- Email: [email protected]
IDENTIFICATION OF NEUROPHYSIOLOGICAL BIOMARKERS OF MCI USING RESTING STATE EEG
P882 P3-163
Poster Presentations: Tuesday, July 26, 2016 IDENTIFICATION OF NEUROPHYSIOLOGICAL BIOMARKERS OF MCI USING RESTING STATE EEG
Shani Waninger1, Seppo Ahlfors2, Maja Stikic1, Stephanie Korszen1, Chris Berka1, David H. Salat2, Ajay Verma3, 1Advanced Brain Monitoring, Carlsbad, CA, USA; 2MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; 3Biogen, Cambridge, MA, USA. Contact e-mail: [email protected] Background: Identification of biomarkers of early stage and prodromal Alzheimer’s disease remains a priority as it is anticipated that new investigational therapeutics may show the greatest promise of efficacy in early disease states. Increased sensitivity in identifying mild cognitive impairment (MCI) in the early stages is critical for identification and stratification of patient populations sensitive to disease modifying therapeutic intervention. A pilot, multimodal study was conducted addressing utility of EEG and other neuroimaging biomarkers by comparing a group of MCI patients to a healthy control (HC) group using resting state EEG data acquisition and analysis including: power spectral density (PSD); source localization using LORETA; and development of a linear Discriminant Function Analysis (lDFA) model for to discriminate between MCI and HC groups. Methods: The EEG data were recorded at resting state with eyes open and eyes closed at a sampling rate of 256 Hz, using a standard 10-20 montage on B-Alert X-24Ò EEG system. Absolute and relative PSDs were calculated by fast Fourier transform and LORETA analysis performed using NeuroGuide software. Variables extracted from the resting state data were grouped together into a feature vector and the most discriminative variables selected to construct the lDFA model. The trained classifier was validated using auto-validation to check the feasibility of the model by testing it on the training data and leave-one-outcross-validation. Results: Significant differences were observed between MCI and healthy groups PSD frequency bands including increase in theta and slow alpha, particularly in the temporal region of the brain. The most significant increase (p<.0001) was mapped to the middle and superior temporal gyrus and fusiform gyrus using LORETA analysis. Application of the lDFA model to the dataset resulted in an overall accuracy of 91.06% for the model using autovalidation. When leave-one-out cross-validation is applied, the overall accuracy of the model is only slightly reduced to 85.11%. Conclusions: The current preliminary results provide additional empirical support for the potential utility of EEG to yield objective biomarkers relevant to tracking pathophysiolgical processes associated with MCI. Additional studies with increased sample size and longitudinal follow up of the patients is of particular value for further validation of EEG biomarkers in neurodegenerative disease.
P3-164
IMPAIRED DRIVING CAPACITY IN EARLY STAGE ALZHEIMER’S IS ASSOCIATED WITH DECREASED CORTICAL RESPONSIVENESS TO SIMULATED SELF-MOVEMENT
Roberto Fernandez-Romero, Daniel J. Cox, University of Virginia, Charlottesville, VA, USA. Contact e-mail: [email protected] Background: The loss of driving capacity is a direct and devastating consequence of the visual-perceptual impairments that occur in early stage Alzheimer’s disease (EAD). These visuospatial deficits are the result of extrastriate cortical dysfunction and reflect the distribution of AD pathology in posterior cortical areas. We have previously shown that event related potentials (ERP) to simulated self-
movement are an effective way to measure extrastriate cortical responsiveness. These ERPs are characterized by a negative wave peaking approximately 200 ms after motion onset (N200 response) and its amplitude can differentiate normal cognitive aging from early stage AD. We have now used a virtual-reality driving simulator to assess vehicular driving capacity in aging and EAD, as well as ERPs to investigate the neurophysiological correlates of impaired driving performance. Methods: EAD patients and older normal controls (ONC) took a virtual reality driving evaluation that incorporates multiple cognitive, visual and motor tests. Composite scores are calculated based on normative data and used to make a determination of driving capacity. We also recorded ERPs evoked by optic flow stimuli (OF-ERP) that simulate self-motion and measure N200 response amplitude and latency. Results: While most ONC subjects were deemed fit to drive, most EAD subjects were not. Compared to controls, EAD subjects had significantly lower driving scores and smaller N200 amplitudes. Furthermore, there was a highly significantly correlation between driving scores and N200 amplitudes. Conclusions: Virtual reality driving assessments can effectively measure driving capacity in EAD and OFERPs can clearly differentiate EAD from normal controls. Significant correlations between vehicular driving scores and N200 amplitudes support the role of extrastriate cortical dysfunction in impaired driving capacity and the potential use of ERPs as screening tools for selective functional impairments and as biomarkers of AD.
