Detection of early Alzheimer's disease in MCI patients by the combination of MMSE and an episodic memory test

Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network

This study aimed to address the issue of larger prediction errors existing in intelligent predictive tasks related to Alzheimer's disease (AD). A cohort of 487 enrolled participants was categorized into three groups: normal control (138 individuals), mild cognitive impairment (238 patients), and AD (111 patients) in this study. An improved multifeature squeeze-and-excitation-dilated residual network (MFSE-DRN) was proposed for two important AD predictions: clinical scores and conversion probability. The model was characterized as three modules: squeeze-and-excitation-dilated residual block (SE-DRB), multifusion pooling (MF-Pool), and multimodal feature fusion. To assess its performance, the proposed model was compared with two other novel models: ranking convolutional neural network (RCNN) and 3D vision geometrical group network (3D-VGGNet). Our method showed the best performance in the two AD predicted tasks. For the clinical scores prediction, the root-mean-square errors (RMSEs) and mean absolute errors (MAEs) of mini-mental state examination (MMSE) and AD assessment scale-cognitive 11-item (ADAS-11) were 1.97, 1.46 and 4.20, 3.19 within 6 months; 2.48, 1.69 and 4.81, 3.44 within 12 months; 2.67, 1.86 and 5.81, 3.83 within 24 months; 3.02, 2.03 and 5.09, 3.43 within 36 months, respectively. At the AD conversion probability prediction, the prediction accuracies within 12, 24, and 36 months reached to 88.0, 85.5, and 88.4%, respectively. The AD predication would play a great role in clinical applications.

Distinct neuroanatomical correlates of interference-related verbal episodic memory test in healthy older adults

Verbal learning test can include a trial of interference process that intrude initial learning and impose additional cognitive stress. However, it has been unclear whether the multiple memory processes underly different brain structural bases. We measured performances of word retrieval that represents distinct memory processes (initial learning, interference, and retention) and regional gray matter morphology from 230 cognitively unimpaired older adults. We identified three distinct multivariate pattern modes using canonical correlation analysis that map correspondence between memory and brain morphometry. The first mode comprised weights highly loaded on temporal lobe and overall performances. The second mode reflected subcortical volumes and initial learning performances. The third mode comprised thickness in the lateral prefrontal and parietal cortex and captured an ability to resist retroactive interference effect. While overall test performance reflected the temporal lobe and whole-gray matter volume, the interim trial of interference signifies neural correlates extending to subcortical and frontoparietal regions.

Association of time-varying sleep duration and cognitive function with mortality in the elderly: a 12-year community-based cohort study

BACKGROUND

Sleeping problems and cognitive impairment are common in elders. Baseline sleep duration and cognitive status are predictors of mortality. But few studies have explored whether longitudinal changes in sleep duration and cognitive function are related to mortality in older adults. The present study investigated the time-varying relationships of sleep duration and cognitive function with subsequent mortality among community-dwelling elders by using 12 years of repeated-measure data.

METHODS

Taichung Community Health Study for Elders (TCHS-E) is a retrospective, population-based cohort that started in 2009 (wave 1) with a total of 912 elders aged 65 years or above. Follow up was conducted in 2010 (wave 2), 2018 (wave 3), and 2020 (wave 4). Sleep duration and Mini-Mental State Examination (MMSE) forms were executed at baseline and three visits during follow-up. Time-varying Cox proportional hazards regression estimated adjusted hazard ratios (HRs) of mortality with 95% confidence intervals (CIs).

RESULTS

During about 12 years (9,396 person-years) follow-up, 329 deaths from all causes were documented, including 102 deaths due to expanded cardiovascular disease (CVD). In the multivariable-adjusted, time-varying Cox proportional hazard model, the adjusted HR values of all-cause mortality were 1.47 (1.02-2.12) for sleep duration > 9 h/day (vs. 7 h/day) and 1.81 (1.26-2.59) for MMSE < 27 (vs. 30). The adjusted HR values of the expanded CVD mortality were 2.91 (1.24-6.83) for MMSE of 29; 2.69 (1.20-6.05) for MMSE of 27-28; and 4.32 (95% CI: 1.92-9.74) for MMSE < 27. The dose-dependent relationship was significant (p < 0.001). The combinations of sleep duration longer than 9 h/day and MMSE < 27 were linked with the highest risks for expanded CVD and all-cause mortality.

CONCLUSIONS

Long sleep duration and low cognitive function were jointly and independently linked with higher risk of mortality in elders residing in community.

Psychosocial assessment practices for hematopoietic stem cell transplantation: a national survey study

Psychosocial health predicts and contributes to medical outcomes for patients undergoing hematopoietic stem cell transplantation (HSCT). Yet, there are no standards for psychosocial assessments or support for both patients and caregivers across the care continuum. To examine the current state of psychosocial care, clinicians were sent a survey of their psychosocial assessment practices for patients and caregivers undergoing HSCT via the Listservs of professional organizations. Descriptive statistics and bivariate analyses were performed to summarize the findings. While 96% of participants reported routine pre-HSCT psychosocial assessment of patients, only 10.6% routinely used a validated transplant risk-assessment measure. Just 27% routinely performed follow-up psychosocial assessments. In contrast, only 47% of participants routinely assessed the psychosocial needs of family caregivers pre-HSCT, and 13% routinely performed follow-up assessments for caregivers. Most (90%) reported social workers were the primary providers of assessments. While patient-report measures were used for evaluation, the majority of assessments were clinical interviews. No significant differences were found between programs that treated adult and pediatric patients versus those only treating adult patients. Our findings highlight the lack of standard psychosocial practices for patients and family caregivers undergoing HSCT and we offer recommendations to fill this gap.

Normative Data for a Neuropsychological Test Battery in the French Aging Population: 20-Year Follow-Up From the "PROgnostic OF Cardiovascular and Cerebrovascular Events" Study

OBJECTIVE

The study aimed to present relevant norms for the evaluation of pathological aging in the French population over a 20-year period, utilizing the "PROgnostic OF cardiovascular and cerebrovascular events" test and questionnaire.

METHODS

Three neuropsychological evaluations were administered over 20 years with 929 participants at the first cognitive evaluation (62-69 years old), 631 at the second (71-78 years old), and 293 at the third (81-88 years old). The tests and questionnaires were administered in the following order: McNair's Cognitive Complaints Questionnaire, Depression Questionnaire of Pichot, Mini Mental State Examination, Free and Cued Selective Reminding test, Benton Visual Retention Test, Digit-Symbol Substitution Test of the WAIS-III, Trail Making Test, Stroop Test, Verbal Fluency, and the Similarities subtest of the WAIS-III.

RESULTS

Normative data were presented at three time points of the repeated evaluation over 20 years (62-69 years, 71-78 years, and 81-88 years) and four educational levels (no diploma, primary school certificate, certificate of professional aptitude, and baccalaureate and above). The data showed a significant effect of educational level in all neuropsychological tests regardless of age. Gender primarily affected memory, Stroop scores, and Similarities scores.

