Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification.

INTRODUCTION: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. METHODS: 1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). RESULTS: Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. CONCLUSIONS: The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.

A Transformer Approach for Cognitive Impairment Classification and Prediction.

INTRODUCTION: Early classification and prediction of Alzheimer disease (AD) and amnestic mild cognitive impairment (aMCI) with noninvasive approaches is a long-standing challenge. This challenge is further exacerbated by the sparsity of data needed for modeling. Deep learning methods offer a novel method to help address these challenging multiclass classification and prediction problems. METHODS: We analyzed 3 target feature-sets from the National Alzheimer Coordinating Center (NACC) dataset: (1) neuropsychological (cognitive) data; (2) patient health history data; and (3) the combination of both sets. We used a masked Transformer-encoder without further feature selection to classify the samples on cognitive status (no cognitive impairment, aMCI, AD)-dynamically ignoring unavailable features. We then fine-tuned the model to predict the participants' future diagnosis in 1 to 3 years. We analyzed the sensitivity of the model to input features via Feature Permutation Importance. RESULTS: We demonstrated (1) the masked Transformer-encoder was able to perform prediction with sparse input data; (2) high multiclass current cognitive status classification accuracy (87% control, 79% aMCI, 89% AD); (3) acceptable results for 1- to 3-year multiclass future cognitive status prediction (83% control, 77% aMCI, 91% AD). CONCLUSION: The flexibility of our methods in handling inconsistent data provides a new venue for the analysis of cognitive status data.

Classification of Mild Cognitive Impairment and Alzheimer's Disease Using Manual Motor Measures.

INTRODUCTION: Manual motor problems have been reported in mild cognitive impairment (MCI) and Alzheimer's disease (AD), but the specific aspects that are affected, their neuropathology, and potential value for classification modeling is unknown. The current study examined if multiple measures of motor strength, dexterity, and speed are affected in MCI and AD, related to AD biomarkers, and are able to classify MCI or AD. METHODS: Fifty-three cognitively normal (CN), 33 amnestic MCI, and 28 AD subjects completed five manual motor measures: grip force, Trail Making Test A, spiral tracing, finger tapping, and a simulated feeding task. Analyses included (1) group differences in manual performance; (2) associations between manual function and AD biomarkers (PET amyloid ß, hippocampal volume, and APOE ε4 alleles); and (3) group classification accuracy of manual motor function using machine learning. RESULTS: Amnestic MCI and AD subjects exhibited slower psychomotor speed and AD subjects had weaker dominant hand grip strength than CN subjects. Performance on these measures was related to amyloid ß deposition (both) and hippocampal volume (psychomotor speed only). Support vector classification well-discriminated control and AD subjects (area under the curve of 0.73 and 0.77, respectively) but poorly discriminated MCI from controls or AD. CONCLUSION: Grip strength and spiral tracing appear preserved, while psychomotor speed is affected in amnestic MCI and AD. The association of motor performance with amyloid ß deposition and atrophy could indicate that this is due to amyloid deposition in and atrophy of motor brain regions, which generally occurs later in the disease process. The promising discriminatory abilities of manual motor measures for AD emphasize their value alongside other cognitive and motor assessment outcomes in classification and prediction models, as well as potential enrichment of outcome variables in AD clinical trials.

[Classification of mild cognitive impairment and normal cognition using an automated voice-based testing application].

