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Category learning brain potentials as neurocognitive markers for pathologic aging
S216
Poster Presentations P1
between the two groups. Results: Regional analyses were performed and direct comparisons of the Jacobian maps revealed that the AAMI group demonstrated significantly greater longitudinal atrophy in the right frontal and right temporal lobes as well as the anterior and posterior cingulate gyrus relative to the NC group (p<0.05; corrected for multiple comparisons using permutation tests). In contrast, the NC group did not show any area of greater brain volume loss relative to the AAMI group. Conclusions: The diagnosis of AAMI was associated with increased rate of brain atrophy relative to NC subjects in regions that are affected in Alzheimer’s disease (AD). AAMI may be diagnostically useful in identifying individuals with early signs of underlying pathology who may be in the prodromal stages of AD. P1-326
MULTI-MODALITY FUSION OF NEUROIMAGING DATA IN PREDICTING ABNORMAL COGNITIVE DECLINE IN AGING
Matthew MacCarthy1, Jeffrey Petrella2, Forrest Sheldon2, Jennifer Shaffer1, Murali Doraiswamy2, Vince Calhoun3, 1Duke University School of Medicine, Durham, North Carolina, United States; 2Duke University Medical Center, Durham, North Carolina, United States; 3The Mind Research Network, Albuquerque, New Mexico, United States. Background: Over the past decade, neuroimaging has been extensively employed in investigating Alzheimer’s disease and the progression from Mild Cognitive Impairment to AD. However, recent therapeutic trials indicate that therapies may be more effective at the preclinical stage of the disease, prior to significant neuronal loss and symptom onset. Thus, there has been increasing emphasis on earlier diagnosis and the transition from normal aging to MCI. Recently, a novel data fusion method developed by Calhoun et al (2009) using parallel Independent Component Analysis (ICA) has shown potential in discovering characteristics that contribute to neurological disease by encompassing the power of whole-brain image analysis and incorporating multiple data types into a single model. Methods: To examine characteristics that may predict a decline from normal aging to MCI, we obtained data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and used the Fusion ICAToolbox to analyze the baseline MRI and FDG-PET data of 103 normal study participants (M/F: 63/40, mean (sd), age: 75.8 (4.73), baseline ADASCog: 10.4 (4.24), baseline MMSE: 29.0 (1.12)). 19 of these patients showed cognitive decline as defined by a CDR sum-of-boxes score greater than or equal to 1 at either 36-or 48-month follow-up visits, and 11 converted to MCI during the follow-up period. Results: ICA analysis identified a total of 32 independently contributory components (5 MRI components and 27 PET components). Statistical analysis of numerical loading parameters from these components revealed several independent components with significant correlations to CDR-SB score. As expected, the medial temporal lobe was identified on MRI (p<.05). Significant PET components included frontal (p ¼ .0034) and inferior temporoparietal (p<.03) areas. Notably, when CDRSB scores of the 11 converter patients were considered alone in a multiple regression with all 32 components, only the posterior singulate region on PET retained an individually significant contribution (p ¼ .0117). Conclusions: We present a novel analysis of neuroimaging data in cognitively normal subjects, using ICA to investigate the progression from normal aging to MCI. Our data suggest that the areas noted above may play a significant role in the pathophysiologic progression of very early cognitive decline.
P1-327
CATEGORY LEARNING BRAIN POTENTIALS AS NEUROCOGNITIVE MARKERS FOR PATHOLOGIC AGING
Robert Morrison1, Krishna Bharani1, Dietta Chihade1, Kevin Nuechterlein1, Sandra Weintraub2, Paul Reber2, Ken Paller2, 1Loyola University Chicago, Chicago, Illinois, United States; 2Northwestern University, Chicago, Illinois, United States. Background: A challenge for treating Alzheimer’s disease is to develop neurocognitive markers to identify at-risk individuals before significant neural damage. Our research program involves measuring event-related
potentials (ERPs) recorded during tasks requiring long-term memory and executive functions in younger adults and in older adults both with and without mild cognitive impairment or Alzheimer’s diagnoses. Methods: In this study we tested healthy younger (m ¼ 21 years) and older (m ¼ 71 years) adults using a rule-based category-learning task where participants learn to categorize visual gratings using trial-by-trial feedback while brain potentials were recorded. We have previously characterized the ERPs sensitive to strategy and categorization accuracy in this task. Previous functional magnetic resonance imaging studies with this task have demonstrated the importance of prefrontal cortex and medial temporal lobe, two areas implicated in pathological aging including Alzheimer’s disease. Results: Older adults showed lower accuracy and longer RTs than did younger adults, but there were two distinct subgroups. The Rule subgroup learned slightly more slowly than younger adults, but showed equivalent asymptotic accuracy. The No-Rule subgroup did not learn and showed near chance performance throughout the task. We calculated ERPs time-locked to the stimulus, response and feedback. ERPs for the Rule subgroup showed a stimulus-locked Late Positive Complex (LPC) larger for correct than incorrect trials, similar to that observed for younger adults. The Rule subgroup also showed a response-locked contingent negative variation difference between correct/ incorrect trials, but this time smaller than that observed in younger adults. Likewise, there were reliable feedback-locked P300 and LPC correct/incorrect differences for the Rule-subgroup, but they were also smaller than that in younger adults suggesting decreased rule-learning confidence (despite similar accuracy across groups). The No-Rule subgroup showed ERPs characteristic of chance performance. Conclusions: The current study suggests that rule-based category learning may be effective for identifying individuals at increased risk for mild cognitive impairment and probable Alzheimer’s disease. Event-related potentials recorded during this task may provide a more sensitive measure of changes in cognition compared to simple neuropsychological tests by measuring neural function even when participants perform well, thus providing an objective measure of confidence in learning. P1-328
