- Email: [email protected]
PREDICTING PROGRESSION FROM MILD COGNITIVE IMPAIRMENT (MCI) TO ALZHEIMER’S DISEASE USING PERFUSION BRAIN SPECT IMAGING ANALYSIS OF THE INITIAL HOSPITAL VISIT
P1106
Poster Presentations: Wednesday, July 27, 2016
Figure 3. Top) Performance of four model classes subject to individual and combined input modalities. The numbers and the black lines indicate the mean and standard error, respectively, of correlation values of each model over 10-folds. The baseline models (LR, SVM, and RF) use left and right HC volumes + mean CT values based on Automated Anatomical Labeling (AAL) atlas (www.cyceron.fr/index.php/en/plateforme-en/freeware) as input. Whereas the artificial NN models use HC segmentation masks and CT values based on custom atlas (see Fig. 2) with total dimensionality > 22500 for the combined input case. All models were trained with a nested-inner loop that searched for optimal hyperparameters. *implies that input data was normalized to center to the mean and component wise scale to unit variance. Bottom) Correlation plots for 4 models with HC+CT input for all subjects from concatenated list of heldout subsets from each fold.
(mean, median, std. error, see Fig. 3) indicate that NN [.58,.61,.06] outperforms the other three models: LR [.53,.51,.07], SVM: [.54,.55,.08], and RF:[.52,.54,.10]. Significant improvement is seen with HC modality with NN model compared to baseline models. All four model classes show boost in performance when HC and CT modalities are combined. Conclusions: The correlation scores validate the utility of NN as prediction model. The proposed modular architecture of the NN provides a framework for multimodal analysis, and can be extended to incorporate other data modalities and/or predicting other clinical scales. P4-211
POSTERIOR ATROPHY SCALE CAN PREDICT THE RATE OF ALZHEIMER’S DISEASE PROGRESSION
Jeewon Suh1, Young Ho Park2, Hang Rae Kim1, SangYun Kim2, 1Seoul National University Hospital, Seoul, Republic of Korea; 2Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. Contact e-mail: [email protected] Background: Rate of disease progression in Alzheimer’s disease is associated with poor prognosis in terms of mortality and caregiver dependency (Hui JS, 2003), (Holtzer R, 2003). Rapid progressive
AD patients have different characteristics compared with slow progressive patients. Some researchers found that brain morphological changes correlate with rate of AD progression. Recent Voxel-based morphometry (VBM) analysis demonstrated that AD patients with rapid cognitive decline had a more extensive cortical atrophy than slow decliners, especially in the medial occipitoparietal areas (Kinkingnehum S, 2008). Nonetheless, there is a limit to apply VBM analysis in individual patients. Recently, easily applicable posterior visual rating scale is developed (Koedam, 2011). Several studies demonstrate that AD patients showed significant differences in posterior atrophy scale with healthy control(Lehmann M, 2012) and showed that visual rating of atrophy can be used to predict conversion to AD in MCI patients(Lehmann M, 2013). In this study we investigated the relation between visual rating scale of brain atrophy and progression rate of disease in early stage of AD patients. Methods: 106 AD patients with initial MMSE score 21 to 26 were followed up for 1 year. We measured posterior atrophy scale and 1 year follow-up MMSE score to see whether brain atrophy according to PA scale is associated with rapid cognitive decline after 1 year. Results: Rapid cognitive decline (3 score drop in MMSE test) at 1 year follow-up occurred in 27 subjects (25.5%). In logistic regression analysis, posterior atrophy in Brain MRI was associated with rapid cognitive decline in 1 year follow-up (regression coefficient 3.59, 95% CI 1.23 to 10.44, p¼0.019). While, medial temporal lobe atrophy scale did not show statistically significant association with rapid cognitive decline (regression coefficient 4.16, 95% CI 0.92 to 18.90, p¼0.065). Conclusions: An easily measurable posterior visual rating scale can predict the rate of disease progression in early AD patients. P4-212
PREDICTING PROGRESSION FROM MILD COGNITIVE IMPAIRMENT (MCI) TO ALZHEIMER’S DISEASE USING PERFUSION BRAIN SPECT IMAGING ANALYSIS OF THE INITIAL HOSPITAL VISIT