CONCLUSION

This study highlights the importance of educational level and gender in the evaluation of the memory and executive function of elderly persons. Furthermore, the presented norms consider the self-report cognitive complaints and depression symptoms over a long period of life.

Gamma neuromodulation improves episodic memory and its associated network in amnestic mild cognitive impairment: a pilot study

Amnestic mild cognitive impairment (aMCI) is a predementia stage of Alzheimer's disease associated with dysfunctional episodic memory and limited treatment options. We aimed to characterize feasibility, clinical, and biomarker effects of noninvasive neurostimulation for aMCI. 13 individuals with aMCI received eight 60-minute sessions of 40-Hz (gamma) transcranial alternating current stimulation (tACS) targeting regions related to episodic memory processing. Feasibility, episodic memory, and plasma Alzheimer's disease biomarkers were assessed. Neuroplastic changes were characterized by resting-state functional connectivity (RSFC) and neuronal excitatory/inhibitory balance. Gamma tACS was feasible and aMCI participants demonstrated improvement in multiple metrics of episodic memory, but no changes in biomarkers. Improvements in episodic memory were most pronounced in participants who had the highest modeled tACS-induced electric fields and exhibited the greatest changes in RSFC. Increased RSFC was also associated with greater hippocampal excitability and higher baseline white matter integrity. This study highlights initial feasibility and the potential of gamma tACS to rescue episodic memory in an aMCI population by modulating connectivity and excitability within an episodic memory network.

Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis

OBJECTIVE

The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer's Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans).

METHODS

Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time.

RESULTS

There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression.

Psychosocial Assessment Practices for Hematopoietic Stem Cell Transplantation: A National Survey Study

Psychosocial health predicts and contributes to medical outcomes for patients undergoing hematopoietic stem cell transplantation (HSCT). Yet, there are no standards for psychosocial assessments or support for both patients and caregivers across the care continuum. To examine the current state of psychosocial care, clinicians were sent a survey of their psychosocial assessment practices for patients and caregivers undergoing HSCT via the Listservs of professional organizations. Descriptive statistics and bivariate analyses were performed to summarize the findings. While 96% of participants reported routine pre-HSCT psychosocial assessment of patients, only 10.6% routinely used a validated transplant risk-assessment measure. Just 27% routinely performed follow-up psychosocial assessments. In contrast, only 47% of participants routinely assessed the psychosocial needs of family caregivers pre-HSCT, and 13% routinely performed follow-up assessments for caregivers. Most (90%) reported social workers were the primary providers of assessments. While patient-report measures were used for evaluation, the majority of assessments were clinical interviews. No significant differences were found between programs that treated adult and pediatric patients versus those only treating adult patients. Our findings highlight the lack of standard psychosocial practices for patients and family caregivers undergoing HSCT and we offer recommendations to fill this gap.

Impact of word properties on list learning: An explanatory item analysis

OBJECTIVE

A variety of factors affect list learning performance and relatively few studies have examined the impact of word selection on these tests. This study examines the effect of both language and memory processing of individual words on list learning.

METHOD

Item-response data from 1,219 participants, Mage = 74.41 (SD = 7.13), Medu = 13.30 (SD = 2.72), in the Harmonized Cognitive Assessment Protocol were used. A Bayesian generalized (non)linear multilevel modeling framework was used to specify the measurement and explanatory item-response theory models. Explanatory effects on items due to learning over trials, serial position of words, and six word properties obtained through the English Lexicon Project were modeled.

RESULTS

A two parameter logistic (2PL) model with trial-specific learning effects produced the best measurement fit. Evidence of the serial position effect on word learning was observed. Robust positive effects on word learning were observed for body-object integration while robust negative effects were observed for word frequency, concreteness, and semantic diversity. A weak negative effect of average age of acquisition and a weak positive effect for the number of phonemes in the word were also observed.

CONCLUSIONS

Results demonstrate that list learning performance depends on factors beyond the repetition of words. Identification of item factors that predict learning could extend to a range of test development problems including translation, form equating, item revision, and item bias. In data harmonization efforts, these methods can also be used to help link tests via shared item features and testing of whether these features are equally explanatory across samples. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

A Multi-Modal and Multi-Atlas Integrated Framework for Identification of Mild Cognitive Impairment

Background: Multi-modal neuroimaging with appropriate atlas is vital for effectively differentiating mild cognitive impairment (MCI) from healthy controls (HC). Methods: The resting-state functional magnetic resonance imaging (rs-fMRI) and structural MRI (sMRI) of 69 MCI patients and 61 HC subjects were collected. Then, the gray matter volumes obtained from the sMRI and Hurst exponent (HE) values calculated from rs-fMRI data in the Automated Anatomical Labeling (AAL-90), Brainnetome (BN-246), Harvard–Oxford (HOA-112) and AAL3-170 atlases were extracted, respectively. Next, these characteristics were selected with a minimal redundancy maximal relevance algorithm and a sequential feature collection method in single or multi-modalities, and only the optimal features were retained after this procedure. Lastly, the retained characteristics were served as the input features for the support vector machine (SVM)-based method to classify MCI patients, and the performance was estimated with a leave-one-out cross-validation (LOOCV). Results: Our proposed method obtained the best 92.00% accuracy, 94.92% specificity and 89.39% sensitivity with the sMRI in AAL-90 and the fMRI in HOA-112 atlas, which was much better than using the single-modal or single-atlas features. Conclusion: The results demonstrated that the multi-modal and multi-atlas integrated method could effectively recognize MCI patients, which could be extended into various neurological and neuropsychiatric diseases.

Mild cognitive impairment in patients with acute heart failure does not limit the effectiveness of early phase II cardiac rehabilitation

BACKGROUND

Cardiac rehabilitation (CR) is commonly performed in patients with heart failure (HF) with mild cognitive impairment (MCI). However, whether MCI diminishes the benefit of early phase II CR is unclear.

AIM

This study aimed to clarify whether MCI diminishes the benefit of early phase II CR in patients hospitalized for HF.

DESIGN

The design of the work is a case-control study.

SETTING

All HF patients who underwent CR in acute care hospitals in Japan from April 2016 to March 2021.

POPULATION

Among the 574 patients who underwent CR, 204 were included in this study. Exclusion criteria were age <65 years, dependence for activities of daily living (ADLs) prior to admission, diagnosis of dementia or delirium, mini-mental state examination (MMSE) score at the commencement of CR<19, missing data, in-hospital death, and transfer to another department during hospitalization.

METHODS

Patients were divided into two groups, those with MCI (MCI group, N.=134) and those without MCI (non-MCI group, N.=70), based on MMSE score at the commencement of CR. Cognitive impairment was defined as a score of <19. MCI was defined as an MMSE score between 19 and 26, and normal cognitive function was defined as MMSE >26. The primary outcomes were the 6-minute walking distance (6MWD), Barthel Index (BI), and Short Physical Performance Battery (SPPB). All patients underwent guideline-based CR programs.

