Whole-brain atrophy rate in Alzheimer disease: identifying fast progressors

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

Early- and Late-Onset Alzheimer's Disease: Two Sides of the Same Coin?

Early-onset Alzheimer's disease (EOAD), defined as Alzheimer's disease onset before 65 years of age, has been significantly less studied than the "classic" late-onset form (LOAD), although EOAD often presents with a more aggressive disease course, caused by variants in the APP, PSEN1, and PSEN2 genes. EOAD has significant differences from LOAD, including encompassing diverse phenotypic manifestations, increased genetic predisposition, and variations in neuropathological burden and distribution. Phenotypically, EOAD can be manifested with non-amnestic variants, sparing the hippocampi with increased tau burden. The aim of this article is to review the different genetic bases, risk factors, pathological mechanisms, and diagnostic approaches between EOAD and LOAD and to suggest steps to further our understanding. The comprehension of the monogenic form of the disease can provide valuable insights that may serve as a roadmap for understanding the common form of the disease.

Preliminary evidence for preserved synaptic density in late-life depression

Late-life depression has been consistently associated with lower gray matter volume, the origin of which remains largely unexplained. Recent in-vivo PET findings in early-onset depression and Alzheimer's Disease suggest that synaptic deficits contribute to the pathophysiology of these disorders and may therefore contribute to lower gray matter volume in late-life depression. Here, we investigate synaptic density in vivo for the first time in late-life depression using the synaptic vesicle glycoprotein 2A receptor radioligand 11C-UCB-J. We included 24 currently depressed adults with late-life depression (73.0 ± 6.2 years, 16 female, geriatric depression scale = 19.5 ± 6.8) and 36 age- and gender-matched healthy controls (70.4 ± 6.2 years, 21 female, geriatric depression scale = 2.7 ± 2.9) that underwent simultaneous 11C-UCB-J positron emission tomography (PET) and 3D T1- and T2-FLAIR weighted magnetic resonance (MR) imaging on a 3-tesla PET-MR scanner. We used analyses of variance to test for 11C-UCB-J binding and gray matter volumes differences in regions implicated in depression. The late-life depression group showed a trend in lower gray matter volumes in the hippocampus (p = 0.04), mesial temporal (p = 0.02) and prefrontal cortex (p = 0.02) compared to healthy control group without surviving correction for multiple comparison. However, no group differences in 11C-UCB-J binding were found in these regions nor were any associations between 11C-UCB-J and depressive symptoms. Our data suggests that, in contrast to Alzheimer's Disease, lower gray matter volume in late-life depression is not associated with synaptic density changes. From a therapeutic standpoint, preserved synaptic density in late-life depression may be an encouraging finding.

A bibliometric analysis of international publication trends in brain atrophy research (2008-2023)

Background

Brain atrophy is a type of neurological and psychiatric disorder characterized by a decrease in brain tissue volume and weight for various reasons and can have a serious impact on the quality of life of patients. Although there are many studies on brain atrophy, there is a lack of relevant bibliometric studies. Therefore, this study aims to provide a visual analysis of global trends in brain atrophy research over the past 16 years.

Methods

CiteSpace and VOSviewer were used to visually analyze publication output, scientific collaborations, cocitations, publishing journals, and keywords to determine the current status and future trends of brain atrophy research. Materials published from 2008 to 2023 were collected from the Web of Science Core Collection (WoSCC) database. This study placed no restrictions on the types of literature and focused on English language publications.

Results

A total of 3,371 publications were included in the analysis. From 2008 to 2023, the number of publications increased annually. In terms of national and academic institutions, universities in the United States and University College London rank first in publication out. Barkhof Frederik and Zivadinov Robert are the most prolific researchers in this field. The publication with the highest cocitation strength is "Deep gray matter volume loss drives disability worsening in multiple sclerosis." Keyword clustering analysis showed that "Alzheimer's disease" and "multiple sclerosis" are current popular topics. The analysis of emergent words indicates that "cerebral small vessel disease," "neurodegeneration," and "cortex/gray matter volume" may become hot research topics in the coming years.

Conclusion

This study analyses papers on brain atrophy from the past 16 years, providing a new perspective for research in this field. In the past 16 years, research on brain atrophy has received increasing attention. The quality of articles in this field is generally high. Extensive national cooperation already exists. The statistical results indicate that a stable core author group in the field of brain atrophy has almost formed.

Comparative evaluation of clinical and cerebrospinal fluid biomarker characteristics in rapidly and non-rapidly progressive Alzheimer's disease

BACKGROUND

Rapidly progressive forms of Alzheimer's disease (rpAD) are increasingly recognized and may have a prevalence of up to 30% of patients among all patients with Alzheimer's disease (AD). However, insights about risk factors, underlying pathophysiological processes, and clinical characteristics of rpAD remain controversial. This study aimed to gain a comprehensive picture of rpAD and new insights into the clinical manifestation to enable a better interpretation of disease courses in clinical practice as well as in future clinical studies.

METHODS

Patients (n = 228) from a prospective observational study on AD were selected and categorized into rpAD (n = 67) and non-rpAD (n = 161) disease groups. Patients were recruited through the German Creutzfeldt-Jakob disease surveillance center and the memory outpatient clinic of the Göttingen University Medical Center, representing diverse phenotypes of the AD population. Biomarkers and clinical presentation were assessed using standardized protocols. A drop of ≥ MMSE 6 points within 12 months defined rapid progressors.

RESULTS

Lower CSF Amyloid beta 1-42 concentrations (p = 0.048), lower Amyloid beta 42/40 ratio (p = 0.038), and higher Tau/Amyloid-beta 1-42 ratio, as well as pTau/Amyloid-beta 1-42 ratio (each p = 0.004) were associated with rpAD. Analyzes in a subset of the cohort (rpAD: n = 12; non-rpAD: n = 31) showed higher CSF NfL levels in rpAD (p = 0.024). Clinically, rpAD showed earlier impairment of functional abilities (p < 0.001) and higher scores on the Unified Parkinson's Disease Rating Scale III (p < 0.001), indicating pronounced extrapyramidal motor symptoms. Furthermore, cognitive profiles (adjusted for overall cognitive performance) indicated marked deficits in semantic (p = 0.008) and phonematic (0.023) verbal fluency tests as well as word list learning (p = 0.007) in rpAD compared to non-rpAD. The distribution of APOE genotypes did not differ significantly between groups.

CONCLUSIONS

Our results suggest that rpAD is associated with distinct cognitive profiles, earlier occurrence of non-cognitive symptoms, extrapyramidal motoric disturbance, and lower Amyloid-beta 1-42 concentrations in the CSF. The findings may help to characterize a distinct phenotype of rpAD and estimate prognosis based on clinical characteristics and biomarker results. However, an important future goal should be a unified definition for rpAD to enable targeted study designs and better comparability of the results.

The APOE4 effect: structural brain differences in Alzheimer's disease according to the age at symptom onset

BACKGROUND AND PURPOSE

How the APOE genotype can differentially affect cortical and subcortical memory structures in biomarker-confirmed early-onset (EOAD) and late-onset (LOAD) Alzheimer's disease (AD) was assessed.

METHOD

Eighty-seven cerebrospinal fluid (CSF) biomarker-confirmed AD patients were classified according to their APOE genotype and age at onset. 28 were EOAD APOE4 carriers (+EOAD), 21 EOAD APOE4 non-carriers (-EOAD), 23 LOAD APOE4 carriers (+LOAD) and 15 LOAD APOE4 non-carriers (-LOAD). Grey matter (GM) volume differences were analyzed using voxel-based morphometry in Papez circuit regions. Multiple regression analyses were performed to determine the relation between GM volume loss and cognition.

