- Email: [email protected]
The contribution of amygdala atrophy to the emotional agnosia in subjects with mild cognitive impairment
S290
Poster Presentations P1
Background: Accumulated evidence from fMRI studies has demonstrated that Alzheimer’s disease (AD) is associated with abnormalities of brain functional networks (FNs) in medial temporal lobe, frontal, temporal, and parietal cortices. Identifying brain FNs affected by mild cognitive impairment (MCI) and optimally utilizing them in diagnosis could potentially improve early detection of AD. This study applied independent component analysis (ICA) to extract brain FNs from fMRI data of MCI and cognitively normal (CN) elderly subjects during their performance of a simple semantic memory task and resting visual fixation. Advanced pattern classification techniques identified the optimal combination of FNs for diagnostic determination. Methods: A manifold based classification algorithm was used to classify fMRI data of 12 MCIs and 12 CNs. A group ICA was used to extract FNs which were encoded by spatial independent components (ICs) and a back-reconstruction technique was used to compute subject specific FNs. The FNs of each individual were used as bases for spanning a linear subspace, referred to as a functional connectivity pattern (FCP), which facilitated a comprehensive characterization of temporal signals of fMRI data. The FCPs of different individuals were analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a k-nearest neighbor classifier, a forward component selection technique was used to select ICs for constructing the most discriminative FCP whose discriminative power was measured using leave-one-out cross validation. Results: We identified a FCP spanned by 7 FNs, including temporal, parietal, and frontal regions, which were most characteristic of MCI. This combined cognitive challenge and resting state FCP correctly distinguished 20 out 24 subjects, cross-validated using leave-one-out method. This result is better than those obtained by the classifiers built on FCPs spanned by either all available FNs (50%) or any individual FN (the best performance: 54.2%). Conclusions: The manifold based classification method has the potential to identify MCI associated FNs, which could serve as surrogate biomarkers for early detection of individuals at risk for AD. Furthermore, the FCP spanned by multiple discriminative FNs has superior diagnostic value compared to either any individual FN or the FCP spanned by all available FNs. P1-399
THE CONTRIBUTION OF AMYGDALA ATROPHY TO THE EMOTIONAL AGNOSIA IN SUBJECTS WITH MILD COGNITIVE IMPAIRMENT
Zuzana Nedelska1, Daniel Horinek2, Alexandra Varjassyova1, R. J. Brownlow, Jr.,2 Frank Trollman2, Ondrej Bradac3, Jan Laczo1, Hana Magerova1, Jan Krasensky4, Zdenek Seidl4, Jiri Lisy5, Miloslav Rocek5, Jakub Hort1, 1Memory Disorders Unit, Department of Neurology, Charles University in Prague, 2nd Medical School, Prague, Czech Republic, Prague, Czech Republic; 2Laboratory for Neuroimaging, Department of Pathophisiology, Charles University in Prague, 2nd Medical School, Prague, Czech Republic, Prague, Czech Republic; 3Department of Neurosurgery, Central Military Hospital, Prague, Czech Republic, Prague, Czech Republic; 4Department of Diagnostic Imaging, Charles University in Prague, 1nd Medical School, Prague, Czech Republic, Prague, Czech Republic; 5Department of Diagnostic Imaging, Charles University in Prague, 2nd Medical School, Prague, Czech Republic, Prague, Czech Republic. Contact e-mail: [email protected]