RESULTS

On admission, MCI patients had significantly lower BI (P<0.01, confidence interval [CI]: 4.9-20.4) and SPPB (P<0.01, CI: 1.1-3.1), despite being independent for ADLs before admission. In addition, 6MWD (P<0.01, CI: 31.2-97.2), BI (P=0.01, CI: 1.0-8.4), and SPPB (P<0.01, CI: 0.6-2.5) were significantly lower in the MCI group at the time of discharge. However, after propensity score matching to adjust for baseline characteristics, no significant differences in any primary outcome were found between the two groups.

CONCLUSIONS

The BI, SPPB, and 6MWD improvements due to CR were similar, regardless of MCI.

CLINICAL REHABILITATION IMPACT

Our results may inform the selection of appropriate rehabilitation interventions for patients with HF and MCI.

Sleep-disordered breathing was associated with lower health-related quality of life and cognitive function in a cross-sectional study of older adults

Background and objective

The clinical significance of sleep‐disordered breathing (SDB) in older age is uncertain. This study determined the prevalence and associations of SDB with mood, daytime sleepiness, quality of life (QOL) and cognition in a relatively healthy older Australian cohort.

Methods

A cross‐sectional analysis was conducted from the Study of Neurocognitive Outcomes, Radiological and retinal Effects of Aspirin in Sleep Apnoea. Participants completed an unattended limited channel sleep study to measure the oxygen desaturation index (ODI) to define mild (ODI 5–15) and moderate/severe (ODI ≥ 15) SDB, the Centre for Epidemiological Studies Scale, the Epworth Sleepiness Scale, the 12‐item Short‐Form for QOL and neuropsychological tests.

Results

Of the 1399 participants (mean age 74.0 years), 36% (273 of 753) of men and 25% (164 of 646) of women had moderate/severe SDB. SDB was associated with lower physical health‐related QOL (mild SDB: beta coefficient [β] −2.5, 95% CI −3.6 to −1.3, p < 0.001; moderate/severe SDB: β −1.8, 95% CI −3.0 to −0.6, p = 0.005) and with lower global composite cognition (mild SDB: β −0.1, 95% CI −0.2 to 0.0, p = 0.022; moderate/severe SDB: β −0.1, 95% CI −0.2 to 0.0, p = 0.032) compared to no SDB. SDB was not associated with daytime sleepiness nor depression.

Conclusion

SDB was associated with lower physical health‐related quality of life and cognitive function. Given the high prevalence of SDB in older age, assessing QOL and cognition may better delineate subgroups requiring further management, and provide useful treatment target measures for this age group.

The clinical implications for sleep‐disordered breathing (SDB) in older age remain uncertain. This study of healthy community‐dwelling older Australians reports significant associations between SDB and a lower physical health‐related quality of life, in contrast to other studies of SDB in older age, and between SDB and lower cognitive function.

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Evaluation of the Accuracy of Cognitive Screening Tests in Detecting Dementia Associated with Alzheimer's Disease: A Hierarchical Bayesian Latent Class Meta-Analysis

BACKGROUND

Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) are neuropsychological tests commonly used by physicians for screening cognitive dysfunction of Alzheimer's disease (AD). Due to different imperfect reference standards, the performance of MoCA and MMSE do not reach consensus. It is necessary to evaluate the consistence and differentiation of MoCA and MMSE in the absence of a gold standard for AD.

OBJECTIVE

We aimed to assess the accuracy of MoCA and MMSE in screening AD without a gold standard reference test.

METHODS

Studies were identified from PubMed, Web of Science, CNKI, Chinese Wanfang Database, China Science and Technology Journal Database, and Cochrane Library. Our search was limited to studies published in English and Chinese before August 2021. A hierarchical Bayesian latent class model was performed in meta-analysis when the gold standard was absent.

RESULTS

A total of 67 studies comprising 5,554 individuals evaluated for MoCA and 76,862 for MMSE were included in this meta-analysis. The pooled sensitivity was 0.934 (95% CI 0.906 to 0.954) for MoCA and 0.883 (95% CI 0.859 to 0.903) for MMSE, while the pooled specificity was 0.899 (95% CI 0.859 to 0.928) for MoCA and 0.903 (95% CI 0.879 to 0.923) for MMSE. MoCA was useful to rule out dementia associated with AD with lower negative likelihood ratio (LR-) (0.074, 95% CI 0.051 to 0.108). MoCA showed better performance with higher diagnostic odds ratio (DOR) (124.903, 95% CI 67.459 to 231.260).

CONCLUSION

MoCA had better performance than MMSE in screening dementia associated with AD from patients with mild cognitive impairment or healthy controls.

Multimodal Early Alzheimer's Detection, a Genetic Algorithm Approach with Support Vector Machines

Alzheimer's disease (AD) is a neurodegenerative disease that mainly affects older adults. Currently, AD is associated with certain hypometabolic biomarkers, beta-amyloid peptides, hyperphosphorylated tau protein, and changes in brain morphology. Accurate diagnosis of AD, as well as mild cognitive impairment (MCI) (prodromal stage of AD), is essential for early care of the disease. As a result, machine learning techniques have been used in recent years for the diagnosis of AD. In this research, we propose a novel methodology to generate a multivariate model that combines different types of features for the detection of AD. In order to obtain a robust biomarker, ADNI baseline data, clinical and neuropsychological assessments (1024 features) of 106 patients were used. The data were normalized, and a genetic algorithm was implemented for the selection of the most significant features. Subsequently, for the development and validation of the multivariate classification model, a support vector machine model was created, and a five-fold cross-validation with an AUC of 87.63% was used to measure model performance. Lastly, an independent blind test of our final model, using 20 patients not considered during the model construction, yielded an AUC of 100%.

Mini-Mental State Examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI)

BACKGROUND

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings.

OBJECTIVES

To determine the accuracy of the Mini Mental State Examination for the early detection of dementia in people with mild cognitive impairment SEARCH METHODS: We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data.

SELECTION CRITERIA

We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve.

MAIN RESULTS

In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis.

AUTHORS' CONCLUSIONS

Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer's Disease

The early diagnosis of Alzheimer's disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer's patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer's disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.