RESULTS

Significantly more mammillary body atrophy in +EOAD compared to -EOAD is reported. The medial temporal and posterior cingulate cortex showed less GM in +LOAD compared to -LOAD. Medial temporal GM volume loss was also found in +EOAD compared to -LOAD. With an exception for +EOAD, medial temporal GM was strongly associated with episodic memory in the three groups, whilst posterior cingulate cortex GM volume was more related with visuospatial abilities. Visuospatial abilities and episodic memory were also associated with the anterior thalamic nucleus in -LOAD.

CONCLUSIONS

Our results show that the APOE genotype has a significant effect on GM integrity as a function of age of disease onset. Specifically, whilst LOAD APOE4 genotype is mostly associated with increased medial temporal and parietal atrophy compared to -LOAD, for EOAD APOE4 might have a more specific effect on subcortical (mammillary body) structures. The findings suggest that APOE genotype needs to be taken into account when classifying patients by age at onset.

Interpretable brain disease classification and relevance-guided deep learning

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy.

Deep learning algorithm reveals probabilities of stage-specific time to conversion in individuals with neurodegenerative disease LATE

Introduction

Limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy (LATE) is a recently defined neurodegenerative disease. Currently, there is no effective way to make a prognosis of time to stage-specific future conversions at an individual level.

Methods

After using the Kaplan-Meier estimation and log-rank test to confirm the heterogeneity of LATE progression, we developed a deep learning-based approach to assess the stage-specific probabilities of time to LATE conversions for different subjects.

Results

Our approach could accurately estimate the disease incidence and transition to next stages: the concordance index was at least 82% and the integrated Brier score was less than 0.14. Moreover, we identified the top 10 important predictors for each disease conversion scenario to help explain the estimation results, which were clinicopathologically meaningful and most were also statistically significant.

Discussion

Our study has the potential to provide individualized assessment for future time courses of LATE conversions years before their actual occurrence.

Associations of multiple visual rating scales based on structural magnetic resonance imaging with disease severity and cerebrospinal fluid biomarkers in patients with Alzheimer’s disease

The relationships between multiple visual rating scales based on structural magnetic resonance imaging (sMRI) with disease severity and cerebrospinal fluid (CSF) biomarkers in patients with Alzheimer’s disease (AD) were ambiguous. In this study, a total of 438 patients with clinically diagnosed AD were recruited. All participants underwent brain sMRI scan, and medial temporal lobe atrophy (MTA), posterior atrophy (PA), global cerebral atrophy-frontal sub-scale (GCA-F), and Fazekas rating scores were visually evaluated. Meanwhile, disease severity was assessed by neuropsychological tests such as the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Clinical Dementia Rating (CDR). Among them, 95 patients were tested for CSF core biomarkers, including Aβ_1–42, Aβ_1–40, Aβ_1–42/Aβ_1–40, p-tau, and t-tau. As a result, the GCA-F and Fazekas scales showed positively significant correlations with onset age (r = 0.181, p < 0.001; r = 0.411, p < 0.001, respectively). Patients with late-onset AD (LOAD) showed higher GCA-F and Fazekas scores (p < 0.001, p < 0.001). With regard to the disease duration, the MTA and GCA-F were positively correlated (r = 0.137, p < 0.05; r = 0.106, p < 0.05, respectively). In terms of disease severity, a positively significant association emerged between disease severity and the MTA, PA GCA-F, and Fazekas scores (p < 0.001, p < 0.001, p < 0.001, p < 0.05, respectively). Moreover, after adjusting for age, gender, and APOE alleles, the MTA scale contributed to moderate to severe AD in statistical significance independently by multivariate logistic regression analysis (p < 0.05). The model combining visual rating scales, age, gender, and APOE alleles showed the best performance for the prediction of moderate to severe AD significantly (AUC = 0.712, sensitivity = 51.5%, specificity = 84.6%). In addition, we observed that the MTA and Fazekas scores were associated with a lower concentration of Aβ_1–42 (p < 0.031, p < 0.022, respectively). In summary, we systematically analyzed the benefits of multiple visual rating scales in predicting the clinical status of AD. The visual rating scales combined with age, gender, and APOE alleles showed best performance in predicting the severity of AD. MRI biomarkers in combination with CSF biomarkers can be used in clinical practice.

Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease

BACKGROUND

The progression of Alzheimer's disease (AD) varies in different patients at different stages, which makes predicting the time of disease conversions challenging.

OBJECTIVE

We established an algorithm by leveraging machine learning techniques to predict the probability of the conversion time to next stage for different subjects during a given period.

METHODS

Firstly, we used Kaplan-Meier (KM) estimation to get the transition curves of different AD stages, and calculated Log-rank statistics to test whether the progression rate between different stages was identical. This quantitatively confirmed the progression rates known in the literature. Then, we developed an approach based on deep learning model, DeepSurv, to predict the probabilities of time-to-conversion. Finally, to help interpret the deep learning model in our approach, we identified important variables contributing the most to the DeepSurv prediction, whose significance were validated with the analysis of variance (ANOVA).

RESULTS

Our machine learning approach predicted the time to conversion with a high accuracy. For each of the different stages, the concordance index (CI) of our approach was at least 86%, and the integrated Brier score (IBS) was less than 0.1. To facilitate interpretability of the prediction results, our approach identified the top 10 variables for each disease conversion scenario, which were clinicopathologically meaningful, and most of them were also statistically significant.

CONCLUSION

Our study has the potential to provide individualized prediction for future time course of AD conversions years before their actual occurrence, thus facilitating personalized prevention and intervention strategies to slow down the progression of AD.

Inter- and Intra-Scanner Variability of Automated Brain Volumetry on Three Magnetic Resonance Imaging Systems in Alzheimer's Disease and Controls

Magnetic Resonance Imaging (MRI) has become part of the clinical routine for diagnosing neurodegenerative disorders. Since acquisitions are performed at multiple centers using multiple imaging systems, detailed analysis of brain volumetry differences between MRI systems and scan-rescan acquisitions can provide valuable information to correct for different MRI scanner effects in multi-center longitudinal studies. To this end, five healthy controls and five patients belonging to various stages of the AD continuum underwent brain MRI acquisitions on three different MRI systems (Philips Achieva dStream 1.5T, Philips Ingenia 3T, and GE Discovery MR750w 3T) with harmonized scan parameters. Each participant underwent two subsequent MRI scans per imaging system, repeated on three different MRI systems within 2 h. Brain volumes computed by icobrain dm (v5.0) were analyzed using absolute and percentual volume differences, Dice similarity (DSC) and intraclass correlation coefficients, and coefficients of variation (CV). Harmonized scans obtained with different scanners of the same manufacturer had a measurement error closer to the intra-scanner performance. The gap between intra- and inter-scanner comparisons grew when comparing scans from different manufacturers. This was observed at image level (image contrast, similarity, and geometry) and translated into a higher variability of automated brain volumetry. Mixed effects modeling revealed a significant effect of scanner type on some brain volumes, and of the scanner combination on DSC. The study concluded a good intra- and inter-scanner reproducibility, as illustrated by an average intra-scanner (inter-scanner) CV below 2% (5%) and an excellent overlap of brain structure segmentation (mean DSC > 0.88).

Age-at-Onset and APOE-Related Heterogeneity in Pathologically Confirmed Sporadic Alzheimer Disease

OBJECTIVE

To characterize age-related clinical heterogeneity in Alzheimer disease (AD) and determine whether it is modified by APOE genotype or concomitant non-AD pathology, we analyzed data from 1,750 patients with sporadic, pathologically confirmed severe AD.