Background: Ability to recognize facial emotions declines over the course of Alzheimer’s disease (AD). The aim of our study was to determine: 1) how facial emotion recognition is impaired in relation to amygdalar atrophy in subjects with amnestic mild cognitive impairment (aMCI) and 2) whether capability of facial emotion identification test might be a profitable tool in early diagnosis of AD. Methods: We examined 23 aMCI subjects further subdivided into aMCI multiple domain- aMCImd (n-16), aMCI single domain MCI- aMCIsd (n-7) groups and 15 age-matched, normal controls (NC). All subjects underwent standard neuropsychological evaluation and facial emotion identification test. MRI scans were obtained and particular brain structure volumes were segmented and measured using fully automated segmentation software.Further,special attention was paid to amygdalae and hippocampi.Volumes were correlated with facial emotion identification test results and subgroups were compared mutually. Results: Differences in hippocampal volumes (p < 0,05) between NC and aMCIsd subjects were observed. Further, differences were observed in both amygdalar (p < 0,05) and hippocampal volumes (p < 0,05) between NC and aMCmd .Total Facial emotion identification test score correlated with right amygdalar volume in aMCIsd (r ¼ 0,804617, p < 0,05). In addition to that, recognition of particular facial emotions dependent on amygdalar laterality was revealed; surprise correlated with right amygdalar volume (r ¼ 0,546312, p < 0,05); and sorrow correlated with left amygdalar volume (r ¼ 0,499867, p < 0,05) in aMCImd subjects. Conclusions: Facial emotion identification impairment is in a relation with the degree of amygdalar atrophy which can be reliably quantified in subjects with aMCI. Facial emotion recognition testing supported with automated volumetry might be promising clinical tool for evaluating subjects at risk of AD among heterogenous MCI population. All MCI subjects should receive special attention and communication skills training. P1-400
CSF ABETA AND TAU, HIPPOCAMPAL ATROPHY AND LATERAL VENTRICLE ENLARGEMENT IN PARKINSON’S DISEASE WITH AND WITHOUT MILD COGNITIVE IMPAIRMENT
Mona K. Beyer1, Kristy S. Hwang2, Sona Babakchanian2, Paul M. Thompson3, Jeffrey L. Cummings2, Ezra Mulugeta1, Jan P. Larsen1, Kolbjorn S. Bronnick1, Dag Aarsland4, Guido Alves1, Liana G. Apostolova2, 1 Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway; 2Mary S Easton Center for Alzheimer’s Disease Research, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; 3 Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA; 4Psychiatric Clinic, Stavanger University Hospital, Stavanger, Norway. Contact e-mail: [email protected] Background: ParkWest is a large multicenter study of newly diagnosed drugnaı¨ve Parkinson’s disease (PD) subjects. Baseline imaging data analyses showed that cognitively normal PD subjects (PDCN) and PD subjects with mild cognitive impairment (PDMCI) have significant hippocampal atrophy and ventricular enlargement. Whether these changes correlate with known cerebrospinal fluid (CSF) neurodegenerative biomarkers is not known. Methods: We analyzed baseline T1-weighted MR data of all ParkWest subjects who provided CSF at baseline. Our sample consisted of 73 PDCN and 18 PDMCI subjects. We used Abeta triplex assay (Human Ab peptide Ultra-Sensitive, 4G8 antibody) and ELISA InnotestÒ hTAU-Ag (t-tau) and phosphotau(181P) (p-tau). The hippocampi and lateral ventricles were segmented with two novel automated segmentation techniques and further analyzed with the radial distance approach. We applied linear regression models to study the associations between CSF biomarkers and hippocampal and ventricular radial distance while adjusting for center. For multiple comparison correction we used permutations with threshold p < 0.01. Results: T-tau showed a significant negative association with left hippocampal radial distance in the pooled sample (pcorrected ¼ 0.05). Abeta38, Abeta40, Abeta42 and p-tau showed no significant associations with hippocampal radial distance. In the pooled ventricular analysis Abeta38 and Abeta42 showed significant negative associations with the occipital (Abeta38 left pcorrected ¼ 0.02, right pcorrected ¼ 0.03; Abeta42 left pcorrected ¼ 0.04, right pcorrected ¼ 0.03) and frontal horns (Abeta38 left pcorrected ¼ 0.007, right pcorrected ¼ 0.01; Abeta42 left pcorrected ¼ 0.01, right pcorrected ¼ 0.007). Abeta40
