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48 Educational Differences in Digital Clock Drawing for the Command Condition: A Bayesian Network Analysis
- Emily F Matusz, Brandon E Frank, Catherine Dion, Udell Holmes III, Yonah Joffe, Sabyasachi Bandyopadhyay, Parisa Rashidi, Patrick Tighe, David J Libon, Catherine C Price
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 727-728
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Objective:
Research shows that highly educated individuals have at least 20 graphomotor features associated with clock drawing with hands set for '10 after 11' (Davoudi et al., 2021). Research has yet to understand clock drawing features in individuals with fewer years of education. In the current study, we compared older adults with < 8 years of education to those with > 9 years of education on number and pattern of graphomotor feature relationships in the clock drawing command condition.
Participants and Methods:Participants age 65+ from the University of Florida (UF) and UF Health (N= 10,491) completed both command and copy conditions of the digital Clock Drawing Test (dCDT) as a part of a federally-funded investigation. Participants were categorized into two education groups: < 8 years of education (n= 304) and > 9 years of education (n= 10,187). Propensity score matching was then used to match participants from each subgroup (n= 266 for each subgroup) on the following demographic characteristics: age, sex, race, and ethnicity (n= 532, age= 74.99±6.21, education= 10.41±4.45, female= 42.7%, non-white= 32.0%). Network models were derived using Bayesian Structure Learning (BSL) with the hill-climbing algorithm to obtain optimal directed acyclic graphs (DAGs) from all possible solutions in each subgroup for the dCDT command condition.
Results:Both education groups retained 13 of 91 possible edges (14.29%). For the < 8 years of education group (education= 6.65±1.74, ASA= 3.08±0.35), the network included 3 clock face (CF), 7 digit, and 3 hour hand (HH) and minute hand (MH) independent, or “parent,” features connected to the retained edges (BIC= -7395.24). In contrast, the > 9 years of education group (education= 14.17±2.88, ASA= 2.90±0.46) network retained 1 CF, 6 digit, 5 HH and MH, and 1 additional parent features representing the total number of pen strokes (BIC= -6689.92). Both groups showed that greater distance from the HH to the center of the clock also had greater distance from the MH to the center of the clock [ßz(< 8 years)= 0.73, ßz(> 9 years)= 0.76]. Groups were similar in the size of the digit height relative to the distance of the digits to the CF [ßz(< 8 years)= 0.27, ßz(> 9 years)= 0.56]. Larger HH angle was associated with larger MH angle across groups [ßz(< 8 years)= 0.28, ßz(> 9 years)= 0.23].
Conclusions:Education groups differed in the ratio of dCDT parent feature types. Specifically, copy clock production in older adults with < 8 years of education relied more heavily on CF parent features. In contrast, older adults with > 9 years of education relied more heavily on HH and MH parent features. Individuals with < 8 years of education may more infrequently present the concept of time in the clock drawing command condition. This study highlights the importance of considering education level in interpreting dCDT scores and features.
96 Proof of Principle: Can Paragraph Recall Pauses and Speech Frequencies Correctly Classify Cognitively Compromised Older Adults?
- Leeor Hershkovich, Sabyasachi Bandyopadhyay, Jack Wittmayer, Patrick Tighe, David J Libon, Catherine C Price, Parisa Rashidi
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 767-768
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Objective:
Recent research has found that machine learning based analysis of patient speech can be used to classify Alzheimer’s Disease. We know of no studies, however, which systematically explore the value of pausing events in speech for detecting cognitive limitations. Using retrospectively acquired voice data from paragraph memory tests, we created two types of pause features: a) the number and duration of pauses, and b) frequency components in speech immediately following pausing. Multiple machine learning models were used to assess how these features could effectively discriminate individuals classified into two groups: Cognitively Compromised versus Cognitively Well.
Participants and Methods:Participants (age> 65 years, n= 67) completed the Newcomer paragraph memory test and a neuropsychological protocol as part of a federally funded prospective IRB approved investigation at the University of Florida. Participant vocal recordings were acquired for the immediate and delay conditions of the test. Speaker diarization was performed on the immediate free recall test condition to separate voices of patients from examiners. Features extracted from both test conditions included a) 3 pause characteristics (total number of pauses, total pause duration, and length of the longest pause), and b) 20 Mel Frequency Cepstral Coefficients (MFCC) pertaining to speech immediately (2.7 seconds) following pauses. These were combined with demographics (age, sex, race, education, and handedness) to create a total of 105 features that were used as inputs for multiple machine learning analytic models (random forest, logistic regression, naive Bayes, AdaBoost, Gradient Boost, and multi-layered perceptron). External neuropsychological metrics were used to initially classify Cognitively Compromised (i.e., < -1.0 standard deviation on > two of five test metrics: total immediate, delay, discrimination Hopkins Verbal Learning Test-Revised (HVLT-R),
Controlled Oral Word Association (COWA) test, category fluency ('animals')). Pearson Product Moment Correlations were used to assess the linear relationships between pauses and speech frequency categories and neuropsychological metrics.
