Differentiating Alzheimer’s Disease and Frontotemporal Dementia: Receiver Operator Characteristic Curve Analysis of Four Rating Scales

1998 ◽  
Vol 9 (3) ◽  
pp. 164-174 ◽  
Author(s):  
W. Michael Hooten ◽  
Constantine G. Lyketsos
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Rating Scales ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Curve Analysis ◽  
Receiver Operator Characteristic

scholarly journals Toward operationalizing deficient emotional self-regulation in newly referred adults with ADHD: A receiver operator characteristic curve analysis

2020 ◽  
Vol 63 (1) ◽  
Author(s):  
Joseph Biederman ◽  
Maura DiSalvo ◽  
K. Yvonne Woodworth ◽  
Ronna Fried ◽  
Mai Uchida ◽  
...  
Keyword(s):  
Adult Adhd ◽  
Rating Scales ◽  
Characteristic Curve ◽  
Self Regulation ◽  
Autistic Traits ◽  
Executive Dysfunction ◽  
Receiver Operator Characteristic Curve ◽  
Emotional Dysregulation ◽  
Receiver Operator Characteristic ◽  
Low Level

Abstract Background. A growing body of research suggests that deficient emotional self-regulation (DESR) is common and morbid among attention-deficit/hyperactivity disorder (ADHD) patients. The main aim of the present study was to assess whether high and low levels of DESR in adult ADHD patients can be operationalized and whether they are clinically useful. Methods. A total of 441 newly referred 18- to 55-year-old adults of both sexes with Diagnostic and Statistical Manual of Mental Disorders: Fifth Edition (DSM-5) ADHD completed self-reported rating scales. We operationalized DESR using items from the Barkley Current Behavior Scale. We used receiver operator characteristic (ROC) curves to identify the optimal cut-off on the Barkley Emotional Dysregulation (ED) Scale to categorize patients as having high- versus low-level DESR and compared demographic and clinical characteristics between the groups. Results. We averaged the optimal Barkley ED Scale cut-points from the ROC curve analyses across all subscales and categorized ADHD patients as having high- (N = 191) or low-level (N = 250) DESR (total Barkley ED Scale score ≥8 or <8, respectively). Those with high-level DESR had significantly more severe symptoms of ADHD, executive dysfunction, autistic traits, levels of psychopathology, and worse quality of life compared with those with low-level DESR. There were no major differences in outcomes among medicated and unmedicated patients. Conclusions. High levels of DESR are common in adults with ADHD and when present represent a burdensome source of added morbidity and disability worthy of further clinical and scientific attention.

scholarly journals A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease

2019 ◽  
Vol 5 (2) ◽  
pp. eaau7220 ◽  
Author(s):  
Nicholas J. Ashton ◽  
Alejo J. Nevado-Holgado ◽  
Imelda S. Barber ◽  
Steven Lynham ◽  
Veer Gupta ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Resolution Mass Spectrometry ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Feature Model ◽  
Therapeutic Trials ◽  
Positron Emission ◽  
Proteomic Study ◽  
Peptide Fractionation

A blood-based assessment of preclinical disease would have huge potential in the enrichment of participants for Alzheimer’s disease (AD) therapeutic trials. In this study, cognitively unimpaired individuals from the AIBL and KARVIAH cohorts were defined as Aβ negative or Aβ positive by positron emission tomography. Nontargeted proteomic analysis that incorporated peptide fractionation and high-resolution mass spectrometry quantified relative protein abundances in plasma samples from all participants. A protein classifier model was trained to predict Aβ-positive participants using feature selection and machine learning in AIBL and independently assessed in KARVIAH. A 12-feature model for predicting Aβ-positive participants was established and demonstrated high accuracy (testing area under the receiver operator characteristic curve = 0.891, sensitivity = 0.78, and specificity = 0.77). This extensive plasma proteomic study has unbiasedly highlighted putative and novel candidates for AD pathology that should be further validated with automated methodologies.