AIM: An easy-to-use tool that can detect cognitive decline in mild cognitive impairment (MCI) is required. In this study, we aimed to construct a machine learning model that discriminates between MCI and cognitively normal (CN) individuals using spoken answers to questions and speech features. METHODS: Participants of ≥50 years of age were recruited from the Silver Human Resource Center. The Japanese Version of the Mini-Mental State Examination (MMSE-J) and Clinical Dementia Rating (CDR) were used to obtain clinical information. We developed a research application that presented neuropsychological tasks via automated voice guidance and collected the participants' spoken answers. The neuropsychological tasks included time orientation, sentence memory tasks (immediate and delayed recall), and digit span memory-updating tasks. Scores and speech features were obtained from spoken answers. Subsequently, a machine learning model was constructed to classify MCI and CN using various classifiers, combining the participants' age, gender, scores, and speech features. RESULTS: We obtained a model using Gaussian Naive Bayes, which classified typical MCI (CDR 0.5, MMSE ≤26) and typical CN (CDR 0 and MMSE ≥29) with an area under the curve (AUC) of 0.866 (accuracy 0.75, sensitivity 0.857, specificity 0.712). CONCLUSIONS: We built a machine learning model that can classify MCI and CN using spoken answers to neuropsychological questions. Easy-to-use MCI detection tools could be developed by incorporating this model into smartphone applications and telephone services.

NIA-AA Alzheimer's Disease Framework: Clinical Characterization of Stages.

BACKGROUND: To operationalize the National Institute on Aging - Alzheimer's Association (NIA-AA) Research Framework for Alzheimer's Disease 6-stage continuum of clinical progression for persons with abnormal amyloid. METHODS: The Mayo Clinic Study of Aging is a population-based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We evaluated persons without dementia having 3 consecutive clinical visits. Measures for cross-sectional categories included objective cognitive impairment (OBJ) and function (FXN). Measures for change included subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (ΔNBS). We calculated frequencies of the stages using different cutoff points and assessed stability of the stages over 15 months. RESULTS: Among 243 abnormal amyloid participants, the frequencies of the stages varied with age: 66 to 90% were classified as stage 1 at age 50 but at age 80, 24 to 36% were stage 1, 32 to 47% were stage 2, 18 to 27% were stage 3, 1 to 3% were stage 4 to 6, and 3 to 9% were indeterminate. Most stage 2 participants were classified as stage 2 because of abnormal ΔOBJ only (44-59%), whereas 11 to 21% had SCD only, and 9 to 13% had ΔNBS only. Short-term stability varied by stage and OBJ cutoff points but the most notable changes were seen in stage 2 with 38 to 63% remaining stable, 4 to 13% worsening, and 24 to 41% improving (moving to stage 1). INTERPRETATION: The frequency of the stages varied by age and the precise membership fluctuated by the parameters used to define the stages. The staging framework may require revisions before it can be adopted for clinical trials. ANN NEUROL 2021;89:1145-1156.

Harmonizing neuropsychological assessment for mild neurocognitive disorders in Europe.

INTRODUCTION: Harmonized neuropsychological assessment for neurocognitive disorders, an international priority for valid and reliable diagnostic procedures, has been achieved only in specific countries or research contexts. METHODS: To harmonize the assessment of mild cognitive impairment in Europe, a workshop (Geneva, May 2018) convened stakeholders, methodologists, academic, and non-academic clinicians and experts from European, US, and Australian harmonization initiatives. RESULTS: With formal presentations and thematic working-groups we defined a standard battery consistent with the U.S. Uniform DataSet, version 3, and homogeneous methodology to obtain consistent normative data across tests and languages. Adaptations consist of including two tests specific to typical Alzheimer's disease and behavioral variant frontotemporal dementia. The methodology for harmonized normative data includes consensus definition of cognitively normal controls, classification of confounding factors (age, sex, and education), and calculation of minimum sample sizes. DISCUSSION: This expert consensus allows harmonizing the diagnosis of neurocognitive disorders across European countries and possibly beyond.

Predicting Mental Decline Rates in Mild Cognitive Impairment From Baseline MRI Volumetric Data.