METHODS

In this retrospective cohort study, regression and mixed effects models assessed effects of estimated age at onset, APOE genotype, and their interaction on standardized clinical, cognitive, and pathologic outcome measures from the National Alzheimer's Coordinating Center database.

RESULTS

A bimodal distribution of age at onset frequency in APOE ε4- cases showed best separation at age 63. Using this age cutoff, cases were grouped as ε4- early-onset AD (EOAD) (n = 169), ε4+ EOAD (n = 273), ε4- late-onset AD (LOAD) (n = 511), and ε4+ LOAD (n = 797). Patients with EOAD were more likely than patients with LOAD to present with noncognitive behavioral or motor symptoms or nonmemory cognitive complaints, and had more executive dysfunction, but less language impairment on objective cognitive testing. Age at onset and ε4- genotype were independently associated with lower baseline Mini-Mental State Examination scores and greater functional impairment and patients with EOAD had faster cognitive and functional decline than patients with LOAD regardless of APOE genotype. Patients with EOAD were more likely than patients with LOAD to receive a non-AD clinical diagnosis even though they were more likely to have pure AD without concomitant vascular or other non-AD neurodegenerative pathology.

CONCLUSIONS

Early-onset sporadic AD is associated with a greater likelihood of an atypical, non-memory-dominant clinical presentation, especially in the absence of the APOE ε4 allele, which may lead to misattribution to non-AD underlying pathology.

Modifiable, Non-Modifiable, and Clinical Factors Associated with Progression of Alzheimer's Disease

There is an extensive literature relating to factors associated with the development of Alzheimer's disease (AD), but less is known about factors which may contribute to its progression. This review examined the literature with regard to 15 factors which were suggested by PubMed search to be positively associated with the cognitive and/or neuropathological progression of AD. The factors were grouped as potentially modifiable (vascular risk factors, comorbidities, malnutrition, educational level, inflammation, and oxidative stress), non-modifiable (age at clinical onset, family history of dementia, gender, Apolipoprotein E ɛ4, genetic variants, and altered gene regulation), and clinical (baseline cognitive level, neuropsychiatric symptoms, and extrapyramidal signs). Although conflicting results were found for the majority of factors, a positive association was found in nearly all studies which investigated the relationship of six factors to AD progression: malnutrition, genetic variants, altered gene regulation, baseline cognitive level, neuropsychiatric symptoms, and extrapyramidal signs. Whether these or other factors which have been suggested to be associated with AD progression actually influence the rate of decline of AD patients is unclear. Therapeutic approaches which include addressing of modifiable factors associated with AD progression should be considered.

Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) represents a cluster of various vascular disorders with different pathological backgrounds. The advanced vasculature net of cerebral vessels, including small arteries, capillaries, arterioles and venules, is usually affected. Processes of oxidation underlie the pathology of CSVD, promoting the degenerative status of the epithelial layer. There are several classifications of cerebral small vessel diseases; some of them include diseases such as Binswanger’s disease, leukoaraiosis, cerebral microbleeds (CMBs) and lacunar strokes. This paper presents the characteristics of CSVD and the impact of the current knowledge of this topic on the diagnosis and treatment of patients.

Effect of APOE ε4 genotype on amyloid-β and tau accumulation in Alzheimer's disease

Background

To assess the effects of apolipoprotein E (ApoE) ε4 genotype on amyloid-β (Aβ) and tau burden and their longitudinal changes in Alzheimer’s disease (AD) spectrum.

Methods

Among 272 individuals who underwent PET scans (¹⁸F-florbetaben for Aβ and ¹⁸F-flortaucipir for tau) and ApoE genotyping, 187 individuals completed 2-year follow-up PET scans. After correcting for the partial volume effect, we compared the standardized uptake value ratio (SUVR) for Aβ and tau burden between the ε4+ and ε4− groups. By using a linear mixed-effect model, we measured changes in SUVR in the ApoE ε4+ and ε4− groups.

Results

The ε4+ group showed greater baseline Aβ burden in the diffuse cortical regions and greater tau burden in the lateral, and medial temporal, cingulate, and insula cortices. Tau accumulation rate was higher in the parietal, occipital, lateral, and medial temporal cortices in the ε4+ group. In Aβ+ individuals, baseline tau burden was greater in the medial temporal cortex, while Aβ burden was conversely greater in the ε4− group. Tau accumulation rate was higher in the ε4+ group in a small region in the lateral temporal cortex. The effect of ApoE ε4 on enhanced tau accumulation persisted even after adjusting for the global cortical Aβ burden.

Conclusions

Progressive tau accumulation may be more prominent in ε4 carriers, particularly in the medial and lateral temporal cortices. ApoE ε4 allele has differential effects on the Aβ burden depending on the existing amyloidosis and may enhance vulnerability to progressive tau accumulation in the AD spectrum independent of Aβ.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s13195-020-00710-6.

Automated age- and sex-specific volumetric estimation of regional brain atrophy: workflow and feasibility

Objectives

An automated workflow for age- and sex-specific estimation of regional brain volume changes from structural MRI relative to a standard population is presented and evaluated for feasibility.

Methods

T1w MRI scans are preprocessed in a standardized way comprising gray matter (GM) segmentation, normalization, modulation, and spatial smoothing. Resulting GM images are then compared to precomputed age- and sex-specific GM templates derived from the population-based Nathan Kline Institute Rockland Sample, and voxel-wise z-maps are compiled. z-maps are color-coded and fused with the subject’s T1w images. The rate of technical success of the proposed workflow was evaluated in 1330 subjects of the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Furthermore, medial temporal atrophy (MTA) was assessed using the color-coded maps and with the MTA visual rating scale in these subjects. Sensitivities and specificity of color-coded maps and MTA scale were compared using McNemar’s test.

Results

One test dataset was excluded due to severe motion artifacts. Out of the remaining 1329 datasets, atrophy map generation was successful in 1323 ADNI subjects (99.5%). Sensitivity for AD diagnosis (71.4 % vs. 53.3%, p < 0.0001 for left; 70.4% vs. 55.3%, p < 0.0001 for right hemisphere) and for MCI (45.4% vs. 17.4, p < 0.0001 for left; 43.5% vs. 14.6%, p < 0.0001 for right hemisphere) based on medial temporal atrophy assessment in color-coded maps was significantly higher than for MTA visual rating scale, while specificity was lower (78.4% vs. 93.8%, p < 0.0001 for left; 79.4% vs. 95.8%, p < 0.0001 for right hemisphere). The workflow is named veganbagel and is published as open-source software with an integrated PACS interface.

Conclusions

Automated brain volume change estimation with the proposed workflow is feasible and technically dependable. It provides high potential for radiologic assessment of brain volume changes and neurodegenerative diseases.

Key Points

• A workflow combining techniques from voxel-based morphometry and population-based neuroimaging data is feasible and technically highly dependable.

• The workflow is provided as open-source software, named veganbagel.

• Sensitivity of medial temporal atrophy assessment in atrophy maps from veganbagel exceeds the sensitivity of MTA visual rating scale for the diagnosis of Alzheimer’s disease.

High blood pressure predicts hippocampal atrophy rate in cognitively impaired elders

INTRODUCTION

Understanding relationships among blood pressure (BP), cognition, and brain volume could inform Alzheimer's disease (AD) management.

METHODS

We investigated Alzheimer's Disease Neuroimaging Initiative (ADNI) participants: 200 controls, 346 mild cognitive impairment (MCI), and 154 AD. National Alzheimer's Co-ordinating Center (NACC) participants were separately analyzed: 1098 controls, 2297 MCI, and 4845 AD. Relationships between cognition and BP were assessed in both cohorts and BP and atrophy rates in ADNI. Multivariate mixed linear-regression models were fitted with joint outcomes of BP (systolic, diastolic, and pulse pressure), cognition (Mini-Mental State Examination, Logical Memory, and Digit Symbol) and atrophy rate (whole-brain, hippocampus).

