Predictors That a Diagnosis of Mild Cognitive Impairment Will Remain Stable 3 Years Later

Cerebrospinal fluid neurofilament light predicts longitudinal diagnostic change in patients with psychiatric and neurodegenerative disorders

OBJECTIVE

People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown.

METHODS

We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other.

RESULTS

Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone.

CONCLUSIONS

CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.

Characterizing mild cognitive impairment to predict incident dementia in adults with bipolar disorder: What should the benchmark be?

Objective: Although mild cognitive impairment (MCI) is generally considered a risk state for dementia, its prevalence and association with dementia are impacted by the number of tests and cut-points used to assess cognition and define "impairment," and sources of norms. Here, we investigate how these methodological variations impact estimates of incident dementia in adults with bipolar disorder (BD), a vulnerable population with pre-existing cognitive deficits and increased dementia risk. Method: Neuropsychological data from 148 adults with BD and 13,610 healthy controls (HC) were drawn from the National Alzheimer's Coordinating Center. BD participants' scores were standardized against published norms and again using regression-based norms generated from HC within the same catchment area as individual BD patients ("site-specific norms"), varying the number of within-domain tests (one vs. two) and the cut-points (-1 vs. -1.5 SD) used to operationalize MCI. Results: Site-specific norms were more sensitive to incident dementia (88.6%-94.3%) than published norms (74.3%-88.6%), but only when using a "single test" definition of impairment. Specificity (22.1%-74.3%), accuracy (37.8%-68.9%), and positive predictive values (26.1%-38.3%) were overall poor. Applying a "single test" definition of impairment resulted in better negative predictive values using site-specific (92.3%-93.3%) than published norms (83.6%-86.2%), and a substantial increase in relative risk of incident dementia relative to published norms. Conclusions: Neuropsychologists should define "impairment" as scores below -1.0 or -1.5 SD on at least two within-domain measures when using published norms to interpret cognitive performance in adults with BD.

Mild Cognitive Impairment, Reversion Rates, and Associated Factors: Comparison of Two Diagnostic Approaches

BACKGROUND

As mild cognitive impairment (MCI) is typically used to identify prodromal stages of dementia, it is essential to identify MCI criteria with high diagnostic stability and prediction of dementia. Moreover, further investigation into pinpointing key factors for reversion is required to foresee future prognosis of MCI patients accurately.

OBJECTIVE

To explore disparities in diagnostic stability by examining reversion rates produced by two operationalizations of the MCI definition: the widely applied Petersen criteria and a version of the Neuropsychological (NP) criteria and to identify cognitive, lifestyle, and health related factors for reversion.

METHODS

MCI was retrospectively classified in a sample from the Swedish community-based study Good Aging in Skåne with the Petersen criteria (n = 744, median follow-up = 7.0 years) and the NP criteria (n = 375, median follow-up, 6.7 years), respectively. Poisson regression models estimated the effect of various factors on the likelihood of incident reversion.

RESULTS

Reversion rates were 323/744 (43.4%, 95% confidence intervals (CI): 39.8; 47.0) and 181/375 (48.3% 95% CI: 43.2; 53.5) for the Petersen criteria and NP criteria, respectively. Participants with impairment in a single cognitive domain, regular alcohol consumption, living with someone, older age, and lower body mass index had a higher likelihood of reverting to normal.

CONCLUSION

Reversion rates were similar for Petersen and NP criteria indicating that one definition is not superior to the other regarding diagnostic stability. Additionally, the results highlight important aspects such as multiple domain MCI, cohabitation, and the role of alcohol on predicting the trajectory of those diagnosed with MCI.

Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI

Accurate diagnosis of the initial phase of Alzheimer's disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer's and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01-.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson's correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study's primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.

Verbal fluency response times predict incident cognitive impairment

Introduction

In recent decades, researchers have defined novel methods for scoring verbal fluency tasks. In this work, we evaluate novel scores based on speed of word responses.

Methods

We transcribed verbal fluency recordings from 641 cases of incident cognitive impairment (ICI) and matched controls, all participants in a large national epidemiological study. Timing measurements of utterances were used to calculate a speed score for each recording. Traditional raw and speed scores were entered into Cox proportional hazards (CPH) regression models predicting time to ICI.

