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Automated index of semantic verbal fluency in mild cognitive impairment and Alzheimer's disease
Poster Presentations: P3 the results of the comparative analysis of the three groups (normal, MCI and dementia) will be published in subsequent studies.
P3-271
AUTOMATED INDEX OF SEMANTIC VERBAL FLUENCY IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE
Serguei Pakhomov1, Laura Hemmy1, Michael Kuskowski2, Rosebud Roberts3, Ronald Petersen3, Bradley Boeve3, David Knopman3, 1 University of Minnesota, Minneapolis, Minnesota, United States; 2 Veterans Administration, Minneapolis, Minnesota, United States; 3Mayo Clinic, Rochester, Minnesota, United States. Background: Semantic verbal fluency (SVF), widely reported in AD and MCI, often show early and disproportionate decline relative to other language, attention, and executive abilities. Successful performance on the SVF test depends on how well conceptual information is organized into related clusters and whether the patient is able to use an efficient strategy to access these clusters. Current methods for clustering and switching behavior assessment are labor-intensive and subjective. We developed and tested an automated computational linguistic approach to cluster words generated on the SVF test. Methods: Participants were a random sample from the Mayo Clinic Alzheimer’s Disease Registry and the Mayo Clinic Study of Aging (20 probable AD, 21 MCI, 19 controls; controlled for age - mean 72 y.o.). All participants underwent a cognitive assessment including the paper-andpencil SVF test subsequently converted to electronic form and a short test of mental status (STMS). The test was analyzed using a novel automatic clustering assessment tool based on semantic relatedness between pairs of words calculated using a variant of principal components analysis. Traditional SVF scores were compared to automatically computed mean cluster size (MCS) and a measure of cumulative relatedness between all pairs of words (CuRel) using logistic regression modeling to distinguish between diagnostic groups and linear regression to predict STMS scores. Results: Classification of MCI vs. controls with SVF score as the only predictor in logistic regression model resulted in smaller area under the curve (AUC) estimates for (MCI vs. controls AUC ¼ 0.70; AD vs. controls AUC ¼ 0.87) than classification with SVF/CuRel score and MCS as predictors (MCI vs. controls AUC ¼ 0.70 and 0.77 for CuRel; AD vs. controls AUC ¼ 0.97 and 0.90 for CuRel). Linear regression models that included the SVF score together with MCS resulted in significantly better fit with STMS scores than models with SVF score as the sole predictor (r 2 ¼ 0.55 vs. r 2 ¼ 0.38, respectively). Conclusions: Our preliminary study indicates that automatically assessed clustering behavior on the SVF test provides complementary information to traditional SVF scoring. Our computerized approach is objective, reproducible and easily scalable to large numbers of participants.
P3-272
LONGITUDINAL PROFILING OF MILD CONGITIVE IMPAIRMENT SUBTYPES
Shannon Klekociuk1, Mathew Summers2, 1University of Tasmania, Launceston, Australia; 2University of Tasmania and Wicking Dementia Research and Education Centre, Launceston, Tasmania, Australia. Background: Recently it was suggested that longitudinal cognitive assessments, although not essential for an MCI classification, are preferable to tracking the trajectory of MCI individuals (Albert et al., 2011). This aligns with previous findings that report instability among MCI cohorts, particularly in cross-sectional studies (Saunders and Summers, 2011; de Jager and Budge, 2005). The sensitivity and specificity of MCI classification is known to be enhanced by assessing multiple cognitive domains. The aim of the present study was to examine the neuropsychological profile of MCI subtypes 12 months after a classification assessment. Methods: A total of 121 participants aged 60 - 90 years (47 male, 74 female) completed a battery of tests previously used for
P555
MCI classification, and were classified as: amnestic MCI (a-MCI; n¼22); non-amnestic MCI (na-MCI; n¼25); multi-domain MCI (mdMCI; n¼23); and healthy controls (n¼51). At 12 months following initial assessment, individuals were assessed using a different battery of neuropsychological tests of visual and verbal memory, working memory, attention, and executive function. Results: Analysis of the 12-month test data by MANOVA indicate that the md-MCI displayed persistent impairments to visual and verbal episodic memory, target detection and attentional control, verbal short-term memory, verbal working memory, and semantic language processing. The a-MCI group displayed a persistent impairment to visual and verbal episodic memory. The na-MCI group displayed a persistent impairment to working memory, but no ongoing impairment to attentional processing or executive functions. Conclusions: The results of the present study indicate that 12 months after initial MCI classification, participants identified as md-MCI continue to display impairments to episodic memory, attentional control, short-term memory capacity, working memory, and semantic language processing. Further, the results indicate that the a-MCI group continues to display impairments to episodic memory processing 12 months after initial screening. The na-MCI group appears to be less stable, with some evidence of recovery of function within 12 months emerging in this group. Further follow up will provide more insight into the neuropsychological profile of the MCI subtypes and stability of their classification.