PURPOSE: In mild cognitive impairment (MCI), identifying individuals at high risk for progressive cognitive deterioration can be useful for prognostication and intervention. This study quantitatively characterizes cognitive decline rates in MCI and tests whether volumetric data from baseline magnetic resonance imaging (MRI) can predict accelerated cognitive decline. METHODS: The authors retrospectively examined Alzheimer Disease Neuroimaging Initiative data to obtain serial Mini-Mental Status Exam (MMSE) scores, diagnoses, and the following baseline MRI volumes: total intracranial volume, whole-brain and ventricular volumes, and volumes of the hippocampus, entorhinal cortex, fusiform gyrus, and medial temporal lobe. Subjects with <24 months or <4 measurements of MMSE data were excluded. Predictive modeling of fast cognitive decline (defined as >0.6/year) from baseline volumetric data was performed on subjects with MCI using a single hidden layer neural network. RESULTS: Among 698 baseline MCI subjects, the median annual decline in the MMSE score was 1.3 for converters to dementia versus 0.11 for stable MCI (P<0.001). A 0.6/year threshold captured dementia conversion with 82% accuracy (sensitivity 79%, specificity 85%, area under the receiver operating characteristic curve 0.88). Regional volumes on baseline MRI predicted fast cognitive decline with a test accuracy of 71%. DISCUSSION: An MMSE score decrease of >0.6/year is associated with MCI-to-dementia conversion and can be predicted from baseline MRI.

Data-driven identification of subtypes of executive function across typical development, attention deficit hyperactivity disorder, and autism spectrum disorders.

BACKGROUND: Impairment of executive function (EF), the goal-directed regulation of thoughts, actions, and emotions, drives negative outcomes and is common across neurodevelopmental disorders including attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). A primary challenge to its amelioration is heterogeneity in symptom expression within and across disorders. Parsing this heterogeneity is necessary to attain diagnostic precision, a goal of the NIMH Research Domain Criteria Initiative. We aimed to identify transdiagnostic subtypes of EF that span the normal to impaired spectrum and establish their predictive and neurobiological validity. METHODS: Community detection was applied to clinical parent-report measures in 8-14-year-old children with and without ADHD and ASD from two independent cohorts (discovery N = 320; replication N = 692) to identify subgroups with distinct behavioral profiles. Support vector machine (SVM) classification was used to predict subgroup membership of unseen cases. Preliminary neurobiological validation was obtained with existing functional magnetic resonance imaging (fMRI) data on a subsample (N = 84) by testing hypotheses about sensitivity of EF subgroups versus DSM categories. RESULTS: We observed three transdiagnostic EF subtypes characterized by behavioral profiles that were defined by relative weakness in: (a) flexibility and emotion regulation; (b) inhibition; and (c) working memory, organization, and planning. The same tripartite structure was also present in the typically developing children. SVM trained on the discovery sample and tested on the replication sample classified subgroup membership with 77.0% accuracy. Split-half SVM classification on the combined sample (N = 1,012) yielded 88.9% accuracy (this SVM is available for public use). As hypothesized, frontal-parietal engagement was better distinguished by EF subtype than DSM diagnosis and the subgroup characterized with inflexibility failed to modulate right IPL activation in response to increased executive demands. CONCLUSIONS: The observed transdiagnostic subtypes refine current diagnostic nosology and augment clinical decision-making for personalizing treatment of executive dysfunction in children.

Profiles of Normal Cognition in Essential Tremor.

OBJECTIVES: Patients with essential tremor exhibit heterogeneous cognitive functioning. Although the majority of patients fall under the broad classification of cognitively "normal," essential tremor is associated with increased risk for mild cognitive impairment and dementia. It is possible that patterns of cognitive performance within the wide range of normal functioning have predictive utility for mild cognitive impairment or dementia. These cross-sectional analyses sought to determine whether cognitive patterns, or "clusters," could be identified among individuals with essential tremor diagnosed as cognitively normal. We also determined whether such clusters, if identified, were associated with demographic or clinical characteristics of patients. METHODS: Elderly subjects with essential tremor (age >55 years) underwent comprehensive neuropsychological testing. Domain means (memory, executive function, attention, visuospatial abilities, and language) from 148 individuals diagnosed as cognitively normal were partitioned using k-means cluster analysis. Individuals in each cluster were compared according to cognitive functioning (domain means and test scores), demographic factors, and clinical variables. RESULTS: There were three clusters. Cluster 1 (n = 64) was characterized by comparatively low memory scores (p < .001), Cluster 2 (n = 39) had relatively low attention and visuospatial scores (p < .001), and Cluster 3 (n = 45) exhibited consistently high performance across all domains. Cluster 1 had lower Montreal Cognitive Assessment scores and reported more prescription medication use and lower balance confidence. CONCLUSIONS: Three patterns of cognitive functioning within the normal range were evident and tracked with certain clinical features. Future work will examine the extent to which such patterns predict conversion to mild cognitive impairment and/or dementia.