Ruka Nakata, Takayuki Yuasa, Yoko Nakao, Katsuhiro Ichinose, Itsuro Tomita, Hideyo Satoh, Akira Satoh, Makoto Ochi, Makiko Seto, Tsujihata Mitsuhiro, Nagasaki Kita Hospital, Nagasaki, Japan. Contact e-mail: [email protected] Background: Mild Cognitive Impairment (MCI) has been proposed as a transitional stage between the cognitive changes of normal aging and dementia. 10 to 15% of MCI patients progress to Alzheimer’s disease (AD) within one year. The aim of this study was to investigate whether perfusion brain SPECT imaging at the initial hospital visit could predict progression from MCI to AD. Methods: The SPECT device used in this study was Infina Hawkeye 4, General Electronics, with rotating two-headed gamma camera with a fan beam collimator (64x64). The data were acquired for 20 minutes, beginning 20 minutes after the intravenous administration of 123I- IMP (111MBq). The easy Z-score Imaging System (eZIS) and Voxel Based Stereotactic Extraction Estimation (vbSEE) were used in this study for the quantitative assessment of brain SPECT images. More than 20% decrease in the extent % comparison to the control subjects was used to assess the hypoperfusion in the vbSEE analysis. To determine the frequent hypoperfusion regions in the vbSEE analysis, 80 hemispheres of 40 patients with probable AD (29 female, 11 male, age range 52-82) were analyzed. Nine regions were selected as the frequent
Poster Presentations: Wednesday, July 27, 2016
hypoperfusion regions including angular gyrus, supramarginal gyrus, inferior parietal lobule, precuneus, middle temporal gyrus, superior parietal lobule, superior temporal gyrus, cingulated gyrus. Results: Among 82 MCI patients (male 21, female 61), 36 patients showed abnormal SPECT (A group) and 46 patients showed normal SPECT (N group). The age and MMSE were not significantly different between the groups. Progression from MCI to AD was observed for the period of 1-5 years. Among 61 patients observed for one year, 17 patients progressed from MCI to AD, including 6 patients in N group (35.3%) and 11 patients in A group (64.7%). The Logrank test demonstrated a significant difference in survival curve between the group A and N (P value¼0.00842; p<0.01). Conclusions: The eZIS and vbSEE analysis of SPECT imaging at the initial hospital visit in MCI patients is a useful tool for predicting the progression to AD in the near future. P4-213
PROGRESSION OF CEREBRAL SMALL-VESSEL DISEASE CORRELATES WITH CORTICAL THINNING AND COGNITIVE IMPAIRMENT IN PARKINSON’S DISEASE
P1107
1 National Neuroscience Institute, Singapore, Singapore; 2University of Cambridge, Cambridge, United Kingdom; 3National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore. Contact e-mail: [email protected]
Background: Nearly 30% of patients with mild stage Parkinson’s disease (PD) develop dementia. The risk factors and pathophysiology of dementia in PD remains unclear. Recent studies have suggested that cerebral small-vessel disease (SVD) (Hanganu et al., 2014; Staals et al., 2015) may be an important risk factor for PD dementia. These studies have shown associations between individual markers of SVD (white matter disease, lacunes, perivascular spaces) in affecting cognition, however the findings are non-consistent and inconclusive (Kandiah et al., 2014; Dalaker et al., 2009). The heterogeneity in findings could be related to the use of different SVD quantifications scales and insensitive quantification measures of structural neuroimaging. To understand the pathophysiological relationship between SVD and cognition in PD, it would be imperative to study the different components of SVD as a composite measure on the progression of cerebral atrophy patterns and clinical-cognitive outcomes.
Heidi Foo1, Elijah Mak2, Ting Ting Yong1, Ming Ching Wen1, Russell J. Chander1, Wing Lok Au1, Louis Tan1, Nagaendran Kandiah3,
Figure 1. Vertex-wise comparisons of percentage change in cortical thinning between PD Slow-SVD and PD Rapid-SVD progression over 18 months. Baseline cortical thickness showed no significant differences except for the left parahippocampal gyrus. The colour bar shows the logarithmic scale of P-values (-log10).
Figure 2. Vertex-wise correlations between percent of cortical thinning and longitudinal decline in PD Rapid- SVD in MMSE, Delayed recall, Clock drawing, Digit span backwards, and in UPDRS; in PD Slow-SVD in Maze time and Maze error.