RESULTS

ADNI MCI and AD patients with greater baseline systolic BP had higher hippocampal atrophy rates ([r, P value]; 0.2, 0.005 and 0.2, 0.04, respectively). NACC AD patients with lower systolic BP had lower cognitive scores (0.1, 0.0003).

DISCUSSION

Higher late-life BP may be associated with faster decline in cognitively impaired elders.

Integrated Flexible Hybrid Silicone-Textile Dual-Resonant Sensors and Switching Circuit for Wearable Neurodegeneration Monitoring Systems

This paper describes the design, development, and testing of flexible hybrid silicone-textile sensors and a flexible switching circuit that were integrated into a wearable system for monitoring neurodegenerative diseases. A total of 6 planar monopole antenna sensors were fabricated that propagates at two separate resonant frequencies: 800 MHz and 2.1 GHz respectively. In addition, 2 switching circuits, each having 3 switches and 4 SMA breakout boards, were assembled and placed on the wearable neurodegeneration monitoring system. Each switching circuit connects 3 sensors to a single port on a vector network analyzer (VNA) that is used to generate and receive microwave signals. Experiments were performed using the wearable device with the developed sensors and switching circuit on phantoms mimicking two common physiological changes in the brain caused by neurodegenerative diseases: 1) brain atrophy and 2) lateral ventricle enlargement. The dual nature of the sensors' resonance allows it to detect both brain atrophy and lateral ventricle enlargement separately at different operating frequency. This provides the advantage of minimizing the number of sensor elements needed to monitor neurodegenerative disease. The use of a switching circuit also allows for quick and convenient measurements by choosing which sensors are active for ports 1 and 2 on the VNA respectively. In addition to being low-cost, the flexibility of the materials used in fabrication allows the sensors and switching circuit to be conformal to the patient's head. Results from the experiments indicates that the sensors and switching circuit were working successfully when integrated into the wearable device.

Prominent Non-Memory Deficits in Alzheimer's Disease Are Associated with Faster Disease Progression

BACKGROUND

Alzheimer's disease (AD) is a heterogeneous disorder.

OBJECTIVE

To investigate whether cognitive AD subtypes are associated with different rates of disease progression.

METHODS

We included 1,066 probable AD patients from the Amsterdam Dementia Cohort (n = 290), Alzheimer's Disease Neuroimaging Initiative (n = 268), Dementia Competence Network (n = 226), and University of California, San Francisco (n = 282) with available follow-up data. Patients were previously clustered into two subtypes based on their neuropsychological test results: one with most prominent memory impairment (n = 663) and one with most prominent non-memory impairment (n = 403). We examined associations between cognitive subtype and disease progression, as measured with repeated Mini-Mental State Examination (MMSE) and Clinical Dementia Rating scale sum of boxes (CDR sob), using linear mixed models. Furthermore, we investigated mortality risk associated with subtypes using Cox proportional hazard analyses.

RESULTS

Patients were 71±9 years old; 541 (51%) were female. At baseline, pooled non-memory patients had worse MMSE scores (23.1±0.1) and slightly worse CDR sob (4.4±0.1) than memory patients (MMSE 24.0±0.1; p < 0.001; CDR sob 4.1±0.1; p < 0.001). During follow-up, pooled non-memory patients showed steeper annual decline in MMSE (-2.8±0.1) and steeper annual increase in CDR sob (1.8±0.1) than memory patients (MMSE - 1.9±0.1; pinteraction<0.001; CDR sob 1.3±0.1; pinteraction<0.001). Furthermore, the non-memory subtype was associated with an increased risk of mortality compared with the memory subtype at trend level (HR = 1.36, CI = 1.00-1.85, p = 0.05).

CONCLUSIONS

AD patients with most prominently non-memory impairment show faster disease progression and higher risk of mortality than patients with most prominently memory impairment.

Measurement Variability Following MRI System Upgrade

Major hardware/software changes to MRI platforms, either planned or unplanned, will almost invariably occur in longitudinal studies. Our objective was to assess the resulting variability on relevant imaging measurements in such context, specifically for three Siemens Healthcare Magnetom Trio upgrades to the Prisma^fit platform. We report data acquired on three healthy volunteers scanned before and after three different platform upgrades. We assessed differences in image signal [contrast-to-noise ratio (CNR)] on T1-weighted images (T1w) and fluid-attenuated inversion recovery images (FLAIR); brain morphometry on T1w image; and small vessel disease (white matter hyperintensities; WMH) on FLAIR image. Prisma^fit upgrade resulted in higher (30%) and more variable neocortical CNR and larger brain volume and thickness mainly in frontal areas. A significant relationship was observed between neocortical CNR and neocortical volume. For FLAIR images, no significant CNR difference was observed, but WMH volumes were significantly smaller (-68%) after Prisma^fit upgrade, when compared to results on the Magnetom Trio. Together, these results indicate that Prisma^fit upgrade significantly influenced image signal, brain morphometry measures and small vessel diseases measures and that these effects need to be taken into account when analyzing results from any longitudinal study undergoing similar changes.

Spatial patterns of progressive brain volume loss after moderate-severe traumatic brain injury

Traumatic brain injury leads to significant loss of brain volume, which continues into the chronic stage. This can be sensitively measured using volumetric analysis of MRI. Here we: (i) investigated longitudinal patterns of brain atrophy; (ii) tested whether atrophy is greatest in sulcal cortical regions; and (iii) showed how atrophy could be used to power intervention trials aimed at slowing neurodegeneration. In 61 patients with moderate-severe traumatic brain injury (mean age = 41.55 years ± 12.77) and 32 healthy controls (mean age = 34.22 years ± 10.29), cross-sectional and longitudinal (1-year follow-up) brain structure was assessed using voxel-based morphometry on T₁-weighted scans. Longitudinal brain volume changes were characterized using a novel neuroimaging analysis pipeline that generates a Jacobian determinant metric, reflecting spatial warping between baseline and follow-up scans. Jacobian determinant values were summarized regionally and compared with clinical and neuropsychological measures. Patients with traumatic brain injury showed lower grey and white matter volume in multiple brain regions compared to controls at baseline. Atrophy over 1 year was pronounced following traumatic brain injury. Patients with traumatic brain injury lost a mean (± standard deviation) of 1.55% ± 2.19 of grey matter volume per year, 1.49% ± 2.20 of white matter volume or 1.51% ± 1.60 of whole brain volume. Healthy controls lost 0.55% ± 1.13 of grey matter volume and gained 0.26% ± 1.11 of white matter volume; equating to a 0.22% ± 0.83 reduction in whole brain volume. Atrophy was greatest in white matter, where the majority (84%) of regions were affected. This effect was independent of and substantially greater than that of ageing. Increased atrophy was also seen in cortical sulci compared to gyri. There was no relationship between atrophy and time since injury or age at baseline. Atrophy rates were related to memory performance at the end of the follow-up period, as well as to changes in memory performance, prior to multiple comparison correction. In conclusion, traumatic brain injury results in progressive loss of brain tissue volume, which continues for many years post-injury. Atrophy is most prominent in the white matter, but is also more pronounced in cortical sulci compared to gyri. These findings suggest the Jacobian determinant provides a method of quantifying brain atrophy following a traumatic brain injury and is informative in determining the long-term neurodegenerative effects after injury. Power calculations indicate that Jacobian determinant images are an efficient surrogate marker in clinical trials of neuroprotective therapeutics.

A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER)

Background:

Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD).

Objective:

We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression.

Methods:

The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available.

Results:

The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment.

Conclusion:

WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.