Association Between Neuropsychiatric Symptom Trajectory and Conversion to Alzheimer Disease.

INTRODUCTION: Neuropsychiatric symptoms (NPS) are both common in mild cognitive impairment and Alzheimer disease (AD). Studies have shown that some NPS such as apathy and depression are a key indicator for progression to AD. METHODS: We compared Neuropsychiatric Inventory (NPI) total score and NPI subdomain score between mild cognitive impairment-converters (MCI-C) and mild cognitive impairment-nonconverters (MCI-NC) longitudinally for 6 years using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In addition to the NPI, Mini-Mental State Examination (MMSE) scores were also compared to find out if MMSE scores would differ between different NPI groups. Lastly, a linear regression model was done on MMSE and NPI total score to establish a relationship between MMSE and NPI total score. RESULTS: The results in this study showed that NPI total scores between MCI-C and MCI-NC differed significantly throughout 6 years. MCI-C subjects had a higher mean NPI total score and lower MMSE score compared with MCI-NC subjects. In addition, MMSE scores were significantly different between the 3 groups of NPI total score. Subjects who have a high NPI score have the lowest mean MMSE score, thus demonstrating that NPI scores do indeed affect MMSE scores. Further analyses using a regression model revealed that a unit change in NPI total score lead to 0.1 to 0.3 decrease in MMSE. DISCUSSION: On the basis of the findings, this study showed evidence that increase in NPS burden (reflected by increase in NPI) over time predicts conversion to AD, whereas stability of symptoms (reflected by stable NPI score) favors nonconversion. Further study should investigate the underlying mechanisms that drive both NPS burden and cognitive decline.

Cognitive reserve and working memory in cognitive performance of adults with subjective cognitive complaints: longitudinal structural equation modeling.

OBJECTIVE: To study the influence of cognitive reserve (CR) on cognitive performance of individuals with subjective cognitive complaints (SCCs) within a period of 36 months. DESIGN: We used a general linear model repeated measures procedure to analyze the differences in performance between three assessments. We used a longitudinal structural equation modeling to analyze the relationship between CR and cognitive performance at baseline and at two follow-up assessments. SETTING: Participants with SCCs were recruited and assessed in primary care health centers. PARTICIPANTS: A total of 212 participants older than 50 years with SCCs. MEASUREMENTS: Cognitive reserve data were collected with an ad hoc questionnaire administered to the subjects in an interview. General cognitive performance (GCP), episodic memory (EM), and working memory (WM) have been evaluated. The Mini-Mental State Examination and the total score of Spanish version of the Cambridge Cognitive Examination evaluated the GCP. Episodic memory was assessed with the Spanish version of the California Verbal Learning. Working memory was evaluated by the counting span task and the listening span task. RESULTS: The satisfactory fit of the proposed model confirmed the direct effects of CR on WM and GCP at baseline, as well as indirect effects on EM and WM at first and second follow-up. Indirect effects of CR on other cognitive constructs via WM were observed over time. CONCLUSION: The proposed model is useful for measuring the influence of CR on cognitive performance over time. Cognitive response acquired throughout life may influence cognitive performance in old age and prevent cognitive deterioration, thus increasing processing resources via WM.