Poster Presentations: Wednesday, July 27, 2016
Figure 3. Top) Performance of four model classes subject to individual and combined input modalities. The numbers and the black lines indicate the mean and standard error, respectively, of correlation values of each model over 10-folds. The baseline models (LR, SVM, and RF) use left and right HC volumes + mean CT values based on Automated Anatomical Labeling (AAL) atlas (www.cyceron.fr/index.php/en/plateforme-en/freeware) as input. Whereas the artificial NN models use HC segmentation masks and CT values based on custom atlas (see Fig. 2) with total dimensionality > 22500 for the combined input case. All models were trained with a nested-inner loop that searched for optimal hyperparameters. *implies that input data was normalized to center to the mean and component wise scale to unit variance. Bottom) Correlation plots for 4 models with HC+CT input for all subjects from concatenated list of heldout subsets from each fold.
(mean, median, std. error, see Fig. 3) indicate that NN [.58,.61,.06] outperforms the other three models: LR [.53,.51,.07], SVM: [.54,.55,.08], and RF:[.52,.54,.10]. Significant improvement is seen with HC modality with NN model compared to baseline models. All four model classes show boost in performance when HC and CT modalities are combined. Conclusions: The correlation scores validate the utility of NN as prediction model. The proposed modular architecture of the NN provides a framework for multimodal analysis, and can be extended to incorporate other data modalities and/or predicting other clinical scales. P4-211
POSTERIOR ATROPHY SCALE CAN PREDICT THE RATE OF ALZHEIMER’S DISEASE PROGRESSION
Jeewon Suh1, Young Ho Park2, Hang Rae Kim1, SangYun Kim2, 1Seoul National University Hospital, Seoul, Republic of Korea; 2Clinical Neuroscience Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea. Contact e-mail: [email protected] Background: Rate of disease progression in Alzheimer’s disease is associated with poor prognosis in terms of mortality and caregiver dependency (Hui JS, 2003), (Holtzer R, 2003). Rapid progressive
AD patients have different characteristics compared with slow progressive patients. Some researchers found that brain morphological changes correlate with rate of AD progression. Recent Voxel-based morphometry (VBM) analysis demonstrated that AD patients with rapid cognitive decline had a more extensive cortical atrophy than slow decliners, especially in the medial occipitoparietal areas (Kinkingnehum S, 2008). Nonetheless, there is a limit to apply VBM analysis in individual patients. Recently, easily applicable posterior visual rating scale is developed (Koedam, 2011). Several studies demonstrate that AD patients showed significant differences in posterior atrophy scale with healthy control(Lehmann M, 2012) and showed that visual rating of atrophy can be used to predict conversion to AD in MCI patients(Lehmann M, 2013). In this study we investigated the relation between visual rating scale of brain atrophy and progression rate of disease in early stage of AD patients. Methods: 106 AD patients with initial MMSE score 21 to 26 were followed up for 1 year. We measured posterior atrophy scale and 1 year follow-up MMSE score to see whether brain atrophy according to PA scale is associated with rapid cognitive decline after 1 year. Results: Rapid cognitive decline (3 score drop in MMSE test) at 1 year follow-up occurred in 27 subjects (25.5%). In logistic regression analysis, posterior atrophy in Brain MRI was associated with rapid cognitive decline in 1 year follow-up (regression coefficient 3.59, 95% CI 1.23 to 10.44, p¼0.019). While, medial temporal lobe atrophy scale did not show statistically significant association with rapid cognitive decline (regression coefficient 4.16, 95% CI 0.92 to 18.90, p¼0.065). Conclusions: An easily measurable posterior visual rating scale can predict the rate of disease progression in early AD patients. P4-212
PREDICTING PROGRESSION FROM MILD COGNITIVE IMPAIRMENT (MCI) TO ALZHEIMER’S DISEASE USING PERFUSION BRAIN SPECT IMAGING ANALYSIS OF THE INITIAL HOSPITAL VISIT