Results

Concordance of the CPH model with speed scores was 0.599, an improvement of 3.4% over a model with only raw scores and demographics. Scores with significant effects included animals raw and speed scores, and letter F speed score.

Discussion

Novel verbal fluency scores based on response times could enable use of remotely administered fluency tasks for early detection of cognitive decline.

Highlights

 The current work evaluates prognostication with verbal fluency speed scores. These speed scores improve survival models predicting cognitive decline. Cases with progressive decline have some characteristics suggestive of Alzheimer's disease. The subset of acute decliners is probably pathologically heterogeneous.

Do perceptions of cognitive changes matter in self-management behaviors among persons with mild cognitive impairment?

BACKGROUND AND OBJECTIVES

This secondary analysis examined 1) the association between illness perceptions (perceived understanding and cause of mild cognitive impairment [MCI]) and self-management behaviors for cognitive health, and 2) whether sociodemographic and clinical factors moderate such relationships among persons with MCI.

RESEARCH DESIGN AND METHODS

We conducted a cross-sectional study of 85 participants using baseline data from the Return of Amyloid Imaging Scan Results (RAISR) Study. The coherence and causality subscales of the Revised Illness Perceptions Questionnaires were used. Self-management behaviors (dietary changes, physical activity, mental activities, dietary supplements) were assessed using the Risk Evaluation and Education for ALzheimer's disease health behavior measure. Sociodemographic and clinical information was extracted from patients' medical records. We performed hierarchical linear regression and binary logistic regression.

RESULTS

We found no main effects for illness perceptions and self-management of cognitive health. Interaction effects were detected, including: 1) coherence and age on the total number of self-management behaviors (b = 0.01, p = 0.04) and on physical activity (p = 0.04, OR = 1.02, 95% CI = [1.00, 1.03]), 2) causality and age on dietary supplements (p = 0.03, OR = 1.31, 95% CI = [1.02, 1.67]), and 3) causality and education on mental activities (p = 0.02, OR = 0.44, 95% CI = [0.22, 0.88]).

IMPLICATION AND DISCUSSIONS

Findings suggest that age and education moderate the relationship between illness perceptions and self-management behaviors. Healthcare professionals should consider subjective perceptions about MCI in light of sociodemographic and clinical factors when discussing cognitive health self-management.

Cerebrovascular and Neurodegenerative Pathologies in Long-Term Stable Mild Cognitive Impairment

BACKGROUND

Mild cognitive impairment (MCI) is considered by some to be a prodromal phase of a progressive disease (i.e., neurodegeneration) resulting in dementia; however, a substantial portion of individuals (ranging from 5-30%) remain cognitively stable over the long term (sMCI). The etiology of sMCI is unclear but may be linked to cerebrovascular disease (CVD), as evidence from longitudinal studies suggest a significant proportion of individuals with vasculopathy remain stable over time.

OBJECTIVE

To quantify the presence of neurodegenerative and vascular pathologies in individuals with long-term (>5-year) sMCI, in a preliminary test of the hypothesis that CVD may be a contributor to non-degenerative cognitive impairment. We expect frequent vasculopathy at autopsy in sMCI relative to neurodegenerative disease, and relative to individuals who convert to dementia.

METHODS

In this retrospective study, using data from the National Alzheimer's Coordinating Center, individuals with sMCI (n = 28) were compared to those with MCI who declined over a 5 to 9-year period (dMCI; n = 139) on measures of neurodegenerative pathology (i.e., Aβ plaques, neurofibrillary tangles, TDP-43, and cerebral amyloid angiopathy) and CVD (infarcts, lacunes, microinfarcts, hemorrhages, and microbleeds).

RESULTS

Alzheimer's disease pathology (Aβ plaques, neurofibrillary tangles, and cerebral amyloid angiopathy) was significantly higher in the dMCI group than the sMCI group. Microinfarcts were the only vasculopathy associated with group membership; these were more frequent in sMCI.

CONCLUSION

The most frequent neuropathology in this sample of long-term sMCI was microinfarcts, tentatively suggesting that silent small vessel disease may characterize non-worsening cognitive impairment.