P3-273
WHITE MATTER CORRELATES OF A NEW SCORING METRIC FOR TRAIL-MAKING TEST-B
Stephen Correia1, Amanda Rabinowitz2, David Ahern2, Paul Malloy2, Stephen Salloway2, Sean Deoni2, 1Alpert Medical School, Brown University, Providence, Rhode Island, United States; 2Brown University, Providence, Rhode Island, United States. Background: The Trail-Making Test Part B (TMT-B) is a neuropsychological test of executive function commonly used in research and clinical practice. However, the traditional TMT-B scoring metric has limited research utility among individuals with dementia who score at the floor (i.e., 300 seconds) because this score masks performance variability that could be used in statistical analyses. For example, a person who completes 75% of the items in 300s presumably has somewhat better executive function than one who completes only 15%. We developed a new TMT-B efficiency score (TMT-Be) to capture this variability. TMT-Be takes into account move-efficiency [ratio of correct moves (Mc) to commission errors (Ec)], time efficiency [time (T) per correct move], and omission errors (Eo). The formula is as follows: TMT-Be¼ [(Mc/(24-Ec))(T/Mc)]+Eo (derivation) TMT-Be¼[T/(24-Ec)]+Eo (computation)where: 24Mc>0, 24T300s, 0Eo 23. We conducted an initial test of convergent validity of our new TMT-Be score against the standard TMT-B raw score (TMT-Bs) by correlating them with MRI indices of anterior and posterior white matter integrity in a group of elderly participants with and without cognitive impairment. Methods: Ten participants (mean age¼79.9, mean education¼13.3 years, 70% female, CDR¼0.0-0.5) completed TMT-B administered according to standard procedures, and underwent MRI with diffusion-tensor imaging (DTI). We computed quantitative DTI tractography metrics in the genu and splenium using Analyze 10.0 and then correlated TMT-Bs and TMT-Be scores with mean fractional anisotropy (FA) for both fiber bundles. Results: The correlation between TMT-Be and mean FA was significant in genu (r¼.66, P<.05) but only a trend in the splenium (r¼.54, p¼.11). In contrast, the correlation between TMT-Bs and mean FA was significant in splenium (r¼.82, P<.005) but only a trend in genu (r¼.52, P¼.12). Conclusions: Both TMT-B scores correlated with white matter integrity in the corpus callosum with TMT-Be showing a significant association with frontal (genu) fibers. These results support the validity of TMT-Be as a measure of executive functioning
P3-271
AUTOMATED INDEX OF SEMANTIC VERBAL FLUENCY IN MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE
Serguei Pakhomov1, Laura Hemmy1, Michael Kuskowski2, Rosebud Roberts3, Ronald Petersen3, Bradley Boeve3, David Knopman3, 1 University of Minnesota, Minneapolis, Minnesota, United States; 2 Veterans Administration, Minneapolis, Minnesota, United States; 3Mayo Clinic, Rochester, Minnesota, United States. Background: Semantic verbal fluency (SVF), widely reported in AD and MCI, often show early and disproportionate decline relative to other language, attention, and executive abilities. Successful performance on the SVF test depends on how well conceptual information is organized into related clusters and whether the patient is able to use an efficient strategy to access these clusters. Current methods for clustering and switching behavior assessment are labor-intensive and subjective. We developed and tested an automated computational linguistic approach to cluster words generated on the SVF test. Methods: Participants were a random sample from the Mayo Clinic Alzheimer’s Disease Registry and the Mayo Clinic Study of Aging (20 probable AD, 21 MCI, 19 controls; controlled for age - mean 72 y.o.). All participants underwent a cognitive assessment including the paper-andpencil SVF test subsequently converted to electronic form and a short test of mental status (STMS). The test was analyzed using a novel automatic clustering assessment tool based on semantic relatedness between pairs of words calculated using a variant of principal components analysis. Traditional SVF scores were compared to automatically computed mean cluster size (MCS) and a measure of cumulative relatedness between all pairs of words (CuRel) using logistic regression modeling to distinguish between diagnostic groups and linear regression to predict STMS scores. Results: Classification of MCI vs. controls with SVF score as the only predictor in logistic regression model resulted in smaller area under the curve (AUC) estimates for (MCI vs. controls AUC ¼ 0.70; AD vs. controls AUC ¼ 0.87) than classification with SVF/CuRel score and MCS as predictors (MCI vs. controls AUC ¼ 0.70 and 0.77 for CuRel; AD vs. controls AUC ¼ 0.97 and 0.90 for CuRel). Linear regression models that included the SVF score together with MCS resulted in significantly better fit with STMS scores than models with SVF score as the sole predictor (r 2 ¼ 0.55 vs. r 2 ¼ 0.38, respectively). Conclusions: Our preliminary study indicates that automatically assessed clustering behavior on the SVF test provides complementary information to traditional SVF scoring. Our computerized approach is objective, reproducible and easily scalable to large numbers of participants.