Association of Brain Magnetic Resonance Imaging Signs With Cognitive Outcomes in Persons With Nonimpaired Cognition and Mild Cognitive Impairment

IMPORTANCE

Brain atrophy and vascular lesions contribute to dementia and mild cognitive impairment (MCI) in clinical referral populations. Prospective evidence in older general populations is limited.

OBJECTIVE

To evaluate which magnetic resonance imaging (MRI) signs are independent risk factors for dementia and MCI.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study included 1553 participants sampled from the Atherosclerosis Risk in Communities Study who had brain MRI scans and were dementia free during visit 5 (June 2011 to September 2013). Participants' cognitive status was evaluated through visit 6 (June 2016 to December 2017).

EXPOSURES

Brain regional volumes, microhemorrhages, white matter hyperintensity (WMH) volumes, and infarcts measured on 3-T MRI.

MAIN OUTCOMES AND MEASURES

Cognitive status (dementia, MCI, or nonimpaired cognition) was determined from in-person evaluations. Dementia among participants who missed visit 6 was identified via dementia surveillance and hospital discharge or death certificate codes. Cox proportional hazards models were used to evaluate the risk of dementia in 3 populations: dementia-free participants (N = 1553), participants with nonimpaired cognition (n = 1014), and participants with MCI (n = 539). Complementary log-log models were used for risk of MCI among participants with nonimpaired cognition who also attended visit 6 (n = 767). Models were adjusted for demographic variables, apolipoprotein E ε4 alleles, vascular risk factors, depressive symptoms, and heart failure.

RESULTS

Overall, 212 incident dementia cases were identified among 1553 participants (mean [SD] age at visit 5, 76 [5.2] years; 946 [60.9%] women; 436 [28.1%] African American) with a median (interquartile range) follow-up period of 4.9 (4.3-5.2) years. Significant risk factors of dementia included lower volumes in the Alzheimer disease (AD) signature region, including hippocampus, entorhinal cortex, and surrounding structures (hazard ratio [HR] per 1-SD decrease, 2.40; 95% CI, 1.89-3.04), lobar microhemorrhages (HR, 1.90; 95% CI, 1.30-2.77), higher volumes of WMH (HR per 1-SD increase, 1.44; 95% CI, 1.23-1.69), and lacunar infarcts (HR, 1.66; 95% CI, 1.20-2.31). The AD signature region volume was also consistently associated with both MCI and progression from MCI to dementia, while subcortical microhemorrhages and infarcts contributed less to the progression from MCI to dementia.

CONCLUSIONS AND RELEVANCE

In this study, lower AD signature region volumes, brain microhemorrhages, higher WMH volumes, and infarcts were risk factors associated with dementia in older community-based residents. Vascular changes were more important in the development of MCI than in its progression to dementia, while AD-related signs were important in both stages.

Alzheimer's disease diagnosis based on the Hippocampal Unified Multi-Atlas Network (HUMAN) algorithm

BACKGROUND

Hippocampal atrophy is a supportive feature for the diagnosis of probable Alzheimer's disease (AD). However, even for an expert neuroradiologist, tracing the hippocampus and measuring its volume is a time consuming and extremely challenging task. Accordingly, the development of reliable fully-automated segmentation algorithms is of paramount importance.

MATERIALS AND METHODS

The present study evaluates (i) the precision and the robustness of the novel Hippocampal Unified Multi-Atlas Network (HUMAN) segmentation algorithm and (ii) its clinical reliability for AD diagnosis. For these purposes, we used a mixed cohort of 456 subjects and their T1 weighted magnetic resonance imaging (MRI) brain scans. The cohort included 145 controls (CTRL), 217 mild cognitive impairment (MCI) subjects and 94 AD patients from Alzheimer's Disease Neuroimaging Initiative (ADNI). For each subject the baseline, repeat, 12 and 24 month follow-up scans were available.

RESULTS

HUMAN provides hippocampal volumes with a 3% precision; volume measurements effectively reveal AD, with an area under the curve (AUC) AUC₁ = 0.08 ± 0.02. Segmented volumes can also reveal the subtler effects present in MCI subjects, AUC₂ = 0.76 ± 0.05. The algorithm is stable and reproducible over time, even for 24 month follow-up scans.

CONCLUSIONS

The experimental results demonstrate HUMAN is a precise segmentation algorithm, besides hippocampal volumes, provided by HUMAN, can effectively support the diagnosis of Alzheimer's disease and become a useful tool for other neuroimaging applications.

Amsterdam Dementia Cohort: Performing Research to Optimize Care

The Alzheimer center of the VU University Medical Center opened in 2000 and was initiated to combine both patient care and research. Together, to date, all patients forming the Amsterdam Dementia Cohort number almost 6,000 individuals. In this cohort profile, we provide an overview of the results produced based on the Amsterdam Dementia Cohort. We describe the main results over the years in each of these research lines: 1) early diagnosis, 2) heterogeneity, and 3) vascular factors. Among the most important research efforts that have also impacted patients' lives and/or the research field, we count the development of novel, easy to use diagnostic measures such as visual rating scales for MRI and the Amsterdam IADL Questionnaire, insight in different subgroups of AD, and findings on incidence and clinical sequelae of microbleeds. Finally, we describe in the outlook how our research endeavors have improved the lives of our patients.

Development of a core outcome set for disease modification trials in mild to moderate dementia: a systematic review, patient and public consultation and consensus recommendations

BACKGROUND

There is currently no disease-modifying treatment available to halt or delay the progression of the disease pathology in dementia. An agreed core set of the best-available and most appropriate outcomes for disease modification would facilitate the design of trials and ensure consistency across disease modification trials, as well as making results comparable and meta-analysable in future trials.

OBJECTIVES

To agree a set of core outcomes for disease modification trials for mild to moderate dementia with the UK dementia research community and patient and public involvement (PPI).

DATA SOURCES

We included disease modification trials with quantitative outcomes of efficacy from (1) references from related systematic reviews in workstream 1; (2) searches of the Cochrane Dementia and Cognitive Improvement Group study register, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Latin American and Caribbean Health Sciences Literature and PsycINFO on 11 December 2015, and clinical trial registries [International Standard Randomised Controlled Trial Number (ISRCTN) and clinicaltrials.gov] on 22 and 29 January 2016; and (3) hand-searches of reference lists of relevant systematic reviews from database searches.

REVIEW METHODS

The project consisted of four workstreams. (1) We obtained related core outcome sets and work from co-applicants. (2) We systematically reviewed published and ongoing disease modification trials to identify the outcomes used in different domains. We extracted outcomes used in each trial, recording how many used each outcome and with how many participants. We divided outcomes into the domains measured and searched for validation data. (3) We consulted with PPI participants about recommended outcomes. (4) We presented all the synthesised information at a conference attended by the wider body of National Institute for Health Research (NIHR) dementia researchers to reach consensus on a core set of outcomes.

RESULTS

We included 149 papers from the 22,918 papers screened, referring to 125 individual trials. Eighty-one outcomes were used across trials, including 72 scales [31 cognitive, 12 activities of daily living (ADLs), 10 global, 16 neuropsychiatric and three quality of life] and nine biological techniques. We consulted with 18 people for PPI. The conference decided that only cognition and biological markers are core measures of disease modification. Cognition should be measured by the Mini Mental State Examination (MMSE) or the Alzheimer's Disease Assessment Scale - Cognitive subscale (ADAS-Cog), and brain changes through structural magnetic resonance imaging (MRI) in a subset of participants. All other domains are important but not core. We recommend using the Neuropsychiatric Inventory for neuropsychiatric symptoms: the Disability Assessment for Dementia for ADLs, the Dementia Quality of Life Measure for quality of life and the Clinical Dementia Rating scale to measure dementia globally.

