Volumetric MRI vs clinical predictors of Alzheimer disease in mild cognitive impairment

A Case for Automated Segmentation of MRI Data in Neurodegenerative Diseases: Type II GM1 Gangliosidosis

BACKGROUND

Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproducible results. However, since these analysis pipelines were trained on or run based on atlases consisting of neurotypical controls, it is important to evaluate how accurate these methods are for neurodegenerative diseases. In this study, we compared five fully automated segmentation pipelines, including FSL, Freesurfer, volBrain, SPM12, and SimNIBS, with a manual segmentation process in GM1 gangliosidosis patients and neurotypical controls.

METHODS

We analyzed 45 MRI scans from 16 juvenile GM1 gangliosidosis patients, 11 MRI scans from 8 late-infantile GM1 gangliosidosis patients, and 19 MRI scans from 11 neurotypical controls. We compared the results for seven brain structures, including volumes of the total brain, bilateral thalamus, ventricles, bilateral caudate nucleus, bilateral lentiform nucleus, corpus callosum, and cerebellum.

RESULTS

We found volBrain's vol2Brain pipeline to have the strongest correlations with the manual segmentation process for the whole brain, ventricles, and thalamus. We also found Freesurfer's recon-all pipeline to have the strongest correlations with the manual segmentation process for the caudate nucleus. For the cerebellum, we found a combination of volBrain's vol2Brain and SimNIBS' headreco to have the strongest correlations, depending on the cohort. For the lentiform nucleus, we found a combination of recon-all and FSL's FIRST to give the strongest correlations, depending on the cohort. Lastly, we found segmentation of the corpus callosum to be highly variable.

CONCLUSIONS

Previous studies have considered automated segmentation techniques to be unreliable, particularly in neurodegenerative diseases. However, in our study, we produced results comparable to those obtained with a manual segmentation process. While manual segmentation processes conducted by neuroradiologists remain the gold standard, we present evidence to the capabilities and advantages of using an automated process that includes the ability to segment white matter throughout the brain or analyze large datasets, which pose feasibility issues to fully manual processes. Future investigations should consider the use of artificial intelligence-based segmentation pipelines to determine their accuracy in GM1 gangliosidosis, lysosomal storage disorders, and other neurodegenerative diseases.

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BACKGROUND

Mild cognitive impairment (MCI) confers a high annual risk of 10-15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease.

OBJECTIVE

The aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia.

METHODS

Baseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients.

RESULTS

Out of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters.

CONCLUSION

Combining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

Can Brain Volume-Driven Characteristic Features Predict the Response of Alzheimer's Patients to Repetitive Transcranial Magnetic Stimulation? A Pilot Study

This study is a post-hoc examination of baseline MRI data from a clinical trial investigating the efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for patients with mild-moderate Alzheimer's disease (AD). Herein, we investigated whether the analysis of baseline MRI data could predict the response of patients to rTMS treatment. Whole-brain T1-weighted MRI scans of 75 participants collected at baseline were analyzed. The analyses were run on the gray matter (GM) and white matter (WM) of the left and right dorsolateral prefrontal cortex (DLPFC), as that was the rTMS application site. The primary outcome measure was the Alzheimer's disease assessment scale-cognitive subscale (ADAS-Cog). The response to treatment was determined based on ADAS-Cog scores and secondary outcome measures. The analysis of covariance showed that responders to active treatment had a significantly lower baseline GM volume in the right DLPFC and a higher GM asymmetry index in the DLPFC region compared to those in non-responders. Logistic regression with a repeated five-fold cross-validated analysis using the MRI-driven features of the initial 75 participants provided a mean accuracy of 0.69 and an area under the receiver operating characteristic curve of 0.74 for separating responders and non-responders. The results suggest that GM volume or asymmetry in the target area of active rTMS treatment (DLPFC region in this study) may be a weak predictor of rTMS treatment efficacy. These results need more data to draw more robust conclusions.

Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80

The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.

Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning

BACKGROUND

Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge.

OBJECTIVE

To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD.

METHODS

Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model.

RESULTS

Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11).

CONCLUSION

A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Threats to external validity in the neuroprediction of substance use treatment outcomes

Background: Tools predicting individual relapse risk would invaluably inform clinical decision-making (e.g. level-of-care) in substance use treatment. Studies of neuroprediction - use of neuromarkers to predict individual outcomes - have the dual potential to create such tools and inform etiological models leading to new treatments. However, financial limitations, statistical power demands, and related factors encourage restrictive selection criteria, yielding samples that do not fully represent the target population. This problem may be further compounded by a lack of statistical optimism correction in neuroprediction research, resulting in predictive models that are overfit to already-restricted samples.Objectives: This systematic review aims to identify potential threats to external validity related to restrictive selection criteria and underutilization of optimism correction in the existing neuroprediction literature targeting substance use treatment outcomes.Methods: Sixty-seven studies of neuroprediction in substance use treatment were identified and details of sample selection criteria and statistical optimism correction were extracted.Results: Most publications were found to report restrictive selection criteria (e.g. excluding psychiatric (94% of publications) and substance use comorbidities (69% of publications)) that would rule-out a considerable portion of the treatment population. Furthermore, only 21% of publications reported optimism correction.Conclusion: Restrictive selection criteria and underutilization of optimism correction are common in the existing literature and may limit the generalizability of identified neural predictors to the target population whose treatment they would ultimately inform. Greater attention to the inclusivity and generalizability of addiction neuroprediction research, as well as new opportunities provided through open science initiatives, have the potential to address this issue.

APOE alleles are associated with sex-specific structural differences in brain regions affected in Alzheimer's disease and related dementia

Alzheimer's disease is marked by intracellular tau aggregates in the medial temporal lobe (MTL) and extracellular amyloid aggregates in the default network (DN). Here, we examined codependent structural variations between the MTL's most vulnerable structure, the hippocampus (HC), and the DN at subregion resolution in individuals with Alzheimer's disease and related dementia (ADRD). By leveraging the power of the approximately 40,000 participants of the UK Biobank cohort, we assessed impacts from the protective APOE ɛ2 and the deleterious APOE ɛ4 Alzheimer's disease alleles on these structural relationships. We demonstrate ɛ2 and ɛ4 genotype effects on the inter-individual expression of HC-DN co-variation structural patterns at the population level. Across these HC-DN signatures, recurrent deviations in the CA1, CA2/3, molecular layer, fornix's fimbria, and their cortical partners related to ADRD risk. Analyses of the rich phenotypic profiles in the UK Biobank cohort further revealed male-specific HC-DN associations with air pollution and female-specific associations with cardiovascular traits. We also showed that APOE ɛ2/2 interacts preferentially with HC-DN co-variation patterns in estimating social lifestyle in males and physical activity in females. Our structural, genetic, and phenotypic analyses in this large epidemiological cohort reinvigorate the often-neglected interplay between APOE ɛ2 dosage and sex and link APOE alleles to inter-individual brain structural differences indicative of ADRD familial risk.

Change in brain amyloid load and cognition in patients with amnestic mild cognitive impairment: a 3-year follow-up study

Background

Our aim was to investigate the discriminative value of ¹⁸F-Flutemetamol PET in longitudinal assessment of amyloid beta accumulation in amnestic mild cognitive impairment (aMCI) patients, in relation to longitudinal cognitive changes.

