Query: Cognitive diagnosis model

title: Diagnosis independent Alzheimer disease biomarker signature in cognitively normal elderly people.
abstract: To identify biomarker patterns typical for Alzheimer disease (AD) in an independent, unsupervised way, without using information on the clinical diagnosis.  Mixture modeling approach.  Alzheimer's Disease Neuroimaging Initiative database.  Cognitively normal persons, patients with AD, and individuals with mild cognitive impairment.  Cerebrospinal fluid derived beta amyloid protein 1 42, total tau protein, and phosphorylated tau(181P) protein concentrations were used as biomarkers on a clinically well characterized data set. The outcome of the qualification analysis was validated on 2 additional data sets, 1 of which was autopsy confirmed.  Using the US Alzheimer's Disease Neuroimaging Initiative data set, a cerebrospinal fluid beta amyloid protein 1 42/phosphorylated tau(181P) biomarker mixture model identified 1 feature linked to AD, while the other matched the "healthy" status. The AD signature was found in 90% 72% and 36% of patients in the AD, mild cognitive impairment, and cognitively normal groups, respectively. The cognitively normal group with the AD signature was enriched in apolipoprotein E epsilon4 allele carriers. Results were validated on 2 other data sets. In 1 study consisting of 68 autopsy confirmed AD cases, 64 of 68 patients (94% sensitivity) were correctly classified with the AD feature. In another data set with patients (n 57) with mild cognitive impairment followed up for 5 years, the model showed a sensitivity of 100% in patients progressing to AD.  The mixture modeling approach, totally independent of clinical AD diagnosis, correctly classified patients with AD. The unexpected presence of the AD signature in more than one third of cognitively normal subjects suggests that AD pathology is active and detectable earlier than has heretofore been envisioned.

title: Measurement of psychological disorders using cognitive diagnosis models.
abstract: Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article presents the development of a new cognitive diagnosis model for use in psychological assessment the DINO (deterministic input; noisy "or" gate) model which, as an illustrative example, is applied to evaluate and diagnose pathological gamblers. As part of this example, a demonstration of the estimates obtained by cognitive diagnosis models is provided. Such estimates include the probability an individual meets each of a set of dichotomous Diagnostic and Statistical Manual of Mental Disorders (text revision [DSM IV TR] American Psychiatric Association, 2000) criteria, resulting in an estimate of the probability an individual meets the DSM IV TR definition for being a pathological gambler. Furthermore, a demonstration of how the hypothesized underlying factors contributing to pathological gambling can be measured with the DINO model is presented, through use of a covariance structure model for the tetrachoric correlation matrix of the dichotomous latent variables representing DSM IV TR criteria.

title: The trajectory of gait speed preceding mild cognitive impairment.
abstract: To compare the trajectory of motor decline, as measured by gait speed and finger tapping speed, between elderly people who developed mild cognitive impairment (MCI) and those who remained cognitively intact. We also sought to determine the approximate time at which the decline in motor function accelerated in persons who developed MCI.  Longitudinal cohort study.  Participants were 204 healthy seniors (57.8% women) from the Oregon Brain Aging Study evaluated for up to 20 years using annual neurologic, neuropsychological, and motor examinations.  The pattern of motor decline with aging was compared using a mixed effects model with an interaction term for age and a clinical diagnosis of MCI. The time before diagnosis of MCI, when the change in gait or finger tapping speed accelerates, was assessed using a mixed effects model with a change point for men and women, separately and combined, who developed MCI.  The rates of change, with aging, in gait speed (P .001) and finger tapping speed in the dominant hand (P .003) and nondominant hand (P .001) were significantly different between participants who developed MCI (converters) and those who did not (nonconverters) Using a change point analysis for MCI converters, the decrease in gait speed accelerated by 0.023 m/s/y (P .001) occurring 12.1 years before the onset of MCI. An acceleration in gait speed decline occurred earlier in men than women. For tapping speed, the change point occurred after the onset of MCI for both dominant and nondominant hands when men and women were combined.  Motor decline as indexed by gait speed accelerates up to 12 years before MCI. Longitudinal changes in motor function may be useful in the early detection of dementia during preclinical stages, when the utility of disease modifying therapies would be greatest.