Poster Presentations P1
Background: Accumulated evidence from fMRI studies has demonstrated that Alzheimer’s disease (AD) is associated with abnormalities of brain functional networks (FNs) in medial temporal lobe, frontal, temporal, and parietal cortices. Identifying brain FNs affected by mild cognitive impairment (MCI) and optimally utilizing them in diagnosis could potentially improve early detection of AD. This study applied independent component analysis (ICA) to extract brain FNs from fMRI data of MCI and cognitively normal (CN) elderly subjects during their performance of a simple semantic memory task and resting visual fixation. Advanced pattern classification techniques identified the optimal combination of FNs for diagnostic determination. Methods: A manifold based classification algorithm was used to classify fMRI data of 12 MCIs and 12 CNs. A group ICA was used to extract FNs which were encoded by spatial independent components (ICs) and a back-reconstruction technique was used to compute subject specific FNs. The FNs of each individual were used as bases for spanning a linear subspace, referred to as a functional connectivity pattern (FCP), which facilitated a comprehensive characterization of temporal signals of fMRI data. The FCPs of different individuals were analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a k-nearest neighbor classifier, a forward component selection technique was used to select ICs for constructing the most discriminative FCP whose discriminative power was measured using leave-one-out cross validation. Results: We identified a FCP spanned by 7 FNs, including temporal, parietal, and frontal regions, which were most characteristic of MCI. This combined cognitive challenge and resting state FCP correctly distinguished 20 out 24 subjects, cross-validated using leave-one-out method. This result is better than those obtained by the classifiers built on FCPs spanned by either all available FNs (50%) or any individual FN (the best performance: 54.2%). Conclusions: The manifold based classification method has the potential to identify MCI associated FNs, which could serve as surrogate biomarkers for early detection of individuals at risk for AD. Furthermore, the FCP spanned by multiple discriminative FNs has superior diagnostic value compared to either any individual FN or the FCP spanned by all available FNs. P1-399
THE CONTRIBUTION OF AMYGDALA ATROPHY TO THE EMOTIONAL AGNOSIA IN SUBJECTS WITH MILD COGNITIVE IMPAIRMENT
Zuzana Nedelska1, Daniel Horinek2, Alexandra Varjassyova1, R. J. Brownlow, Jr.,2 Frank Trollman2, Ondrej Bradac3, Jan Laczo1, Hana Magerova1, Jan Krasensky4, Zdenek Seidl4, Jiri Lisy5, Miloslav Rocek5, Jakub Hort1, 1Memory Disorders Unit, Department of Neurology, Charles University in Prague, 2nd Medical School, Prague, Czech Republic, Prague, Czech Republic; 2Laboratory for Neuroimaging, Department of Pathophisiology, Charles University in Prague, 2nd Medical School, Prague, Czech Republic, Prague, Czech Republic; 3Department of Neurosurgery, Central Military Hospital, Prague, Czech Republic, Prague, Czech Republic; 4Department of Diagnostic Imaging, Charles University in Prague, 1nd Medical School, Prague, Czech Republic, Prague, Czech Republic; 5Department of Diagnostic Imaging, Charles University in Prague, 2nd Medical School, Prague, Czech Republic, Prague, Czech Republic. Contact e-mail: [email protected]
