scholarly journals Deficits in color detection in patients with Alzheimer disease

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262226
Author(s):  
Hee Jin Kim ◽  
Jae Hyun Ryou ◽  
Kang Ta Choi ◽  
Sun Mi Kim ◽  
Jee Taek Kim ◽  
...  
Keyword(s):  
Color Vision ◽  
Area Under The Curve ◽  
Alzheimer’S Dementia ◽  
Early Screening ◽  
Roc Curve Analysis ◽  
Alzheimer's Dementia ◽  
Threshold Test ◽  
Color Detection ◽  
Early Biomarker ◽  
State Examination

Deficits in color vision and related retinal changes hold promise as early screening biomarkers in patients with Alzheimer’s disease. This study aimed to determine a cut-off score that can screen for Alzheimer’s dementia using a novel color vision threshold test named the red, green, and blue (RGB) modified color vision plate test (RGB-vision plate). We developed the RGB-vision plate consisting of 30 plates in which the red and green hues of Ishihara Plate No.22 were sequentially adjusted. A total of 108 older people participated in the mini-mental state examination (MMSE), Ishihara plate, and RGB-vision plate. For the analyses, the participants were divided into two groups: Alzheimer’s dementia (n = 42) and healthy controls (n = 38). K-means cluster analysis and ROC curve analysis were performed to identify the most appropriate cut-off score. As a result, the cut-off screening score for Alzheimer’s dementia on the RGB-vision plate was set at 25, with an area under the curve of 0.773 (p<0.001). Moreover, there was a negative correlation between the RGB-vision plate thresholds and MMSE scores (r = -0.36, p = 0.02). In conclusion, patients with Alzheimer’s dementia had a deficit in color vision. The RGB-vision plate is a potential early biomarker that may adequately detect Alzheimer’s dementia.

scholarly journals Recognition of Alzheimer’s Dementia From the Transcriptions of Spontaneous Speech Using fastText and CNN Models

2021 ◽  
Vol 3 ◽  
Author(s):  
Amit Meghanani ◽  
C. S. Anoop ◽  
Angarai Ganesan Ramakrishnan
Keyword(s):  
Spontaneous Speech ◽  
Alzheimer’S Dementia ◽  
Mel Frequency Cepstral Coefficients ◽  
Text Data ◽  
Alzheimer's Dementia ◽  
Dementia Patients ◽  
Input Sentence ◽  
N Gram ◽  
Mean Square Errors ◽  
State Examination

Alzheimer’s dementia (AD) is a type of neurodegenerative disease that is associated with a decline in memory. However, speech and language impairments are also common in Alzheimer’s dementia patients. This work is an extension of our previous work, where we had used spontaneous speech for Alzheimer’s dementia recognition employing log-Mel spectrogram and Mel-frequency cepstral coefficients (MFCC) as inputs to deep neural networks (DNN). In this work, we explore the transcriptions of spontaneous speech for dementia recognition and compare the results with several baseline results. We explore two models for dementia recognition: 1) fastText and 2) convolutional neural network (CNN) with a single convolutional layer, to capture the n-gram-based linguistic information from the input sentence. The fastText model uses a bag of bigrams and trigrams along with the input text to capture the local word orderings. In the CNN-based model, we try to capture different n-grams (we use n = 2, 3, 4, 5) present in the text by adapting the kernel sizes to n. In both fastText and CNN architectures, the word embeddings are initialized using pretrained GloVe vectors. We use bagging of 21 models in each of these architectures to arrive at the final model using which the performance on the test data is assessed. The best accuracies achieved with CNN and fastText models on the text data are 79.16 and 83.33%, respectively. The best root mean square errors (RMSE) on the prediction of mini-mental state examination (MMSE) score are 4.38 and 4.28 for CNN and fastText, respectively. The results suggest that the n-gram-based features are worth pursuing, for the task of AD detection. fastText models have competitive results when compared to several baseline methods. Also, fastText models are shallow in nature and have the advantage of being faster in training and evaluation, by several orders of magnitude, compared to deep models.

Alzheimer's dementia: Performance on the Mini-Mental State Examination.

1987 ◽  
Vol 55 (1) ◽  
pp. 96-100 ◽  
Author(s):  
Evelyn L. Teng ◽  
Helena C. Chui ◽  
Lon S. Schneider ◽  
Laura E. Metzger
Keyword(s):  
Mental State ◽  
Mini Mental State Examination ◽  
Alzheimer’S Dementia ◽  
Alzheimer's Dementia ◽  
State Examination

The Mini-Cog versus the Mini-Mental State Examination and the Clock Drawing Test in daily clinical practice: screening value in a German Memory Clinic

2011 ◽  
Vol 24 (5) ◽  
pp. 766-774 ◽  
Author(s):  
Monika Milian ◽  
Anna-Maria Leiherr ◽  
Guido Straten ◽  
Stephan Müller ◽  
Thomas Leyhe ◽  
...  
Keyword(s):  
Mental State ◽  
Mini Mental State Examination ◽  
Alzheimer’S Dementia ◽  
Discriminatory Power ◽  
Memory Clinic ◽  
Alzheimer's Dementia ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test ◽  
State Examination

