- Email: [email protected]
Hippocampal deformity in nonsemantic primary progressive aphasia
Poster Presentations: P2 Lewy bodies (DLB), core features and several non-motor symptoms of Parkinson disease (PD) frequently precede cognitive impairment. F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and I-123 iodoamphetamine (IMP) single photon emission computed tomography (SPECT) are helpful for the differential diagnosis of AD and DLB. But it is little known about imaging differential diagnosis among mild cognitive impairment (MCI) patients. Methods: We constructed the normal databases of FDG-PET and IMP-SPECT. By using the database we examined 9 AD patients, 9 DLB patients, 8 prodromal AD patients who converted to AD within 3 years, and 9 mild DLB patients with some core features of DLB and/or non-motor symptoms of PD. Then the regional uptake reductions of FDG/IMP were calculated by NEUROSTAT. Results: In AD and DLB patients, receiver operatorating characteristic analysis of lateral and medial occipital regions (l-OR and m-OR) using either FDG-PET or IMP-SPECT was useful to distinguish AD and DLB. We defined the cut-off points of maximum FDG/IMP uptake reductions of AD in the l-OR and m-OR, respectively, and examined the prevalence of FDG/IMP uptake reduction in the l-OR or m-OR of prodromal AD and mild DLB patients. Regarding FDG-PET, the prevalence of uptake reduction in both the l-OR and m-OR was significantly higher in mild DLB patients than in prodromal AD patients. (l-OR: prodromal AD 12.5% vs mild DLB 66.7%, P<0.05, m-OR: prodromal AD 12.5% vs mild DLB 77.8%, P<0.05). Regarding IMPSPECT, the prevalence of uptake reduction in the m-OR was significantly higher in mild DLB patients than in prodromal AD patients (prodromal AD 0% vs mild DLB 66.7%, P<0.05), but not in the l-OR (prodromal AD 12.5% vs mild DLB 55.6%). Conclusions: Both FDG-PET and IMPSPECT are useful for differential diagnosis between AD and DLB. For differential diagnosis of MCI patients with AD and DLB, FDG-PET may be more useful than IMP-SPECT.
P2-089
MACHINE-LEARNING CLASSIFICATION OF MR SCANS IN ALZHEIMER’S DISEASE BASED ON TENSOR-BASED MORPHOMETRY
Motonobu Fujishima1, Norihide Maikusa1, Noriko Chida2, Hiroshi Matsuda1, Fumio Yamashita3, Takeshi Iwatsubo4, Japanese Alzheimer’s Disease Neuroimaging Initiative2,1National Center of Neurology and Psychiatry, Kodaira, Japan; 2Research Association for Biotechnology, Minato-ku, Japan; 3Iwate Medical University, Morioka, Japan; 4The University of Tokyo, Bunkyo-ku, Japan. Contact e-mail: [email protected] Background: Machine learning classification of MR brain scans could help physicians perform differential diagnosis of Alzheimer’s disease (AD). We assessed the ability of brain morphological measures from tensor-based morphometry (TBM) for classification of patients with AD and mild cognitive impairment (MCI) from healthy elderly controls, using dataset from the Japanese Alzheimer’s Disease Neuroimaging Initiative. Methods: Three-dimensional T1-weighted MPRAGE scans of 95 patients with AD, 198 patients with MCI and 136 healthy elderly controls were acquired at 38 clinical sites in Japan using 1.5T MR scanners. ATBM approach was applied to extract voxel-wise, relative volume differences between each individual’s brain and a study-specific brain template, as a three-dimensional Jacobian map. 95 patients with AD were compared with 95 healthy elderly controls, and 136 patients with MCI were compared with 136 healthy controls. We adopted a linear support vector machine (SVM) and Gaussian process classifiers (GPC) to differentiate whole-brain Jacobian maps of patients with AD and MCI from those of healthy controls. Leave-one-out cross-validation was employed to evaluate the performance of our trained classifiers. Results: With the approaches using SVM and GPC, we obtained classification accuracies of 82.1% (sensitivity/specificity: 84.2%/80.0%) and 83.2% (sensitivity/specificity: 82.1%/84.2%), respectively, for differentiating AD from healthy controls. Similarly, classification accuracies for discriminating MCI from healthy controls were 71.3% (sensitivity/specificity: 72.1%/70.6%) and 73.2% (sensitivity/specificity: 72.8%/73.5%), respectively. Conclusions: Results of this study indicate that machine learning classification
P375
using SVM and GPC offers promise as a novel diagnostic support tool for differentiating patients with AD and MCI from healthy elderly controls. Although we used only morphological measures at a single timepoint, we will use longitudinal changes of serial structural MR scans, FDG PET scans or cerebrospinal fluid in the future for the achievement of more accurate classification.
