor control.
AB - BACKGROUND: Hippocampal and amygdala subfields variably affect cognitive impairment in neurodegenerative diseases. Subfields of these regions can be well segmented using modern neuroimaging tools but their role in neurodegenerative disease is under active investigation. In this study, we identified hippocampal and amygdala subregions predictive of cognitive performance and motor symptoms severity measured by MoCA (Montreal Cognitive Assessment) and MDS-UPDRS III, respectively, in patients with dementia with Lewy Bodies (DLB). METHOD: We selected all participants with probable DLB (N = 48, mean age = 71±7 years, 15% female) enrolled in the Dementia with Lewy Bodies Consortium (DLBC) as of July 2022, with concurrent measures of 3D T1 MRI sequence, MoCA, and MDS-UPDRS III scores. We performed cortical reconstruction and volumetric segmentation of hippocampal subfields and nuclei of the amygdala using FreeSurfer (v 7.2). We used combat harmonization to account for site and scanner differences. We trained and applied a bootstrapped, bidirectional stepwise regression model of 29 predictor variables comprised of sub-fields and mean cortical thickness against MoCA and MDS-UPDRS III, respectively, with an 80-20 train-test split ratio, and 5000 repetitions, corrected for age and sex. RESULT: Subfield segmentation is shown in Figure 1A. The best fitting model for MoCA included mean cortical thickness, parasubiculum, hippocampal and amygdala transition area, corticoamygdaloid transition area, and CA3 body (Figure 1B, adjusted R2 = 0.51). The best fitting model for MDS-UPDRS III included the cortical nucleus of the amygdala and CA1 body (Figure 1C, adjusted R2 = 0.22). This model was considered a poor fit. We considered MoCA for further analysis and closely predicted scores in our 20% partitioned test sample (Figure 2A, R2 = 0.38). CONCLUSION: We report model-based selection of hippocampal and amygdala subfields to predict MoCA scores in DLB. Atrophy in these regions has been associated with global cognitive deficit in mild cognitive impairment and Alzheimer disease cohorts. The model fit for MDS-UPDRS III scores was poor, providing evidence that these brain regions do not serve a role in motor control.
UR - http://www.scopus.com/inward/record.url?scp=85201064087&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85201064087&partnerID=8YFLogxK
U2 - 10.1002/alz.082764
DO - 10.1002/alz.082764
M3 - Article
C2 - 39120947
AN - SCOPUS:85201064087
SN - 1552-5260
VL - 19
SP - e082764
JO - Alzheimer's and Dementia
JF - Alzheimer's and Dementia
ER -