Methods

We investigated the change in ¹⁸F-Flutemetamol uptake and cognitive impairment in aMCI patients over time up to 3 years which enabled us to investigate possible association between changes in brain amyloid load and cognition over time. Thirty-four patients with aMCI (mean age 73.4 years, SD 6.6) were examined with ¹⁸F-Flutemetamol PET scan, brain MRI and cognitive tests at baseline and after 3-year follow-up or earlier if the patient had converted to Alzheimer´s disease (AD). ¹⁸F-Flutemetamol data were analyzed both with automated region-of-interest analysis and voxel-based statistical parametric mapping.

Results

¹⁸F-flutemetamol uptake increased during the follow-up, and the increase was significantly higher in patients who were amyloid positive at baseline as compared to the amyloid-negative ones. At follow-up, there was a significant association between ¹⁸F-Flutemetamol uptake and MMSE, logical memory I (immediate recall), logical memory II (delayed recall) and verbal fluency. An association was seen between the increase in ¹⁸F-Flutemetamol uptake and decline in MMSE and logical memory I scores.

Conclusions

In the early phase of aMCI, presence of amyloid pathology at baseline strongly predicted amyloid accumulation during follow-up, which was further paralleled by cognitive declines. Inversely, some of our patients remained amyloid negative also at the end of the study without significant change in ¹⁸F-Flutemetamol uptake or cognition. Future studies with longer follow-up are needed to distinguish whether the underlying pathophysiology of aMCI in such patients is other than AD.

Brain ventricles, CSF and cognition: a narrative review

The brain ventricles are structures that have been related to cognition since antiquity. They are essential components in the development and maintenance of brain functions. The aging process runs with the enlargement of ventricles and is related to a less selective blood-cerebrospinal fluid barrier and then a more toxic cerebrospinal fluid environment. The study of brain ventricles as a biological marker of aging is promissing because they are structures easily identified in neuroimaging studies, present good inter-rater reliability, and measures of them can identify brain atrophy earlier than cortical structures. The ventricular system also plays roles in the development of dementia, since dysfunction in the clearance of beta-amyloid protein is a key mechanism in sporadic Alzheimer's disease. The morphometric and volumetric studies of the brain ventricles can help to distinguish between healthy elderly and persons with mild cognitive impairment (MCI) and dementia. Brain ventricle data may contribute to the appropriate allocation of individuals in groups at higher risk for MCI-dementia progression in clinical trials and to measuring therapeutic responses in these studies, as well as providing differential diagnosis, such as normal pressure hydrocephalus. Here, we reviewed the pathophysiology of healthy aging and cognitive decline, focusing on the role of the choroid plexus and brain ventricles in this process.

Differential Effects of APOE Genotype on MicroRNA Cargo of Cerebrospinal Fluid Extracellular Vesicles in Females With Alzheimer's Disease Compared to Males

Multiple biological factors, including age, sex, and genetics, influence Alzheimer's disease (AD) risk. Of the 6.2 million Americans living with Alzheimer's dementia in 2021, 3.8 million are women and 2.4 million are men. The strongest genetic risk factor for sporadic AD is apolipoprotein E-e4 (APOE-e4). Female APOE-e4 carriers develop AD more frequently than age-matched males and have more brain atrophy and memory loss. Consequently, biomarkers that are sensitive to biological risk factors may improve AD diagnostics and may provide insight into underlying mechanistic changes that could drive disease progression. Here, we have assessed the effects of sex and APOE-e4 on the miRNA cargo of cerebrospinal fluid (CSF) extracellular vesicles (EVs) in AD. We used ultrafiltration (UF) combined with size exclusion chromatography (SEC) to enrich CSF EVs (e.g., Flotillin+). CSF EVs were isolated from female and male AD or controls (CTLs) that were either APOE-e3,4 or -e3,3 positive (n = 7/group, 56 total). MiRNA expression levels were quantified using a custom TaqMan™ array that assayed 190 miRNAs previously found in CSF, including 25 miRNAs that we previously validated as candidate AD biomarkers. We identified changes in the EV miRNA cargo that were affected by both AD and sex. In total, four miRNAs (miR-16-5p, -331-3p, -409-3p, and -454-3p) were significantly increased in AD vs. CTL, independent of sex and APOE-e4 status. Pathway analysis of the predicted gene targets of these four miRNAs with identified pathways was highly relevant to neurodegeneration (e.g., senescence and autophagy). There were also three miRNAs (miR-146b-5p, -150-5p, and -342-3p) that were significantly increased in females vs. males, independent of disease state and APOE-e4 status. We then performed a statistical analysis to assess the effect of APOE genotype in AD within each sex and found that APOE-e4 status affects different subsets of CSF EV miRNAs in females vs. males. Together, this study demonstrates the complexity of the biological factors associated with AD risk and the impact on EV miRNAs, which may contribute to AD pathophysiology.

Prediction Models for Conversion From Mild Cognitive Impairment to Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Background and Purpose

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder with no cure, and available treatments are only able to postpone the progression of the disease. Mild cognitive impairment (MCI) is considered to be a transitional stage preceding AD. Therefore, prediction models for conversion from MCI to AD are desperately required. These will allow early treatment of patients with MCI before they develop AD. This study performed a systematic review and meta-analysis to summarize the reported risk prediction models and identify the most prevalent factors for conversion from MCI to AD.

Methods

We systematically reviewed the studies from the databases of PubMed, CINAHL Plus, Web of Science, Embase, and Cochrane Library, which were searched through September 2021. Two reviewers independently identified eligible articles and extracted the data. We used the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist for the risk of bias assessment.

Results

In total, 18 articles describing the prediction models for conversion from MCI to AD were identified. The dementia conversion rate of elderly patients with MCI ranged from 14.49 to 87%. Models in 12 studies were developed using the data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). C-index/area under the receiver operating characteristic curve (AUC) of development models were 0.67–0.98, and the validation models were 0.62–0.96. MRI, apolipoprotein E genotype 4 (APOE4), older age, Mini-Mental State Examination (MMSE) score, and Alzheimer’s Disease Assessment Scale cognitive (ADAS-cog) score were the most common and strongest predictors included in the models.

Conclusion

In this systematic review, many prediction models have been developed and have good predictive performance, but the lack of external validation of models limited the extensive application in the general population. In clinical practice, it is recommended that medical professionals adopt a comprehensive forecasting method rather than a single predictive factor to screen patients with a high risk of MCI. Future research should pay attention to the improvement, calibration, and validation of existing models while considering new variables, new methods, and differences in risk profiles across populations.

Automatized FACEmemory® scoring is related to Alzheimer's disease phenotype and biomarkers in early-onset mild cognitive impairment: the BIOFACE cohort

Background

FACEmemory® is the first computerized, self-administered verbal episodic memory test with voice recognition. It can be conducted under minimal supervision and contains an automatic scoring system to avoid administrator errors. Moreover, it is suitable for discriminating between cognitively healthy and amnestic mild cognitive impairment (MCI) individuals, and it is associated with Alzheimer’s disease (AD) cerebrospinal fluid (CSF) biomarkers. This study aimed to determine whether FACEmemory scoring is related to performance on classical memory tests and to AD biomarkers of brain magnetic resonance imaging (MRI) and CSF in patients with early-onset MCI (EOMCI).

Methods

Ninety-four patients with EOMCI from the BIOFACE study completed FACEmemory, classical memory tests (the Spanish version of the Word Free and Cued Selective Reminding Test -FCSRT-, the Word List from the Wechsler Memory Scale, third edition, and the Spanish version of the Rey–Osterrieth Complex Figure Test), and a brain MRI. Eighty-two individuals also underwent a lumbar puncture.