Background: Ability to recognize facial emotions declines over the course of Alzheimer’s disease (AD). The aim of our study was to determine: 1) how facial emotion recognition is impaired in relation to amygdalar atrophy in subjects with amnestic mild cognitive impairment (aMCI) and 2) whether capability of facial emotion identification test might be a profitable tool in early diagnosis of AD. Methods: We examined 23 aMCI subjects further subdivided into aMCI multiple domain- aMCImd (n-16), aMCI single domain MCI- aMCIsd (n-7) groups and 15 age-matched, normal controls (NC). All subjects underwent standard neuropsychological evaluation and facial emotion identification test. MRI scans were obtained and particular brain structure volumes were segmented and measured using fully automated segmentation software.Further,special attention was paid to amygdalae and hippocampi.Volumes were correlated with facial emotion identification test results and subgroups were compared mutually. Results: Differences in hippocampal volumes (p < 0,05) between NC and aMCIsd subjects were observed. Further, differences were observed in both amygdalar (p < 0,05) and hippocampal volumes (p < 0,05) between NC and aMCmd .Total Facial emotion identification test score correlated with right amygdalar volume in aMCIsd (r ¼ 0,804617, p < 0,05). In addition to that, recognition of particular facial emotions dependent on amygdalar laterality was revealed; surprise correlated with right amygdalar volume (r ¼ 0,546312, p < 0,05); and sorrow correlated with left amygdalar volume (r ¼ 0,499867, p < 0,05) in aMCImd subjects. Conclusions: Facial emotion identification impairment is in a relation with the degree of amygdalar atrophy which can be reliably quantified in subjects with aMCI. Facial emotion recognition testing supported with automated volumetry might be promising clinical tool for evaluating subjects at risk of AD among heterogenous MCI population. All MCI subjects should receive special attention and communication skills training. P1-400
CSF ABETA AND TAU, HIPPOCAMPAL ATROPHY AND LATERAL VENTRICLE ENLARGEMENT IN PARKINSON’S DISEASE WITH AND WITHOUT MILD COGNITIVE IMPAIRMENT
Mona K. Beyer1, Kristy S. Hwang2, Sona Babakchanian2, Paul M. Thompson3, Jeffrey L. Cummings2, Ezra Mulugeta1, Jan P. Larsen1, Kolbjorn S. Bronnick1, Dag Aarsland4, Guido Alves1, Liana G. Apostolova2, 1 Norwegian Centre for Movement Disorders, Stavanger University Hospital, Stavanger, Norway; 2Mary S Easton Center for Alzheimer’s Disease Research, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA; 3 Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA, USA; 4Psychiatric Clinic, Stavanger University Hospital, Stavanger, Norway. Contact e-mail: [email protected] Background: ParkWest is a large multicenter study of newly diagnosed drugnaı¨ve Parkinson’s disease (PD) subjects. Baseline imaging data analyses showed that cognitively normal PD subjects (PDCN) and PD subjects with mild cognitive impairment (PDMCI) have significant hippocampal atrophy and ventricular enlargement. Whether these changes correlate with known cerebrospinal fluid (CSF) neurodegenerative biomarkers is not known. Methods: We analyzed baseline T1-weighted MR data of all ParkWest subjects who provided CSF at baseline. Our sample consisted of 73 PDCN and 18 PDMCI subjects. We used Abeta triplex assay (Human Ab peptide Ultra-Sensitive, 4G8 antibody) and ELISA InnotestÒ hTAU-Ag (t-tau) and phosphotau(181P) (p-tau). The hippocampi and lateral ventricles were segmented with two novel automated segmentation techniques and further analyzed with the radial distance approach. We applied linear regression models to study the associations between CSF biomarkers and hippocampal and ventricular radial distance while adjusting for center. For multiple comparison correction we used permutations with threshold p < 0.01. Results: T-tau showed a significant negative association with left hippocampal radial distance in the pooled sample (pcorrected ¼ 0.05). Abeta38, Abeta40, Abeta42 and p-tau showed no significant associations with hippocampal radial distance. In the pooled ventricular analysis Abeta38 and Abeta42 showed significant negative associations with the occipital (Abeta38 left pcorrected ¼ 0.02, right pcorrected ¼ 0.03; Abeta42 left pcorrected ¼ 0.04, right pcorrected ¼ 0.03) and frontal horns (Abeta38 left pcorrected ¼ 0.007, right pcorrected ¼ 0.01; Abeta42 left pcorrected ¼ 0.01, right pcorrected ¼ 0.007). Abeta40