Mapping progressive brain structural changes in early Alzheimer's disease and mild cognitive impairment

Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.

Associations of Midlife Dietary Patterns with Incident Dementia and Brain Structure: Findings from the UK Biobank Study

BACKGROUND

At present, the results on the associations between dietary patterns and the risk of dementia are inconsistent, and studies on the associations between dietary patterns and brain structures are limited.

OBJECTIVE

We aimed to investigate the associations of midlife dietary patterns with incident dementia and brain structures.

METHODS

Based on the UK Biobank Study, we investigated the 1) prospective associations of four healthy dietary pattern indices (healthy plant-based diet index [hPDI], Mediterranean diet score [MDS], Recommended food score [RFS], and Mediterranean-Dietary Approaches to Stop Hypertension Intervention [DASH] Intervention for Neurodegenerative Delay Diet [MIND]) with incident dementia (identified using linked hospital data; N = 114,684; mean age, 56.8 years; 55.5% females) using Cox proportional-hazards regressions and the 2) cross-sectional associations of these dietary pattern indices with brain structures (estimated using magnetic resonance imaging; N = 18,214; mean age, 55.9 years; 53.1% females) using linear regressions. A series of covariates were adjusted, and several sensitivity analyses were conducted.

RESULTS

A total of 481 (0.42%) participants developed dementia during the average 9.4-year follow-up. Although the associations were not statistically significant, all dietary patterns exerted protective effects against incident dementia (all hazard ratios < 1). Furthermore, higher dietary pattern indices were significantly associated with larger regional brain volumes, including volumes of gray matter in the parietal and temporal cortices and volumes of the hippocampus and thalamus. The main results were confirmed via sensitivity analyses.

CONCLUSIONS

Greater adherence to hPDI, MDS, RFS, and MIND was individually associated with larger brain volumes in specific regions. This study shows a comprehensive picture of the consistent associations of midlife dietary patterns with the risk of dementia and brain health, underscoring the potential benefits of a healthy diet in the prevention of dementia.

Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

Neuroimaging and verbal memory assessment in healthy aging adults using a portable low-field MRI scanner and a web-based platform: results from a proof-of-concept population-based cross-section study

Consumer wearables and health monitors, internet-based health and cognitive assessments, and at-home biosample (e.g., saliva and capillary blood) collection kits are increasingly used by public health researchers for large population-based studies without requiring intensive in-person visits. Alongside reduced participant time burden, remote and virtual data collection allows the participation of individuals who live long distances from hospital or university research centers, or who lack access to transportation. Unfortunately, studies that include magnetic resonance neuroimaging are challenging to perform remotely given the infrastructure requirements of MRI scanners, and, as a result, they often omit socially, economically, and educationally disadvantaged individuals. Lower field strength systems (< 100 mT) offer the potential to perform neuroimaging at a participant's home, enabling more accessible and equitable research. Here we report the first use of a low-field MRI "scan van" with an online assessment of paired-associate learning (PAL) to examine associations between brain morphometry and verbal memory performance. In a sample of 67 individuals, 18-93 years of age, imaged at or near their home, we show expected white and gray matter volume trends with age and find significant (p < 0.05 FWE) associations between PAL performance and hippocampus, amygdala, caudate, and thalamic volumes. High-quality data were acquired in 93% of individuals, and at-home scanning was preferred by all individuals with prior MRI at a hospital or research setting. Results demonstrate the feasibility of remote neuroimaging and cognitive data collection, with important implications for engaging traditionally under-represented communities in neuroimaging research.