P2-090
HIPPOCAMPAL DEFORMITY IN NONSEMANTIC PRIMARY PROGRESSIVE APHASIA
Adam Christensen1, Kathryn Alpert2, Emily Rogalski3, Derin Cobia2, Sandra Weintraub2, Marsel Mesulam4, Lei Wang3, 1Northwestern University, Feinberg School of Medicine, Chicago, Illinois, United States; 2 Northwestern University, Chicago, Illinois, United States; 3Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States; 4 Northwestern Feinberg School of Medicine, Chicago, Illinois, United States. Contact e-mail: [email protected] Background: Primary progressive aphasia (PPA), a clinical dementia syndrome primarily affecting cortical language regions, is associated with both Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) pathology. In the former case, PPA has been associated with hippocampal pathology (1). Here we report the largest neuroimaging study of hippocampal integrity using volume and shape deformation measurements in PPA patients. Methods: T1-weighted MPRAGE images were collected from 37 PPA patients with non-semantic clinical subtypes and 32 healthy controls in the Northwestern PPA Program. We generated the hippocampal surface and corresponding subfields (CA1, CA2-4+GD, subiculum) in all subjects using FS-LDDMM (2) with atlas selection and minor manual correction. Individual subject surfaces were adjusted for intracranial volume. Hippocampal volume was calculated as volume enclosed within its surface, and hippocampal shape measures were obtained using principal component analysis. Group differences and group-by-hemisphere interactions were assessed using repeated-measures ANOVA, covarying for age and education. Finally, we correlated hippocampal shape measures with nonverbal memory scores and a measure of aphasia severity. Results: Shape comparisons revealed significant deformity for PPA subjects compared to controls (F¼2.2,p¼.038), particularly in the left anterior hippocampus (Figure 1). Post-hoc subfield analysis attributed this to deformation of CA1 and CA2-4+GD, but not subiculum. Furthermore, significant left-ward hemispheric asymmetry of shape deformation was found in CA1. Groups did not differ in overall hippocampal volume, irrespective of hemisphere. However, a significant group-by-hemisphere interaction was driven by leftward asymmetry of volume loss within the PPA group (F¼5.1,p¼.027). Correlations of shape with nonverbal memory and aphasia severity scores were not statistically significant. Conclusions: Hippocampal shape analysis revealed significant regional volume loss in CA1 and CA2-4+GD subfields in PPA. Greater, left-ward asymmetrical atrophy in PPA is consistent with known cortical patterns of the disease. These
Figure 1. Shape deformation patterns in the PPA vs. control subjects. Cooler shades represent greater inward deformation of the PPA group relative to controls.
P376
Poster Presentations: P2
patterns differ from those in dementia of the Alzheimer type (DAT), where there is atrophy of the CA1 and subiculum subfields. This suggests that hippocampal damage in PPA is different from that in DAT, and may reflect disparities in underlying neuropathology. References: 1. Gefen, T., Gasho, K., Rademaker, A., Lalehzari, M., Weintraub, S., Rogalski, E., Wieneke, C., et al. (2012). Clinically concordant variations of Alzheimer pathology in aphasic versus amnestic dementia. Brain : a journal of neurology, 135(Pt 5), 1554-65. http://dx.doi.org/10.1093/brain/aws076. 2. Khan, A. R., Wang, L., & Beg, M. F. (2008). FreeSurfer-initiated fullyautomated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping. NeuroImage, 41(3), 735-46. http://dx. doi.org/10.1016/j.neuroimage.2008.03.024 P2-091
MILD COGNITIVE IMPAIRMENT SUBTYPES: AN MEG STUDY
Jose Antonio Cabranes1, Maria Eugenia Garcia2, Pablo Cuesta3, Nazareth Castellanos4, Pilar Garces3, Sara Aurtenexte4, Ricardo Bajo4, Maria Luisa Delgado5, Pedro Montejo6, Alberto Marcos7, Ana Barabash7, Fernando Maest u4, Alberto Fernandez8, 1Hospital Clınico San Carlos, Madrid, Spain; 2Center for Biomedical Technology (CTB), Madrid, Spain; 3 Center for Biomedical Technology, Pozuelo de Alarcon, Spain; 4Center for Biomedical Technology, Pozuelo de Alarcon, Spain; 5Seniors Center of the District of Chamartin, Madrid, Spain; 6Memory Decline Prevention Center of Madrid, Madrid, Spain; 7Hospital Clınico San Carlos, Madrid, Spain; 8 Universidad Complutense de Madrid, Madrid, Spain. Contact e-mail: [email protected] Background: Previous studies of the dementia continuum have characterized the early disruption of the brain oscillatory activity at the stage of Mild cognitive impairment (MCI). Reduction in power in posterior regions in the alpha band has been one of the landmarks of the Alzheimer Disease accompanied by the anteriorization of the theta band power. However, little is known about the neurophysiological differences between single and multidomain MCI patients.Our goal is to study the differences in oscillatory magnetic activity between amnestic single and multidomain MCI. This will allow us to test whether the effect of the impairment in a single cognitive domain or in a more widespread functional impairment can be reflected in specific neurophysiological profiles. Methods: A total of 105 subjects underwent a magnetoencephalography (MEG) recording: 36 healthy controls, 33 amnestic MCI (aMCI) and 36 multidomain MCI (mMCI). The groups were well matched for education and age. 3 minutes resting state eyes closed were recorded at 1000Hz sampling rate through 306 channels Elekta-Neuromag MEG system. Recordings were online bandpass filtered (0.1 - 330 Hz), offline filtered with a spatial filter (tSSS, corr ¼ 0.9, t ¼ 10s) and segmented in 4 seconds trials. EOG, Muscle and Jumps artifacts were rejected by means of Fieldtrip and Matlab custom-written scripts. Visual inspection among survival trials was realized and MEG power spectrum was calculated through mtmfft approach with dpss windowing and1 Hz smoothing. Results: We found an increase of power in delta, theta, alpha (anterior areas) and beta bands in mMCI group compared to the Control group, who show and increase in alpha frequency in posterior areas. Similarly, aMCI present more power in theta and alpha bands (in anterior areas) whereas beta band was increased in posterior areas in the control group.Finally, we compared mMCI to aMCI finding an increase of theta power in mMCI group. Conclusions: These results suggest that the pattern of activity of the mMCI is closer to the one previously reported in AD’s than the aMCI ones.
P2-092
NEUROANATOMICAL CORRELATES OF THE BEHAVIORAL AND PSYCHIATRIC SYMPTOMS IN ALZHEIMER’S DISEASE: A VOXEL-BASED MORPHOMETRY STUDY
Xiaochen Hu1, Beate Newport2, Dix Meiberth1, Frank Jessen1, 1University of Bonn, Bonn, Germany; 2German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. Contact e-mail: [email protected]
Background: Behavioural and psychiatric symptoms (BPS) are frequently observed in the clinical course of Alzheimer’s disease (AD). However, previous studies on neuroanatomical underpinnings of BPS in AD have revealed inconsistent results, which might be biased by the image pre-processing steps and the small samples. The current study aimed to assess the relationship between regional grey matter volume (GMV) atrophy and BPS in a large sample of 424 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants. Methods: Structural MRI images and the scores of neuropsychiatric inventory questionnaire (NPI-Q) of altogether 85 AD, 208 patients with mild cognitive impairment (MCI), and 131 healthy controls (HC) were collected from the ADNI website. In contrast to the previous studies in this field, we used improved image pre-processing strategies, including the new segmentation and DARTEL normalization tools from SPM8. Voxel-based multiple regression analyses were used to characterize the association between GMV atrophy and each NPI-Q symptoms across the whole sample, with age, gender and total intracranial volume as covariates of non-interest. The results were exclusively masked with regions directly related to general cognitive deterioration, as expressed by the correlation with the Mini-Mental-State-Examination (MMSE). A statistic threshold of p<0.05 (cluster level family wise error corrected) was applied. Results: Agitation was associated with GMV loss in the bilateral precuneus, the left frontal and insula cortices. Depression was related to GMV decreases in the left frontal cortex. Aberrant motor behaviour was associated with GMV atrophy in bilateral medial orbitofrontal cortices, bilateral putamen and the right inferior frontal gyrus. Conclusions: The current study has shown the neuroanatomical underpinnings of specific BPS by using advanced VBM techniques within a large public available database (ADNI). Our results contribute to the poor understanding of the pathology of BPS in AD.