Association between Sour Taste SNP KCNJ2-rs236514, Diet Quality and Mild Cognitive Impairment in an Elderly Cohort

Differences in sour-taste thresholds have been identified in cognition-related diseases. Diet is a modulator of cognitive health, and taste perception influences dietary preferences and habits. Heritable genetics and polymorphisms in the KCNJ2 gene involved in the transduction of sour taste have been linked to variations in sour taste and non-gustatory functions. However, relationships between sour taste genetics, mild cognitive impairment, and diet quality are yet to be elucidated. This study investigated the associations between the presence of the KCNJ2-rs236514 variant (A) allele, diet quality indices, and mild cognitive impairment evaluated by the Mini-Mental State Examination (MMSE), in a secondary cross-sectional analysis of data from the Retirement Health & Lifestyle Study. Data from 524 elderly Australians (≥65y) were analyzed, using standard least squares regression and nominal logistic regression modeling, with demographic adjustments applied. Results showed that the presence of the KCNJ2-A allele is associated with increased proportions of participants scoring in the range indicative of mild or more severe cognitive impairment (MMSE score of ≤26) in the total cohort, and males. These associations remained statistically significant after adjusting for age, sex, and diet quality indices. The absence of association between the KCNJ2-A allele and cognitive impairment in women may be related to their higher diet quality scores in all indices. The potential link between sour taste genotype and cognitive impairment scores may be due to both oral and extra-oral functions of sour taste receptors. Further studies are required on the role and relationship of neurotransmitters, sour taste genotypes and sour taste receptors in the brain, and dietary implications, to identify potential risk groups or avenues for therapeutic or prophylactic interventions.

Gesturing helps memory encoding in aMCI

Encoding in episodic memory is a step often impaired in patients with amnestic Mild Cognitive Impairment (aMCI). However, procedural memory processes are still relatively preserved. In line with previous research on the enactment effect, we investigated the potential benefit of encoding words combined with imitative gestures on episodic memory. Based on the Grober and Buschke's free/cued recall procedure, we developed the Symbiosis test in which 13 patients with aMCI and 16 healthy elderly participants learned 32 words belonging to 16 different semantic categories either in a verbal encoding (A) or a bimodal (B; verbal and motor imitation) condition, using a blocked ABBA/BAAB procedure. Overall, memory retrieval was better in healthy participants than in patients with aMCI, and better for cued retrieval in the bimodal encoding (gesture cues) than the verbal encoding (category cues) condition, but there was no interaction effect between group and encoding conditions. These results show that performing concomitant gestures can enhance cued episodic memory retrieval in patients with aMCI and in healthy elderly controls. The Symbiosis test broadens the scope of the enactment effect, from action phrases to isolated words learning in patients with aMCI. Future work should investigate how bimodal encoding provides novel perspectives for memory rehabilitation in patients with aMCI.

The Independent and Interactive Associations of Bilingualism and Sex on Cognitive Performance in Hispanics/Latinos of the Hispanic Community Health Study/Study of Latinos

Sixty percent of Hispanics/Latinos are bilingual which research suggests may confer certain cognitive advantages. Female sex confers cognitive advantages in verbal learning and memory compared to male sex, regardless of race or ethnicity. Understanding the independent and interactive associations of bilingualism and sex with cognition may aid in predicting cognitive aging in Hispanics/Latinos. We examined baseline (2008-2011) data from the Hispanic Community Health Study/Study of Latinos, a multicenter, prospective community-based study. Our analyses included 6,110 males and females ≥45 years old who self-reported birth and parents' origin outside of the continental US, Spanish as their first language, and were evaluated in Spanish. Bilingualism was assessed along a Likert scale (1 = only Spanish to 4 = English>Spanish) for language proficiency (reading/spoken) and patterns of use (thinking/socializing). Cognitive testing included verbal learning, memory, fluency, and Digit Symbol Substitution (DSS). Linear regression models adjusted for relevant confounders, the complex survey design, and sampling weights. Participants' self-reported language proficiency was Spanish better than English, while patterns of use suggested more Spanish than English. Higher language proficiency was associated with higher performance on all cognitive indices while higher patterns of use associated with higher fluency and DSS scores (p-values < 0.01). Female sex was associated with higher performance on all cognitive indices (p-values < 0.05). There were no significant interactions with bilingualism (regardless of metric) by sex on cognition. For Hispanics/Latinos residing in the continental US and reporting birth and parents' origin elsewhere, bilingualism and female sex have independent cognitive benefits that are important to consider when evaluating cognitive performance.

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD). We hypothesized that an independent linguistic battery comprising of only the language components or subtests of popular test batteries could give a better clinical diagnosis for MCI-AD compared to using an exhaustive battery of tests. As such, we combined multiple clinical datasets and performed Exploratory Factor Analysis (EFA) to extract the underlying linguistic constructs from a combination of the Consortium to Establish a Registry for Alzheimer's disease (CERAD), Wechsler Memory Scale (WMS) Logical Memory (LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic variables with distinct underlying linguistic constructs that show Cronbach's alpha of 0.74 on the MCI-AD group and 0.87 on the healthy control group. Our machine learning evaluation showed a robust AUC of 0.97 when controlled for age, sex, race, and education, and a clinically reliable AUC of 0.88 without controlling for age, sex, race, and education. Overall, the linguistic battery showed a better diagnostic result compared to the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR), and a combination of MMSE and CDR.

Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records

About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinely-collected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.

The Pattern of Mu Rhythm Modulation During Emotional Destination Memory: Comparison Between Mild Cognitive Impairment Patients and Healthy Controls

Leading theories of affect development and empirical studies suggest that emotion can enhance memory in older adults. Destination memory which is defined as the ability to remember to whom we told a piece of information is being found to be compromised in aging. In the present study, we sought to assess destination memory using emotional stimuli (Emotional Destination Memory, EDM) in 16 older adults with mild cognitive impairment (MCI) and 16 healthy controls and shed light onto its potential neurophysiological aspects. We measured Mu suppression in frontal and temporal regions via EEG in real time while participants performed the task of EDM. Results showed no group differences in task performance but significant differences in fronto-temporal activations, specifically in electrodes F7 and F8. Differential Mu rhythm pattern was observed between healthy controls and MCI with the first exhibiting Mu suppression and the last Mu enhancement. Furthermore, Mu enhancement in temporal electrodes within the MCI group was associated with lower scores on EDM. The absence of group differences in the task can be explained by the fact that even if there are underlying structural or functional deficits in the MCI group, these deficits are manifested only at neurophysiological level and not at a behavioral level, which is a common pattern in the process of cognitive decline in its initial phases. The overall findings reveal that, even if there are not any behavioral decrements in MCI patients, they show reduced activations in fronto-temporal regions and this can be attributed to general impairment in emotional destination memory due to possible mirror neuron deficiency.

Deep language space neural network for classifying mild cognitive impairment and Alzheimer-type dementia

It has been quite a challenge to diagnose Mild Cognitive Impairment due to Alzheimer's disease (MCI) and Alzheimer-type dementia (AD-type dementia) using the currently available clinical diagnostic criteria and neuropsychological examinations. As such we propose an automated diagnostic technique using a variant of deep neural networks language models (DNNLM) on the verbal utterances of affected individuals. Motivated by the success of DNNLM on natural language tasks, we propose a combination of deep neural network and deep language models (D2NNLM) for classifying the disease. Results on the DementiaBank language transcript clinical dataset show that D2NNLM sufficiently learned several linguistic biomarkers in the form of higher order n-grams to distinguish the affected group from the healthy group with reasonable accuracy on very sparse clinical datasets.

