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.

Alzheimer's Disease Classification Based on Image Transformation and Features Fusion

It has become an inevitable trend for medical personnel to analyze and diagnose Alzheimer's disease (AD) in different stages by combining functional magnetic resonance imaging (fMRI) and artificial intelligence technologies such as deep learning in the future. In this paper, a classification method was proposed for AD based on two different transformation images of fMRI and improved the 3DPCANet model and canonical correlation analysis (CCA). The main ideas include that, firstly, fMRI images were preprocessed, and subsequently, mean regional homogeneity (mReHo) and mean amplitude of low-frequency amplitude (mALFF) transformation were performed for the preprocessed images. Then, mReHo and mALFF images were extracted features using the improved 3DPCANet, and these two kinds of the extracted features were fused by CCA. Finally, the support vector machine (SVM) was used to classify AD patients with different stages. Experimental results showed that the proposed approach was robust and effective. Classification accuracy for significant memory concern (SMC) vs. mild cognitive impairment (MCI), normal control (NC) vs. AD, and NC vs. SMC, respectively, reached 95.00%, 92.00%, and 91.30%, which adequately proved the feasibility and effectiveness of the proposed method.

A Diagnosis and Biotype Comparison Across the Psychosis Spectrum: Investigating Volume and Shape Amygdala-Hippocampal Differences from the B-SNIP Study

OBJECTIVE

Brain-based Biotypes for psychotic disorders have been developed as part of the B-SNIP consortium to create neurobiologically distinct subgroups within idiopathic psychosis, independent from traditional phenomenological diagnostic methods. In the current study, we aimed to validate the Biotype model by assessing differences in volume and shape of the amygdala and hippocampus contrasting traditional clinical diagnoses with Biotype classification.

METHODS

A total of 811 participants from 6 sites were included: probands with schizophrenia (n = 199), schizoaffective disorder (n = 122), psychotic bipolar disorder with psychosis (n = 160), and healthy controls (n = 330). Biotype classification, previously developed using cognitive and electrophysiological data and K-means clustering, was used to categorize psychosis probands into 3 Biotypes, with Biotype-1 (B-1) showing reduced neural salience and severe cognitive impairment. MAGeT-Brain segmentation was used to determine amygdala and hippocampal volumetric data and shape deformations.

RESULTS

When using Biotype classification, B-1 showed the strongest reductions in amygdala-hippocampal volume and the most widespread shape abnormalities. Using clinical diagnosis, probands with schizophrenia and schizoaffective disorder showed the most significant reductions of amygdala and hippocampal volumes and the most abnormal hippocampal shape compared with healthy controls. Biotype classification provided the strongest neuroanatomical differences compared with conventional DSM diagnoses, with the best discrimination seen using bilateral amygdala and right hippocampal volumes in B-1.

CONCLUSION

These findings characterize amygdala and hippocampal volumetric and shape abnormalities across the psychosis spectrum. Grouping individuals by Biotype showed greater between-group discrimination, suggesting a promising approach and a favorable target for characterizing biological heterogeneity across the psychosis spectrum.

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

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.

Cognitive Subtyping in Schizophrenia: A Latent Profile Analysis

Cognitive dysfunction is a core feature of schizophrenia. The subtyping of cognitive performance in schizophrenia may aid the refinement of disease heterogeneity. The literature on cognitive subtyping in schizophrenia, however, is limited by variable methodologies and neuropsychological tasks, lack of validation, and paucity of studies examining longitudinal stability of profiles. It is also unclear if cognitive profiles represent a single linear severity continuum or unique cognitive subtypes. Cognitive performance measured with the Brief Assessment of Cognition in Schizophrenia was analyzed in schizophrenia patients (n = 767). Healthy controls (n = 1012) were included as reference group. Latent profile analysis was performed in a schizophrenia discovery cohort (n = 659) and replicated in an independent cohort (n = 108). Longitudinal stability of cognitive profiles was evaluated with latent transition analysis in a 10-week follow-up cohort. Confirmatory factor analysis (CFA) was carried out to investigate if cognitive profiles represent a unidimensional structure. A 4-profile solution was obtained from the discovery cohort and replicated in an independent cohort. It comprised of a "less-impaired" cognitive subtype, 2 subtypes with "intermediate cognitive impairment" differentiated by executive function performance, and a "globally impaired" cognitive subtype. This solution showed relative stability across time. CFA revealed that cognitive profiles are better explained by distinct meaningful profiles than a severity linear continuum. Associations between profiles and negative symptoms were observed. The subtyping of schizophrenia patients based on cognitive performance and its associations with symptomatology may aid phenotype refinement, mapping of specific biological mechanisms, and tailored clinical treatments.

Combining Multimodal Behavioral Data of Gait, Speech, and Drawing for Classification of Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

Gait, speech, and drawing behaviors have been shown to be sensitive to the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, previous studies focused on only analyzing individual behavioral modalities, although these studies suggested that each of these modalities may capture different profiles of cognitive impairments associated with AD.

OBJECTIVE

We aimed to investigate if combining behavioral data of gait, speech, and drawing can improve classification performance compared with the use of individual modality and if each of these behavioral data can be associated with different cognitive and clinical measures for the diagnosis of AD and MCI.

METHODS

Behavioral data of gait, speech, and drawing were acquired from 118 AD, MCI, and cognitively normal (CN) participants.

RESULTS

Combining all three behavioral modalities achieved 93.0% accuracy for classifying AD, MCI, and CN, and only 81.9% when using the best individual behavioral modality. Each of these behavioral modalities was statistically significantly associated with different cognitive and clinical measures for diagnosing AD and MCI.

CONCLUSION

Our findings indicate that these behaviors provide different and complementary information about cognitive impairments such that classification of AD and MCI is superior to using either in isolation.

Factors Affecting Quality of Life Among the Elderly in Long-Term Care Hospitals

BACKGROUND

There are challenges in sustaining person-centered care in aged care settings. Key related issues of concern such as quality of life among the older people in long-term care hospitals and interactions with nursing staff have been described previously.

PURPOSE

This study was designed to explore the factors affecting quality of life among older people living in long-term care hospitals in South Korea.

METHODS

Older adult patients (N = 202) in three long-term care hospitals completed measures of cognitive functions, depression, care dependency, and interactions between nurse and patient and a quality-of-life assessment tool. Univariate analyses were used to examine the relationships among these variables, and a multiple linear regression analysis was used to explore the extent to which these variables predicted quality of life in these patients.