P2-093
QUANTITATIVE REGIONAL VALIDATION OF THE RATING SCALE FOR POSTERIOR CORTICAL ATROPHY
Christiane M€oller1, Wiesje Van der Flier2, Marije Benedictus3, Adriaan Versteeg1, Mike Wattjes4, Esther Koedam3, Frederik Barkhof2, Philip Scheltens2, Hugo Vrenken2, 1VU Medical Center, Amsterdam, Netherlands; 2VU University Medical Center, Amsterdam, Netherlands; 3 VUMC Alzheimer Centre Amsterdam, Amsterdam, Netherlands; 4 VUMC Radiology Department, Amsterdam, Netherlands. Contact e-mail: [email protected] Background: Posterior cortical atrophy is emerging as an important aspect of Alzheimer’s disease (AD). A 4-point visual rating scale for posterior cortical atrophy (PA) on magnetic resonance (MR) images has been recently developed (Koedam, Eur Radiol 2011). We aimed to validate the rating scale through quantitative grey matter (GM) volumetry of the entire posterior region and its anatomical subregions, as well as voxelbased morphometry (VBM). Methods: We included patients with probable
P2-089
MACHINE-LEARNING CLASSIFICATION OF MR SCANS IN ALZHEIMER’S DISEASE BASED ON TENSOR-BASED MORPHOMETRY
Motonobu Fujishima1, Norihide Maikusa1, Noriko Chida2, Hiroshi Matsuda1, Fumio Yamashita3, Takeshi Iwatsubo4, Japanese Alzheimer’s Disease Neuroimaging Initiative2,1National Center of Neurology and Psychiatry, Kodaira, Japan; 2Research Association for Biotechnology, Minato-ku, Japan; 3Iwate Medical University, Morioka, Japan; 4The University of Tokyo, Bunkyo-ku, Japan. Contact e-mail: [email protected] Background: Machine learning classification of MR brain scans could help physicians perform differential diagnosis of Alzheimer’s disease (AD). We assessed the ability of brain morphological measures from tensor-based morphometry (TBM) for classification of patients with AD and mild cognitive impairment (MCI) from healthy elderly controls, using dataset from the Japanese Alzheimer’s Disease Neuroimaging Initiative. Methods: Three-dimensional T1-weighted MPRAGE scans of 95 patients with AD, 198 patients with MCI and 136 healthy elderly controls were acquired at 38 clinical sites in Japan using 1.5T MR scanners. ATBM approach was applied to extract voxel-wise, relative volume differences between each individual’s brain and a study-specific brain template, as a three-dimensional Jacobian map. 95 patients with AD were compared with 95 healthy elderly controls, and 136 patients with MCI were compared with 136 healthy controls. We adopted a linear support vector machine (SVM) and Gaussian process classifiers (GPC) to differentiate whole-brain Jacobian maps of patients with AD and MCI from those of healthy controls. Leave-one-out cross-validation was employed to evaluate the performance of our trained classifiers. Results: With the approaches using SVM and GPC, we obtained classification accuracies of 82.1% (sensitivity/specificity: 84.2%/80.0%) and 83.2% (sensitivity/specificity: 82.1%/84.2%), respectively, for differentiating AD from healthy controls. Similarly, classification accuracies for discriminating MCI from healthy controls were 71.3% (sensitivity/specificity: 72.1%/70.6%) and 73.2% (sensitivity/specificity: 72.8%/73.5%), respectively. Conclusions: Results of this study indicate that machine learning classification
P375
using SVM and GPC offers promise as a novel diagnostic support tool for differentiating patients with AD and MCI from healthy elderly controls. Although we used only morphological measures at a single timepoint, we will use longitudinal changes of serial structural MR scans, FDG PET scans or cerebrospinal fluid in the future for the achievement of more accurate classification.