Results

FACEmemory scoring was moderately correlated with FCSRT scoring. With regard to neuroimaging MRI results, worse execution on FACEmemory was associated with lower cortical volume in the right prefrontal and inferior parietal areas, along with the left temporal and associative occipital areas. Moreover, the total FACEmemory score correlated with CSF AD biomarkers (Aβ1-42/Aβ1-40 ratio, p181-tau, and Aβ1-42/p181-tau ratio). When performance on FACEmemory was compared among the ATN classification groups, significant differences between the AD group and normal and SNAP groups were found.

Conclusions

FACEmemory is a promising tool for detecting memory deficits sensitive to early-onset AD, but it also allows the detection of memory-impaired cases due to other etiologies. Our findings suggest that FACEmemory scoring can detect the AD endophenotype and that it is also associated with AD-related changes in MRI and CSF in patients with EOMCI. The computerized FACEmemory tool might be an opportunity to facilitate early detection of MCI in younger people than 65, who have a growing interest in new technologies.

Classification of Mild Cognitive Impairment With Multimodal Data Using Both Labeled and Unlabeled Samples

Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.

Novel characterization of the relationship between verbal list-learning outcomes and hippocampal subfields in healthy adults

The relationship between hippocampal subfield volumetry and verbal list‐learning test outcomes have mostly been studied in clinical and elderly populations, and remain controversial. For the first time, we characterized a relationship between verbal list‐learning test outcomes and hippocampal subfield volumetry on two large separate datasets of 447 and 1,442 healthy young and middle‐aged adults, and explored the processes that could explain this relationship. We observed a replicable positive linear correlation between verbal list‐learning test free recall scores and CA1 volume, specific to verbal list learning as demonstrated by the hippocampal subfield volumetry independence from verbal intelligence. Learning meaningless items was also positively correlated with CA1 volume, pointing to the role of the test design rather than word meaning. Accordingly, we found that association‐based mnemonics mediated the relationship between verbal list‐learning test outcomes and CA1 volume. This mediation suggests that integrating items into associative representations during verbal list‐learning tests explains CA1 volume variations: this new explanation is consistent with the associative functions of the human CA1.

An investigation of thalamic nuclei volumes and the intrinsic thalamic structural network based on motor subtype in drug naïve patients with Parkinson's disease

INTRODUCTION

This study aimed to investigate the alterations in thalamic nuclei volumes and the intrinsic thalamic structural network in patients with de novo Parkinson's disease (PD) based on their predominant symptoms.

METHODS

We enrolled 65 patients with de novo PD (44 patients with tremor-dominant [TD] subtype and 21 patients with postural instability and gait disturbance [PIGD] subtype) and 20 healthy controls. All subjects underwent three-dimensional T1-weighted magnetic resonance imaging. The thalamic nuclei were segmented using the FreeSurfer program.

RESULTS

We obtained volumetric differences in the thalamic nuclei of each subtype of PD in comparison of healthy control. Volumes of the right and left suprageniculate nuclei were significantly increased, whereas that of the left parafascicular nucleus was decreased in patients with the TD subtype. Volumes of the right and left suprageniculate nuclei and right ventromedial nucleus were significantly increased, whereas those of the right and left parafascicular nuclei volumes were decreased in patients with the PIGD subtype. The measures of the intrinsic thalamic global network were not different between patients with TD PD and healthy controls. However, in patients with the PIGD subtype, the global and local efficiencies were significantly increased compared to healthy controls. Moreover, although there were no differences in thalamic volume and intrinsic thalamic global network between patients with the TD and PIGD variants, we identified significant differences in the intrinsic thalamic local network between the two groups.

CONCLUSIONS

Alterations in thalamic nuclei volumes and the intrinsic thalamic network in patients with PD differed based on their predominant symptoms. These findings might be related to the underlying pathogenesis and suggest that PD is a heterogeneous syndrome.

Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review

We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice.

T2 heterogeneity: a novel marker of microstructural integrity associated with cognitive decline in people with mild cognitive impairment

BACKGROUND

Early Alzheimer's disease (AD) diagnosis is vital for development of disease-modifying therapies. Prior to significant brain tissue atrophy, several microstructural changes take place as a result of Alzheimer's pathology. These include deposition of amyloid, tau and iron, as well as altered water homeostasis in tissue and some cell death. T2 relaxation time, a quantitative MRI measure, is sensitive to these changes and may be a useful non-invasive, early marker of tissue integrity which could predict conversion to dementia. We propose that different microstructural changes affect T2 in opposing ways, such that average 'midpoint' measures of T2 are less sensitive than measuring distribution width (heterogeneity). T2 heterogeneity in the brain may present a sensitive early marker of AD pathology.

METHODS

In this cohort study, we tested 97 healthy older controls, 49 people with mild cognitive impairment (MCI) and 10 with a clinical diagnosis of AD. All participants underwent structural MRI including a multi-echo sequence for quantitative T2 assessment. Cognitive change over 1 year was assessed in 20 participants with MCI. T2 distributions were modelled in the hippocampus and thalamus using log-logistic distribution giving measures of log-median value (midpoint; T2μ) and distribution width (heterogeneity; T2σ).

RESULTS

We show an increase in T2 heterogeneity (T2σ; p < .0001) in MCI compared to healthy controls, which was not seen with midpoint (T2μ; p = .149) in the hippocampus and thalamus. Hippocampal T2 heterogeneity predicted cognitive decline over 1 year in MCI participants (p = .018), but midpoint (p = .132) and volume (p = .315) did not. Age affects T2, but the effects described here are significant even after correcting for age.

CONCLUSIONS

We show that T2 heterogeneity can identify subtle changes in microstructural integrity of brain tissue in MCI and predict cognitive decline over a year. We describe a new model that considers the competing effects of factors that both increase and decrease T2. These two opposing forces suggest that previous conclusions based on T2 midpoint may have obscured the true potential of T2 as a marker of subtle neuropathology. We propose that T2 heterogeneity reflects microstructural integrity with potential to be a widely used early biomarker of conditions such as AD.

Predicting Alzheimer's disease based on survival data and longitudinally measured performance on cognitive and functional scales

This study assessed how well longitudinally taken cognitive and functional scales from people with mild cognitive impairment (MCI) predict conversion to Alzheimer's disease (AD). Participants were individuals with baseline MCI from the Alzheimer's Disease Neuroimaging Initiative. Scales included the Alzheimer Disease Assessment Scale-Cognitive (ADAS-Cog) 11 and 13, the Mini Mental State Examination (MMSE), and the Functional Assessment Questionnaire (FAQ). A joint modelling approach compared performance on the four scales for dynamic prediction of risk for AD. The goodness of fit measures included log likelihood, the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The area under the curve (AUC) of the receiver operating characteristic assessed predictive accuracy. The parameter α in the ADAS-Cog11, ADAS-Cog13, MMSE, and FAQ joint models was statistically significant. Joint MMSE and FAQ models had better goodness of fit. FAQ had the best predictive accuracy. Cognitive and functional impairment assessment scales are strong screening predictors when repeated measures are available. They could be useful for predicting risk for AD in primary healthcare.

MRI-based brain volumetry and retinal optical coherence tomography as the biomarkers of outcome in acute methanol poisoning

BACKGROUND

Basal ganglia lesions are typical findings on magnetic resonance imaging (MRI) of the brain in survivors of acute methanol poisoning. However, no data are available on the association between the magnitude of damaged brain regions, serum concentrations of markers of acute methanol toxicity, oxidative stress, neuroinflammation, and the rate of retinal nerve ganglion cell loss.