EVENT-RELATED POTENTIALS PREDICT PERSONS AT GENETIC RISK FOR ALZHEIMER’S DISEASE
Claire Murphy1, Charlie Morgan2, 1SDSU/UCSD Joint Doctoral Program, San Diego, California, United States; 2San Diego State University, San Diego, California, United States. Background: Alzheimer’s disease (AD) is rising at an alarming rate as the world’s population ages, presenting significant public health issues with profound economic consequences for individuals and for the global economy. As successful interventions become available, the ability to determine who is at risk for the disease, the point of disease onset, disease progression, and the effectiveness of interventions are critical. Neuropathological changes in AD begin in entorhinal and transentorhinal areas and in the olfactory bulb, regions critical for processing olfactory information. The most important genetic risk factor for AD is the apolipoprotein e4 allele. Recent studies suggest that event-related potentials (ERPs) may reflect subtle changes in brain function prior to disease onset in mild cognitive impairment. This study investigated the ability of ERPs recorded during tasks that challenged the olfactory system to predict genetic status for development of AD. Methods: Participants were 65 years and older, screened for anosmia and genotyped for the ApoE e4 allele. A series of olfactory tasks, progressing from more sensory to the more cognitive were used to elicit ERPs, including an active detection task, apassive task, an odor identification task, and an odor recognition memory task. Results: P3 (and other component) latencies were significantly longer in e4+ adults. In the sensory task, N1 as well as P3 was significantly longer in the e4+ than in the e4-individuals. In the recognition memory task, ERP topography indicated activity in older adults with the e4+ allele that suggested greater effort in cognitive processing. Binary logistic regression demonstrated good ApoE status classification rates for all odor tasks, with excellent classification rates for tasks that involved semantic processing of odor. Conclusions: The findings support impairment in brain processing during odor tasks in e4+ individuals who do not
Poster Presentations P1
between the two groups. Results: Regional analyses were performed and direct comparisons of the Jacobian maps revealed that the AAMI group demonstrated significantly greater longitudinal atrophy in the right frontal and right temporal lobes as well as the anterior and posterior cingulate gyrus relative to the NC group (p<0.05; corrected for multiple comparisons using permutation tests). In contrast, the NC group did not show any area of greater brain volume loss relative to the AAMI group. Conclusions: The diagnosis of AAMI was associated with increased rate of brain atrophy relative to NC subjects in regions that are affected in Alzheimer’s disease (AD). AAMI may be diagnostically useful in identifying individuals with early signs of underlying pathology who may be in the prodromal stages of AD. P1-326
MULTI-MODALITY FUSION OF NEUROIMAGING DATA IN PREDICTING ABNORMAL COGNITIVE DECLINE IN AGING
Matthew MacCarthy1, Jeffrey Petrella2, Forrest Sheldon2, Jennifer Shaffer1, Murali Doraiswamy2, Vince Calhoun3, 1Duke University School of Medicine, Durham, North Carolina, United States; 2Duke University Medical Center, Durham, North Carolina, United States; 3The Mind Research Network, Albuquerque, New Mexico, United States. Background: Over the past decade, neuroimaging has been extensively employed in investigating Alzheimer’s disease and the progression from Mild Cognitive Impairment to AD. However, recent therapeutic trials indicate that therapies may be more effective at the preclinical stage of the disease, prior to significant neuronal loss and symptom onset. Thus, there has been increasing emphasis on earlier diagnosis and the transition from normal aging to MCI. Recently, a novel data fusion method developed by Calhoun et al (2009) using parallel Independent Component Analysis (ICA) has shown potential in discovering characteristics that contribute to neurological disease by encompassing the power of whole-brain image analysis and incorporating multiple data types into a single model. Methods: To examine characteristics that may predict a decline from normal aging to MCI, we obtained data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and used the Fusion ICAToolbox to analyze the baseline MRI and FDG-PET data of 103 normal study participants (M/F: 63/40, mean (sd), age: 75.8 (4.73), baseline ADASCog: 10.4 (4.24), baseline MMSE: 29.0 (1.12)). 19 of these patients showed cognitive decline as defined by a CDR sum-of-boxes score greater than or equal to 1 at either 36-or 48-month follow-up visits, and 11 converted to MCI during the follow-up period. Results: ICA analysis identified a total of 32 independently contributory components (5 MRI components and 27 PET components). Statistical analysis of numerical loading parameters from these components revealed several independent components with significant correlations to CDR-SB score. As expected, the medial temporal lobe was identified on MRI (p<.05). Significant PET components included frontal (p ¼ .0034) and inferior temporoparietal (p<.03) areas. Notably, when CDRSB scores of the 11 converter patients were considered alone in a multiple regression with all 32 components, only the posterior singulate region on PET retained an individually significant contribution (p ¼ .0117). Conclusions: We present a novel analysis of neuroimaging data in cognitively normal subjects, using ICA to investigate the progression from normal aging to MCI. Our data suggest that the areas noted above may play a significant role in the pathophysiologic progression of very early cognitive decline.