Amyloid burden identifies neuropsychological phenotypes at increased risk of progression to Alzheimer's disease in mild cognitive impairment patients

Purpose

The extent of amyloid burden associated with cognitive impairment in amnestic mild cognitive impairment is unknown. The primary aim of the study was to determine the extent to which amyloid burden is associated to the cognitive impairment. The secondary objective was to test the relationship between amyloid accumulation and memory or cognitive impairment.

Materials and methods

In this prospective study 66 participants with amnestic mild cognitive impairment underwent clinical, neuropsychological and PET amyloid imaging tests. Composite scores assessing memory and non-memory domains were used to identify two clinical classes of neuropsychological phenotypes expressing different degree of cognitive impairment. Detection of amyloid status and definition of optimal amyloid ± cutoff for discrimination relied on unsupervised k-means clustering method.

Results

Threshold for identifying low and high amyloid retention groups was of SUVr = 1.3. Aß + participants showed poorer global cognitive and episodic memory performance than subjects with low amyloid deposition. Aß positivity significantly identified individuals with episodic memory impairment with a sensitivity and specificity of 80 and 79%, (χ2 = 21.48; P < 0.00001). Positive and negative predictive values were 82 and 76%, respectively. Amyloid deposition increased linearly as function of memory impairment with a rate of 0.13/ point of composite memory score (R = −44, P = 0.0003).

Conclusion

The amyloid burden of SUVr = 1.3 allows early identification of subjects with episodic memory impairment which might predict progression from MCI to Alzheimer’s disease.

Trial registration

EudraCT 2015-001184-39.

Differentiated Regional Homogeneity in Progressive Mild Cognitive Impairment: A Study With Post Hoc Label

PURPOSE

Mild cognitive impairment (MCI) is considered to be the clinical transition stage between patients with cognitively intact geriatrics and Alzheimer's disease (AD). When observed longitudinally, however, a certain proportion of patients with MCI are expected to revert to a cognitively intact state (MCI_R) while others either remain in the MCI state (MCI_S) or deteriorate into AD (MCI_P). It is worthwhile to investigate the divergence in the brain activities of these MCI groups with different post hoc labels.

METHODS

In this study, we employed the regional homogeneity (ReHo) measure to explore the characteristics of local brain activity in these MCI groups.

RESULTS

Compared to age-matched normal controls, our results exhibited that (1) in MCI_R group, ReHo index showed an increase in the left insula and a decrease in the left superior temporal gyrus; (2) in MCI_S group, ReHo index increased in the left orbital part of the inferior frontal gyrus (IFG_orb) and decreased in the left inferior parietal lobe; and (3) in MCI_P group, ReHo index elevated in the left IFG_orb and decreased in the left putamen.

CONCLUSIONS

The distinct ReHo changes in the individual MCI groups indicated a potential evidence for differentially active interventions for a specific patient with MCI.

Clinical validity of delayed recall tests as a gateway biomarker for Alzheimer's disease in the context of a structured 5-phase development framework

Although Alzheimer's disease criteria promote the use of biomarkers, their maturity in clinical routine still needs to be assessed. In the light of the oncology framework, we conducted a literature review on measures used to assess delayed recall impairment due to medial temporal lobe dysfunction (i.e., free and cued word list recall tests). Ample evidence is available for phases 1 (rationale for use), 2 (discriminative ability), and 3 (early detection ability) for many of the tests in routine use. Evidence about phase 4 (performance in real world) and phase 5 (quantify impact and costs) is yet to come. Administration procedures have been standardized and cutoff scores are well validated in large Alzheimer's disease and mild cognitive impaired series. Some aspects (e.g., different task formats), however, hamper the comparability of results among different populations and the reproducibility between laboratories. No definite guideline for their use can thus be proposed at the moment. Accordingly, the maturity of such markers is not yet sufficient and requires future investigation to promote the proper use of memory measures in clinical settings.

Advancing Alzheimer's research: A review of big data promises

OBJECTIVE

To review the current state of science using big data to advance Alzheimer's disease (AD) research and practice. In particular, we analyzed the types of research foci addressed, corresponding methods employed and study findings reported using big data in AD.

METHOD

Systematic review was conducted for articles published in PubMed from January 1, 2010 through December 31, 2015. Keywords with AD and big data analytics were used for literature retrieval. Articles were reviewed and included if they met the eligibility criteria.

RESULTS

Thirty-eight articles were included in this review. They can be categorized into seven research foci: diagnosing AD or mild cognitive impairment (MCI) (n=10), predicting MCI to AD conversion (n=13), stratifying risks for AD (n=5), mining the literature for knowledge discovery (n=4), predicting AD progression (n=2), describing clinical care for persons with AD (n=3), and understanding the relationship between cognition and AD (n=3). The most commonly used datasets are AD Neuroimaging Initiative (ADNI) (n=16), electronic health records (EHR) (n=11), MEDLINE (n=3), and other research datasets (n=8). Logistic regression (n=9) and support vector machine (n=8) are the most used methods for data analysis.

CONCLUSION

Big data are increasingly used to address AD-related research questions. While existing research datasets are frequently used, other datasets such as EHR data provide a unique, yet under-utilized opportunity for advancing AD research.

δ scores predict mild cognitive impairment and Alzheimer's disease conversions from nondemented states

INTRODUCTION

We tested the latent variable "δ" (for "dementia")'s ability to predict conversion to "mild cognitive impairment" (MCI) and Alzheimer's disease (AD).

METHODS

An ethnicity equivalent d homolog ("dEQ") was constructed in n = 1113 Mexican- American (MA) and n = 1958 non-Hispanic white (NHW) participants in the Texas Alzheimer's Research and Care Consortium. "Normal Controls" (NC) (n = 1276) and MCI cases (n = 611) were followed annually for up to 6 years [m = 4.7(0.6)].

RESULTS

22.0% (n = 281) of NC converted to "MCI" or "AD". 17.3%( n = 106) of MCI converted to "AD." Independently of covariates, each quintile increase in the dEQ scores of NC increased the odds of conversion by 52%. Each quintile increase in the dEQ scores of MCI cases increased the odds of conversion to AD almost three-fold.

DISCUSSION

Baseline δ scores predict MCI and AD conversions from nondemented states in MA and NHW.

Predicting probable Alzheimer's disease using linguistic deficits and biomarkers

BACKGROUND

The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls.

RESULTS

Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM).

CONCLUSIONS

Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.