LIMITATIONS

Most of the trials included participants with Alzheimer's disease, so recommendations may not apply to other types of dementia. We did not conduct economic analyses. The PPI consultation was limited to members of the Alzheimer's Society Research Network.

CONCLUSIONS

Cognitive outcomes and biological markers form the core outcome set for future disease modification trials, measured by the MMSE or ADAS-Cog, and structural MRI in a subset of participants.

FUTURE WORK

We envisage that the core set may be superseded in the future, particularly for other types of dementia. There is a need to develop an algorithm to compare scores on the MMSE and ADAS-Cog.

STUDY REGISTRATION

The project was registered with Core Outcome Measures in Effectiveness Trials [ www.comet-initiative.org/studies/details/819?result=true (accessed 7 April 2016)]. The systematic review protocol is registered as PROSPERO CRD42015027346.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Retrospective Diagnosis of Parkinsonian Syndromes Using Whole-Brain Atrophy Rates

Objective: The absence of markers for ante-mortem diagnosis of idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) results in these disorders being commonly mistaken for each other, particularly in the initial stages. We aimed to investigate annualized whole-brain atrophy rates (a-WBAR) in these disorders to aid in the diagnosis between IPD vs. PSP and MSA. Methods: Ten healthy controls, 20 IPD, 39 PSP, and 41 MSA patients were studied using Structural Imaging Evaluation with Normalization of Atrophy (SIENA). SIENA is an MRI-based algorithm that quantifies brain tissue volume and does not require radiotracers. SIENA has been shown to have a low estimation error for atrophy rate over the whole brain (0.5%). Results: In controls, the a-WBAR was 0.37% ± 0.28 (CI 95% 0.17-0.57), while in IPD a-WBAR was 0.54% ± 0.38 (CI 95% 0.32-0.68). The IPD patients did not differ from the controls. In PSP, the a-WBAR was 1.93% ± 1.1 (CI 95% 1.5-2.2). In MSA a-WBAR was 1.65% ± 0.9 (CI 95%1.37-1.93). MSA did not differ from PSP. The a-WBAR in PSP and MSA were significantly higher than in IPD (p < 0.001). a-WBAR 0.6% differentiated patients with IPD from those with PSA and MSA with 91% sensitivity and 80% specificity. Conclusions: a-WBAR within the normal range is unlikely to be observed in PSP or MSA. a-WBAR may add a potential retrospective application to improve the diagnostic accuracy of MSA and PSP vs. IPD during the first year of clinical assessment.

Demographic and clinical characteristics related to cognitive decline in Alzheimer disease in China: A multicenter survey from 2011 to 2014

Alzheimer disease (AD) is the most frequent cause of dementia. AD diagnosis, progression, and treatment have not been analyzed nationwide in China. The primary aim of this study was to analyze demographic and clinical characteristics related to cognitive decline in AD patients treated at outpatient clinics in China.We performed a retrospective study of 1993 AD patients at 10 cognitive centers across 8 cities in China from March 2011 to October 2014. Of these, 891 patients were followed for more than 1 year.The mean age at diagnosis was 72.0 ± 10.0 years (range 38-96 years), and the mean age at onset of AD was 69.8 ± 9.5 years. Most patients (65.1%) had moderate to severe symptoms at the time of diagnosis, and mean Mini-Mental State Examination at diagnosis was 15.7 ± 7.7. AD patients showed significant cognitive decline at 12 months after diagnosis. Having more than 9 years of formal education was an independent risk factor related to rapid cognitive decline [odds ratio (OR) = 1.80; 95% confidence interval (95% CI): 1.11-2.91]. Early-onset AD patients experienced more rapid cognitive decline than late-onset patients (OR = 1.83; 95% CI: 1.09-3.06).Most AD patients in China had moderate to severe symptoms at the time of diagnosis and experienced significant cognitive decline within 1 year. Rapid cognitive decline in AD was related to having a higher educational level and younger age of onset.

The identification of cognitive subtypes in Alzheimer's disease dementia using latent class analysis

OBJECTIVE

Alzheimer's disease (AD) is a heterogeneous disorder with complex underlying neuropathology that is still not completely understood. For better understanding of this heterogeneity, we aimed to identify cognitive subtypes using latent class analysis (LCA) in a large sample of patients with AD dementia. In addition, we explored the relationship between the identified cognitive subtypes, and their demographical and neurobiological characteristics.

METHODS

We performed LCA based on neuropsychological test results of 938 consecutive probable patients with AD dementia using Mini-Mental State Examination as the covariate. Subsequently, we performed multinomial logistic regression analysis with cluster membership as dependent variable and dichotomised demographics, APOE genotype, cerebrospinal fluid biomarkers and MRI characteristics as independent variables.

RESULTS

LCA revealed eight clusters characterised by distinct cognitive profile and disease severity. Memory-impaired clusters-mild-memory (MILD-MEM) and moderate-memory (MOD-MEM)-included 43% of patients. Memory-spared clusters mild-visuospatial-language (MILD-VILA), mild-executive (MILD-EXE) and moderate-visuospatial (MOD-VISP) -included 29% of patients. Memory-indifferent clusters mild-diffuse (MILD-DIFF), moderate-language (MOD-LAN) and severe-diffuse (SEV-DIFF) -included 28% of patients. Cognitive clusters were associated with distinct demographical and neurobiological characteristics. In particular, the memory-spared MOD-VISP cluster was associated with younger age, APOE e4 negative genotype and prominent atrophy of the posterior cortex.

CONCLUSIONS

Using LCA, we identified eight distinct cognitive subtypes in a large sample of patients with AD dementia. Cognitive clusters were associated with distinct demographical and neurobiological characteristics.

Measuring Global Brain Atrophy with the Brain Volume/Cerebrospinal Fluid Index: Normative Values, Cut-Offs and Clinical Associations

BACKGROUND

Global brain atrophy is present in normal aging and different neurodegenerative disorders such as Alzheimer's disease (AD) and is becoming widely used to monitor disease progression.

SUMMARY

The brain volume/cerebrospinal fluid index (BV/CSF index) is validated in this study as a measurement of global brain atrophy. We tested the ability of the BV/CSF index to detect global brain atrophy, investigated the influence of confounders, provided normative values and cut-offs for mild, moderate and severe brain atrophy, and studied associations with different outcome variables. A total of 1,009 individuals were included [324 healthy controls, 408 patients with mild cognitive impairment (MCI) and 277 patients with AD]. Magnetic resonance images were segmented using FreeSurfer, and the BV/CSF index was calculated and studied both cross-sectionally and longitudinally (1-year follow-up). Both AD patients and MCI patients who progressed to AD showed greater global brain atrophy compared to stable MCI patients and controls. Atrophy was associated with older age, larger intracranial volume, less education and presence of the ApoE ε4 allele. Significant correlations were found with clinical variables, CSF biomarkers and several cognitive tests.

KEY MESSAGES

The BV/CSF index may be useful for staging individuals according to the degree of global brain atrophy, and for monitoring disease progression. It also shows potential for predicting clinical changes and for being used in the clinical routine.

Different patterns of cortical gray matter loss over time in behavioral variant frontotemporal dementia and Alzheimer's disease

We examined patterns of cortical thickness loss and cognitive decline over time in 19 patients with Alzheimer's disease (AD), 10 with behavioral variant frontotemporal dementia (bvFTD), and 34 controls with a mean interval of 2.1 ± 0.4 years. We measured vertexwise and regional cortical thickness changes of 6 lobar regions of interest between groups with the longitudinal FreeSurfer pipeline. Compared with controls, AD and bvFTD had a steeper rate of cognitive decline and showed faster cortical thinning per year. Decrease of thickness over time was highest in AD and generalized throughout the whole brain, most pronounced posteriorly, whereas bvFTD patients had a more selective loss in frontal cortex and in anterior parts of the temporal lobes. In a direct comparison, AD patients showed faster cortical thinning in the insula, temporal, and parietal regions, whereas bvFTD patients only showed faster cortical thinning in the orbitofrontal gyrus. Decline of cognitive performances was in line with cortical thinning and deteriorated the most in AD patients.