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed dementia and those who did not. DESIGN: Mann-Whitney U and ML analysis. Nine ML algorithms were evaluated using a 10-fold stratified validation procedure. Performance metrics (accuracy, recall, F-1 score, and Cohen's kappa) were computed for each algorithm, and graphic metrics (ROC and precision-recall curves) and features analysis were computed for the best-performing algorithm. SETTING: Primary care health centers. PARTICIPANTS: 128 participants: 78 cognitively unimpaired and 50 with MCI. MEASUREMENTS: Diagnosis at baseline, months from the baseline assessment until the 3rd follow-up or development of dementia, gender, age, Charlson Comorbidity Index, Neuropsychiatric Inventory-Questionnaire (NPI-Q) individual items, NPI-Q total severity, and total stress score and Geriatric Depression Scale-15 items (GDS-15) total score. RESULTS: 30 participants developed dementia, while 98 did not. Most of the participants who developed dementia were diagnosed at baseline with amnestic multidomain MCI. The Random Forest Plot model provided the metrics that best predicted conversion to dementia (e.g. accuracy=.88, F1=.67, and Cohen's kappa=.63). The algorithm indicated the importance of the metrics, in the following (decreasing) order: months from first assessment, age, the diagnostic group at baseline, total NPI-Q severity score, total NPI-Q stress score, and GDS-15 total score. CONCLUSIONS: ML is a valuable technique for detecting the risk of conversion to dementia in MCI patients. Some NPS proxies, including NPI-Q total severity score, NPI-Q total stress score, and GDS-15 total score, were deemed as the most important variables for predicting conversion, adding further support to the hypothesis that some NPS are associated with a higher risk of dementia in MCI.

A combination of 3-D discrete wavelet transform and 3-D local binary pattern for classification of mild cognitive impairment.

BACKGROUND: The detection of Alzheimer's Disease (AD) in its formative stages, especially in Mild Cognitive Impairments (MCI), has the potential of helping the clinicians in understanding the condition. The literature review shows that the classification of MCI-converts and MCI-non-converts has not been explored profusely and the maximum classification accuracy reported is rather low. Thus, this paper proposes a Machine Learning approach for classifying patients of MCI into two groups one who converted to AD and the others who are not diagnosed with any signs of AD. The proposed algorithm is also used to distinguish MCI patients from controls (CN). This work uses the Structural Magnetic Resonance Imaging data. METHODS: This work proposes a 3-D variant of Local Binary Pattern (LBP), called LBP-20 for extracting features. The method has been compared with 3D-Discrete Wavelet Transform (3D-DWT). Subsequently, a combination of 3D-DWT and LBP-20 has been used for extracting features. The relevant features are selected using the Fisher Discriminant Ratio (FDR) and finally the classification has been carried out using the Support Vector Machine. RESULTS: The combination of 3D-DWT with LBP-20 results in a maximum accuracy of 88.77. Similarly, the proposed combination of methods is also applied to distinguish MCI from CN. The proposed method results in the classification accuracy of 90.31 in this data. CONCLUSION: The proposed combination is able to extract relevant distribution of microstructures from each component, obtained with the use of DWT and thereby improving the classification accuracy. Moreover, the number of features used for classification is significantly less as compared to those obtained by 3D-DWT. The performance of the proposed method is measured in terms of accuracy, specificity and sensitivity and is found superior in comparison to the existing methods. Thus, the proposed method may contribute to effective diagnosis of MCI and may prove advantageous in clinical settings.

Cerebrospinal Fluid Biomarkers for the Diagnosis and Classification of Alzheimer's Disease Spectrum.