A support vector machine-based method to identify mild cognitive impairment with multi-level characteristics of magnetic resonance imaging

Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer's disease (AD). Non-invasive diagnostic methods are desirable to identify MCI for early therapeutic interventions. In this study, we proposed a support vector machine (SVM)-based method to discriminate between MCI patients and normal controls (NCs) using multi-level characteristics of magnetic resonance imaging (MRI). This method adopted a radial basis function (RBF) as the kernel function, and a grid search method to optimize the two parameters of SVM. The calculated characteristics, i.e., the Hurst exponent (HE), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and gray matter density (GMD), were adopted as the classification features. A leave-one-out cross-validation (LOOCV) was used to evaluate the classification performance of the method. Applying the proposed method to the experimental data from 29 MCI patients and 33 healthy subjects, we achieved a classification accuracy of up to 96.77%, with a sensitivity of 93.10% and a specificity of 100%, and the area under the curve (AUC) yielded up to 0.97. Furthermore, the most discriminative features for classification were found to predominantly involve default-mode regions, such as hippocampus (HIP), parahippocampal gyrus (PHG), posterior cingulate gyrus (PCG) and middle frontal gyrus (MFG), and subcortical regions such as lentiform nucleus (LN) and amygdala (AMYG). Therefore, our method is promising in distinguishing MCI patients from NCs and may be useful for the diagnosis of MCI.

Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia

The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer's Dementia with 98.7% accuracy.

Neuroanatomical correlates of awareness of illness in patients with amnestic mild cognitive impairment who will or will not convert to Alzheimer's disease

BACKGROUND

Awareness of cognitive deficits may be reduced in mild cognitive impairment (MCI). This may have a detrimental effect on illness course and may be a predictor of subsequent conversion to AD. Although neuropsychological correlates have been widely investigated, no evidence of a neuroanatomical basis of the phenomenon has been reported yet. This study was aimed at investigating the neuroanatomical correlates of deficit awareness in amnestic MCI to determine whether they constitute risk factors for conversion to AD.

METHOD

A sample of 36 first-diagnosis amnestic MCI patients were followed for five years. At the first diagnostic visit they were administered an extensive diagnostic and clinical procedure and the Memory Insight Questionnaire (MIQ), measuring a total index and four sub-indices, to investigate awareness of deficits in dementia; they also underwent a high resolution T1-weighted Magnetic Resonance Imaging (MRI) investigation. Grey matter brain volumes were analysed on a voxel-by-voxel basis using Statistical Parametric Mapping 8. Data of 10 converter patients (CONV) and those of 26 non converter patients (NOCONV) were analysed using multiple regression models.

RESULTS

At baseline, self-awareness of memory deficits was poorer in CONV compared to NOCONV. Furthermore, reduced awareness of cognitive deficits in CONV correlated with reduced grey matter volume of the anterior cingulate (memory deficit awareness), right pars triangularis of the inferior frontal cortex (memory deficit awareness) and cerebellar vermis (total awareness), whereas in NOCONV it correlated with reduced grey matter volume of left superior (total awareness) and middle (language deficit awareness) temporal areas. Further, at baseline self-awareness of memory deficits were poorer in CONV than in NOCONV.

CONCLUSIONS

Defective awareness of cognitive deficits is underpinned by different mechanisms in CONV and NOCONV amnestic MCI patients. Our data support the hypothesis that poor awareness of cognitive deficit is a predictor of subsequent conversion to AD.

Mini-Mental State Examination (MMSE) for the detection of Alzheimer's disease and other dementias in people with mild cognitive impairment (MCI)

BACKGROUND

Dementia is a progressive global cognitive impairment syndrome. In 2010, more than 35 million people worldwide were estimated to be living with dementia. Some people with mild cognitive impairment (MCI) will progress to dementia but others remain stable or recover full function. There is great interest in finding good predictors of dementia in people with MCI. The Mini-Mental State Examination (MMSE) is the best-known and the most often used short screening tool for providing an overall measure of cognitive impairment in clinical, research and community settings.

OBJECTIVES

To determine the diagnostic accuracy of the MMSE at various thresholds for detecting individuals with baseline MCI who would clinically convert to dementia in general, Alzheimer's disease dementia or other forms of dementia at follow-up.

SEARCH METHODS

We searched ALOIS (Cochrane Dementia and Cognitive Improvement Specialized Register of diagnostic and intervention studies (inception to May 2014); MEDLINE (OvidSP) (1946 to May 2014); EMBASE (OvidSP) (1980 to May 2014); BIOSIS (Web of Science) (inception to May 2014); Web of Science Core Collection, including the Conference Proceedings Citation Index (ISI Web of Science) (inception to May 2014); PsycINFO (OvidSP) (inception to May 2014), and LILACS (BIREME) (1982 to May 2014). We also searched specialized sources of diagnostic test accuracy studies and reviews, most recently in May 2014: MEDION (Universities of Maastricht and Leuven, www.mediondatabase.nl), DARE (Database of Abstracts of Reviews of Effects, via the Cochrane Library), HTA Database (Health Technology Assessment Database, via the Cochrane Library), and ARIF (University of Birmingham, UK, www.arif.bham.ac.uk). No language or date restrictions were applied to the electronic searches and methodological filters were not used as a method to restrict the search overall so as to maximize sensitivity. We also checked reference lists of relevant studies and reviews, tracked citations in Scopus and Science Citation Index, used searches of known relevant studies in PubMed to track related articles, and contacted research groups conducting work on MMSE for dementia diagnosis to try to locate possibly relevant but unpublished data.

SELECTION CRITERIA

We considered longitudinal studies in which results of the MMSE administered to MCI participants at baseline were obtained and the reference standard was obtained by follow-up over time. We included participants recruited and clinically classified as individuals with MCI under Petersen and revised Petersen criteria, Matthews criteria, or a Clinical Dementia Rating = 0.5. We used acceptable and commonly used reference standards for dementia in general, Alzheimer's dementia, Lewy body dementia, vascular dementia and frontotemporal dementia.

DATA COLLECTION AND ANALYSIS

We screened all titles generated by the electronic database searches. Two review authors independently assessed the abstracts of all potentially relevant studies. We assessed the identified full papers for eligibility and extracted data to create two by two tables for dementia in general and other dementias. Two authors independently performed quality assessment using the QUADAS-2 tool. Due to high heterogeneity and scarcity of data, we derived estimates of sensitivity at fixed values of specificity from the model we fitted to produce the summary receiver operating characteristic curve.

MAIN RESULTS

In this review, we included 11 heterogeneous studies with a total number of 1569 MCI patients followed for conversion to dementia. Four studies assessed the role of baseline scores of the MMSE in conversion from MCI to all-cause dementia and eight studies assessed this test in conversion from MCI to Alzheimer´s disease dementia. Only one study provided information about the MMSE and conversion from MCI to vascular dementia. For conversion from MCI to dementia in general, the accuracy of baseline MMSE scores ranged from sensitivities of 23% to 76% and specificities from 40% to 94%. In relationship to conversion from MCI to Alzheimer's disease dementia, the accuracy of baseline MMSE scores ranged from sensitivities of 27% to 89% and specificities from 32% to 90%. Only one study provided information about conversion from MCI to vascular dementia, presenting a sensitivity of 36% and a specificity of 80% with an incidence of vascular dementia of 6.2%. Although we had planned to explore possible sources of heterogeneity, this was not undertaken due to the scarcity of studies included in our analysis.