OBJECTIVES

To investigate the association between MRI-based volumetry of the basal ganglia, retinal nerve fibre layer (RNFL) thickness and prognostic laboratory markers of outcomes in acute methanol poisoning.

METHODS

MRI-based volumetry of putamen, nucleus caudatus and globus pallidus was performed and compared with laboratory parameters of severity of poisoning and acute serum markers of oxidative damage of lipids (8-isoprostan, MDA, HHE, HNE), nucleic acids (8-OHdG, 8-OHG, 5-OHMU), proteins (o-Thyr, NO-Thyr, Cl-Thyr) and leukotrienes (LTC4, LTD4, LTE4, LTB4), as well as with the results of RNFL measurements by optic coherence tomography (OCT) in 16 patients with acute methanol poisoning (Group I) and in 28 survivors of poisoning two years after discharge with the same markers measured within the follow-up examination (Group II). The control group consisted of 28 healthy subjects without methanol poisoning.

RESULTS

The survivors of acute methanol poisoning had significantly lower volumes of basal ganglia than the controls. The patients with MRI signs of methanol-induced toxic brain damage had significantly lower volumes of basal ganglia than those without these signs. A positive correlation was found between the volume of putamen and arterial blood pH on admission (r = 0.45; p = 0.02 and r = 0.44; p = 0.02 for left and right putamen, correspondingly). A negative correlation was present between the volumes of putamen and acute serum lactate (r = -0.63; p < 0.001 and r = -0.59; p = 0.01), creatinine (r = -0.53; p = 0.01 and r = -0.47; p = 0.01) and glucose (r = -0.55; p < 0.001 and r = -0.50; p = 0.01) concentrations. The volume of basal ganglia positively correlated with acute concentrations of markers of lipoperoxidation (8-isoprostan: r = 0.61; p < 0.05 and r = 0.59; p < 0.05 for left and right putamen, correspondingly) and inflammation (leukotriene LTB4: r = 0.61; p < 0.05 and r = 0.61; p < 0.05 for left and right putamen, correspondingly). The higher the volume of the basal ganglia, the higher the thickness of the RNFL, with the strongest positive association between global RNFL and the volume of putamen bilaterally (all p < 0.01). In the follow-up markers of oxidative stress and inflammation, only o-Thyr concentration negatively correlated with the volume of putamen bilaterally (r = -0.39; p < 0.05 and r = -0.37; p < 0.05 for left and right putamen, correspondingly).

CONCLUSION

In survivors of acute methanol poisoning with signs of toxic brain damage, the magnitude of affected areas correlated with acute parameters of severity of poisoning, markers of oxidative stress and neuroinflammation. There was a positive association between the basal ganglia volume and the thickness of RNFL, making OCT an important screening test and MRI-based volumetry the confirmative diagnostic method for the detection of CNS sequelae of methanol poisoning.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

A Novel Ensemble-Based Machine Learning Algorithm to Predict the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Socio-Demographic Characteristics, Clinical Information, and Neuropsychological Measures

Background: Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD. Methods: The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. Results: The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. Conclusions: The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.

Analyzing the chance of developing dementia among geriatric people: a cross-sectional pilot study in Bangladesh

AIM

Alzheimer's disease is the most common form of dementia, representing 60-80% of cases, and ageing is the primary risk factor for the development of Alzheimer's disease. The objective of this study was to examine the chance of developing dementia (i.e. mild cognitive impairment (MCI), Alzheimer's disease) among geriatric people in Bangladesh.

METHODS

This study included 390 adult citizens of Bangladesh (age range: 60-70 years). The Takeda Three Colors Combination (TTCC) test was used to detect the prevalence of MCI and mild dementia among the subjects, and then the Clinical Dementia Rating was used to determine the level of dementia.

RESULTS

The subjects who were aged 60-65 years included 154 with MCI, 76 with mild dementia, 1 with moderate dementia, 4 with severe dementia, and 29 without dementia. The subjects who were aged 66-70 years included 75 with MCI, 36 with mild dementia, 0 with moderate dementia, 2 with severe dementia, and 13 without dementia. The sensitivity of the TTCC was 75% and 58% for the mild dementia and MCI groups, respectively, and the specificity was 52%. The odds ratio of incorrect responses to the TTCC was 3.42 (95% confidence interval: 1.63-7.21) for subjects with mild dementia compared those without dementia. However, the TTCC outcomes revealed no significant differences between the MCI and non-dementia groups. The results showed no significant associations between cognitive decline/developing dementia and social status/occupation.

CONCLUSION

The outcomes of this study indicated that most of the subjects had MCI or mild dementia and were farmers aged 60-65 years.

Spared emotional perception in patients with Alzheimer's disease is associated with negative caregiver outcomes

OBJECTIVES

Caregivers (CGs) for patients with Alzheimer's disease (AD) often experience negative mental health and relationship outcomes. Additionally, emotional perception abilities are often compromised in early AD; the relationships between these deficits and CG outcomes are unclear. The present study investigated the relationship between emotional perception abilities in AD participants and CG well-being.

METHODS

Participants included 28 individuals with AD, their spousal CGs, and 30 older controls (OCs). Patients and controls completed the Montreal Cognitive Assessment and Advanced Clinical Solutions: Social Perception subtest. CGs completed questionnaires related to relationship satisfaction, burden, depression, and patient neuropsychiatric symptoms and activities of daily living.

RESULTS

The patient group performed significantly worse than OCs on measures of cognition and emotional perception. Several significant relationships emerged between AD participant emotional perception and CG outcomes. Higher CG depression was associated with greater overall emotional perception abilities (r = .39, p = .041). Caregiver burden was positively correlated with AD participants' ability to label the emotional tones of voices (r = .47, p = .015). Relationship satisfaction was not significantly correlated with emotional perception.

DISCUSSION

This study replicated earlier findings of impaired emotional perception abilities in AD participants. However, preserved abilities in emotional perception were associated greater CG depression and burden. Interestingly, the CGs satisfaction with the marital relationship did not appear to be influenced by changes in emotional perception. Higher emotional engagement among couples in which one spouse has cognitive impairment may contribute to increased negative interactions and in turn a greater sense of burden and depression, while leaving the marital relationship preserved.

Positron emission tomography-guided magnetic resonance spectroscopy in Alzheimer disease

OBJECTIVE

To determine whether the level of metabolites in magnetic resonance spectroscopy (MRS) is a representative marker of underlying pathological changes identified in positron emission tomographic (PET) images in Alzheimer disease (AD).

METHODS

We performed PET-guided MRS in cases of probable AD, mild cognitive impairment (MCI), and healthy controls (HC). All participants were imaged by 11 C-Pittsburgh compound B (11 C-PiB) and 18 F-fluorodeoxyglucose (18 F-FDG) PET followed by 3T MRS. PET images were assessed both visually and using standardized uptake value ratios (SUVRs). MRS voxels were placed in regions with maximum abnormality on amyloid (Aβ+) and FDG (hypometabolic) areas on PET scans. Corresponding normal areas were selected in controls. The ratios of total N-acetyl (tNA) group, myoinositol (mI), choline, and glutamate + glutamine over creatine (Cr) were compared between these regions.