BACKGROUND: Cerebrospinal fluid (CSF) biomarkers are increasingly used in clinical practice for the diagnosis of Alzheimer's disease (AD). We aimed to 1) determine cutoff values of CSF biomarkers for AD, 2) investigate their clinical utility by estimating a concordance with amyloid positron emission tomography (PET), and 3) apply ATN (amyloid/tau/neurodegeneration) classification based on CSF results. METHODS: We performed CSF analysis in 51 normal controls (NC), 23 mild cognitive impairment (MCI) and 65 AD dementia (ADD) patients at the Samsung Medical Center in Korea. We attempted to develop cutoff of CSF biomarkers for differentiating ADD from NC using receiver operating characteristic analysis. We also investigated a concordance between CSF and amyloid PET results and applied ATN classification scheme based on CSF biomarker abnormalities to characterize our participants. RESULTS: CSF Aß42, total tau (t-tau) and phosphorylated tau (p-tau) significantly differed across the three groups. The area under curve for the differentiation between NC and ADD was highest in t-tau/Aß42 (0.994) followed by p-tau/Aß42 (0.963), Aß42 (0.960), t-tau (0.918), and p-tau (0.684). The concordance rate between CSF Aß42 and amyloid PET results was 92%. Finally, ATN classification based on CSF biomarker abnormalities led to a majority of NC categorized into A-T-N-(73%), MCI as A+T-N-(30%)/A+T+N+(26%), and ADD as A+T+N+(57%). CONCLUSION: CSF biomarkers had high sensitivity and specificity in differentiating ADD from NC and were as accurate as amyloid PET. The ATN subtypes based on CSF biomarkers may further serve to predict the prognosis.

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aß42), Aß40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aß40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.

Prevalence and determinants of cognitive impairment in the euthymic phase of bipolar disorders: results from the FACE-BD cohort.

BACKGROUND: Cognitive deficits are a well-established feature of bipolar disorders (BD), even during periods of euthymia, but risk factors associated with cognitive deficits in euthymic BD are still poorly understood. We aimed to validate classification criteria for the identification of clinically significant cognitive impairment, based on psychometric properties, to estimate the prevalence of neuropsychological deficits in euthymic BD, and identify risk factors for cognitive deficits using a multivariate approach. METHODS: We investigated neuropsychological performance in 476 euthymic patients with BD recruited via the French network of BD expert centres. We used a battery of tests, assessing five domains of cognition. Five criteria for the identification of neuropsychological impairment were tested based on their convergent and concurrent validity. Uni- and multivariate logistic regressions between cognitive impairment and several clinical and demographic variables were performed to identify risk factors for neuropsychological impairment in BD. RESULTS: One cut-off had satisfactory psychometric properties and yielded a prevalence of 12.4% for cognitive deficits in euthymic BD. Antipsychotics use were associated with the presence of a cognitive deficit. CONCLUSIONS: This is the first study to validate a criterion for clinically significant cognitive impairment in BD. We report a lower prevalence of cognitive impairment than previous studies, which may have overestimated its prevalence. Patients with euthymic BD and cognitive impairment may benefit from cognitive remediation.

Screening for cognitive dysfunction in systemic lupus erythematosus: the Montreal Cognitive Assessment Questionnaire and the Informant Questionnaire on Cognitive Decline in the Elderly.

BACKGROUND: Cognitive dysfunction (CD) is among the most common neuropsychiatric manifestations of systemic lupus erythematosus (SLE). Traditional neuropsychological testing and the Automated Neuropsychologic Assessment Metrics (ANAM) have been used to assess CD but neither is an ideal screening test. The Montreal Cognitive Assessment Questionnaire (MoCA) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) are brief and inexpensive tests. This study evaluated the MoCA and IQCODE as screening tools. METHODS: SLE patients fulfilling American College of Rheumatology (ACR) classification criteria were evaluated using the ANAM as the reference standard. The performance characteristics of the MoCA and IQCODE were assessed in comparison with normal controls (NCs) and rheumatoid arthritis (RA) patients. Four different definitions of CD were utilized. RESULTS: In total, 78 patients were evaluated. MoCA and ANAM scores were significantly correlated ( r = 0.51, p < 0.001). At the optimal cutoff, the sensitivity of the MoCA was ≥ 90% (depending on definition of CD) vs RA patients and ≥83% vs NCs. ANAM and IQCODE scores did not correlate ( p = 0.8152). IQCODE sensitivities were low for both RA patients and NCs regardless of definition and cutoff used. CONCLUSION: The MoCA appears to be a promising and practical screening tool for identification of patients with SLE at risk for CD.