RESULTS

The significant factors associated with quality of life were found to be cognitive functions (r = .373, p < .001), care dependency (r = .350, p < .001), and depression (r = -.504, p < .001). The regression model with depression and care dependency as predictor variables accounted for 25.7% of the variance in quality of life.

CONCLUSIONS/IMPLICATIONS FOR PRACTICE

The correlation found in this study between quality of life and depression and care dependency provides additional evidentiary support for developing and applying nursing interventions that reduce depression and care dependency in older adult populations.

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 Aβ42, 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/Aβ42 (0.994) followed by p-tau/Aβ42 (0.963), Aβ42 (0.960), t-tau (0.918), and p-tau (0.684). The concordance rate between CSF Aβ42 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 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 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.

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.

Skill Profiles of College Students With a History of Developmental Language Disorder and Developmental Dyslexia

Developmental language disorder (DLD) and developmental dyslexia (DD) are two prevalent subtypes of Specific Learning Disabilities (SLDs; Diagnostic and Statistical Manual of Mental Disorders [5th ed.; DSM-5]). Yet, little information is available regarding the distinct challenges faced by adults with DLD and/or DD in college. The purpose of the present report is to characterize the relative strengths and challenges of college students with a history of DLD and/or DD, as this information is critical for providing appropriate institutional support. We examined the cognitive skill profiles of 352 college students (ages 18-35 years), using standardized and research-validated measures of reading, spoken language, nonverbal cognition, and self-reported childhood diagnostic history. We classified college students as having DLD (n = 50), and/or DD (n = 40), or as typically developed adults (n = 132) according to procedures described for adults with DLD and DD. A structural equation model determined the cognitive, language, and reading measures predicted by the classification group. Adults with DLD demonstrated poor verbal working memory and speeded sentence-level reading. Adults with DD primarily demonstrated deficits in phonology-based skills. These results indicate that adults with DLD and/or DD continue to face similar challenges as they did during childhood, and thus may benefit from differentially targeted accommodations in college.

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.

Latent Profiles of Cognitive Control, Episodic Memory, and Visual Perception Across Psychiatric Disorders Reveal a Dimensional Structure

Although meta-analyses suggest that schizophrenia (SZ) is associated with a more severe neurocognitive phenotype than mood disorders such as bipolar disorder, considerable between-subject heterogeneity exists in the phenotypic presentation of these deficits across mental illnesses. Indeed, it is unclear whether the processes that underlie cognitive dysfunction in these disorders are unique to each disease or represent a common neurobiological process that varies in severity. Here we used latent profile analysis (LPA) across 3 distinct cognitive domains (cognitive control, episodic memory, and visual integration; using data from the CNTRACS consortium) to identify distinct profiles of patients across psychotic illnesses. LPA was performed on a sample of 223 psychosis patients (59 with Type I bipolar disorder, 88 with SZ, and 76 with schizoaffective disorder). Seventy-three healthy control participants were included for comparison but were not included in sample LPA. Three latent profiles ("Low," "Moderate," and "High" ability) were identified as the underlying covariance across the 3 domains. The 3-profile solution provided highly similar fit to a single continuous factor extracted by confirmatory factor analysis, supporting a unidimensional structure. Diagnostic ratios did not significantly differ between profiles, suggesting that these profiles cross diagnostic boundaries (an exception being the Low ability profile, which had only one bipolar patient). Profile membership predicted Brief Psychiatric Rating Scale and Young Mania Rating Scale symptom severity as well as everyday communication skills independent of diagnosis. Biological, clinical and methodological implications of these findings are discussed.

Metabolic Profiles Help Discriminate Mild Cognitive Impairment from Dementia Stage in Alzheimer's Disease

Accurate differentiation between neurodegenerative diseases is developing quickly and has reached an effective level in disease recognition. However, there has been less focus on effectively distinguishing the prodromal state from later dementia stages due to a lack of suitable biomarkers. We utilized the Disease State Index (DSI) machine learning classifier to see how well quantified metabolomics data compares to clinically used cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD). The metabolic profiles were quantified for 498 serum and CSF samples using proton nuclear magnetic resonance spectroscopy. The patient cohorts in this study were dementia (with a clinical AD diagnosis) (N = 359), mild cognitive impairment (MCI) (N = 96), and control patients with subjective memory complaints (N = 43). DSI classification was conducted for MCI (N = 51) and dementia (N = 214) patients with low CSF amyloid-β levels indicating AD pathology and controls without such amyloid pathology (N = 36). We saw that the conventional CSF markers of AD were better at classifying controls from both dementia and MCI patients. However, quantified metabolic subclasses were more effective in classifying MCI from dementia. Our results show the consistent effectiveness of traditional CSF biomarkers in AD diagnostics. However, these markers are relatively ineffective in differentiating between MCI and the dementia stage, where the quantified metabolomics data provided significant benefit.

Neuropsychological subtypes of incident mild cognitive impairment in the Mayo Clinic Study of Aging

INTRODUCTION

We evaluated whether incident mild cognitive impairment (MCI) subtypes could be empirically derived in the Mayo Clinic Study of Aging.

METHODS

We performed cluster analysis on neuropsychological data from 506 participants with incident MCI.

RESULTS

The 3-cluster solution resulted in (1) amnestic, (2) dysexecutive, (3) dysnomic subtypes. The 4-cluster solution produced these same three groups and a fourth group with subtle cognitive impairment (SCI). The SCI cluster was a subset of the amnestic cluster and distinct from well-matched cognitively unimpaired participants based on memory and global z-score area under the receiver operating characteristic curve analyses and probability of progression to MCI/dementia.

DISCUSSION

We empirically identified three neuropsychological subtypes of MCI that share some features with MCI subtypes identified in the Alzheimer's Disease Neuroimaging Initiative. The fourth subtype with SCI in the Mayo Clinic Study of Aging differed from the fourth cluster-derived normal group in Alzheimer's Disease Neuroimaging Initiative and could represent a group to target with early interventions.

Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders

Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition techniques such as principal component analysis toward higher complexity, non-linear decomposition algorithms. With the arrival of the deep learning paradigm, it has become possible to extract high-level abstract features directly from MRI images that internally describe the distribution of data in low-dimensional manifolds. In this work, we try a new exploratory data analysis of AD based on deep convolutional autoencoders. We aim at finding links between cognitive symptoms and the underlying neurodegeneration process by fusing the information of neuropsychological test outcomes, diagnoses, and other clinical data with the imaging features extracted solely via a data-driven decomposition of MRI. The distribution of the extracted features in different combinations is then analyzed and visualized using regression and classification analysis, and the influence of each coordinate of the autoencoder manifold over the brain is estimated. The imaging-derived markers could then predict clinical variables with correlations above 0.6 in the case of neuropsychological evaluation variables such as the MMSE or the ADAS11 scores, achieving a classification accuracy over 80% for the diagnosis of AD.

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.

Mild cognitive impairment: A concept analysis

AIM

To clarify the concept of mild cognitive impairment (MCI) and identify its attributes to enhance understanding of its implications for nursing practice and research.

BACKGROUND

MCI is a concept that has evolved, thus clarification of this concept is essential for the advancement of nursing science.

DESIGN

Walker and Avant's eight steps of concept analysis strategy was used.

DATA SOURCE

Manuals of mental disorders and databases such as PubMed, Springer, PsychINFO, Cumulative Index to Nursing and Allied Health Literature, and Education Resources Information Center (ERIC) from 1982 to 2018.

REVIEW METHODS

A literature search was conducted using keywords such as "mild," "cognitive," "impairment," and "deterioration."

RESULTS

The concept of MCI is defined as the transitional state between cognitive state normal for age and the early manifestation of dementia states. It is characterized by the presence of objective and subjective evidence of impairment in one or multiple cognitive domains, independence in daily activities, can be reversible, and is a risk factor for dementia.

CONCLUSIONS

Clarification of MCI serves as a framework for identification, treatment, and interventions that may support healthy aging in older adults.

Predictors and Implications of Accelerated Cognitive Aging

Aging is a major risk factor for both normal and pathological cognitive decline. However, individuals vary in their rate of age-related decline. We developed an easily interpretable composite measure of cognitive age, and related both the level of cognitive age and cognitive slope to sociodemographic, genetic, and disease indicators and examine its prediction of dementia transition. Using a sample of 19,594 participants from the Health and Retirement Study, cognitive age was derived from a set of performance tests administered at each wave. Our findings reveal different conclusions as they relate to levels versus slopes of cognitive age, with more pronounced differences by sex and race/ethnicity for absolute levels of cognitive decline rather than for rates of declines. We also find that both level and slope of cognitive age are inversely related to education, as well as increased for persons with APOE ε4 and/or diabetes. Finally, results show that the slope in cognitive age predicts subsequent dementia among non-demented older adults. Overall, our study suggests that this measure is applicable to cross-sectional and longitudinal studies on cognitive aging, decline, and dementia with the goal of better understanding individual differences in cognitive decline.

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

We built and validated a deep learning algorithm predicting the individual diagnosis of Alzheimer's disease (AD) and mild cognitive impairment who will convert to AD (c-MCI) based on a single cross-sectional brain structural MRI scan. Convolutional neural networks (CNNs) were applied on 3D T1-weighted images from ADNI and subjects recruited at our Institute (407 healthy controls [HC], 418 AD, 280 c-MCI, 533 stable MCI [s-MCI]). CNN performance was tested in distinguishing AD, c-MCI and s-MCI. High levels of accuracy were achieved in all the classifications, with the highest rates achieved in the AD vs HC classification tests using both the ADNI dataset only (99%) and the combined ADNI + non-ADNI dataset (98%). CNNs discriminated c-MCI from s-MCI patients with an accuracy up to 75% and no difference between ADNI and non-ADNI images. CNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Our method performed well without any prior feature engineering and regardless the variability of imaging protocols and scanners, demonstrating that it is exploitable by not-trained operators and likely to be generalizable to unseen patient data. CNNs may accelerate the adoption of structural MRI in routine practice to help assessment and management of patients.

Long-term cognitive trajectories and heterogeneity in patients with schizophrenia and their unaffected siblings

OBJECTIVE

This study aimed to assess the heterogeneity and stability of cognition in patients with a non-affective psychotic disorder and their unaffected siblings. In addition, we aimed to predict the cognitive subtypes of siblings by their probands.

METHOD

Assessments were conducted at baseline, 3 and 6 years in 1119 patients, 1059 siblings and 586 controls from the Genetic Risk and Outcome of Psychosis (GROUP) study. Group-based trajectory modeling was applied to identify trajectories and clustered multinomial logistic regression analysis was used for prediction modeling. A composite score of eight neurocognitive tests was used to measure cognitive performance.

RESULTS

Five stable cognitive trajectories ranging from severely altered to high cognitive performance were identified in patients. Likewise, four stable trajectories ranging from moderately altered to high performance were found in siblings. Siblings had a higher risk of cognitive alteration when patients' alteration was mild (OR = 2.21), moderate (OR = 5.70), and severe (OR = 10.07) compared with patients with intact cognitive function. The familial correlation coefficient between pairs of index patients and their siblings was 0.27 (P = 0.003).

CONCLUSIONS

The cognitive profiles identified in the current study might be suitable as endophenotypes and could be used in future genetic studies and predicting functional and clinical outcomes.

Empirical evidence for discrete neurocognitive subgroups in patients with non-psychotic major depressive disorder: clinical implications

BACKGROUND

Neuropsychological deficits are present across various cognitive domains in major depressive disorder (MDD). However, a consistent and specific profile of neuropsychological abnormalities has not yet been established.

METHODS

We assessed cognition in 170 patients with non-psychotic MDD using the Brief Assessment of Cognition in Schizophrenia and the scores were compared with those of 42 patients with schizophrenia as a reference for severity of cognitive impairment. Hierarchical cluster analysis was conducted to determine whether there are discrete neurocognitive subgroups in MDD. We then compared the subgroups in terms of several clinical factors and social functioning.

RESULTS

Three distinct neurocognitive subgroups were found: (1) a mild impairment subgroup with near-normative performance and mild dysfunction in motor speed; (2) a selective impairment subgroup, which exhibited preserved working memory and executive function, but moderate to severe deficits in verbal memory, motor speed, verbal fluency, and attention/information processing speed; and (3) a global impairment subgroup with moderate to severe deficits across all neurocognitive domains, comparable with deficits in schizophrenia. The global impairment subgroup was characterized by lower pre-morbid intelligence quotient (IQ). Moreover, a significant difference between groups was observed in premorbid IQ (p = 0.003), antidepressant dose (p = 0.043), antipsychotic dose (p = 0.013), or anxiolytic dose (p < 0.001).