Predictors of Mild Cognitive Impairment Stability, Progression, or Reversion in the Lothian Birth Cohort 1936

BACKGROUND

Mild cognitive impairment (MCI) describes a borderland between healthy cognition and dementia. Progression to and reversion from MCI is relatively common but more research is required to understand the factors affecting this fluidity and improve clinical care interventions.

OBJECTIVE

We explore these transitions in MCI status and their predictive factors over a six-year period in a highly-phenotyped longitudinal study, the Lothian Birth Cohort 1936.

METHODS

MCI status was derived in the LBC1936 at ages 76 (n = 567) and 82 years (n = 341) using NIA-AA diagnostic guidelines. Progressions and reversions between healthy cognition and MCI over the follow-up period were assessed. Multinomial logistic regression assessed the effect of various predictors on the likelihood of progressing, reverting, or maintaining cognitive status.

RESULTS

Of the 292 participants who completed both time points, 41 (14%) participants had MCI at T1 and 56 (19%) at T2. Over the follow-up period, 74%remained cognitively healthy, 12%transitioned to MCI, 7%reverted to healthy cognition, and 7%maintained their baseline MCI status. Findings indicated that membership of these transition groups was affected by age, cardiovascular disease, and number of depressive symptoms.

CONCLUSION

Findings that higher baseline depressive symptoms increase the likelihood of reverting from MCI to healthy cognition indicate that there may be an important role for the treatment of depression for those with MCI. However, further research is required to identify prevention strategies for those at high risk of MCI and inform effective interventions that increase the likelihood of reversion to, and maintenance of healthy cognition.

Classification and Graphical Analysis of Alzheimer's Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

Graphical, voxel, and region-based analysis has become a popular approach to studying neurodegenerative disorders such as Alzheimer's disease (AD) and its prodromal stage [mild cognitive impairment (MCI)]. These methods have been used previously for classification or discrimination of AD in subjects in a prodromal stage called stable MCI (MCIs), which does not convert to AD but remains stable over a period of time, and converting MCI (MCIc), which converts to AD, but the results reported across similar studies are often inconsistent. Furthermore, the classification accuracy for MCIs vs. MCIc is limited. In this study, we propose combining different neuroimaging modalities (sMRI, FDG-PET, AV45-PET, DTI, and rs-fMRI) with the apolipoprotein-E genotype to form a multimodal system for the discrimination of AD, and to increase the classification accuracy. Initially, we used two well-known analyses to extract features from each neuroimage for the discrimination of AD: whole-brain parcelation analysis (or region-based analysis), and voxel-wise analysis (or voxel-based morphometry). We also investigated graphical analysis (nodal and group) for all six binary classification groups (AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs). Data for a total of 129 subjects (33 AD, 30 MCIs, 31 MCIc, and 35 HCs) for each imaging modality were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) homepage. These data also include two APOE genotype data points for the subjects. Moreover, we used the 2-mm AICHA atlas with the NiftyReg registration toolbox to extract 384 brain regions from each PET (FDG and AV45) and sMRI image. For the rs-fMRI images, we used the DPARSF toolbox in MATLAB for the automatic extraction of data and the results for REHO, ALFF, and fALFF. We also used the pyClusterROI script for the automatic parcelation of each rs-fMRI image into 200 brain regions. For the DTI images, we used the FSL (Version 6.0) toolbox for the extraction of fractional anisotropy (FA) images to calculate a tract-based spatial statistic. Moreover, we used the PANDA toolbox to obtain 50 white-matter-region-parcellated FA images on the basis of the 2-mm JHU-ICBM-labeled template atlas. To integrate the different modalities and different complementary information into one form, and to optimize the classifier, we used the multiple kernel learning (MKL) framework. The obtained results indicated that our multimodal approach yields a significant improvement in accuracy over any single modality alone. The areas under the curve obtained by the proposed method were 97.78, 96.94, 95.56, 96.25, 96.67, and 96.59% for AD vs. HC, MCIs vs. MCIc, AD vs. MCIc, AD vs. MCIs, HC vs. MCIc, and HC vs. MCIs binary classification, respectively. Our proposed multimodal method improved the classification result for MCIs vs. MCIc groups compared with the unimodal classification results. Our study found that the (left/right) precentral region was present in all six binary classification groups (this region can be considered the most significant region). Furthermore, using nodal network topology, we found that FDG, AV45-PET, and rs-fMRI were the most important neuroimages, and showed many affected regions relative to other modalities. We also compared our results with recently published results.