P2-090
HIPPOCAMPAL DEFORMITY IN NONSEMANTIC PRIMARY PROGRESSIVE APHASIA
Adam Christensen1, Kathryn Alpert2, Emily Rogalski3, Derin Cobia2, Sandra Weintraub2, Marsel Mesulam4, Lei Wang3, 1Northwestern University, Feinberg School of Medicine, Chicago, Illinois, United States; 2 Northwestern University, Chicago, Illinois, United States; 3Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States; 4 Northwestern Feinberg School of Medicine, Chicago, Illinois, United States. Contact e-mail: [email protected] Background: Primary progressive aphasia (PPA), a clinical dementia syndrome primarily affecting cortical language regions, is associated with both Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD) pathology. In the former case, PPA has been associated with hippocampal pathology (1). Here we report the largest neuroimaging study of hippocampal integrity using volume and shape deformation measurements in PPA patients. Methods: T1-weighted MPRAGE images were collected from 37 PPA patients with non-semantic clinical subtypes and 32 healthy controls in the Northwestern PPA Program. We generated the hippocampal surface and corresponding subfields (CA1, CA2-4+GD, subiculum) in all subjects using FS-LDDMM (2) with atlas selection and minor manual correction. Individual subject surfaces were adjusted for intracranial volume. Hippocampal volume was calculated as volume enclosed within its surface, and hippocampal shape measures were obtained using principal component analysis. Group differences and group-by-hemisphere interactions were assessed using repeated-measures ANOVA, covarying for age and education. Finally, we correlated hippocampal shape measures with nonverbal memory scores and a measure of aphasia severity. Results: Shape comparisons revealed significant deformity for PPA subjects compared to controls (F¼2.2,p¼.038), particularly in the left anterior hippocampus (Figure 1). Post-hoc subfield analysis attributed this to deformation of CA1 and CA2-4+GD, but not subiculum. Furthermore, significant left-ward hemispheric asymmetry of shape deformation was found in CA1. Groups did not differ in overall hippocampal volume, irrespective of hemisphere. However, a significant group-by-hemisphere interaction was driven by leftward asymmetry of volume loss within the PPA group (F¼5.1,p¼.027). Correlations of shape with nonverbal memory and aphasia severity scores were not statistically significant. Conclusions: Hippocampal shape analysis revealed significant regional volume loss in CA1 and CA2-4+GD subfields in PPA. Greater, left-ward asymmetrical atrophy in PPA is consistent with known cortical patterns of the disease. These
Figure 1. Shape deformation patterns in the PPA vs. control subjects. Cooler shades represent greater inward deformation of the PPA group relative to controls.
P376
Poster Presentations: P2
patterns differ from those in dementia of the Alzheimer type (DAT), where there is atrophy of the CA1 and subiculum subfields. This suggests that hippocampal damage in PPA is different from that in DAT, and may reflect disparities in underlying neuropathology. References: 1. Gefen, T., Gasho, K., Rademaker, A., Lalehzari, M., Weintraub, S., Rogalski, E., Wieneke, C., et al. (2012). Clinically concordant variations of Alzheimer pathology in aphasic versus amnestic dementia. Brain : a journal of neurology, 135(Pt 5), 1554-65. http://dx.doi.org/10.1093/brain/aws076. 2. Khan, A. R., Wang, L., & Beg, M. F. (2008). FreeSurfer-initiated fullyautomated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping. NeuroImage, 41(3), 735-46. http://dx. doi.org/10.1016/j.neuroimage.2008.03.024 P2-091
MILD COGNITIVE IMPAIRMENT SUBTYPES: AN MEG STUDY
Jose Antonio Cabranes1, Maria Eugenia Garcia2, Pablo Cuesta3, Nazareth Castellanos4, Pilar Garces3, Sara Aurtenexte4, Ricardo Bajo4, Maria Luisa Delgado5, Pedro Montejo6, Alberto Marcos7, Ana Barabash7, Fernando Maest u4, Alberto Fernandez8, 1Hospital Clınico San Carlos, Madrid, Spain; 2Center for Biomedical Technology (CTB), Madrid, Spain; 3 Center for Biomedical Technology, Pozuelo de Alarcon, Spain; 4Center for Biomedical Technology, Pozuelo de Alarcon, Spain; 5Seniors Center of the District of Chamartin, Madrid, Spain; 6Memory Decline Prevention Center of Madrid, Madrid, Spain; 7Hospital Clınico San Carlos, Madrid, Spain; 8 Universidad Complutense de Madrid, Madrid, Spain. Contact e-mail: [email protected] Background: Previous studies of the dementia continuum have characterized the early disruption of the brain oscillatory activity at the stage of Mild cognitive impairment (MCI). Reduction in power in posterior regions in the alpha band has been one of the landmarks of the Alzheimer Disease accompanied by the anteriorization of the theta band power. However, little is known about the neurophysiological differences between single and multidomain MCI patients.Our goal is to study the differences in oscillatory magnetic activity between amnestic single and multidomain MCI. This will allow us to test whether the effect of the impairment in a single cognitive domain or in a more widespread functional impairment can be reflected in specific neurophysiological profiles. Methods: A total of 105 subjects underwent a magnetoencephalography (MEG) recording: 36 healthy controls, 33 amnestic MCI (aMCI) and 36 multidomain MCI (mMCI). The groups were well matched for education and age. 3 minutes resting state eyes closed were recorded at 1000Hz sampling rate through 306 channels Elekta-Neuromag MEG system. Recordings were online bandpass filtered (0.1 - 330 Hz), offline filtered with a spatial filter (tSSS, corr ¼ 0.9, t ¼ 10s) and segmented in 4 seconds trials. EOG, Muscle and Jumps artifacts were rejected by means of Fieldtrip and Matlab custom-written scripts. Visual inspection among survival trials was realized and MEG power spectrum was calculated through mtmfft approach with dpss windowing and1 Hz smoothing. Results: We found an increase of power in delta, theta, alpha (anterior areas) and beta bands in mMCI group compared to the Control group, who show and increase in alpha frequency in posterior areas. Similarly, aMCI present more power in theta and alpha bands (in anterior areas) whereas beta band was increased in posterior areas in the control group.Finally, we compared mMCI to aMCI finding an increase of theta power in mMCI group. Conclusions: These results suggest that the pattern of activity of the mMCI is closer to the one previously reported in AD’s than the aMCI ones.