CONCLUSIONS

These results suggest the presence of multiple neurocognitive subgroups in non-psychotic MDD with unique profiles, one of which exhibits deficits comparable to those of schizophrenia. The results of the present study may help guide future efforts to target these disabling symptoms using different treatments.

The impact of cognitive impairment on the physical ageing process

BACKGROUND

Physical decline and cognitive degeneration characterise the ageing process.

AIM

Physical parameters, performance and the functional indexes were studied in relation to age in healthy and cognitively impaired older persons to understand the interactions and changes during normal ageing, cognitive decline and progression to frailty.

METHODS

Cross-sectional analysis was performed on a data registry of an ambulatory Memory Diagnosis Centre. The quantitative gait characteristics at usual pace, body composition parameters, disability scales (activity of daily living and instrumental activity of daily living) and Rockwood frailty index were compared in cognitively healthy (CHI), mild cognitively impaired, mildly and moderately demented < 80-years old and > 80-years old adults.

RESULTS

Quality of gait deteriorated with age in CHI and cognitively impaired. Skeletal muscle mass index decreased when cognitive status worsened. Disability and frailty correlated with increasing cognitive impairment. Age, gender, cognitive impairment, body composition and Rockwood's Frailty scale had a combined forecasting effect, as well as the individual effect on the gait characteristics. Disability score, Frailty index, skeletal muscle mass and skeletal muscle mass index, gait speed, normalised mean step length and swing time variability in mildly demented < 80-years old adults mirrored the parameters in the CHI > 80-years old.

CONCLUSION

Quantitative gait characteristics, muscle mass and disabilities change along with cognitive impairment, frailty and age. A more rapid physical ageing process accompanies cognitive decline. Therefore, gait characteristics should be age-referenced and studies on gait in older persons should include muscle mass, frailty and cognitive parameters.

Classification of Alzheimer's Disease Stages: An Approach Using PCA-Based Algorithm

Early diagnosis of Alzheimer's disease (AD) allows individuals and their health managers to manage healthier medication. We proposed an approach for classification of AD stages, with respect to principal component analysis (PCA)-based algorithm. The PCA has been extensively applied as the most auspicious face-recognition algorithm. For the proposed algorithm, 100 images of 10 children were transformed for feature extraction and covariance matrix was constructed to obtain eigenvalues. The eigenvector provided a useful framework for face recognition. For the classification of AD stages, magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) data were obtained from Alzheimer's Disease Neuroimaging Initiative database. Hippocampus is one of the most affected regions by AD. Thus, we selected clusters of voxels from the "hippocampus" of AD screening stage (mild cognitive impairment), AD stage 1, AD stage 2, and AD stage 3. By using eigenvectors corresponding to maximum eigenvalues of fMRI data, the purposed algorithm classified the voxels of AD stages effectively.

A hybrid data mining model for diagnosis of patients with clinical suspicion of dementia

BACKGROUND AND OBJECTIVE

Given the phenomenon of aging population, dementias arise as a complex health problem throughout the world. Several methods of machine learning have been applied to the task of predicting dementias. Given its diagnostic complexity, the great challenge lies in distinguishing patients with some type of dementia from healthy people. Particularly in the early stages, the diagnosis positively impacts the quality of life of both the patient and the family. This work presents a hybrid data mining model, involving the mining of texts integrated to the mining of structured data. This model aims to assist specialists in the diagnosis of patients with clinical suspicion of dementia.

METHODS

The experiments were conducted from a set of 605 medical records with 19 different attributes about patients with cognitive decline reports. Firstly, a new structured attribute was created from a text mining process. It was the result of clustering the patient's pathological history information stored in an unstructured textual attribute. Classification algorithms (naïve bayes, bayesian belief networks and decision trees) were applied to obtain Alzheimer's disease and mild cognitive impairment predictive models. Ensemble methods (Bagging, Boosting and Random Forests) were used in order to improve the accuracy of the generated models. These methods were applied in two datasets: one containing only the original structured data; the other containing the original structured data with the inclusion of the new attribute resulting from the text mining (hybrid model).

RESULTS

The models' accuracy metrics obtained from the two different datasets were compared. The results evidenced the greater effectiveness of the hybrid model in the diagnostic prediction for the pathologies of interest.

CONCLUSIONS

When analysing the different methods of classification and clustering used, the better rates related to the precision and sensitivity of the pathologies under study were obtained with hybrid models with support of ensemble methods.

Association between white matter lesions and cerebral Aβ burden

INTRODUCTION

White matter lesions (WMLs), detected as hyperintensities on T2-weighted MRI, represent small vessel disease in the brain and are considered a potential risk factor for memory and cognitive impairment in older adults. The purpose of this study was to evaluate the association between WMLs and cerebral amyloid-β (Aβ) burden in patients with cognitive impairment.

METHODS

A total of 83 patients with cognitive impairment, who underwent brain MRI and F-18 florbetaben PET, were included prospectively: 19 patients were cognitively unimpaired, 30 exhibited mild cognitive impairment (MCI), and 34 exhibited dementia. The Fazekas scale was used to quantify WMLs on T2-weighted brain MR images. Cerebral Aβ burden was quantitatively estimated using volume-of-interest analysis. Differences in cerebral Aβ burden were evaluated between low-WML (Fazekas scale ≤1) and high-WML (Fazekas scale ≥2) groups. The relationship between the Fazekas rating and cerebral Aβ burden was evaluated using linear regression analysis after adjusting for age and sex.

RESULTS

In the overall cohort, the high-WML group exhibited significantly higher Aβ burden compared with the low-WML group (P = 0.011) and cerebral Aβ burden was positively correlated with Fazekas rating (β = 0.299, P = 0.006). In patients with MCI, the high-WML group exhibited significantly higher Aβ burden compared with the low-WML group (P = 0.019) and cerebral Aβ burden was positively correlated with Fazekas rating (β = 0.517, P = 0.003).

CONCLUSION

The presence of WMLs was associated with cerebral Aβ burden in patients with MCI. Our findings suggest that small vessel disease in the brain is related to Alzheimer's disease pathology.