Ruka Nakata, Takayuki Yuasa, Yoko Nakao, Katsuhiro Ichinose, Itsuro Tomita, Hideyo Satoh, Akira Satoh, Makoto Ochi, Makiko Seto, Tsujihata Mitsuhiro, Nagasaki Kita Hospital, Nagasaki, Japan. Contact e-mail: [email protected] Background: Mild Cognitive Impairment (MCI) has been proposed as a transitional stage between the cognitive changes of normal aging and dementia. 10 to 15% of MCI patients progress to Alzheimer’s disease (AD) within one year. The aim of this study was to investigate whether perfusion brain SPECT imaging at the initial hospital visit could predict progression from MCI to AD. Methods: The SPECT device used in this study was Infina Hawkeye 4, General Electronics, with rotating two-headed gamma camera with a fan beam collimator (64x64). The data were acquired for 20 minutes, beginning 20 minutes after the intravenous administration of 123I- IMP (111MBq). The easy Z-score Imaging System (eZIS) and Voxel Based Stereotactic Extraction Estimation (vbSEE) were used in this study for the quantitative assessment of brain SPECT images. More than 20% decrease in the extent % comparison to the control subjects was used to assess the hypoperfusion in the vbSEE analysis. To determine the frequent hypoperfusion regions in the vbSEE analysis, 80 hemispheres of 40 patients with probable AD (29 female, 11 male, age range 52-82) were analyzed. Nine regions were selected as the frequent
Poster Presentations: Wednesday, July 27, 2016
hypoperfusion regions including angular gyrus, supramarginal gyrus, inferior parietal lobule, precuneus, middle temporal gyrus, superior parietal lobule, superior temporal gyrus, cingulated gyrus. Results: Among 82 MCI patients (male 21, female 61), 36 patients showed abnormal SPECT (A group) and 46 patients showed normal SPECT (N group). The age and MMSE were not significantly different between the groups. Progression from MCI to AD was observed for the period of 1-5 years. Among 61 patients observed for one year, 17 patients progressed from MCI to AD, including 6 patients in N group (35.3%) and 11 patients in A group (64.7%). The Logrank test demonstrated a significant difference in survival curve between the group A and N (P value¼0.00842; p<0.01). Conclusions: The eZIS and vbSEE analysis of SPECT imaging at the initial hospital visit in MCI patients is a useful tool for predicting the progression to AD in the near future. P4-213
PROGRESSION OF CEREBRAL SMALL-VESSEL DISEASE CORRELATES WITH CORTICAL THINNING AND COGNITIVE IMPAIRMENT IN PARKINSON’S DISEASE
P1107
1 National Neuroscience Institute, Singapore, Singapore; 2University of Cambridge, Cambridge, United Kingdom; 3National Neuroscience Institute, Tan Tock Seng Hospital, Singapore, Singapore. Contact e-mail: [email protected]
Background: Nearly 30% of patients with mild stage Parkinson’s disease (PD) develop dementia. The risk factors and pathophysiology of dementia in PD remains unclear. Recent studies have suggested that cerebral small-vessel disease (SVD) (Hanganu et al., 2014; Staals et al., 2015) may be an important risk factor for PD dementia. These studies have shown associations between individual markers of SVD (white matter disease, lacunes, perivascular spaces) in affecting cognition, however the findings are non-consistent and inconclusive (Kandiah et al., 2014; Dalaker et al., 2009). The heterogeneity in findings could be related to the use of different SVD quantifications scales and insensitive quantification measures of structural neuroimaging. To understand the pathophysiological relationship between SVD and cognition in PD, it would be imperative to study the different components of SVD as a composite measure on the progression of cerebral atrophy patterns and clinical-cognitive outcomes.
Heidi Foo1, Elijah Mak2, Ting Ting Yong1, Ming Ching Wen1, Russell J. Chander1, Wing Lok Au1, Louis Tan1, Nagaendran Kandiah3,
Figure 1. Vertex-wise comparisons of percentage change in cortical thinning between PD Slow-SVD and PD Rapid-SVD progression over 18 months. Baseline cortical thickness showed no significant differences except for the left parahippocampal gyrus. The colour bar shows the logarithmic scale of P-values (-log10).
Figure 2. Vertex-wise correlations between percent of cortical thinning and longitudinal decline in PD Rapid- SVD in MMSE, Delayed recall, Clock drawing, Digit span backwards, and in UPDRS; in PD Slow-SVD in Maze time and Maze error.