ABSTRACTBackground: The aim of this study was to compare the screening value of the Mini-Cog, Clock Drawing Test (CDT), Mini-Mental State Examination (MMSE) and the algorithm MMSE and/or CDT to separate elderly people with dementia from healthy depending on test time, type and severity of dementia, and demographic variables in a German Memory Clinic.Methods: Data from a heterogeneous patient sample and healthy participants (n = 502) were retrospectively analyzed. Of the 438 patients with dementia, 49.1% of the dementia diagnoses were Alzheimer's dementia and 50.9% were non-Alzheimer's dementia. Sixty-four participants were classified as cognitively unimpaired. The CDT and an extraction of the 3-item recall of the MMSE were used to constitute the Mini-Cog algorithm.Results: Overall, the Mini-Cog showed significantly higher discriminatory power (86.8%) than the MMSE (72.6% at a cut-off ≤ 24 and 79.2% at ≤ 25, respectively) and CDT (78.1%) (each p < 0.01) and did not perform worse than the algorithm MMSE and/or CDT (each p > 0.05). The specificity of the Mini-Cog (100.0%) was similar to that of the MMSE (100.0% for both cut-offs) and CDT (96.9%) (p = 0.154). For all age and educational groups the Mini-Cog outmatched the CDT and MMSE, and was less affected by education than MMSE and less susceptible for the dementia stage than the CDT.Conclusion: The Mini-Cog proved to have superior discriminatory power than either CDT or MMSE and is demonstrated to be a valid “short” screening instrument taking 3 to 4 minutes to administer in the geriatric setting.

scholarly journals Isoprostanes as Biomarker for White Matter Injury in Extremely Preterm Infants

2021 ◽  
Vol 8 ◽  
Author(s):  
Caterina Coviello ◽  
Serafina Perrone ◽  
Giuseppe Buonocore ◽  
Simona Negro ◽  
Mariangela Longini ◽  
...  
Keyword(s):  
Lipid Peroxidation ◽  
White Matter ◽  
Preterm Infants ◽  
Neurodevelopmental Outcome ◽  
Area Under The Curve ◽  
White Matter Injury ◽  
Roc Curve Analysis ◽  
Extremely Preterm ◽  
Extremely Preterm Infants ◽  
Early Biomarker

Background and Aim: Preterm white matter is vulnerable to lipid peroxidation-mediated injury. F2-isoprostanes (IPs), are a useful biomarker for lipid peroxidation. Aim was to assess the association between early peri-postnatal IPs, white matter injury (WMI) at term equivalent age (TEA), and neurodevelopmental outcome in preterm infants.Methods: Infants with a gestational age (GA) below 28 weeks who had an MRI at TEA were included. IPs were measured in cord blood (cb) at birth and on plasma (pl) between 24 and 48 h after birth. WMI was assessed using Woodward MRI scoring system. Multiple regression analyses were performed to assess the association between IPs with WMI and then with BSITD-III scores at 24 months corrected age (CA). Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of pl-IPs for the development of WMI.Results: Forty-four patients were included. cb-IPs were not correlated with WMI score at TEA, whereas higher pl-IPs and lower GA predicted higher WMI score (p = 0.037 and 0.006, respectively) after controlling for GA, FiO2 at sampling and severity of IVH. The area under the curve was 0.72 (CI 95% = 0.51–0.92). The pl-IPs levels plotted curve indicated that 31.8 pg/ml had the best predictive threshold with a sensitivity of 86% and a specificity of 60%, to discriminate newborns with any WMI from newborns without WMI. IPs were not associated with outcome at 24 months.Conclusion: Early measurement of pl-IPs may help discriminate patients showing abnormal WMI score at TEA, thus representing an early biomarker to identify newborns at risk for brain injury.

scholarly journals A novel olfactory threshold test for screening cognitive decline among elderly people

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254357
Author(s):  
Sun Mi Kim ◽  
Hye Ri Kim ◽  
Hyun Jin Min ◽  
Kyung Soo Kim ◽  
Jae-Chan Jin ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Elderly People ◽  
Area Under The Curve ◽  
Disease Assessment ◽  
Olfactory Threshold ◽  
Screening Process ◽  
Threshold Test ◽  
Neurocognitive Tests ◽  
Function Test ◽  
Early Biomarker

Olfactory impairment is associated with dementia and is a potential early biomarker of cognitive decline. We developed a novel olfactory threshold test called Sniff Bubble using rose odor-containing beads made with 2-phenylethyl alcohol. We aimed to define cut-off scores for this tool to help identify cognitive decline among elderly people. In total, 162 elderly people (mean age ± SD: 73.04 ± 8.73 years) were administered olfactory threshold and neurocognitive tests. For analyses, we divided the participants into two groups based on cognitive functioning, namely cognitive decline (n = 44) and normal cognition (n = 118) groups. The Sniff Bubble and YSK olfactory function test for olfactory threshold and the Structured Clinical Interview for DSM-5 Disorders-Clinician Version and Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease assessment packet for neurocognitive functioning were used. We used K-means cluster analyses and receiver operating characteristic (ROC) analyses to identify the most appropriate cut-off value. We established a positive correlation between the Sniff Bubble and neurocognitive function test scores (r = 0.431, p < 0.001). We defined the cut-off score, using the ROC curve analyses for Sniff Bubble scores, at 3 and higher with an area under the curve of 0.759 (p < 0.001). The Sniff Bubble test can adequately detect cognitive decline in elderly people and may be used clinically as the first step in the screening process.

Sign in / Sign up

Export Citation Format

Share Document