RESULTS

Aβ + regions had significantly higher (p = 0.02) mI/Cr and lower tNA/Cr (p = 0.02), whereas in hypometabolic areas only tNA/Cr was reduced (p = 0.003). Multiple regression analysis adjusting for sex, age, and education showed mI/Cr was only associated with 11 C-PiB SUVR (p < 0.0001). tNA/Cr, however, was associated with both PiB (p = 0.0003) and 18 F-FDG SUVR (p = 0.006). The level of mI/Cr was not significantly different between MCI and AD (p = 0.28), but tNA/Cr showed significant decline from HC to MCI to AD (p = 0.001, p = 0.04).

INTERPRETATION

mI/Cr has significant temporal and spatial associations with Aβ and could potentially be considered as a disease state biomarker. tNA is an indicator of early neurodegenerative changes and might have a role as disease stage biomarker and also as a valuable surrogate marker for treatment response. Ann Neurol 2018;83:771-778.

Cortical Thickness and Microstructural White Matter Changes Detect Amnestic Mild Cognitive Impairment

Both the apolipoprotein E (APOE) ɛ4 allele and amnestic mild cognitive impairment (aMCI) are considered to be risk factors for Alzheimer's disease (AD). The primary aim of this study was to determine whether the aMCI-related abnormality in gray matter (GM) cortical thickness and white matter (WM) tracts integrity would be modified by the APOE genotype. A total of 146 older adults, including 64 aMCI patients (28 ɛ4 carriers and 36 non-carriers) and 82 healthy controls (39 ɛ4 carriers and 43 non-carriers), underwent a standardized clinical interview, neuropsychological battery assessment, and multi-modal brain magnetic resonance imaging scans. Compared with control subjects, the patients with aMCI showed significantly reduced cortical thickness bilaterally in the parahippocampal gyrus and disrupted WM integrity in the limbic tracts (e.g., increased mean diffusivity in the right parahippocampal cingulum and bilateral uncinate fasciculus). However, no significant main effects of the APOE genotype and diagnosis-by-genotype interaction on GM thickness and WM integrity were observed. Further, diffusivity measures of the limbic WM tracts were significantly correlated with the parahippocampal atrophy in aMCI. Importantly, the parahippocampal thickness and diffusivity measures of the limbic WM tracts were significantly correlated with the cognitive performance (i.e., episodic memory Z score) in patients with aMCI. These results demonstrate that WM microstructural disruptions in the limbic tracts are present at the early stage of AD in an APOE-independent manner; and this degeneration may occur progressively, in parallel with parahippocampal atrophy, and may specifically contribute to early initial impairment in episodic memory.

Gender-Specific Degeneration of Dementia-Related Subcortical Structures Throughout the Lifespan

Age-related changes in brain structure are a question of interest to a broad field of research. Structural decline has been consistently, but not unambiguously, linked to functional consequences, including cognitive impairment and dementia. One of the areas considered of crucial importance throughout this process is the medial temporal lobe, and primarily the hippocampal region. Gender also has a considerable effect on volume deterioration of subcortical grey matter (GM) structures, such as the hippocampus. The influence of age×gender interaction on disproportionate GM volume changes might be mediated by hormonal effects on the brain. Hippocampal volume loss appears to become accelerated in the postmenopausal period. This decline might have significant influences on neuroplasticity in the CA1 region of the hippocampus highly vulnerable to pathological influences. Additionally, menopause has been associated with critical pathobiochemical changes involved in neurodegeneration. The micro- and macrostructural alterations and consequent functional deterioration of critical hippocampal regions might result in clinical cognitive impairment-especially if there already is a decline in the cognitive reserve capacity. Several lines of potential vulnerability factors appear to interact in the menopausal period eventually leading to cognitive decline, mild cognitive impairment, or Alzheimer's disease. This focused review aims to delineate the influence of unmodifiable risk factors of neurodegenerative processes, i.e., age and gender, on critical subcortical GM structures in the light of brain derived estrogen effects. The menopausal period appears to be of key importance for the risk of cognitive decline representing a time of special vulnerability for molecular, structural, and functional influences and offering only a narrow window for potential protective effects.

The bumps under the hippocampus

Shown in every neuroanatomy textbook, a key morphological feature is the bumpy ridges, which we refer to as hippocampal dentation, on the inferior aspect of the hippocampus. Like the folding of the cerebral cortex, hippocampal dentation allows for greater surface area in a confined space. However, examining numerous approaches to hippocampal segmentation and morphology analysis, virtually all published 3D renderings of the hippocampus show the inferior surface to be quite smooth or mildly irregular; we have rarely seen the characteristic bumpy structure on reconstructed 3D surfaces. The only exception is a 9.4T postmortem study (Yushkevich et al. [2009]: NeuroImage 44:385-398). An apparent question is, does this indicate that this specific morphological signature can only be captured using ultra high-resolution techniques? Or, is such information buried in the data we commonly acquire, awaiting a computation technique that can extract and render it clearly? In this study, we propose an automatic and robust super-resolution technique that captures the fine scale morphometric features of the hippocampus based on common 3T MR images. The method is validated on 9.4T ultra-high field images and then applied on 3T data sets. This method opens possibilities of future research on the hippocampus and other sub-cortical structural morphometry correlating the degree of dentation with a range of diseases including epilepsy, Alzheimer's disease, and schizophrenia. Hum Brain Mapp 39:472-490, 2018. © 2017 Wiley Periodicals, Inc.

Characterizing biomarker features of cognitively normal individuals with ventriculomegaly

INTRODUCTION

The clinical significance of ventriculomegaly in cognitively normal elderly individuals remains unclear.

METHODS

We selected cognitively normal individuals (n = 425) from the Alzheimer's Disease Neuroimaging Initiative database and calculated Evans index (EI) based on the ratio of the frontal horn and skull diameter. We defined ventriculomegaly as EI ≥ 0.30, and the participants were stratified into EI ≥ 0.30 group and EI < 0.30 group. Neuropsychological, imaging, and fluid biomarker profiles between the two groups were then compared using regression models.

RESULTS

A total of 96 (22.5%) individuals who had ventriculomegaly performed worse on the cognitive tests; showed smaller hippocampal volume but larger caudate, cingulate, and paracentral gyrus volumes; and displayed lower positron emission tomography [18F]fluorodeoxyglucose standardized uptake value ratio but higher amyloid burden represented by higher [18F]florbetapir standardized uptake value ratio and lower cerebrospinal fluid amyloid β 1-42 levels compared to those without ventriculomegaly.

DISCUSSION

Asymptomatic ventriculomegaly might be an early imaging signature of preclinical Alzheimer's disease and/or normal pressure hydrocephalus.

Prediction of Conversion to Alzheimer's Disease with Longitudinal Measures and Time-To-Event Data

BACKGROUND

Identifying predictors of conversion to Alzheimer's disease (AD) is critically important for AD prevention and targeted treatment.

OBJECTIVE

To compare various clinical and biomarker trajectories for tracking progression and predicting conversion from amnestic mild cognitive impairment to probable AD.

METHODS

Participants were from the ADNI-1 study. We assessed the ability of 33 longitudinal biomarkers to predict time to AD conversion, accounting for demographic and genetic factors. We used joint modelling of longitudinal and survival data to examine the association between changes of measures and disease progression. We also employed time-dependent receiver operating characteristic method to assess the discriminating capability of the measures.