Results:Neuropsychology metric classification using -1SD cut-off identified 27% (18/67 participants) as Cognitively Compromised. The Cognitively Compromised group and the Cognitively Well group did not show any difference in distributions of individual pause/frequency features (Mann Whitney U-test, p> 0.11). A negative correlation was found between total duration of short pauses and HVLT total immediate free recall, while a positive correlation was found between MFCC-10 and HVLT total immediate free recall. The best classification model was AdaBoost Classifier which predicted the Cognitively Compromised label with 0.91 area under receiver operating curve, 0.81 accuracy, 0.43 sensitivity, 1.0 specificity, 1.0 precision, 0.6 f1 score.
Conclusions:Pause characteristics and frequency profiles of speech immediately following pauses from a paragraph memory test accurately identified older adults with compromised cognition, as measured by verbal learning and verbal fluency metrics. Furthermore, individuals with reduced HVLT immediate free recall generated more pauses, while individuals who recalled more words had higher power in mid-frequency bands (10th MFCC). Future research needs to replicate how paragraph recall pause characteristics and frequency the profile of speech immediately following pauses potentially provides a low resource alternative to automatic speech recognition models for detecting cognitive impairments.
49 Educational Differences in Digital Clock Drawing for the Copy Condition: A Bayesian Network Analysis
- Emily F Matusz, Brandon E Frank, Catherine Dion, Udell Holmes III, Yonah Joffe, Sabyasachi Bandyopadhyay, Parisa Rashidi, Patrick Tighe, David J Libon, Catherine C Price
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, p. 728
-
- Article
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- You have access Access
- Export citation
-
Objective:
Research shows that highly educated individuals have at least 20 graphomotor features associated with clock drawing with hands set for '10 after 11' (Davoudi et al., 2021). Research has yet to understand clock drawing features in individuals with fewer years of education. In the current study, we compared older adults with < 8 years of education to those with > 9 years of education on number and pattern of graphomotor feature relationships in the clock drawing copy condition.
Participants and Methods:Participants age 65+ from the University of Florida (UF) and UF Health (N= 10,491) completed command and copy digital Clock Drawing Tests (dCDT) as a part of a federally-funded investigation. Participants were categorized into two groups: < 8 years of education (n= 304) and > 9 years of education (n= 10,187). Propensity score matching was used to match participants from each subgroup (n= 266 for each subgroup) on the following: age, sex, race, and ethnicity (n= 532, age= 74.99±6.21, education= 10.41±4.45, female= 42.7%, non-white= 32.0%). Network models were derived using Bayesian Structure Learning (BSL) with the hill-climbing algorithm to obtain optimal directed acyclic graphs (DAGs) from all possible solutions in each subgroup for the dCDT copy condition.
Results:The < 8 years of education group (education= 6.65±1.74, ASA= 3.08±0.35), retained 12 of 91 possible edges (13.19%, BIC= -7775.50). The network retained 2 clock face (CF), 5 digit, and 5 hour hand (HH) and minute hand (MH) independent, or “parent,” features connected to the retained edges. In contrast, the > 9 years of education group (education= 14.17±2.88, ASA= 2.90±0.46) network retained 15 of 91 possible edges (16.48%, BIC= -8261.484). The network retained 2 CF, 6 digit, 4 HH and MH, and an additional 3 total stroke parent features. Both groups showed that greater distance from the HH to the clock center also had greater distance from the MH to the clock center (ßz= 0.73, both). Groups were similar in digit width size relative to digit height [ßz(< 8 years)= 0.72, ßz(> 9 years)= 0.74]. Digit height size related to CF area [ßz(< 8 years)= 0.44, ßz(> 9 years)= 0.62] and CF area related to the digit distance to the CF across groups [ßz(< 8 years)= 0.39, ßz(> 9 years)= 0.46]. Greater distance from the MH to the clock center was associated with smaller MH angle [ßz(< 8 years)= -0.35, ßz(> 9 years)= -0.31], whereas greater digit misplacement was associated with larger MH angle across groups [ßz(< 8 years)= 0.14, ßz(> 9 years)= 0.29].
Conclusions:Education groups differed in the ratio of dCDT parent feature types. Specifically, copy clock production in older adults with < 8 years of education relied more evenly across CF, digit, and MH and HH parent features. In contrast, those with > 9 years of education differed in the additional reliance on total stroke parent features. Individuals with < 8 years of education may more heavily rely upon visual referencing when copying a clock. This study highlights the importance of considering education level in interpreting dCDT scores and features.