P1-327
CATEGORY LEARNING BRAIN POTENTIALS AS NEUROCOGNITIVE MARKERS FOR PATHOLOGIC AGING
Robert Morrison1, Krishna Bharani1, Dietta Chihade1, Kevin Nuechterlein1, Sandra Weintraub2, Paul Reber2, Ken Paller2, 1Loyola University Chicago, Chicago, Illinois, United States; 2Northwestern University, Chicago, Illinois, United States. Background: A challenge for treating Alzheimer’s disease is to develop neurocognitive markers to identify at-risk individuals before significant neural damage. Our research program involves measuring event-related
potentials (ERPs) recorded during tasks requiring long-term memory and executive functions in younger adults and in older adults both with and without mild cognitive impairment or Alzheimer’s diagnoses. Methods: In this study we tested healthy younger (m ¼ 21 years) and older (m ¼ 71 years) adults using a rule-based category-learning task where participants learn to categorize visual gratings using trial-by-trial feedback while brain potentials were recorded. We have previously characterized the ERPs sensitive to strategy and categorization accuracy in this task. Previous functional magnetic resonance imaging studies with this task have demonstrated the importance of prefrontal cortex and medial temporal lobe, two areas implicated in pathological aging including Alzheimer’s disease. Results: Older adults showed lower accuracy and longer RTs than did younger adults, but there were two distinct subgroups. The Rule subgroup learned slightly more slowly than younger adults, but showed equivalent asymptotic accuracy. The No-Rule subgroup did not learn and showed near chance performance throughout the task. We calculated ERPs time-locked to the stimulus, response and feedback. ERPs for the Rule subgroup showed a stimulus-locked Late Positive Complex (LPC) larger for correct than incorrect trials, similar to that observed for younger adults. The Rule subgroup also showed a response-locked contingent negative variation difference between correct/ incorrect trials, but this time smaller than that observed in younger adults. Likewise, there were reliable feedback-locked P300 and LPC correct/incorrect differences for the Rule-subgroup, but they were also smaller than that in younger adults suggesting decreased rule-learning confidence (despite similar accuracy across groups). The No-Rule subgroup showed ERPs characteristic of chance performance. Conclusions: The current study suggests that rule-based category learning may be effective for identifying individuals at increased risk for mild cognitive impairment and probable Alzheimer’s disease. Event-related potentials recorded during this task may provide a more sensitive measure of changes in cognition compared to simple neuropsychological tests by measuring neural function even when participants perform well, thus providing an objective measure of confidence in learning. P1-328
EVENT-RELATED POTENTIALS PREDICT PERSONS AT GENETIC RISK FOR ALZHEIMER’S DISEASE
Claire Murphy1, Charlie Morgan2, 1SDSU/UCSD Joint Doctoral Program, San Diego, California, United States; 2San Diego State University, San Diego, California, United States. Background: Alzheimer’s disease (AD) is rising at an alarming rate as the world’s population ages, presenting significant public health issues with profound economic consequences for individuals and for the global economy. As successful interventions become available, the ability to determine who is at risk for the disease, the point of disease onset, disease progression, and the effectiveness of interventions are critical. Neuropathological changes in AD begin in entorhinal and transentorhinal areas and in the olfactory bulb, regions critical for processing olfactory information. The most important genetic risk factor for AD is the apolipoprotein e4 allele. Recent studies suggest that event-related potentials (ERPs) may reflect subtle changes in brain function prior to disease onset in mild cognitive impairment. This study investigated the ability of ERPs recorded during tasks that challenged the olfactory system to predict genetic status for development of AD. Methods: Participants were 65 years and older, screened for anosmia and genotyped for the ApoE e4 allele. A series of olfactory tasks, progressing from more sensory to the more cognitive were used to elicit ERPs, including an active detection task, apassive task, an odor identification task, and an odor recognition memory task. Results: P3 (and other component) latencies were significantly longer in e4+ adults. In the sensory task, N1 as well as P3 was significantly longer in the e4+ than in the e4-individuals. In the recognition memory task, ERP topography indicated activity in older adults with the e4+ allele that suggested greater effort in cognitive processing. Binary logistic regression demonstrated good ApoE status classification rates for all odor tasks, with excellent classification rates for tasks that involved semantic processing of odor. Conclusions: The findings support impairment in brain processing during odor tasks in e4+ individuals who do not