title: Latent feature representation with stacked auto encoder for AD/MCI diagnosis
abstract: Recently, there have been great interests for computer aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI) Unlike the previous methods that considered simple low level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning based latent feature representation with a stacked auto encoder (SAE) We believe that there exist latent non linear complicated patterns inherent in the low level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre training in deep learning, we can benefit from the target unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine tuning with the target related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC) MCI vs. HC, AD vs. MCI, and MCI converter (MCI C) vs. MCI non converter (MCI NC) In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 for AD/HC, MCI/HC, AD/MCI, and MCI C/MCI NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

title: Neuroimaging markers for the prediction and early diagnosis of Alzheimer's disease dementia
abstract: Alzheimer's disease (AD) is a progressive age related neurodegenerative disease. At the time of clinical manifestation of dementia, significant irreversible brain damage is already present, rendering the diagnosis of AD at early stages of the disease an urgent prerequisite for therapeutic treatment to halt, or at least slow, disease progression. In this review, we discuss various neuroimaging measures that are proving to have potential value as biomarkers of AD pathology for the detection and prediction of AD before the onset of dementia. Recent studies that have identified AD like structural and functional brain changes in elderly people who are cognitively within the normal range or who have mild cognitive impairment (MCI) are discussed. A dynamic sequence model of changes that occur in neuroimaging markers during the different disease stages is presented and the predictive value of multimodal neuroimaging for AD dementia is considered.

title: Combining MRI and CSF measures for classification of Alzheimer's disease and prediction of mild cognitive impairment conversion
abstract: The suggested revision of the NINCDS ADRDA criterion for the diagnosis of Alzheimer's disease (AD) includes at least one abnormal biomarker among magnetic resonance imaging (MRI) positron emission tomography (PET) and cerebrospinal fluid (CSF) We aimed to investigate if the combination of baseline MRI and CSF could enhance the classification of AD compared to using either alone and predict mild cognitive impairment (MCI) conversion at multiple future time points. 369 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI) were included in the study (AD=96, MCI=162 and CTL=111) Freesurfer was used to generate regional subcortical volumes and cortical thickness measures. A total of 60 variables were used for orthogonal partial least squares to latent structures (OPLS) multivariate analysis (57 MRI measures and 3 CSF measures: Ab(42) t tau and p tau) Combining MRI and CSF gave the best results for distinguishing AD vs. CTL. We found an accuracy of 91.8% for the combined model at baseline compared to 81.6% for CSF measures and 87.0% for MRI measures alone. The combined model also gave the best accuracy when distinguishing between MCI vs. CTL (77.6% at baseline. MCI subjects who converted to AD by 12 and 18month follow up were accurately predicted at baseline using an AD vs. CTL model (82.9% and 86.4% respectively) with lower prediction accuracies for those MCI subjects converting by 24 and 36month follow up (75.4% and 68.0% respectively) The overall prediction accuracies for converters and non converters ranged from 58.6% to 66.4% at different time points. Combining MRI and CSF measures in a multivariate model at baseline gave better accuracy for discriminating between AD and CTL, between MCI and CTL and for predicting future conversion from MCI to AD, than using either MRI or CSF separately.

title: Characterization of the 3xTg AD mouse model of Alzheimer's disease: Part 2. Behavioral and cognitive changes
abstract: Alzheimer's disease (AD) is characterized by distinct behavioral and cognitive deficits that differ from those observed in normal aging. Transgenic models of AD are a promising tool in understanding the underlying mechanisms and cause of disease. The triple transgenic mouse model of AD (3xTg AD) is the only model to exhibit both Abeta and tau pathology that is characteristic of the human form. The present study characterized the performance of 3xTg AD mice on several tasks measuring behavioral and cognitive ability. Aged 3xTg AD females exhibited a higher level of fear and anxiety demonstrated by increased restlessness, startle responses, and freezing behaviors. No differences were observed in muscle strength and visuo motor coordination. Understanding the behavioral manifestations that occur in this model of AD may aid in the early diagnosis and appropriate treatment of AD symptomology.