RESULTS

23 of 33 longitudinal clinical and imaging measures are significant predictors of AD conversion beyond demographic and genetic factors. The strong phenotypic and biological predictors are in the cognitive domain (ADAS-Cog; RAVLT), functional domain (FAQ), and neuroimaging domain (middle temporal gyrus and hippocampal volume). The strongest predictor is ADAS-Cog 13 with an increase of one SD in ADAS-Cog 13 increased the risk of AD conversion by 2.92 times.

CONCLUSION

Prediction of AD conversion can be improved by incorporating longitudinal change information, in addition to baseline characteristics. Cognitive measures are consistently significant and generally stronger predictors than imaging measures.

A New MRI-Based Pediatric Subcortical Segmentation Technique (PSST)

Volumetric and morphometric neuroimaging studies of the basal ganglia and thalamus in pediatric populations have utilized existing automated segmentation tools including FIRST (Functional Magnetic Resonance Imaging of the Brain's Integrated Registration and Segmentation Tool) and FreeSurfer. These segmentation packages, however, are mostly based on adult training data. Given that there are marked differences between the pediatric and adult brain, it is likely an age-specific segmentation technique will produce more accurate segmentation results. In this study, we describe a new automated segmentation technique for analysis of 7-year-old basal ganglia and thalamus, called Pediatric Subcortical Segmentation Technique (PSST). PSST consists of a probabilistic 7-year-old subcortical gray matter atlas (accumbens, caudate, pallidum, putamen and thalamus) combined with a customized segmentation pipeline using existing tools: ANTs (Advanced Normalization Tools) and SPM (Statistical Parametric Mapping). The segmentation accuracy of PSST in 7-year-old data was compared against FIRST and FreeSurfer, relative to manual segmentation as the ground truth, utilizing spatial overlap (Dice's coefficient), volume correlation (intraclass correlation coefficient, ICC) and limits of agreement (Bland-Altman plots). PSST achieved spatial overlap scores ≥90% and ICC scores ≥0.77 when compared with manual segmentation, for all structures except the accumbens. Compared with FIRST and FreeSurfer, PSST showed higher spatial overlap (p FDR  < 0.05) and ICC scores, with less volumetric bias according to Bland-Altman plots. PSST is a customized segmentation pipeline with an age-specific atlas that accurately segments typical and atypical basal ganglia and thalami at age 7 years, and has the potential to be applied to other pediatric datasets.

Sex biology contributions to vulnerability to Alzheimer's disease: A think tank convened by the Women's Alzheimer's Research Initiative

More than 5 million Americans are living with Alzheimer's disease (AD) today, and nearly two-thirds of Americans with AD are women. This sex difference may be due to the higher longevity women generally experience; however, increasing evidence suggests that longevity alone is not a sufficient explanation and there may be other factors at play. The Alzheimer's Association convened an expert think tank to focus on the state of the science and level of evidence around gender and biological sex differences for AD, including the knowledge gaps and areas of science that need to be more fully addressed. This article summarizes the think tank discussion, moving forward a research agenda and funding program to better understand the biological underpinnings of sex- and gender-related disparities of risk for AD.

Grey matter atrophy in prodromal stage of dementia with Lewy bodies and Alzheimer's disease

BACKGROUND

Little is known about the patterns of brain atrophy in prodromal dementia with Lewy bodies (pro-DLB).

METHODS

In this study, we used SPM8 with diffeomorphic anatomical registration through exponentiated lie algebra to measure grey matter (GM) volume and investigate patterns of GM atrophy in pro-DLB (n = 28) and prodromal Alzheimer's disease (pro-AD) (n = 27) and compared and contrasted them with those in elderly control subjects (n = 33) (P ≤ 0.05 corrected for family-wise error).

RESULTS

Patients with pro-DLB showed diminished GM volumes of bilateral insulae and right anterior cingulate cortex compared with control subjects. Comparison of GM volume between patients with pro-AD and control subjects showed a more extensive pattern, with volume reductions in temporal (hippocampi and superior and middle gyri), parietal and frontal structures in the former. Direct comparison of prodromal groups suggested that more atrophy was evident in the parietal lobes of patients with pro-AD than patients with pro-DLB. In patients with pro-DLB, we found that visual hallucinations were associated with relative atrophy of the left cuneus.

CONCLUSIONS

Atrophy in pro-DLB involves the insulae and anterior cingulate cortex, regions rich in von Economo neurons, which we speculate may contribute to the early clinical phenotype of pro-DLB.

First magenetic resonance imaging studies on aluminium maltolate-treated aged New Zealand rabbits: an Alzheimer's animal model

BACKGROUND

Alzheimer's disease is a devastative neurodegenerative disorder. To date, there has been no animal model that could unravel the complete disease pathology. Magnetic resonance imaging has played a pivotal role in the quantitative assessment of brain tissue atrophy for a few decades. In particular, temporal lobe atrophy and ventricular dilatation have been found to be sensitive in Alzheimer's disease.

METHODS

The present study focused on the replication of these crucial pathological events to enable disease progression to be diagnosed at an early stage and stopped through the use of potential therapeutic strategies.

RESULT

The objective of this study was to show temporal lobe atrophy and ventricular dilatation in aluminium maltolate-treated aged New Zealand rabbit, and our study was able to demonstrate this for the first time.

CONCLUSION

The present study makes this animal model a substantial one for further molecular level studies and opens up new targets for potential therapeutic strategies.

Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a 'European ADNI study'

BACKGROUND

In the field of Alzheimer's disease (AD), the validation of biomarkers for early AD diagnosis and for use as a surrogate outcome in AD clinical trials is of considerable research interest.

OBJECTIVE

To characterize the clinical profile and genetic, neuroimaging and neurophysiological biomarkers of prodromal AD in amnestic mild cognitive impairment (aMCI) patients enrolled in the IMI WP5 PharmaCog (also referred to as the European ADNI study).

METHODS

A total of 147 aMCI patients were enrolled in 13 European memory clinics. Patients underwent clinical and neuropsychological evaluation, magnetic resonance imaging (MRI), electroencephalography (EEG) and lumbar puncture to assess the levels of amyloid β peptide 1-42 (Aβ42), tau and p-tau, and blood samples were collected. Genetic (APOE), neuroimaging (3T morphometry and diffusion MRI) and EEG (with resting-state and auditory oddball event-related potential (AO-ERP) paradigm) biomarkers were evaluated.

RESULTS

Prodromal AD was found in 55 aMCI patients defined by low Aβ42 in the cerebrospinal fluid (Aβ positive). Compared to the aMCI group with high Aβ42 levels (Aβ negative), Aβ positive patients showed poorer visual (P = 0.001), spatial recognition (P < 0.0005) and working (P = 0.024) memory, as well as a higher frequency of APOE4 (P < 0.0005), lower hippocampal volume (P = 0.04), reduced thickness of the parietal cortex (P < 0.009) and structural connectivity of the corpus callosum (P < 0.05), higher amplitude of delta rhythms at rest (P = 0.03) and lower amplitude of posterior cingulate sources of AO-ERP (P = 0.03).

CONCLUSION

These results suggest that, in aMCI patients, prodromal AD is characterized by a distinctive cognitive profile and genetic, neuroimaging and neurophysiological biomarkers. Longitudinal assessment will help to identify the role of these biomarkers in AD progression.