AUTHORS' CONCLUSIONS

Our review did not find evidence supporting a substantial role of MMSE as a stand-alone single-administration test in the identification of MCI patients who could develop dementia. Clinicians could prefer to request additional and extensive tests to be sure about the management of these patients. An important aspect to assess in future updates is if conversion to dementia from MCI stages could be predicted better by MMSE changes over time instead of single measurements. It is also important to assess if a set of tests, rather than an isolated one, may be more successful in predicting conversion from MCI to dementia.

Longitudinal alteration of amygdalar functional connectivity in mild cognitive impairment subjects revealed by resting-state FMRI

Mild cognitive impairment (MCI) is considered to be the prodromal stage of Alzheimer's disease. The amygdala, which is considered to be a hub, has been shown to have widespread brain connections with many cortical regions. Longitudinal alterations in the functional connectivity of the amygdala remain unclear in MCI. We hypothesized that the impairment in the amygdala-cortical loop would be more severe in a follow-up MCI group than in a baseline MCI group and that these alterations would be related to the disease processes. To test this hypothesis, we used resting-state functional magnetic resonance imaging to investigate alterations in amygdalar connectivity patterns based on longitudinal data from 13 MCI subjects (8 males and 5 females). Compared to the baseline, decreases in functional connectivity were mainly found located between the amygdala and regions at the conjunction of the temporal-occipital system and the regions included in the default mode network in the follow-up MCI individuals. The alterations in the functional connectivity of the identified regions were validated in an independent dataset. Specifically, reduced amygdalar connectivity was significantly correlated with cognitive abilities. These findings indicate that impairments in the functional connectivity of the amygdala may be potential biomarkers of the progression of MCI.

Cognitive disorders and tau-protein expression among retired aluminum smelting workers

OBJECTIVES

To analyze cognitive functions and tau-protein expression in peripheral blood lymphocytes of retired aluminum (Al)-exposed workers.

METHODS

A total of 66 retired Al potroom workers and 70 unexposed controls were investigated. The cognitive functions were assessed with the Mini-Mental State Examination. The tau-protein expression in peripheral blood lymphocyte was analyzed with Western blot.

RESULTS

The cognitive functions of the exposed group were significantly decreased. Twelve mild cognitive impairment cases in the exposed group and four mild cognitive impairment cases in the control group were diagnosed. Significantly higher p-tau181 and p-tau231 levels were detected in the Al-exposed workers than in the control group.

CONCLUSIONS

The study suggests that long-term exposure to Al may cause cognitive disorders and that p-tau181 and p-tau231 might be useful indicators for monitoring cognitive decline in Al-exposed workers.

Early neuropsychological detection of Alzheimer's disease

Lifestyle modification offers a promising way of preventing or delaying Alzheimer's disease (AD). In particular, nutritional interventions can contribute to decrease the risk of dementia. The efficacy of such interventions should be assessed in individuals thought to be prone to AD. It is therefore necessary to identify markers that may help detecting AD as early as possible. This review will focus on subtle neuropsychological changes that may already exist in the predementia phase, and that could point to individuals at risk of dementia. Episodic memory decline appears consistently as the earliest sign of incipient typical AD. An episodic memory test that ensures deep encoding of information and assesses retrieval with free as well as cued recall appears as a useful tool to detect patients at an early stage of AD. Beyond the memory domain, category verbal fluency has been shown to decline early and to predict progression to AD. Moreover, in line with current diagnosis criteria for prodromal AD, combining neuropsychological scores and neuroimaging data allows a better discrimination of future AD patients than neuroimaging or neuropsychological data alone. Altogether, the detection of cognitive changes that are predictive of the typical form of probable AD already in the predementia stage points to at risk people who are the best target for therapeutic interventions, such as nutrition or physical exercise counseling or dietary interventions.

Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample

BACKGROUND

The delineation of the relative temporal trajectories of specific cognitive measures associated with Alzheimer's disease (AD) is important for evaluating preclinical markers and monitoring disease progression.

METHODS

We characterized the temporal trajectories of measures of verbal episodic memory, short-term visual memory, and mental status using data from 895 participants in the Baltimore Longitudinal Study of Aging.

RESULTS

The California Verbal Learning Test (CVLT) immediate recall was the first measure to decline, followed by CVLT delayed recall. However, further along the disease progression scale, CVLT delayed recall and visual memory changed more rapidly than CVLT immediate recall.

CONCLUSIONS

Our findings reconcile reports of early changes in immediate recall with greater reliance on delayed recall performance in clinical settings. Moreover, the utility of cognitive markers in evaluating AD progression depends on the stage of cognitive decline, suggesting that optimal endpoints in therapeutic trials may vary across different stages of the disease process.

Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease

OBJECTIVE

We constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD.

METHOD

Semantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, "generalized" versions of clustering and switching, and a method based on independent components analysis (ICA). Random forest classifiers based on raw scores were compared to "augmented" classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. Receiver operating characteristic (ROC) curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong's test and assessed validity and reliability of the augmented classifier.

RESULTS

Augmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC .97 vs. .95), MCI conversion (AUC .91 vs. .77), CDR-SOB increase (AUC .90 vs. .79), and FCI decrease (AUC .89 vs. .72). Measures of validity and stability over time support the use of the method.

CONCLUSION

Latent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money.

Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease

Alzheimer's disease (AD) is increasingly recognized as a disconnection syndrome, which leads to cognitive impairment due to the disruption of functional activity across large networks or systems of interconnected brain regions. We explored abnormal functional magnetic resonance imaging (fMRI) resting-state dynamics, functional connectivity, and weighted functional networks, in a sample of patients with severe AD (N = 18) and age-matched healthy volunteers (N = 21). We found that patients had reduced amplitude and regional homogeneity of low-frequency fMRI oscillations, and reduced the strength of functional connectivity, in several regions previously described as components of the default mode network, for example, medial posterior parietal cortex and dorsal medial prefrontal cortex. In patients with severe AD, functional connectivity was particularly attenuated between regions that were separated by a greater physical distance; and loss of long distance connectivity was associated with less efficient global and nodal network topology. This profile of functional abnormality in severe AD was consistent with the results of a comparable analysis of data on 2 additional groups of patients with mild AD (N = 17) and amnestic mild cognitive impairment (MCI; N = 18). A greater degree of cognitive impairment, measured by the mini-mental state examination across all patient groups, was correlated with greater attenuation of functional connectivity, particularly over long connection distances, for example, between anterior and posterior components of the default mode network, and greater reduction of global and nodal network efficiency. These results indicate that neurodegenerative disruption of fMRI oscillations and connectivity in AD affects long-distance connections to hub nodes, with the consequent loss of network efficiency. This profile was evident also to a lesser degree in the patients with less severe cognitive impairment, indicating that the potential of resting-state fMRI measures as biomarkers or predictors of disease progression in AD.

A hierarchy of predictors for dementia-free survival in old-age: results of the AgeCoDe study

OBJECTIVE

Progression from cognitive impairment (CI) to dementia is predicted by several factors, but their relative importance and interaction are unclear.