Distinct social-behavioral changes in frontotemporal dementia and early onset Alzheimer's disease

2014 ◽  
Author(s):  
Joseph P. Barsuglia ◽  
Michelle J. Mather ◽  
Hemali V. Panchal ◽  
Aditi Joshi ◽  
Elvira Jimenez ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Frontotemporal Dementia ◽  
Early Onset ◽  
Behavioral Changes ◽  
Early Onset Alzheimer’S Disease

Identify Compounds' Target Against Alzheimer's Disease Based on In-Silico Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 193-208 ◽  
Author(s):  
Yan Hu ◽  
Guangya Zhou ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Qin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Small Molecule ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Learner Model ◽  
Mao B ◽  
Number Of Patients ◽  
Sar Model ◽  
Set Up

Background: Alzheimer's disease swept every corner of the globe and the number of patients worldwide has been rising. At present, there are as many as 30 million people with Alzheimer's disease in the world, and it is expected to exceed 80 million people by 2050. Consequently, the study of Alzheimer’s drugs has become one of the most popular medical topics. Methods: In this study, in order to build a predicting model for Alzheimer’s drugs and targets, the attribute discriminators CfsSubsetEval, ConsistencySubsetEval and FilteredSubsetEval are combined with search methods such as BestFirst, GeneticSearch and Greedystepwise to filter the molecular descriptors. Then the machine learning algorithms such as BayesNet, SVM, KNN and C4.5 are used to construct the 2D-Structure Activity Relationship(2D-SAR) model. Its modeling results are utilized for Receiver Operating Characteristic curve(ROC) analysis. Results: The prediction rates of correctness using Randomforest for AChE, BChE, MAO-B, BACE1, Tau protein and Non-inhibitor are 77.0%, 79.1%, 100.0%, 94.2%, 93.2% and 94.9%, respectively, which are overwhelming as compared to those of BayesNet, BP, SVM, KNN, AdaBoost and C4.5. Conclusion: In this paper, we conclude that Random Forest is the best learner model for the prediction of Alzheimer’s drugs and targets. Besides, we set up an online server to predict whether a small molecule is the inhibitor of Alzheimer's target at http://47.106.158.30:8080/AD/. Furthermore, it can distinguish the target protein of a small molecule.

Easy Screening for Mild Alzheimer's Disease and Mild Cognitive Impairment from Elderly Speech

2018 ◽  
Vol 15 (2) ◽  
pp. 104-110 ◽  
Author(s):  
Shohei Kato ◽  
Akira Homma ◽  
Takuto Sakuma
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Characteristic Curve ◽  
Linear Regression Analysis ◽  
Speech Sound ◽  
The Elderly ◽  
Speech Sounds ◽  
Mel Frequency Cepstral Coefficients

Objective: This study presents a novel approach for early detection of cognitive impairment in the elderly. The approach incorporates the use of speech sound analysis, multivariate statistics, and data-mining techniques. We have developed a speech prosody-based cognitive impairment rating (SPCIR) that can distinguish between cognitively normal controls and elderly people with mild Alzheimer's disease (mAD) or mild cognitive impairment (MCI) using prosodic signals extracted from elderly speech while administering a questionnaire. Two hundred and seventy-three Japanese subjects (73 males and 200 females between the ages of 65 and 96) participated in this study. The authors collected speech sounds from segments of dialogue during a revised Hasegawa's dementia scale (HDS-R) examination and talking about topics related to hometown, childhood, and school. The segments correspond to speech sounds from answers to questions regarding birthdate (T1), the name of the subject's elementary school (T2), time orientation (Q2), and repetition of three-digit numbers backward (Q6). As many prosodic features as possible were extracted from each of the speech sounds, including fundamental frequency, formant, and intensity features and mel-frequency cepstral coefficients. They were refined using principal component analysis and/or feature selection. The authors calculated an SPCIR using multiple linear regression analysis. Conclusion: In addition, this study proposes a binary discrimination model of SPCIR using multivariate logistic regression and model selection with receiver operating characteristic curve analysis and reports on the sensitivity and specificity of SPCIR for diagnosis (control vs. MCI/mAD). The study also reports discriminative performances well, thereby suggesting that the proposed approach might be an effective tool for screening the elderly for mAD and MCI.

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