[Therapeutic cooling is associated with better function in children with cerebral palsy due to birth asphyxia according to the national health care quality registry CPUP]

Hypothermic treatment after birth asphyxia was introduced as a recommended practice in Sweden in 2007. CPUP is a national quality register and surveillance program that encompasses the total population of children with cerebral palsy (CP) in Sweden. In an analysis of CPUP data children with CP and asphyxia treated with cooling were compared to children with CP and asphyxia who were not cooled. A lower proportion of severe motor and cognitive impairments were observed in the group that did receive the cooling/hypothermic treatment.

An updated diagnostic approach to subtype definition of vascular parkinsonism - Recommendations from an expert working group

This expert working group report proposes an updated approach to subtype definition of vascular parkinsonism (VaP) based on a review of the existing literature. The persistent lack of consensus on clear terminology and inconsistent conceptual definition of VaP formed the impetus for the current expert recommendation report. The updated diagnostic approach intends to provide a comprehensive tool for clinical practice. The preamble for this initiative is that VaP can be diagnosed in individual patients with possible prognostic and therapeutic consequences and therefore should be recognized as a clinical entity. The diagnosis of VaP is based on the presence of clinical parkinsonism, with variable motor and non-motor signs that are corroborated by clinical, anatomic or imaging findings of cerebrovascular disease. Three VaP subtypes are presented: (1) The acute or subacute post-stroke VaP subtype presents with acute or subacute onset of parkinsonism, which is typically asymmetric and responds to dopaminergic drugs; (2) The more frequent insidious onset VaP subtype presents with progressive parkinsonism with prominent postural instability, gait impairment, corticospinal, cerebellar, pseudobulbar, cognitive and urinary symptoms and poor responsiveness to dopaminergic drugs. A higher-level gait disorder occurs frequently as a dominant manifestation in the clinical spectrum of insidious onset VaP, and (3) With the emergence of molecular imaging biomarkers in clinical practice, our diagnostic approach also allows for the recognition of mixed or overlapping syndromes of VaP with Parkinson's disease or other neurodegenerative parkinsonisms. Directions for future research are also discussed.

Autism: a transdiagnostic, dimensional, construct of reasoning?

The concept of autism has changed across time, from the Bleulerian concept, which defined it as one of several symptoms of dementia praecox, to the present-day concept representing a pervasive development disorder. The present theoretical contribution to this special issue of EJN on autism introduces new theoretical ideas and discusses them in light of selected prior theories, clinical examples, and recent empirical evidence. The overall aim is to identify some present challenges of diagnostic practice and autism research and to suggest new pathways that may help direct future research. Future research must agree on the definitions of core concepts such as autism and psychosis. A possible redefinition of the concept of autism may be a condition in which the rationale of an individual's behaviour differs qualitatively from that of the social environment due to characteristic cognitive impairments affecting reasoning. A broad concept of psychosis could focus on deviances in the experience of reality resulting from impairments of reasoning. In this light and consistent with recent empirical evidence, it may be appropriate to redefine dementia praecox as a developmental disorder of reasoning. A future challenge of autism research may be to develop theoretical models that can account for the impact of complex processes acting at the social level in addition to complex neurobiological and psychological processes. Such models could profit from a distinction among processes related to (i) basic susceptibility, (ii) adaptive processes and (iii) decompensating factors involved in the development of manifest illness.

Does my older cancer patient have cognitive impairment?

Cancer and impaired cognition are both frequent conditions in old age and consequently coexist to certain degree. The prevalence of impaired cognition increases sharply after the age of 65 and the more advanced form of cognitive impairment; dementia, is exceeding 30% by the age of 85years. Adequate cognition is crucial for understanding important facts and for giving consent for intervention. There are many different stages of cognitive impairment, ranging from subjective cognitive impairment to severe dementia. The mildest stages of cognitive impairment are sometimes reversible but in more severe stages, there is brain damage of some kind, most frequently caused by neurodegenerative disorder such as Alzheimer's disease. Therefore, some kind of evaluation of cognition should be offered to all older individuals with cancer and in need for intervention. In this evaluation, information should also be sought from a close relative. In the earlier stages of cognitive impairment, the individual usually retains ability to give consent and understands information given but in later stages of dementia, a surrogate decision maker is needed. In milder stages of dementia, an individual evaluation is needed for decision of capability for consent. A specific diagnosis of a disorder such as Alzheimer's disease does not in itself preclude the individual from giving consent, the degree of cognitive impairment, impaired judgement and poor insight are more decisive in this regard. It is also important to know the difference of delirium, most often a time limited condition and dementia that usually is progressive.

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares

BACKGROUND

Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. Mild cognitive impairment (MCI) is very frequently a prodromal phase of AD, and existing studies have suggested that people with MCI tend to progress to AD at a rate of about 10-15% per year. However, the ability of clinicians and machine learning systems to predict AD based on MRI biomarkers at an early stage is still a challenging problem that can have a great impact in improving treatments.

METHOD

The proposed system, developed by the SiPBA-UGR team for this challenge, is based on feature standardization, ANOVA feature selection, partial least squares feature dimension reduction and an ensemble of One vs. Rest random forest classifiers. With the aim of improving its performance when discriminating healthy controls (HC) from MCI, a second binary classification level was introduced that reconsiders the HC and MCI predictions of the first level.

RESULTS

The system was trained and evaluated on an ADNI datasets that consist of T1-weighted MRI morphological measurements from HC, stable MCI, converter MCI and AD subjects. The proposed system yields a 56.25% classification score on the test subset which consists of 160 real subjects.

COMPARISON WITH EXISTING METHOD(S)

The classifier yielded the best performance when compared to: (i) One vs. One (OvO), One vs. Rest (OvR) and error correcting output codes (ECOC) as strategies for reducing the multiclass classification task to multiple binary classification problems, (ii) support vector machines, gradient boosting classifier and random forest as base binary classifiers, and (iii) bagging ensemble learning.

CONCLUSIONS

A robust method has been proposed for the international challenge on MCI prediction based on MRI data. The system yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set.

Demographic and Cognitive Profile of Individuals Seeking a Diagnosis of Autism Spectrum Disorder in Adulthood

Little is known about ageing with autism spectrum disorder (ASD). We examined the characteristics of adults referred to a specialist diagnostic centre for assessment of possible ASD, 100 of whom received an ASD diagnosis and 46 did not. Few demographic differences were noted between the groups. Comorbid psychiatric disorders were high in individuals with ASD (58 %) and non-ASD (59 %). Individuals who received an ASD diagnosis had higher self-rated severity of ASD traits than non-ASD individuals. Within the ASD group, older age was associated with higher ratings of ASD traits and better cognitive performance. One interpretation is that general cognitive ability and the development of coping strategies across the lifespan, do not necessarily reduce ASD traits but may mitigate their effects.

Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment

Multimodal classification methods using different modalities of imaging and non-imaging data have recently shown great advantages over traditional single-modality-based ones for diagnosis and prognosis of Alzheimer's disease (AD), as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, to the best of our knowledge, most existing methods focus on mining the relationship across multiple modalities of the same subjects, while ignoring the potentially useful relationship across different subjects. Accordingly, in this paper, we propose a novel learning method for multimodal classification of AD/MCI, by fully exploring the relationships across both modalities and subjects. Specifically, our proposed method includes two subsequent components, i.e., label-aligned multi-task feature selection and multimodal classification. In the first step, the feature selection learning from multiple modalities are treated as different learning tasks and a group sparsity regularizer is imposed to jointly select a subset of relevant features. Furthermore, to utilize the discriminative information among labeled subjects, a new label-aligned regularization term is added into the objective function of standard multi-task feature selection, where label-alignment means that all multi-modality subjects with the same class labels should be closer in the new feature-reduced space. In the second step, a multi-kernel support vector machine (SVM) is adopted to fuse the selected features from multi-modality data for final classification. To validate our method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database using baseline MRI and FDG-PET imaging data. The experimental results demonstrate that our proposed method achieves better classification performance compared with several state-of-the-art methods for multimodal classification of AD/MCI.

Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum

BACKGROUND

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored.

METHOD

Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575).

RESULTS

Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently.

CONCLUSIONS

Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Profiles of cognitive impairments in an older age community sample: A latent class analysis

OBJECTIVE

Person-centered studies that could describe the different patterns of cognitive impairments among older people are lacking. To this end, the current study utilized a person-centered approach to examine the different profiles of cognitive impairment in an older age Chinese community sample. Additionally, the current study also examined whether functional impairments differ across the different profiles.

METHOD

A total of 220 older people (Mage = 70.9 years) who were assessed to have an objective impairment in any of 7 domains (immediate memory, delayed memory, attention, inhibition, verbal fluency, working memory, and processing speed) were entered in a latent class analysis. Subsequently, functional impairment (both self-reported and clinician-rated) between the different profiles of cognitive impairments that emerged from the analyses were compared.

RESULTS

A 4-class solution was chosen based on fit statistics and interpretability. Three profiles were characterized by impairments in cognitive rigidity, memory, and other executive functions, and the 4th with impairments in both executive functions and memory. Furthermore, relative to the non-memory-impaired groups, the memory-impaired groups were significantly more likely to report a higher level of clinician-rated functional impairments even though these groups did not differ significantly in self-reported functional impairments.

CONCLUSIONS

The observed cognitive impairments in the current sample can be classified into 4 distinct profiles along the lines of memory and/or executive functions impairment. The memory-impaired groups were significantly impaired relative to the non-memory-impaired groups, at least in terms of clinician-rated functional outcomes. These findings present some important implications. (PsycINFO Database Record

Heterogeneity of Neuropsychological Impairment in HIV Infection: Contributions from Mild Cognitive Impairment

Despite longstanding acknowledgement of the heterogeneity of HIV-associated neurocognitive disorders (HAND), existing HAND diagnostic methods classify according to the degree of impairment, without regard to the pattern of neuropsychological strengths and weaknesses. Research in mild cognitive impairment (MCI) has demonstrated that classifying individuals into subtypes by both their level and pattern of impairment, using either conventional or statistical methods, has etiologic and prognostic utility. Methods for characterizing the heterogeneity of MCI provide a framework that can be applied to other disorders and may be useful in clarifying some of the current challenges in the study of HAND. A small number of studies have applied these methods to examine the heterogeneity of neurocognitive function among individuals with HIV. Most have supported the existence of multiple subtypes of neurocognitive impairment, with some evidence for distinct clinicodemographic features of these subtypes, but a number of gaps exist. Following a review of diagnostic methods and challenges in the study of HAND, we summarize the literature regarding conventional and empirical subtypes of MCI and HAND and identify directions for future research regarding neurocognitive heterogeneity in HIV infection.

Mild Cognitive Impairment Subtypes in a Cohort of Elderly Essential Tremor Cases

OBJECTIVES

Individuals with essential tremor (ET) exhibit a range of cognitive deficits generally conceptualized as "dysexecutive" or "fronto-subcortical," and thought to reflect disrupted cortico-cerebellar networks. In light of emerging evidence that ET increases risk for Alzheimer's disease (AD), it is critical to more closely examine the nature of specific cognitive deficits in ET, with particular attention to amnestic deficits that may signal early AD.

METHODS

We performed a cross-sectional analysis of baseline data from 128 ET cases (age 80.4±9.5 years) enrolled in a longitudinal, clinical-pathological study. Cases underwent a comprehensive battery of motor-free neuropsychological tests and a functional assessment to inform clinical diagnoses of normal cognition (ET-NC), mild cognitive impairment (MCI) (ET-MCI), or dementia (ET-D). ET-MCI was subdivided into subtypes including: amnestic single-domain (a-MCI), amnestic multi-domain (a-MCI+), non-amnestic single-domain (na-MCI), or non-amnestic multi-domain (na-MCI+).

RESULTS

Ninety-one (71.1%) cases were ET-NC, 24 (18.8%) were ET-MCI, and 13 (10.2%) were ET-D. Within MCI, the a-MCI+ subtype was the most common (13/24; 54.2%) followed by a-MCI (4/24; 16.7%), na-MCI+ (4/24; 16.7%), and na-MCI (3/24; 12.5%). Cases with amnestic MCI demonstrated lower recognition memory Z-scores (-2.4±1.7) than non-amnestic groups (-0.9±1.2) (p=.042).

CONCLUSIONS

Amnestic MCI, defined by impaired memory recall but associated with lower memory storage scores, was the most frequent MCI subtype in our study. Such impairment has not been explicitly discussed in the context of ET and may be an early hallmark of AD. Results have implications for the prognosis of specific cognitive deficits in ET. (JINS, 2017, 23, 390-399).

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions.