title: A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer's disease and mild cognitive impairment
abstract: Population aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer's Disease (AD) have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI) Bayesian networks are well suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer's Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil) The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis.

title: Subjective cognitive concerns and neuropsychiatric predictors of progression to the early clinical stages of Alzheimer disease.
abstract: To examine neuropsychiatric and neuropsychological predictors of progression from normal to early clinical stages of Alzheimer disease (AD) METHODS From a total sample of 559 older adults from the Massachusetts Alzheimer's Disease Research Center longitudinal cohort, 454 were included in the primary analysis: 283 with clinically normal cognition (CN) 115 with mild cognitive impairment (MCI) and 56 with subjective cognitive concerns (SCC) but no objective impairment, a proposed transitional group between CN and MCI. Two latent cognitive factors (memory semantic, attention executive) and two neuropsychiatric factors (affective, psychotic) were derived from the Alzheimer's Disease Centers' Uniform Data Set neuropsychological battery and Neuropsychiatric Inventory brief questionnaire. Factors were analyzed as predictors of time to progression to a worse diagnosis using a Cox proportional hazards regression model with backward elimination. Covariates included baseline diagnosis, gender, age, education, prior depression, antidepressant medication, symptom duration, and interaction terms.  Higher/better memory semantic factor score predicted lower hazard of progression (hazard ratio [HR] 0.4 for 1 standard deviation [SD] increase, p <0.0001) and higher/worse affective factor score predicted higher hazard (HR 1.3 for one SD increase, p 0.01) No other predictors were significant in adjusted analyses. Using diagnosis as a sole predictor of transition to MCI, the SCC diagnosis carried a fourfold risk of progression compared with CN (HR 4.1, p <0.0001) CONCLUSION These results identify affective and memory semantic factors as significant predictors of more rapid progression from normal to early stages of cognitive decline and highlight the subgroup of cognitively normal elderly with SCC as those with elevated risk of progression to MCI.

title: Neurofeedback and Cognitive Attention Training for Children with Attention Deficit Hyperactivity Disorder in Schools
abstract: To evaluate the efficacy of 2 computer attention training systems administered in school for children with attention deficit hyperactivity disorder (ADHD)  Children in second and fourth grade with a diagnosis of ADHD (n 104) were randomly assigned to neurofeedback (NF) (n 34) cognitive training (CT) (n 34) or control (n 36) conditions. A 2 point growth model assessed change from pre post intervention on parent reports (Conners 3 Parent [ Rating Inventory of Executive Function [BRIEF] rating scale) teacher reports (Swanson, Kotkin, Agler, M Flynn and Pelham scale [SKAMP] Conners 3 Teacher [Conners 3 T] and systematic classroom observations (Behavioral Observation of Students in Schools [BOSS] Paired t tests and an analysis of covariance assessed change in medication.  Children who received NF showed significant improvement compared with those in the control condition on the Conners 3 P Attention, Executive Functioning and Global Index, on all BRIEF summary indices, and on BOSS motor/verbal off task behavior. Children who received CT showed no improvement compared to the control condition. Children in the NF condition showed significant improvements compared to those in the CT condition on Conners 3 P Executive Functioning, all BRIEF summary indices, SKAMP Attention, and Conners 3 T Inattention subscales. Stimulant medication dosage in methylphenidate equivalencies significantly increased for children in the CT (8.54 mg) and control (7.05 mg) conditions but not for those in the NF condition (0.29 mg)  Neurofeedback made greater improvements in ADHD symptoms compared to both the control and CT conditions. Thus, NF is a promising attention training treatment intervention for children with ADHD.