Walking ability to predict future cognitive decline in old adults: A scoping review

Early identification of individuals at risk for cognitive decline may facilitate the selection of those who benefit most from interventions. Current models predicting cognitive decline include neuropsychological and/or biological markers. Additional markers based on walking ability might improve accuracy and specificity of these models because motor and cognitive functions share neuroanatomical structures and psychological processes. We reviewed the relationship between walking ability at one point of (mid) life and cognitive decline at follow-up. A systematic literature search identified 20 longitudinal studies. The average follow-up time was 4.5 years. Gait speed quantified walking ability in most studies (n=18). Additional gait measures (n=4) were step frequency, variability and step-length. Despite methodological weaknesses, results revealed that gait slowing (0.68-1.1 m/sec) preceded cognitive decline and the presence of dementia syndromes (maximal odds and hazard ratios of 10.4 and 11.1, respectively). The results indicate that measures of walking ability could serve as additional markers to predict cognitive decline. However, gait speed alone might lack specificity. We recommend gait analysis, including dynamic gait parameters, in clinical evaluations of patients with suspected cognitive decline. Future studies should focus on examining the specificity and accuracy of various gait characteristics to predict future cognitive decline.

Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer's disease

OBJECTIVE

To investigate whether subcortical grey matter atrophy predicts progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), and to compare subcortical volumes between AD, MCI and controls. To assess the correlation between subcortical grey matter volumes and severity of cognitive impairment.

METHODS

We included 773 participants with three-dimensional T1-weighted MRI at 3 T, made up of 181 controls, who had subjective memory symptoms with normal cognition, 201 MCIs and 391 AD. During follow-up (2.0 ± 0.9 years), 35 MCIs converted to AD (progressive MCI) and 160 MCIs remained stable (stable MCI). We segmented volumes of six subcortical structures of the amygdala, thalamus, caudate nucleus, putamen, globus pallidus and nucleus accumbens, and of the hippocampus, using FMRIBs integrated registration and segmentation tool.

RESULTS

Analysis of variances, adjusted for sex and age, showed that all structures, except the globus pallidus, were smaller in AD than in controls. In addition, the amygdala, thalamus, putamen, nucleus accumbens and hippocampus were smaller in MCIs than in controls. Across groups, all subcortical greymatter volumes, except the globus pallidus, showed a positive correlation with cognitive function, as measured by Mini Mental State Examination (MMSE) (0.16<r<0.28, all p<0.05). Cox proportional hazards analyses adjusted for age, sex, education, Cambridge Cognitive Examination-Revised (CAMCOG-R) and MMSE showed that smaller volumes of the hippocampus and nucleus accumbens were associated with increased risk of progression from MCI to AD (HR (95% CI) 1.60 (1.15 to 2.21); 1.60 (1.09 to 2.35), p<0.05).

CONCLUSIONS

In addition to the hippocampus, the nucleus accumbens volume loss was also associated with increased risk of progression from MCI to AD. Furthermore, volume loss of subcortical grey matter structures was associated with severity of cognitive impairment.

Neuroimaging of hippocampal atrophy in early recognition of Alzheimer's disease--a critical appraisal after two decades of research

As a characteristic feature of Alzheimer's disease (AD) hippocampal atrophy (HA) can be demonstrated in the majority of patients by using neuroimaging techniques in particular magnetic resonance imaging (MRI). Hippocampal atrophy is associated with declarative memory deficits and can also be associated with changes of adjacent medial temporal substructures such as the parahippocampal gyrus or the the entorhinal cortex. Similar findings are present in patients with mild cognitive impairment (MCI) albeit to a lesser extent. While these finding facilitate the diagnostic process in patients with clinical suspicious AD, the metric properties of hippocampal atrophy for delineating healthy aging from MCI and mild AD still appear to be rather limited; as such it is not sufficient to establish the diagnosis of AD (and even more so of MCI). This limitation partly refers to methodological issues and partly to the fact that hippocampal tissue integrity is subject to various pathogenetic influences other than AD. Moreover,the effects of hippocampal atrophy on the behavioral level (e.g. cognitive deficits) are modulated by the individual's cognitive reserve. From a clinical standpoint these observations are in line with the hypothesis that the onset and course of AD is influenced by a number of peristatic factors which are partly conceptualized in the concepts of brain and/or cognitive reserve. These complex interactions have to be considered when using the presence of hippocampal atrophy in the routine diagnostic procedure of AD.

Differences in and correlations between cognitive abilities and brain volumes in healthy control, mild cognitive impairment, and Alzheimer disease groups

The purpose of this study is to investigate differences in and correlations between cognitive abilities and brain volumes in healthy control (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD) groups. The Korean Version of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-K), which is used to diagnose AD, was used to measure the cognitive abilities of the study subjects, and the volumes of typical brain components related to AD diagnosis-cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM)-were acquired. Of the CERAD-K subtests, the Boston Naming Test distinguished significantly among the HC, MCI, and AD groups. GM and WM volumes differed significantly among the three groups. There was a significant positive correlation between Boston Naming Test scores and GM and WM volumes. In conclusion, the Boston Naming Test and GM and WM brain volumes differentiated the three tested groups accurately, and there were strong correlations between Boston Naming Test scores and GM and WM volumes. These results will help to establish a test method that differentiates the three groups accurately and is economically feasible.

Six psychotropics for pre-symptomatic & early Alzheimer's (MCI), Parkinson's, and Huntington's disease modification

The quest for neuroprotective drugs to slow the progression of neurodegenerative diseases (NDDs), including Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD), has been largely unrewarding. Preclinical evidence suggests that repurposing quetiapine, lithium, valproate, fluoxetine, donepezil, and memantine for early and pre-symptomatic disease-modification in NDDs may be promising and can spare regulatory barriers. The literature of these psychotropics in early stage and pre-symptomatic AD, PD, and HD is reviewed and propitious findings follow. Mild cognitive impairment (MCI) phase of AD: salutary human randomized controlled trial findings for low-dose lithium and, in selected patients, donepezil await replication. Pre-symptomatic AD: human epidemiological data indicate that lithium reduces AD risk. Animal model studies (AMS) reveal encouraging results for quetiapine, lithium, donepezil, and memantine. Early PD: valproate AMS findings show promise. Pre-symptomatic PD: lithium and valproate AMS findings are encouraging. Early HD: uncontrolled clinical data indicate non-progression with lithium, fluoxetine, donepezil, and memantine. Pre-symptomatic HD: lithium and valproate are auspicious in AMS. Many other promising findings awaiting replication (valproate in MCI; lithium, valproate, fluoxetine in pre-symptomatic AD; lithium in early PD; lithium, valproate, fluoxetine in pre-symptomatic PD; donepezil in early HD; lithium, fluoxetine, memantine in pre-symptomatic HD) are reviewed. Dose- and stage-dependent effects are considered. Suggestions for signal-enhancement in human trials are provided for each NDD stage.

Detection of Alzheimer's disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis

Finding very early biomarkers of Alzheimer's Disease (AD) to aid in individual prognosis is of major interest to accelerate the development of new therapies. Among the potential biomarkers, neurodegeneration measurements from MRI are considered as good candidates but have so far not been effective at the early stages of the pathology. Our objective is to investigate the efficiency of a new MR-based hippocampal grading score to detect incident dementia in cognitively intact patients. This new score is based on a pattern recognition strategy, providing a grading measure that reflects the similarity of the anatomical patterns of the subject under study with dataset composed of healthy subjects and patients with AD. Hippocampal grading was evaluated on subjects from the Three-City cohort, with a followup period of 12 years. Experiments demonstrate that hippocampal grading yields prediction accuracy up to 72.5% (P < 0.0001) 7 years before conversion to AD, better than both hippocampal volume (58.1%, P = 0.04) and MMSE score (56.9%, P = 0.08). The area under the ROC curve (AUC) supports the efficiency of imaging biomarkers with a gain of 8.4 percentage points for hippocampal grade (73.0%) over hippocampal volume (64.6%). Adaptation of the proposed framework to clinical score estimation is also presented. Compared with previous studies investigating new biomarkers for AD prediction over much shorter periods, the very long followup of the Three-City cohort demonstrates the important clinical potential of the proposed imaging biomarker. The high accuracy obtained with this new imaging biomarker paves the way for computer-based prognostic aides to help the clinician identify cognitively intact subjects that are at high risk to develop AD.