Mental retirement and health selection: Analyses from the U.S. Health and Retirement Study

BACKGROUND

Research has recently suggested that retirement may decrease cognitive engagement, resulting in cognitive aging. Few studies have systematically documented whether or how selectivity into retirement shapes the relationship between retirement and cognitive aging.

METHODS

We draw on data from the Health and Retirement Study (1998-2012) to examine the relationship between cognition and retirement for 18,575 labor force participants. Longitudinal regression discontinuity modeling was used to examine performance and decline in episodic memory. Models differentiated three forms of selection bias: indirect and direct selection as well as reverse causation. To further interrogate the disuse hypothesis, we adjust for confounding from health and socioeconomic sources.

RESULTS

Results revealed that individuals who retired over the course of the panel were substantially different in terms of health, wealth and cognition when compared to those who remained employed. However, accounting for observed selection biases, significant associations were found linking longer retirement with more rapid cognitive decline.

DISCUSSION

This study examined respondents who were in the labor force at baseline and transitioned into retirement. Analyses suggested that those who retired over the course of the panel had worse overall functioning, but also experienced more rapid declines after retirement that increased the rate of aging by two-fold, resulting in yearly losses of 3.7% (95% CI = [3.5, 4.0]) of one standard deviation in functioning attributable to retirement. Results are supportive of the view that retirement is associated with more rapid cognitive aging.

MRI-based comparative study of different mild cognitive impairment subtypes: protocol for an observational case-control study

INTRODUCTION

Amnestic mild cognitive impairment (aMCI) and vascular mild cognitive impairment (VaMCI) comprise the 2 main types of mild cognitive impairment (MCI). The first condition generally progresses to Alzheimer's disease, whereas the second is likely to develop into vascular dementia (VD). The brain structure and function of patients with MCI differ from those of normal elderly individuals. However, whether brain structures or functions differ between these 2 MCI subtypes has not been studied. This study is designed to analyse neuroimages of brain in patients with VaMCI and aMCI using multimodality MRI (structural MRI (sMRI), functional MRI and diffusion tensor imaging (DTI)).

METHODS AND ANALYSIS

In this study, 80 participants diagnosed with aMCI, 80 participants diagnosed with VaMCI, and 80 age-matched, gender-matched and education-matched normal controls (NCs) will be recruited to the Hongqi Hospital of Mudanjiang Medical University, Heilongjiang, China. All participants will undergo neuroimaging and neuropsychological evaluations. The primary outcome measures will be (1) microstructural alterations revealed by multimodal MRIs, including sMRI, resting-state functional MRI and DTI; and (2) a neuropsychological evaluation, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Memory and Executive Screening (MES), trail making test, Stroop colour naming condition and Clinical Dementia Rating (CDR) scale, to evaluate global cognition, memory function, attention, visuospatial skills, processing speed, executive function and emotion, respectively.

TRIAL REGISTRATION NUMBER

NCT02706210; Pre-results.

Correlation-Weighted Sparse Group Representation for Brain Network Construction in MCI Classification

Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l₁-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network. Besides, the conventional sparse regularization often overlooks group structure in the brain network, i.e., a set of links (or connections) sharing similar attribute. To address these issues, we propose to construct BFCN by integrating both link strength and group structure information. Specifically, a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity, (2) link strength, and (3) group structure, in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics, as demonstrated by superior MCI classification accuracy of 81.8%. Moreover, our method is promising for its capability in modeling more biologically meaningful sparse brain networks, which will benefit both basic and clinical neuroscience studies.

Profiles of Neuropsychological Functioning in Children and Adolescents with Spina Bifida: Associations with Biopsychosocial Predictors and Functional Outcomes

OBJECTIVES

The current study examined neuropsychological performance among children with spina bifida (SB) to determine biological and functional correlates of distinct "profiles" of cognitive functioning.

METHODS

A total of 95 children with SB myelomeningocele (ages, 8-15 years) completed a neuropsychological assessment battery. Hierarchical and non-hierarchical cluster analyses were used to identify and confirm a cluster solution. Hypothesized predictors of cluster membership included lesion level, number of shunt surgeries, history of seizures, age, ethnicity, socio-economic status, and family stress. Outcomes included independence, academic success, expectations for the future, and quality of life.

RESULTS

Ward's cluster method indicated a three-cluster solution, and was replicated with two other cluster analytic methods. The following labels were applied to the clusters: "average to low average" (n=39), "extremely low to borderline" (n=27), and "broadly average with verbal strength" (n=29). Socio-econimc status, lesion level, and seizure history significantly predicted group membership. Cluster membership significantly predicted independence, academic success, parent expectations for the future, and child reported physical quality of life.

CONCLUSIONS

Findings from this study suggest qualitatively different cognitive profiles exist among children with SB, and the relevance of neuropsychological functioning for day-to-day adaptive functioning and quality of life. Clinical implications and future research are discussed. (JINS, 2016, 22, 804-815).

Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer's Disease

Brain network occupies an important position in representing abnormalities in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Currently, most studies only focused on morphological features of regions of interest without exploring the interregional alterations. In order to investigate the potential discriminative power of a morphological network in AD diagnosis and to provide supportive evidence on the feasibility of an individual structural network study, we propose a novel approach of extracting the correlative features from magnetic resonance imaging, which consists of a two-step approach for constructing an individual thickness network with low computational complexity. Firstly, multi-distance combination is utilized for accurate evaluation of between-region dissimilarity; and then the dissimilarity is transformed to connectivity via calculation of correlation function. An evaluation of the proposed approach has been conducted with 189 normal controls, 198 MCI subjects, and 163 AD patients using machine learning techniques. Results show that the observed correlative feature suggests significant promotion in classification performance compared with cortical thickness, with accuracy of 89.88% and area of 0.9588 under receiver operating characteristic curve. We further improved the performance by integrating both thickness and apolipoprotein E ɛ4 allele information with correlative features. New achieved accuracies are 92.11% and 79.37% in separating AD from normal controls and AD converters from non-converters, respectively. Differences between using diverse distance measurements and various correlation transformation functions are also discussed to explore an optimal way for network establishment.

Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans

Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is critical for identification of patients who are at risk for Alzheimer's disease (AD), so that early treatment can be administered. In this paper, we propose a pMCI/sMCI classification framework that harnesses information available in longitudinal magnetic resonance imaging (MRI) data, which could be incomplete, to improve diagnostic accuracy. Volumetric features were first extracted from the baseline MRI scan and subsequent scans acquired after 6, 12, and 18 months. Dynamic features were then obtained using the 18th month scan as the reference and computing the ratios of feature differences for the earlier scans. Features that are linearly or non-linearly correlated with diagnostic labels are then selected using two elastic net sparse learning algorithms. Missing feature values due to the incomplete longitudinal data are imputed using a low-rank matrix completion method. Finally, based on the completed feature matrix, we build a multi-kernel support vector machine (mkSVM) to predict the diagnostic label of samples with unknown diagnostic statuses. Our evaluation indicates that a diagnosis accuracy as high as 78.2 % can be achieved when information from the longitudinal scans is used-6.6 % higher than the case using only the reference time point image. In other words, information provided by the longitudinal history of the disease improves diagnosis accuracy.

Visuoconstructional Impairment in Subtypes of Mild Cognitive Impairment

Clock Drawing Test performance was examined alongside other neuropsychological tests in mild cognitive impairment (MCI). We tested the hypothesis that clock-drawing errors are related to executive impairment. The current research examined 86 patients with MCI for whom, in prior research, cluster analysis was used to sort patients into dysexecutive (dMCI, n = 22), amnestic (aMCI, n = 13), and multidomain (mMCI, n = 51) subtypes. First, principal components analysis (PCA) and linear regression examined relations between clock-drawing errors and neuropsychological test performance independent of MCI subtype. Second, between-group differences were assessed with analysis of variance (ANOVA) where MCI subgroups were compared to normal controls (NC). PCA yielded a 3-group solution. Contrary to expectations, clock-drawing errors loaded with lower performance on naming/lexical retrieval, rather than with executive tests. Regression analyses found increasing clock-drawing errors to command were associated with worse performance only on naming/lexical retrieval tests. ANOVAs revealed no differences in clock-drawing errors between dMCI versus mMCI or aMCI versus NCs. Both the dMCI and mMCI groups generated more clock-drawing errors than the aMCI and NC groups in the command condition. In MCI, language-related skills contribute to clock-drawing impairment.

[Subtypes of mild cognitive impairment in Parkinson's disease and factors predicting its becoming dementia]

INTRODUCTION

Cognitive impairment may appear at the earliest stages in Parkinson's disease (PD). To assess the prevalence of mild cognitive impairment (MCI) and its different subtypes, as transitional stage, is complicated by the lack of consensus diagnostic criteria.

AIM

To review MCI in PD (MCI-PD), diagnostic criteria and predictive factors of conversion to dementia.

PATIENTS AND METHODS

Systematic review of articles published in Medline (PubMed) using the combination of keywords 'mild cognitive impairment' and 'Parkinson's disease'.

RESULTS

MCI-PD diagnostic criteria published by the Movement Disorders Society are an interesting tool for the diagnosis, in spite they are not validated. Its implementation has the following limitations: 1) the heterogeneity of cognitive deficits described in PD; 2) a variable evolution of cognitive symptoms in PD which difficult the identification of dementia predictors; 3) selection of the more appropriate neuropsychological tests and cut-off points; 4) patient characteristics, disease stage and type of antiparkinsonian treatment.

CONCLUSIONS

Neuropsychological subtypes, neuroimaging, biomarkers or limitation in some instrumental activities seem to be very sensitive for detecting patients with MCI-PD and increased risk of conversion to dementia.

Mild cognitive impairment etiologic subtyping using pragmatic and conventional criteria: preliminary experience in the Florence VAS-COG clinic

BACKGROUND

Mild cognitive impairment (MCI) is an abnormal condition defined by the presence of cognitive decline not severe enough to fit dementia criteria. According to Winblad et al.'s criteria, the clinical distinction of MCI subtypes (amnestic/non-amnestic, single/multiple domain) is based on the cognitive profiling (conventional diagnosis) and infers possible different MCI etiologies. MCI prodromic of vascular dementia (Vasc-MCI) is thought to be characterized by a multiple domain profile. In our outpatient clinic (the "Florence VAS-COG clinic"), the diagnosis of MCI and of its different subtypes (vascular, degenerative, mixed) is based on a comprehensive evaluation of clinical and neuroimaging features (pragmatic diagnosis).

AIMS

To compare the pragmatic and conventional diagnoses in terms of etiologic subtyping of MCI.

METHODS

We retrospectively assessed the agreement between the two diagnoses in 30 MCI patients. Agreement was considered present when degenerative MCI was of the amnestic type (single or multiple domain) and Vasc-MCI was of the multiple domain type (amnestic or non-amnestic MCI).

RESULTS

In 15/30 (50 %) patients, the diagnoses were in disagreement: 5/9 (56 %) patients diagnosed with a degenerative MCI type presented a non-amnestic cognitive profile (4 single domain and 1 multiple domain); 10/21 (48 %) Vasc-MCI were classified as non-amnestic single domain.

CONCLUSIONS

The application of MCI etiologic subtyping using pragmatic or conventional diagnoses leads to different results. In our setting, not all the Vasc-MCI patients have a multiple domain profile. Our preliminary study suggests that the cognitive profile of Vasc-MCI is more heterogeneous than previously suggested.

Inclusion of Neuropsychological Scores in Atrophy Models Improves Diagnostic Classification of Alzheimer's Disease and Mild Cognitive Impairment

Brain atrophy in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are difficult to demarcate to assess the progression of AD. This study presents a statistical framework on the basis of MRI volumes and neuropsychological scores. A feature selection technique using backward stepwise linear regression together with linear discriminant analysis is designed to classify cognitive normal (CN) subjects, early MCI (EMCI), late MCI (LMCI), and AD subjects in an exhaustive two-group classification process. Results show a dominance of the neuropsychological parameters like MMSE and RAVLT. Cortical volumetric measures of the temporal, parietal, and cingulate regions are found to be significant classification factors. Moreover, an asymmetrical distribution of the volumetric measures across hemispheres is seen for CN versus EMCI and EMCI versus AD, showing dominance of the right hemisphere; whereas CN versus LMCI and EMCI versus LMCI show dominance of the left hemisphere. A 2-fold cross-validation showed an average accuracy of 93.9%, 90.8%, and 94.5%, for the CN versus AD, CN versus LMCI, and EMCI versus AD, respectively. The accuracy for groups that are difficult to differentiate like EMCI versus LMCI was 73.6%. With the inclusion of the neuropsychological scores, a significant improvement (24.59%) was obtained over using MRI measures alone.