P3-165
PLASMA BIOMARKER PROFILE OF AMYLOID, TAU, AND NEURONAL PATHOPHYSIOLOGY IN COGNITIVELY NORMAL PRECLINICAL INDIVIDUALS STRATIFIED BY AMYLOID-PET STATUS
Harald Hampel1, Simone Lista2, Henrik Zetterberg3, Kaj Blennow3, Marie-Odile Habert4, Francis Nyasse5, Hovagim Bakardjian6, Foudil Lamari7, Bruno Dubois8, 1Sorbonne Universites, Universite Pierre et Marie Curie, Paris, France; 2Sorbonne Universites, Universite Pierre et Marie Curie (UPMC), Paris, France; 3Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; 4Sorbonne Universites, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Laboratoire d’Imagerie Biomedicale, Paris, France; 5Institut de la Memoire et de la Maladie d’Alzheimer (IM2A), Paris, France; 6IHU-A-ICM - Paris Institute of Translational Neurosciences, H^opital Pitie-Salp^etriere, Paris, France; 7Service de Biochimie Metabolique, H^opital Pitie-Salp^etriere, Paris, France; 8Sorbonne Universites, Universite Pierre et Marie Curie-Paris 6, Paris, France. Contact e-mail: [email protected] Background: Disease-modifying treatments against Alzheimer’s
disease (AD) should be tested during the preclinical stage before the occurrence of widespread irreversible pathological brain damage. Biomarker candidates indicative of the preclinical AD stage are in discovery and development. We performed advanced plasma candidate biomarker characterization in cognitively normal preclinical individuals at increased risk of AD stratified by brain amyloid load to assess amyloid/tau pathophysiology in correlation with neuronal damage. Methods: The mono-centric INSIGHT cohort (Pitie-Salp^etriere University Hospital, Paris) was used with a target population of cognitively normal elderly individuals stratified by amyloid PET (18F-AV-45 [AmyvidÔ]). Plasma samples collected from the INSIGHT cohort were analysed for the neurofilament light (NFL) chain polypeptide – a key structural constituent of the neuronal cytoskeleton and a neurochemical biomarker of large-caliber myelinated axonal loss – Ab1-42/Ab1-40
Poster Presentations: Tuesday, July 26, 2016 IDENTIFICATION OF NEUROPHYSIOLOGICAL BIOMARKERS OF MCI USING RESTING STATE EEG
Shani Waninger1, Seppo Ahlfors2, Maja Stikic1, Stephanie Korszen1, Chris Berka1, David H. Salat2, Ajay Verma3, 1Advanced Brain Monitoring, Carlsbad, CA, USA; 2MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; 3Biogen, Cambridge, MA, USA. Contact e-mail: [email protected] Background: Identification of biomarkers of early stage and prodromal Alzheimer’s disease remains a priority as it is anticipated that new investigational therapeutics may show the greatest promise of efficacy in early disease states. Increased sensitivity in identifying mild cognitive impairment (MCI) in the early stages is critical for identification and stratification of patient populations sensitive to disease modifying therapeutic intervention. A pilot, multimodal study was conducted addressing utility of EEG and other neuroimaging biomarkers by comparing a group of MCI patients to a healthy control (HC) group using resting state EEG data acquisition and analysis including: power spectral density (PSD); source localization using LORETA; and development of a linear Discriminant Function Analysis (lDFA) model for to discriminate between MCI and HC groups. Methods: The EEG data were recorded at resting state with eyes open and eyes closed at a sampling rate of 256 Hz, using a standard 10-20 montage on B-Alert X-24Ò EEG system. Absolute and relative PSDs were calculated by fast Fourier transform and LORETA analysis performed using NeuroGuide software. Variables extracted from the resting state data were grouped together into a feature vector and the most discriminative variables selected to construct the lDFA model. The trained classifier was validated using auto-validation to check the feasibility of the model by testing it on the training data and leave-one-outcross-validation. Results: Significant differences were observed between MCI and healthy groups PSD frequency bands including increase in theta and slow alpha, particularly in the temporal region of the brain. The most significant increase (p<.0001) was mapped to the middle and superior temporal gyrus and fusiform gyrus using LORETA analysis. Application of the lDFA model to the dataset resulted in an overall accuracy of 91.06% for the model using autovalidation. When leave-one-out cross-validation is applied, the overall accuracy of the model is only slightly reduced to 85.11%. Conclusions: The current preliminary results provide additional empirical support for the potential utility of EEG to yield objective biomarkers relevant to tracking pathophysiolgical processes associated with MCI. Additional studies with increased sample size and longitudinal follow up of the patients is of particular value for further validation of EEG biomarkers in neurodegenerative disease.