Cerebral small vessel disease and Alzheimer's disease

Cerebral small vessel disease (CSVD) is a group of pathological processes with multifarious etiology and pathogenesis that are involved into the small arteries, arterioles, venules, and capillaries of the brain. CSVD mainly contains lacunar infarct or lacunar stroke, leukoaraiosis, Binswanger's disease, and cerebral microbleeds. CSVD is an important cerebral microvascular pathogenesis as it is the cause of 20% of strokes worldwide and the most common cause of cognitive impairment and dementia, including vascular dementia and Alzheimer's disease (AD). It has been well identified that CSVD contributes to the occurrence of AD. It seems that the treatment and prevention for cerebrovascular diseases with statins have such a role in the same function for AD. So far, there is no strong evidence-based medicine to support the idea, although increasing basic studies supported the fact that the treatment and prevention for cerebrovascular diseases will benefit AD. Furthermore, there is still lack of evidence in clinical application involved in specific drugs to benefit both AD and CSVD.

Nonlinear cerebral atrophy patterns across the Alzheimer's disease continuum: impact of APOE4 genotype

The progression of Alzheimer's disease (AD) is characterized by complex trajectories of cerebral atrophy that are affected by interactions with age and apolipoprotein E allele ε4 (APOE4) status. In this article, we report the nonlinear volumetric changes in gray matter across the full biological spectrum of the disease, represented by the AD-cerebrospinal fluid (CSF) index. This index reflects the subject's level of pathology and position along the AD continuum. We also evaluated the associated impact of the APOE4 genotype. The atrophy pattern associated with the AD-CSF index was highly symmetrical and corresponded with the typical AD signature. Medial temporal structures showed different atrophy dynamics along the progression of the disease. The bilateral parahippocampal cortices and a parietotemporal region extending from the middle temporal to the supramarginal gyrus presented an initial increase in volume which later reverted. Similarly, a portion of the precuneus presented a rather linear inverse association with the AD-CSF index whereas some other clusters did not show significant atrophy until index values corresponded to positive CSF tau values. APOE4 carriers showed steeper hippocampal volume reductions with AD progression. Overall, the reported atrophy patterns are in close agreement with those mentioned in previous findings. However, the detected nonlinearities suggest that there may be different pathological processes taking place at specific moments during AD progression and reveal the impact of the APOE4 allele.

Genetic determinants of disease progression in Alzheimer's disease

There is a strong genetic basis for late-onset Alzheimer's disease (LOAD); thus far 22 genes/loci have been identified that affect the risk of LOAD. However, the relationships among the genetic variations at these loci and clinical progression of the disease have not been fully explored. In the present study, we examined the relationships of 22 known LOAD genes to the progression of AD in 680 AD patients recruited from the University of Pittsburgh Alzheimer's Disease Research Center. Patients were classified as "rapid progressors" if the Mini-Mental State Examination (MMSE) changed ≥3 points in 12 months and "slow progressors" if the MMSE changed ≤2 points. We also performed a genome-wide association study in this cohort in an effort to identify new loci for AD progression. Association analysis between single nucleotide polymorphisms (SNPs) and the progression status of the AD cases was performed using logistic regression model controlled for age, gender, dementia medication use, psychosis, and hypertension. While no significant association was observed with either APOE*4 (p = 0.94) or APOE*2 (p = 0.33) with AD progression, we found multiple nominally significant associations (p < 0.05) either within or adjacent to seven known LOAD genes (INPP5D, MEF2C, TREM2, EPHA1, PTK2B, FERMT2, and CASS4) that harbor both risk and protective SNPs. Genome-wide association analyses identified four suggestive loci (PAX3, CCRN4L, PIGQ, and ADAM19) at p < 1E-05. Our data suggest that short-term clinical disease progression in AD has a genetic basis. Better understanding of these genetic factors could help to improve clinical trial design and potentially affect the development of disease modifying therapies.

Early onset APOE E4-negative Alzheimer's disease patients show faster cognitive decline on non-memory domains

Age at onset and APOE E4-genotype have been shown to influence clinical manifestation of Alzheimer's disease (AD). We investigated rate of decline in specific cognitive domains according to age at onset and APOE E4-genotype in patients with AD. 199 patients with probable AD underwent at least two annual neuropsychological assessments. Patients were classified according to age-at-onset (≤ 65 years vs >65 years) and APOE genotype (positive vs negative). The neuropsychological test battery compromised tests for memory, language, attention, executive and visuo-spatial functioning. For each domain compound z-scores were calculated, based on the baseline performance of patients. Average duration of follow-up was 1.5 ± 1 years. We used linear mixed models (LMM) to estimate effects of age, APOE and age⁎APOE on cognitive decline over time. At baseline, patients were 65 ± 8 years, 98(49%) were female and MMSE was 22 ± 4. LMM showed that early onset patients declined faster on executive functioning (β ± SE:-0.09 ± 0.06) than late onset patients, but age was not related to decline in the other cognitive domains. APOE E4 negative patients declined faster on language than APOE E4 positive patients (β ± SE:-0.1 ± 0.06). When we took age and APOE genotype into account simultaneously, we found that compared to late onset-E4 positive patients, early onset-E4 negative patients declined faster on language (β ± SE:-0.36 ± 0.1), attention (β ± SE:-0.42 ± 0.1), executive (β ± SE:-0.41 ± 0.1) and visuo-spatial functioning (β ± SE:-0.43 ± 0.1). Late onset-E4 negative and early onset-E4 positive patients showed intermediate rates of decline. We found no differences in decline on memory. We found that patients who develop AD despite absence of the two most important risk factors, show steepest cognitive decline on non-memory cognitive domains.

Magnetization transfer MRI in dementia disorders, Huntington's disease and parkinsonism

Magnetic resonance imaging is the most used technique of neuroimaging. Using recent advances in magnetic resonance application it is possible to investigate several changes in neurodegenerative disease. Among different techniques, magnetization-transfer imaging (MTI), a magnetic resonance acquisition protocol assessing the magnetization exchange between protons bound to water and those bound to macromolecules, is able to identify microstructural brain tissue changes peculiar of neurodegenerative diseases. This review provides a report on the MTI technique and its use in the dementia disorders, Huntington's disease and parkinsonisms, comprehensive of the predictive values of MTI in the identification of early-phase disease.

The metabolic syndrome in a memory clinic population: relation with clinical profile and prognosis

BACKGROUND

The metabolic syndrome (MetS) refers to a cluster of cardiovascular risk factors that is associated with an increased risk of cognitive impairment and dementia. It is unclear however, if the presence of the MetS is associated with a particular clinical profile or a different prognosis in patients with cognitive complaints or early dementia.

OBJECTIVES

To compare 1) the clinical profile and 2) the prognosis of patients attending a memory clinic according to the presence or absence of MetS.

DESIGN

Longitudinal cohort.

SETTING

Memory clinic.

PARTICIPANTS

We included and followed 86 consecutive patients (average age of 66.7 (SD 9.7)) from the Amsterdam Dementia Cohort with an MMSE>22.

MEASUREMENTS

Clinical profile (neuropsychological examination, brain MRI, cerebrospinal fluid (CSF) biomarkers, clinical diagnosis) on an initial standardized diagnostic assessment was compared according to MetS status. Progression to dementia was assessed in initially nondemented patients (subjective complaints n=40, mild cognitive impairment n=24, follow-up available in 59).