Prevalence and Incidence of Mild Cognitive Impairment across Subtypes, Age, and Sex.

OBJECTIVE: The purpose of this study was to report on the prevalence and incidence of mild cognitive impairment (MCI) across age, sex, and subtypes according to various criteria in a population-based sample. METHODS: The sample was drawn from the Swedish Good Aging in Skåne (GÅS) population study, and data from 3,752 participants aged 60 years and more were used to calculate the MCI prevalence. The incidence was calculated using 2,093 participants with 6-year follow-up data. MCI was defined according to the expanded Mayo Clinic criteria: cognitive complaint, objective cognitive impairment (two different criteria depending on the severity of impairment), preserved functional abilities, and no dementia. RESULTS: The prevalence estimates ranged from 5.13 to 29.9% depending on age and severity of impairment. The incidence rates of overall MCI were 22.6 (95% confidence interval [CI]: 19.6-25.9) and 8.67 (95% CI: 7.0-10.7) per 1,000 person-years for less severe and severe cognitive impairment, respectively. The highest prevalence and incidence estimates were found for "non-amnestic MCI single domain." The older age groups had a higher prevalence, and no sex or age differences in MCI incidence were detected. CONCLUSION: Our findings concur with previous research advocating that MCI is a heterogeneous concept, since the prevalence and incidence estimates differed substantially according to age, MCI subtype, and severity of cognitive impairment.

A Multifactor Approach to Mild Cognitive Impairment.

Mild cognitive impairment (MCI) represents an intermediate stage between normal cognition and dementia. Individuals with MCI are at increased risk of conversion to dementia, and the rate of progression of MCI to dementia is dependent on age, gender, and education. MCI may be diagnosed using neuropsychological criteria using cut-offs representing decrements in cognition, or using criteria to assess for a decline in functional status. The ability to determine the status of dementia-related biomarkers has allowed for better staging and prognostication in different forms of MCI. MCI is now recognized as a significant target stage for future therapies. These future therapies aim to reduce the rate of conversion of individuals with MCI to dementia. In this article, we review different conceptions of MCI, the diagnosis and prognostication of MCI, and presently available management approaches for this condition.

Exploring Heterogeneity on the Wisconsin Card Sorting Test in Schizophrenia Spectrum Disorders: A Cluster Analytical Investigation.

OBJECTIVES: The Wisconsin Card Sorting Test (WCST) is a complex measure of executive function that is frequently employed to investigate the schizophrenia spectrum. The successful completion of the task requires the interaction of multiple intact executive processes, including attention, inhibition, cognitive flexibility, and concept formation. Considerable cognitive heterogeneity exists among the schizophrenia spectrum population, with substantive evidence to support the existence of distinct cognitive phenotypes. The within-group performance heterogeneity of individuals with schizophrenia spectrum disorder (SSD) on the WCST has yet to be investigated. A data-driven cluster analysis was performed to characterise WCST performance heterogeneity. METHODS: Hierarchical cluster analysis with k-means optimisation was employed to identify homogenous subgroups in a sample of 210 schizophrenia spectrum participants. Emergent clusters were then compared to each other and a group of 194 healthy controls (HC) on WCST performance and demographic/clinical variables. RESULTS: Three clusters emerged and were validated via altered design iterations. Clusters were deemed to reflect a relatively intact patient subgroup, a moderately impaired patient subgroup, and a severely impaired patient subgroup. CONCLUSIONS: Considerable within-group heterogeneity exists on the WCST. Identification of subgroups of patients who exhibit homogenous performance on measures of executive functioning may assist in optimising cognitive interventions. Previous associations found using the WCST among schizophrenia spectrum participants should be reappraised. (JINS, 2019, 25, 750-760).