P3-272
LONGITUDINAL PROFILING OF MILD CONGITIVE IMPAIRMENT SUBTYPES
Shannon Klekociuk1, Mathew Summers2, 1University of Tasmania, Launceston, Australia; 2University of Tasmania and Wicking Dementia Research and Education Centre, Launceston, Tasmania, Australia. Background: Recently it was suggested that longitudinal cognitive assessments, although not essential for an MCI classification, are preferable to tracking the trajectory of MCI individuals (Albert et al., 2011). This aligns with previous findings that report instability among MCI cohorts, particularly in cross-sectional studies (Saunders and Summers, 2011; de Jager and Budge, 2005). The sensitivity and specificity of MCI classification is known to be enhanced by assessing multiple cognitive domains. The aim of the present study was to examine the neuropsychological profile of MCI subtypes 12 months after a classification assessment. Methods: A total of 121 participants aged 60 - 90 years (47 male, 74 female) completed a battery of tests previously used for
P555
MCI classification, and were classified as: amnestic MCI (a-MCI; n¼22); non-amnestic MCI (na-MCI; n¼25); multi-domain MCI (mdMCI; n¼23); and healthy controls (n¼51). At 12 months following initial assessment, individuals were assessed using a different battery of neuropsychological tests of visual and verbal memory, working memory, attention, and executive function. Results: Analysis of the 12-month test data by MANOVA indicate that the md-MCI displayed persistent impairments to visual and verbal episodic memory, target detection and attentional control, verbal short-term memory, verbal working memory, and semantic language processing. The a-MCI group displayed a persistent impairment to visual and verbal episodic memory. The na-MCI group displayed a persistent impairment to working memory, but no ongoing impairment to attentional processing or executive functions. Conclusions: The results of the present study indicate that 12 months after initial MCI classification, participants identified as md-MCI continue to display impairments to episodic memory, attentional control, short-term memory capacity, working memory, and semantic language processing. Further, the results indicate that the a-MCI group continues to display impairments to episodic memory processing 12 months after initial screening. The na-MCI group appears to be less stable, with some evidence of recovery of function within 12 months emerging in this group. Further follow up will provide more insight into the neuropsychological profile of the MCI subtypes and stability of their classification.
P3-273
WHITE MATTER CORRELATES OF A NEW SCORING METRIC FOR TRAIL-MAKING TEST-B
Stephen Correia1, Amanda Rabinowitz2, David Ahern2, Paul Malloy2, Stephen Salloway2, Sean Deoni2, 1Alpert Medical School, Brown University, Providence, Rhode Island, United States; 2Brown University, Providence, Rhode Island, United States. Background: The Trail-Making Test Part B (TMT-B) is a neuropsychological test of executive function commonly used in research and clinical practice. However, the traditional TMT-B scoring metric has limited research utility among individuals with dementia who score at the floor (i.e., 300 seconds) because this score masks performance variability that could be used in statistical analyses. For example, a person who completes 75% of the items in 300s presumably has somewhat better executive function than one who completes only 15%. We developed a new TMT-B efficiency score (TMT-Be) to capture this variability. TMT-Be takes into account move-efficiency [ratio of correct moves (Mc) to commission errors (Ec)], time efficiency [time (T) per correct move], and omission errors (Eo). The formula is as follows: TMT-Be¼ [(Mc/(24-Ec))(T/Mc)]+Eo (derivation) TMT-Be¼[T/(24-Ec)]+Eo (computation)where: 24Mc>0, 24T300s, 0Eo 23. We conducted an initial test of convergent validity of our new TMT-Be score against the standard TMT-B raw score (TMT-Bs) by correlating them with MRI indices of anterior and posterior white matter integrity in a group of elderly participants with and without cognitive impairment. Methods: Ten participants (mean age¼79.9, mean education¼13.3 years, 70% female, CDR¼0.0-0.5) completed TMT-B administered according to standard procedures, and underwent MRI with diffusion-tensor imaging (DTI). We computed quantitative DTI tractography metrics in the genu and splenium using Analyze 10.0 and then correlated TMT-Bs and TMT-Be scores with mean fractional anisotropy (FA) for both fiber bundles. Results: The correlation between TMT-Be and mean FA was significant in genu (r¼.66, P<.05) but only a trend in the splenium (r¼.54, p¼.11). In contrast, the correlation between TMT-Bs and mean FA was significant in splenium (r¼.82, P<.005) but only a trend in genu (r¼.52, P¼.12). Conclusions: Both TMT-B scores correlated with white matter integrity in the corpus callosum with TMT-Be showing a significant association with frontal (genu) fibers. These results support the validity of TMT-Be as a measure of executive functioning