METHOD

We investigated numerous such factors in the AgeCoDe study, a longitudinal study of general practice patients aged 75+. We used recursive partitioning analysis (RPA) to identify hierarchical patterns of baseline covariates that predicted dementia-free survival.

RESULTS

Among 784 non-demented patients with CI, 157 (20.0%) developed dementia over a follow-up interval of 4.5 years. RPA showed that more severe cognitive compromise, revealed by a Mini-Mental State Examination (MMSE) score < 27.47, was the strongest predictor of imminent dementia. Dementia-free survival time was shortest (mean 2.4 years) in such low-scoring patients who also had impaired instrumental activities of daily living (iADL) and subjective memory impairment with related worry (SMI-w). Patients with identical characteristics but without SMI-w had an estimated mean dementia-free survival time of 3.8 years, which was still shorter than in patients who had subthreshold MMSE scores but intact iADL (4.2-5.2 years).

CONCLUSION

Hierarchical patterns of readily available covariates can predict dementia-free survival in older general practice patients with CI. Although less widely appreciated than other variables, iADL impairment appears to be an especially noteworthy predictor of progression to dementia.

Improvement of dementia screening accuracy of mini-mental state examination by education-adjustment and supplementation of frontal assessment battery performance

This study aimed to investigate whether the demographic variable-adjustment and supplementation of Frontal Assessment Battery (FAB) score can improve the screening ability of Mini-Mental State Examination (MMSE) for dementia and its subtypes. Five hundred forty-one non-demented comparison (NC) and 474 dementia (320 Alzheimer's disease [AD]; 139 non-Alzheimer's disease dementia [NAD]; and 15 mixed AD-NAD dementia) individuals living in the community were included. Education-adjusted MMSE (MMSE-edu) score showed significantly better screening accuracy for overall dementia, AD, and NAD than MMSE raw score. FAB-supplemented MMSE (MMSE-FAB) score had significantly better screening ability for NAD, but not for overall dementia and AD, than MMSE raw score alone. Additional supplementation of FAB to MMSE-edu further increased the ability for overall dementia or NAD screening, but not for AD screening. Further education adjustment of MMSE-FAB also improved its ability for overall dementia, AD, and NAD screening. These results strongly support the usefulness of education-adjustment and supplementation of frontal function assessment to improve screening performance of MMSE for dementia and its subtypes, NAD in particular.

Minimal functional brain differences between older adults with and without mild cognitive impairment during the stroop

This investigation compared the neural correlates of inhibition in normal older adults (OAs) and OAs with mild cognitive impairment (MCI). It was hypothesized the MCI group would require a greater amount of resources for inhibition, and therefore display greater functional activation in specific regions of interest (ROIs). Twenty-six OAs without and 17 with MCI completed the Stroop task during functional neuroimaging, and completed additional out-of-scanner neuropsychological measures. During inhibition, there were minimal functional Magnetic Resonance Imaging (fMRI) differences found between groups in a priori specified ROIs and with post-hoc multiple regression analyses. However, these minimal differences did not survive corrected thresholds. Robust differences were found with several tasks of a neuropsychological screening battery. The results of this study suggest only very minimal group differences in fMRI activation during inhibition which may not reliably identify MCI, and this condition may be best detected by traditional neuropsychological techniques.

Rapid β-amyloid deposition and cognitive impairment after cholinergic denervation in APP/PS1 mice

Although extensive evidence supports the role of β-amyloid (Aβ) in Alzheimer disease (AD), the neurotoxic mechanisms underlying AD pathogenesis are not understood. On the other hand, neuronal loss is the pathologic feature that best correlates with cognitive impairment. We hypothesized that cholinergic neurodegeneration may lead to Aβ deposition and tested this by inducing selective cholinergic lesions in APPswe/PS1dE9 mice with murine p75 saporin (mu p75-SAP). Intracerebroventricular lesions that removed approximately 50% of cholinergic innervation to the cortex and hippocampus were induced in animals with incipient (∼3 months) and marked (∼7 months of age) Aβ deposition. Cranial windows were implanted, and Aβ deposition was monitored in vivo using multiphoton microscopy. Deposition of Aβ was increased as soon as 7 days after the lesion, and this effect was maintained up to 3 months later. Postmortem studies using immunohistochemistry with an anti-Aβ antibody corroborated these findings in both cerebral cortex and hippocampus. Tau phosphorylation was also significantly increased after the lesions. Cholinergic denervation resulted in early memory impairment at 3 months of age that worsened with age (∼7 months); there was a synergistic effect between cholinergic denervation and the presence of APP/PS1 transgenes. Altogether, our data suggest that cholinergic denervation may trigger Aβ deposition and synergistically contribute to cognitive impairment in AD patients.

Suitability of neuropsychological tests in patients with vascular dementia (VaD)

The concept of vascular dementia (VaD) has evolved with the introduction of vascular cognitive impairment (VCI). VaD patients show predominantly frontal cognitive deficits. The executive area is particularly affected, while memory deficits are less frequent in patients with VaD than patients with AD. Several neuropsychological tests are available for the diagnosis and differentiation of dementias, but there are currently no tests developed specifically for VaD. We proposed to evaluate various neuropsychological tests, on the basis of evidence from different studies, in order to clarify the utility of the neuropsychological assessment in vascular dementia.

Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity

Alzheimer's disease (AD), the most prevalent cause of dementia in the elderly, is characterized by progressive cognitive and intellectual deficits. Most patients with mild cognitive impairment (MCI) are thought to be in a very early stage of AD. Resting-state functional magnetic resonance imaging reflects spontaneous brain activities and/or the endogenous/background neurophysiological process of the human brain. Regional Homogeneity (ReHo) can provide a fast method for mapping regional activity across the whole brain. Little has been previously published about where or how spontaneous activity differs between MCI and AD, although many previous fMRI studies have shown that the activity pattern is altered in MCI/AD. In the present study, we first used the ReHo method to explore differences in regional spontaneous activities throughout the whole brain between normal controls (NC) and people with MCI and with AD. A one-way ANOVA was performed to determine the regions in which the ReHo differs between the three groups, and then a post hoc analysis was performed to evaluate differences in the pattern among the three groups. Finally a correlation analysis was done between the ReHo index of these regions and clinical variables in order to evaluate the relationship between ReHo and cognitive measures in the AD and MCI groups. An exploratory classification analysis also demonstrated that ReHo measures were able to correctly separate subjects in 71.4% of the cases. Altered brain spontaneous activations were found in the medial prefrontal cortex, the bilateral posterior cingulate gyrus/precuneus and the left inferior parietal lobule (IPL) in both MCI and AD. In MCI, the ReHo index in the left IPL was higher than that of the NC, which could indicate the presence of a compensatory mechanism in MCI. More obviously, the correlation analysis indicated that the lower the memory and other cognitive abilities, the lower the ReHo in patients with MCI and AD. Combining our findings with the results in earlier studies, we propose that the spontaneous activity pattern in the resting state could potentially be used as a clinical marker for MCI/AD.