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Selected Gray Matter Volumes and Gender but Not Basal Ganglia nor Cerebellum Gyri Discriminate Left Versus Right Cerebral Hemispheres: Multivariate Analyses in human Brains at 3T

Interest in the lateralization of the human brain is evident through a multidisciplinary number of scientific studies. Understanding volumetric brain asymmetries allows the distinction between normal development stages and behavior, as well as brain diseases. We aimed to evaluate volumetric asymmetries in order to select the best gyri able to classify right- versus left cerebral hemispheres. A cross-sectional study performed in 47 right-handed young-adults healthy volunteers. SPM-based software performed brain segmentation, automatic labeling and volumetric analyses for 54 regions involving the cerebral lobes, basal ganglia and cerebellum from each cerebral hemisphere. Multivariate discriminant analysis (DA) allowed the assembling of a predictive model. DA revealed one discriminant function that significantly differentiated left vs. right cerebral hemispheres: Wilks' λ = 0.008, χ(2) (9) = 238.837, P < 0.001. The model explained 99.20% of the variation in the grouping variable and depicted an overall predictive accuracy of 98.8%. With the influence of gender; the selected gyri able to discriminate between hemispheres were middle orbital frontal gyrus (g.), angular g., supramarginal g., middle cingulum g., inferior orbital frontal g., calcarine g., inferior parietal lobule and the pars triangularis inferior frontal g. Specific brain gyri are able to accurately classify left vs. right cerebral hemispheres by using a multivariate approach; the selected regions correspond to key brain areas involved in attention, internal thought, vision and language; our findings favored the concept that lateralization has been evolutionary favored by mental processes increasing cognitive efficiency and brain capacity.

Coalition Against Major Diseases/European Medicines Agency biomarker qualification of hippocampal volume for enrichment of clinical trials in predementia stages of Alzheimer's disease

BACKGROUND

Regulatory qualification of a biomarker for a defined context of use provides scientifically robust assurances to sponsors and regulators that accelerate appropriate adoption of biomarkers into drug development.

METHODS

The Coalition Against Major Diseases submitted a dossier to the Scientific Advice Working Party of the European Medicines Agency requesting a qualification opinion on the use of hippocampal volume as a biomarker for enriching clinical trials in subjects with mild cognitive impairment, incorporating a scientific rationale, a literature review and a de novo analysis of Alzheimer's Disease Neuroimaging Initiative data.

RESULTS

The literature review and de novo analysis were consistent with the proposed context of use, and the Committee for Medicinal Products for Human Use released an opinion in November 2011.

CONCLUSIONS

We summarize the scientific rationale and the data that supported the first qualification of an imaging biomarker by the European Medicines Agency.

Mapping ventricular expansion onto cortical gray matter in older adults

Dynamic changes in the brain's lateral ventricles on magnetic resonance imaging are powerful biomarkers of disease progression in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Ventricular measures can represent accumulation of diffuse brain atrophy with very high effect sizes. Despite having no direct role in cognition, ventricular expansion co-occurs with volumetric loss in gray and white matter structures. To better understand relationships between ventricular and cortical changes over time, we related ventricular expansion to atrophy in cognitively relevant cortical gray matter surfaces, which are more challenging to segment. In ADNI participants, percent change in ventricular volumes at 1-year (N = 677) and 2-year (N = 536) intervals was significantly associated with baseline cortical thickness and volume in the full sample controlling for age, sex, and diagnosis, and in MCI separately. Ventricular expansion in MCI was associated with thinner gray matter in frontal, temporal, and parietal regions affected by AD. Ventricular expansion reflects cortical atrophy in early AD, offering a useful biomarker for clinical trials of interventions to slow AD progression.

Hippocampal spatial position evaluation on MRI for research and clinical practice

In clinical practice as well as in many volumetric studies we use different reorientations of the brain position towards x and y axis on the magnetic resonance imaging (MRI) scans. In order to find out whether it has an overall effect on the resulting 2D data, manual hippocampal area measurements and rotation variability of the brain (in two reoriented axes) and the skull were performed in 23 Alzheimer's disease patients and 31 healthy controls. After the MRI scanning, native brain scans (nat) were reoriented into the two different artificial planes (anterior commissure – posterior commissure axis (AC-PC) and hippocampal horizontal long axis (hipp)). Hippocampal area and temporal horn of the lateral ventricle was measured manually using freeware Image J program. We found that 1) hippocampal area of nat images is larger compared to hipp images, area of the nat images is equal to the AC-PC images and area of the hipp images is smaller compared to AC-PC images, 2) hippocampal area together with the area of the temporal horn for nat images is larger compared to hipp images, area of the hipp images is smaller compared to the AC-PC images and area of the nat images is smaller compared to the AC-PC images. The conclusion is that the measured area of the hippocampus in the native MRI is almost the same as the area of MRI reoriented only into the AC-PC axis. Therefore, when performing 2D area studies of the hippocampus or in the clinical practice we recommend usage of not-reoriented MRI images or to reorient them into the AC-PC axis. Surprising finding was that rotation of both AC-PC and hipp line towards x-axis among patients varies up to 35° and the same is true for the skull rotation so that it is not only a matter of the brain position.

Pre-clinical cognitive phenotypes for Alzheimer disease: a latent profile approach

BACKGROUND

Cognitive profiles for pre-clinical Alzheimer disease (AD) can be used to identify groups of individuals at risk for disease and better characterize pre-clinical disease. Profiles or patterns of performance as pre-clinical phenotypes may be more useful than individual test scores or measures of global decline.

OBJECTIVE

To evaluate patterns of cognitive performance in cognitively normal individuals to derive latent profiles associated with later onset of disease using a combination of factor analysis and latent profile analysis.

METHODS

The National Alzheimer Coordinating Centers collect data, including a battery of neuropsychological tests, from participants at 29 National Institute on Aging-funded Alzheimer Disease Centers across the United States. Prior factor analyses of this battery demonstrated a four-factor structure comprising memory, attention, language, and executive function. Factor scores from these analyses were used in a latent profile approach to characterize cognition among a group of cognitively normal participants (N = 3,911). Associations between latent profiles and disease outcomes an average of 3 years later were evaluated with multinomial regression models. Similar analyses were used to determine predictors of profile membership.

RESULTS

Four groups were identified; each with distinct characteristics and significantly associated with later disease outcomes. Two groups were significantly associated with development of cognitive impairment. In post hoc analyses, both the Trail Making Test Part B, and a contrast score (Delayed Recall - Trails B), significantly predicted group membership and later cognitive impairment.

CONCLUSIONS

Latent profile analysis is a useful method to evaluate patterns of cognition in large samples for the identification of preclinical AD phenotypes; comparable results, however, can be achieved with very sensitive tests and contrast scores.