P2-092
NEUROANATOMICAL CORRELATES OF THE BEHAVIORAL AND PSYCHIATRIC SYMPTOMS IN ALZHEIMER’S DISEASE: A VOXEL-BASED MORPHOMETRY STUDY
Xiaochen Hu1, Beate Newport2, Dix Meiberth1, Frank Jessen1, 1University of Bonn, Bonn, Germany; 2German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany. Contact e-mail: [email protected]
Background: Behavioural and psychiatric symptoms (BPS) are frequently observed in the clinical course of Alzheimer’s disease (AD). However, previous studies on neuroanatomical underpinnings of BPS in AD have revealed inconsistent results, which might be biased by the image pre-processing steps and the small samples. The current study aimed to assess the relationship between regional grey matter volume (GMV) atrophy and BPS in a large sample of 424 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants. Methods: Structural MRI images and the scores of neuropsychiatric inventory questionnaire (NPI-Q) of altogether 85 AD, 208 patients with mild cognitive impairment (MCI), and 131 healthy controls (HC) were collected from the ADNI website. In contrast to the previous studies in this field, we used improved image pre-processing strategies, including the new segmentation and DARTEL normalization tools from SPM8. Voxel-based multiple regression analyses were used to characterize the association between GMV atrophy and each NPI-Q symptoms across the whole sample, with age, gender and total intracranial volume as covariates of non-interest. The results were exclusively masked with regions directly related to general cognitive deterioration, as expressed by the correlation with the Mini-Mental-State-Examination (MMSE). A statistic threshold of p<0.05 (cluster level family wise error corrected) was applied. Results: Agitation was associated with GMV loss in the bilateral precuneus, the left frontal and insula cortices. Depression was related to GMV decreases in the left frontal cortex. Aberrant motor behaviour was associated with GMV atrophy in bilateral medial orbitofrontal cortices, bilateral putamen and the right inferior frontal gyrus. Conclusions: The current study has shown the neuroanatomical underpinnings of specific BPS by using advanced VBM techniques within a large public available database (ADNI). Our results contribute to the poor understanding of the pathology of BPS in AD.
P2-093
QUANTITATIVE REGIONAL VALIDATION OF THE RATING SCALE FOR POSTERIOR CORTICAL ATROPHY
Christiane M€oller1, Wiesje Van der Flier2, Marije Benedictus3, Adriaan Versteeg1, Mike Wattjes4, Esther Koedam3, Frederik Barkhof2, Philip Scheltens2, Hugo Vrenken2, 1VU Medical Center, Amsterdam, Netherlands; 2VU University Medical Center, Amsterdam, Netherlands; 3 VUMC Alzheimer Centre Amsterdam, Amsterdam, Netherlands; 4 VUMC Radiology Department, Amsterdam, Netherlands. Contact e-mail: [email protected] Background: Posterior cortical atrophy is emerging as an important aspect of Alzheimer’s disease (AD). A 4-point visual rating scale for posterior cortical atrophy (PA) on magnetic resonance (MR) images has been recently developed (Koedam, Eur Radiol 2011). We aimed to validate the rating scale through quantitative grey matter (GM) volumetry of the entire posterior region and its anatomical subregions, as well as voxelbased morphometry (VBM). Methods: We included patients with probable