A Pilot Double-Blind Randomized Controlled Trial of Cognitive Training Combined with Transcranial Direct Current Stimulation for Amnestic Mild Cognitive Impairment

BACKGROUND

There is currently no effective intervention for improving memory in people at increased risk for dementia. Cognitive training (CT) has been promising, though effects are modest, particularly at follow-up.

OBJECTIVE

To investigate whether adjunctive non-invasive brain stimulation (transcranial direct current stimulation, tDCS) could enhance the memory benefits of CT in amnestic mild cognitive impairment (aMCI).

METHODS

Participants with aMCI were randomized to receive CT with either Active tDCS (2 mA for 30 min and 0.016 mA for 30 min) or Sham tDCS (0.016 mA for 60 min) for 15 sessions over a period of 5 weeks in a double-blind, sham-controlled, parallel group clinical trial. The primary outcome measure was the California Verbal Learning Task 2nd Edition.

RESULTS

68 participants commenced the intervention. Intention-to-treat (ITT) analysis showed that the CT+Active tDCS group significantly improved at post treatment (p = 0.033), and the CT+Sham tDCS group did not (p = 0.050), but there was no difference between groups. At the 3-month follow-up, both groups showed large-sized memory improvements compared to pre-treatment (CT+Active tDCS: p < 0.01, d = 0.99; CT+Sham tDCS: p < 0.01, d = 0.74), although there was no significant difference between groups.

CONCLUSION

This study found that CT+Active tDCS did not produce greater memory improvement compared to CT+Sham tDCS. Large-sized memory improvements occurred in both conditions at follow-up. One possible interpretation, based on recent novel findings, is that low intensity tDCS (used as 'sham') may have contributed biological effects. Further work should use a completely inert tDCS sham condition.

Plasma Transthyretin as a Predictor of Amnestic Mild Cognitive Impairment Conversion to Dementia

Amnestic mild cognitive impairment (MCI) is a prodromal stage of dementia, with a higher incidence of these patients progressing to Alzheimer’s disease (AD) than normal aging people. A biomarker for the early detection and prediction for this progression is important. We recruited MCI subjects in three teaching hospitals and conducted longitudinal follow-up for 5 years at one-year intervals. Cognitively healthy controls were recruited for comparisom at baseline. Plasma transthyretin (TTR) levels were measured by ELISA. Survival analysis with time to AD conversion as an outcome variable was calculated with the multivariable Cox proportional hazards models using TTR as a continuous variable with adjustment for other covariates and bootstrapping resampling analysis. In total, 184 MCI subjects and 40 sex- and age-matched controls were recruited at baseline. At baseline, MCI patients had higher TTR levels compared with the control group. During the longitudinal follow-ups, 135 MCI patients (73.4%) completed follow-up at least once. The TTR level was an independent predictor for MCI conversion to AD when using TTR as a continuous variable (p = 0.023, 95% CI 1.001–1.007). In addition, in MCI converters, the TTR level at the point when they converted to AD was significantly lower than that at baseline (328.6 ± 66.5 vs. 381.9 ± 77.6 ug/ml, p < 0.001). Our study demonstrates the temporal relationship between the plasma TTR level and the conversion from MCI to AD.

Mild cognitive impairment in primary care: a clinical review

Dementia is projected to become a global health priority but often not diagnosed in its earlier preclinical stage which is mild cognitive impairment (MCI). MCI is generally referred as a transition state between normal cognition and Alzheimer’s disease. Primary care physicians play an important role in its early diagnosis and identification of patients most likely to progress to Alzheimer’s disease while offering evidenced-based interventions that may reverse or halt the progression to further cognitive impairment. The aim of this review is to introduce the concept of MCI in primary care through a case-based clinical review. We discuss the case of a patient with MCI and provide an evidence-based framework for assessment, early recognition and management of MCI while addressing associated risk factors, neuropsychiatric symptoms and prognosis.