RESULTS

35 (41%) patients met the MetS criteria. Demographics were similar between patients with or without the MetS. At baseline, diagnosis, cognitive performance, severity of degenerative or vascular abnormalities on MRI, and CSF amyloid and tau levels did not differ between the groups (all p>0.05). Among nondemented patients, however, MetS was associated with worse performance on executive function, attention & speed and visuoconstructive ability (z-scores, p<0.05). During a mean follow-up of 3.4years a similar proportion of patients with (4; 17%) and without (6; 17%) the MetS progressed to dementia (p=0.45).

CONCLUSION

Among nondemented patients presenting at a memory clinic MetS was associated with slightly worse cognitive performance (worse on tasks assessing executive functions, visuo-constructive ability, attention & speed), but conversion rate to dementia was not increased.

Analysis of the lipid profiles in a section of bovine brain via non-catalytic rapid methylation

The main focus of this study is to mechanistically introduce a new qualitative and quantitative technique for mapping the lipid profile of a sectional brainvianon-catalytic transesterification reaction (i.e., pseudo catalytic reaction in the presence of porous materials).

Predicting progression of Alzheimer's disease using ordinal regression

We propose a novel approach to predicting disease progression in Alzheimer’s disease (AD) – multivariate ordinal regression – which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression – the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = –0.64, ADNI and ρ = –0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12–24 months) and late converters (24–36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.

CR1, ABCA7, and APOE genes affect the features of cognitive impairment in Alzheimer's disease

BACKGROUND

The genetic factors that determine the heterogeneity of cognitive impairment in Alzheimer's disease (AD) patients have been rarely reported. We aimed to investigate the association between top hits of genome-wide association studies (GWAS) and specific cognitive domains in AD patients.

METHODS

We investigated 86 single nucleotide polymorphisms (SNPs) selected from 12 genes (ABCA7, APOE, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, LRAT, MS4A6A, PCDH11X, and PICALM) based on results of the recent GWAS and genotyped in 211 AD cases. We also analyzed results of comprehensive neuropsychological evaluations in all cases. We performed multiple regression analyses.

RESULTS

There were four significant associations between genotypes and phenotypes of AD patients: CR1 SNP rs11803956 correlated with Mini-Mental State Examination (MMSE) score (β=1.718, Pcorrected=0.002); ABCA7 SNP rs3752232 correlated with Rey Complex Figure Test (RCFT) copy score (β=-6.861, Pcorrected=0.013); APOE SNP rs2075650 correlated with the percentile of RCFT copy score (β=14.005, Pcorrected=0.021) and the percentile of total score in phonemic fluency (β=11.052, Pcorrected=0.035).

CONCLUSION

Our results suggest that CR1, ABCA7, and APOE correlate with specific aspects of cognitive impairments in AD patients.

Dysglycemia, brain volume and vascular lesions on MRI in a memory clinic population

OBJECTIVE

It is unclear, if the association between abnormalities in glucose metabolism (dysglycemia) and impaired cognitive functioning is primarily driven by degenerative or vascular brain damage. We therefore examined the relation between dysglycemia and brain volume and vascular lesions on MRI in a memory clinic population.

METHODS

The relations between markers of glycemia (HbA1c and fasting glucose levels) and normalized brain volume, medial temporal lobe atrophy and vascular lesions (white matter hyperintensities, lacunes) were assessed in 274 consecutive patients attending a memory clinic, using linear regression analyses.

RESULTS

Clinical diagnoses were subjective complaints (n=117), mild cognitive impairment (n=62), Alzheimer's disease (n=61) and other type of dementia (n=34). Twenty patients had a history of diabetes. Across the whole study population there was no relation between HbA1c or fasting glucose and the brain MRI measurements, after adjustments for age, sex and diagnostic group. Secondary analyses after stratification by diabetes status, diagnosis and median age (67 years) did not change the results.

CONCLUSION

In this memory clinic population, dysglycemia was not associated with either brain volume or vascular lesions. Apparently, dysglycemia is not associated with a specific class of brain pathology in patients with cognitive complaints.

Rapid cognitive decline in Alzheimer's disease: a literature review

BACKGROUND

The rate of cognitive decline in Alzheimer's disease (AD) varies considerably between individuals. Predicting rapid cognitive decline might help clinicians provide prognostic information, select subjects for trial intervention and/or reduce costs.

METHODS

PubMed and PsycINFO were searched for all the English written studies published until the end of 2010 on rapid cognitive decline in AD and factors associated with it.

RESULTS

More than 300 individual articles were retrieved. We selected 82 relevant studies. The main findings of these papers are that younger, more educated and more impaired patients are more likely to show rapid cognitive decline. ApoE alleles seem not to modify the velocity of clinical progression of dementia, or at most could have a very small effect. No inference can be made for all the other variables analysed.

CONCLUSIONS

There are many studies on rapid cognitive decline. Results are heterogeneous and often contradictory. No reliable conclusions about factors that may be associated with rapid cognitive decline can yet be drawn.

Smell identification function as a severity and progression marker in Alzheimer's disease

BACKGROUND

Olfactory dysfunction, impaired smell identification in particular, is known as a diagnostic and a marker of conversion in Alzheimer's disease (AD). We aimed to evaluate the associations of olfactory identification impairments with cognition, illness severity, and progression in AD patients.

METHODS

Fifty-seven outpatients with late onset mild to moderate AD and 24 elderly non-demented controls (NDC) were assessed, at baseline and after three months, for Mini-Mental State Examination (MMSE), University of Pennsylvania Smell Identification Test (UPSIT), and Bristol Activities of Daily Living and Neuropsychiatry Inventory. AD participants were classified as Rapid Cognitive Decliners (RCD) defined on a priori with a loss of ≥2 points in MMSE within the previous six months.

RESULTS

AD participants had lower olfactory scores than NDC. RCD had lower olfaction scores compared with Non-Rapid Cognitive Decliners (NRCD). Although the baseline UPSIT scores were associated with baseline MMSE scores, it did not interact significantly with change in MMSE over the follow-up period. Using a median split for olfactory scores, the AD participants were classified as Rapid Olfactory Progressors (ROP) (UPSIT ≤ 15) and Slow Olfactory Progressors correlating significantly with RCD/NRCD groups. The ROP group with higher olfactory impairment indicated more symptomatic illness or severity, i.e. lower cognition, higher functional dependence, and presence of behavioral symptoms.

CONCLUSIONS

Our study supports association of smell identification function with cognition and its utility as an adjunct clinical measure to assess severity in AD. Further work, including larger longitudinal studies, is needed to explore its value in predicting AD progression.

Longitudinal changes of cortical thickness in early- versus late-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) has been shown to progress more rapidly than late-onset Alzheimer's disease (LOAD). However, no studies have compared the topography of brain volume reduction over time. The purpose of this 3-year longitudinal study was to compare EOAD and LOAD in terms of their rates of decline in cognitive testing and topography of cortical thinning. We prospectively recruited 36 patients with AD (14 EOAD and 22 LOAD) and 14 normal controls. All subjects were assessed with neuropsychological tests and with magnetic resonance imaging at baseline, Year 1, and Year 3. The EOAD group showed more rapid decline than the LOAD group in attention, language, and frontal-executive tests. The EOAD group also showed more rapid cortical thinning in widespread association cortices. In contrast, the LOAD group presented more rapid cortical thinning than the EOAD group only in the left parahippocampal gyrus. Our study suggests that patients with EOAD show more rapid cortical atrophy than patients with LOAD, which accounts for faster cognitive decline on neuropsychological tests.