P3-164
IMPAIRED DRIVING CAPACITY IN EARLY STAGE ALZHEIMER’S IS ASSOCIATED WITH DECREASED CORTICAL RESPONSIVENESS TO SIMULATED SELF-MOVEMENT
Roberto Fernandez-Romero, Daniel J. Cox, University of Virginia, Charlottesville, VA, USA. Contact e-mail: [email protected] Background: The loss of driving capacity is a direct and devastating consequence of the visual-perceptual impairments that occur in early stage Alzheimer’s disease (EAD). These visuospatial deficits are the result of extrastriate cortical dysfunction and reflect the distribution of AD pathology in posterior cortical areas. We have previously shown that event related potentials (ERP) to simulated self-
movement are an effective way to measure extrastriate cortical responsiveness. These ERPs are characterized by a negative wave peaking approximately 200 ms after motion onset (N200 response) and its amplitude can differentiate normal cognitive aging from early stage AD. We have now used a virtual-reality driving simulator to assess vehicular driving capacity in aging and EAD, as well as ERPs to investigate the neurophysiological correlates of impaired driving performance. Methods: EAD patients and older normal controls (ONC) took a virtual reality driving evaluation that incorporates multiple cognitive, visual and motor tests. Composite scores are calculated based on normative data and used to make a determination of driving capacity. We also recorded ERPs evoked by optic flow stimuli (OF-ERP) that simulate self-motion and measure N200 response amplitude and latency. Results: While most ONC subjects were deemed fit to drive, most EAD subjects were not. Compared to controls, EAD subjects had significantly lower driving scores and smaller N200 amplitudes. Furthermore, there was a highly significantly correlation between driving scores and N200 amplitudes. Conclusions: Virtual reality driving assessments can effectively measure driving capacity in EAD and OFERPs can clearly differentiate EAD from normal controls. Significant correlations between vehicular driving scores and N200 amplitudes support the role of extrastriate cortical dysfunction in impaired driving capacity and the potential use of ERPs as screening tools for selective functional impairments and as biomarkers of AD.
P3-165
PLASMA BIOMARKER PROFILE OF AMYLOID, TAU, AND NEURONAL PATHOPHYSIOLOGY IN COGNITIVELY NORMAL PRECLINICAL INDIVIDUALS STRATIFIED BY AMYLOID-PET STATUS
Harald Hampel1, Simone Lista2, Henrik Zetterberg3, Kaj Blennow3, Marie-Odile Habert4, Francis Nyasse5, Hovagim Bakardjian6, Foudil Lamari7, Bruno Dubois8, 1Sorbonne Universites, Universite Pierre et Marie Curie, Paris, France; 2Sorbonne Universites, Universite Pierre et Marie Curie (UPMC), Paris, France; 3Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; 4Sorbonne Universites, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Laboratoire d’Imagerie Biomedicale, Paris, France; 5Institut de la Memoire et de la Maladie d’Alzheimer (IM2A), Paris, France; 6IHU-A-ICM - Paris Institute of Translational Neurosciences, H^opital Pitie-Salp^etriere, Paris, France; 7Service de Biochimie Metabolique, H^opital Pitie-Salp^etriere, Paris, France; 8Sorbonne Universites, Universite Pierre et Marie Curie-Paris 6, Paris, France. Contact e-mail: [email protected] Background: Disease-modifying treatments against Alzheimer’s
disease (AD) should be tested during the preclinical stage before the occurrence of widespread irreversible pathological brain damage. Biomarker candidates indicative of the preclinical AD stage are in discovery and development. We performed advanced plasma candidate biomarker characterization in cognitively normal preclinical individuals at increased risk of AD stratified by brain amyloid load to assess amyloid/tau pathophysiology in correlation with neuronal damage. Methods: The mono-centric INSIGHT cohort (Pitie-Salp^etriere University Hospital, Paris) was used with a target population of cognitively normal elderly individuals stratified by amyloid PET (18F-AV-45 [AmyvidÔ]). Plasma samples collected from the INSIGHT cohort were analysed for the neurofilament light (NFL) chain polypeptide – a key structural constituent of the neuronal cytoskeleton and a neurochemical biomarker of large-caliber myelinated axonal loss – Ab1-42/Ab1-40