N-acetyl-aspartate and Myo-inositol as Markers of White Matter Microstructural Organization in Mild Cognitive Impairment: Evidence from a DTI-1H-MRS Pilot Study

We implemented a multimodal approach to examine associations between structural and neurochemical changes that could signify neurodegenerative processes related to mild cognitive impairment (MCI). Fifty-nine older adults (60-85 years; 22 MCI) underwent whole-brain structural 3T MRI (T1W, T2W, DTI) and proton magnetic resonance spectroscopy (¹H-MRS). The regions of interest (ROIs) for ¹H-MRS measurements were the dorsal posterior cingulate cortex, left hippocampal cortex, left medial temporal cortex, left primary sensorimotor cortex, and right dorsolateral prefrontal cortex. The findings revealed that subjects in the MCI group showed moderate to strong positive associations between the total N-acetylaspartate to total creatine and the total N-acetylaspartate to myo-inositol ratios in the hippocampus and dorsal posterior cingulate cortex and fractional anisotropy (FA) of WM tracts crossing these regions-specifically, the left temporal tapetum, right corona radiata, and right posterior cingulate gyri. In addition, negative associations between the myo-inositol to total creatine ratio and FA of the left temporal tapetum and right posterior cingulate gyri were observed. These observations suggest that the biochemical integrity of the hippocampus and cingulate cortex is associated with a microstructural organization of ipsilateral WM tracts originating in the hippocampus. Specifically, elevated myo-inositol might be an underlying mechanism for decreased connectivity between the hippocampus and the prefrontal/cingulate cortex in MCI.

The effects of epigenetic age and its acceleration on surface area, cortical thickness, and volume in young adults

DNA methylation age has been used in recent studies as an epigenetic marker of accelerated cellular aging, whose contribution to the brain structural changes was lately acknowledged. We aimed to characterize the association of epigenetic age (i.e. estimated DNA methylation age) and its acceleration with surface area, cortical thickness, and volume in healthy young adults. Using the multi-tissue method (Horvath S. DNA methylation age of human tissues and cell types. 2013. Genome Biol 14), epigenetic age was computed with saliva sample. Epigenetic age acceleration was derived from residuals after adjusting epigenetic age for chronological age. Multiple regression models were computed for 148 brain regions for surface area, cortical thickness, and volume using epigenetic age or accelerated epigenetic age as a predictor and controlling for sex. Epigenetic age was associated with surface area reduction of the left insula. It was also associated with cortical thinning and volume reduction in multiple regions, with prominent changes of cortical thickness in the left temporal regions and of volume in the bilateral orbital gyri. Finally, accelerated epigenetic age was negatively associated with right cuneus gyrus volume. Our findings suggest that understanding the mechanisms of epigenetic age acceleration in young individuals may yield valuable insights into the relationship between epigenetic aging and the cortical change and on the early development of neurocognitive pathology among young adults.

Brain-on-Cloud for automatic diagnosis of Alzheimer's disease from 3D structural magnetic resonance whole-brain scans

BACKGROUND AND OBJECTIVE

Alzheimer's disease accounts for approximately 70% of all dementia cases. Cortical and hippocampal atrophy caused by Alzheimer's disease can be appreciated easily from a T1-weighted structural magnetic resonance scan. Since a timely therapeutic intervention during the initial stages of the syndrome has a positive impact on both disease progression and quality of life of affected subjects, Alzheimer's disease diagnosis is crucial. Thus, this study relies on the development of a robust yet lightweight 3D framework, Brain-on-Cloud, dedicated to efficient learning of Alzheimer's disease-related features from 3D structural magnetic resonance whole-brain scans by improving our recent convolutional long short-term memory-based framework with the integration of a set of data handling techniques in addition to the tuning of the model hyper-parameters and the evaluation of its diagnostic performance on independent test data.

METHODS

For this objective, four serial experiments were conducted on a scalable GPU cloud service. They were compared and the hyper-parameters of the best experiment were tuned until reaching the best-performing configuration. In parallel, two branches were designed. In the first branch of Brain-on-Cloud, training, validation and testing were performed on OASIS-3. In the second branch, unenhanced data from ADNI-2 were employed as independent test set, and the diagnostic performance of Brain-on-Cloud was evaluated to prove its robustness and generalization capability. The prediction scores were computed for each subject and stratified according to age, sex and mini mental state examination.

RESULTS

In its best guise, Brain-on-Cloud is able to discriminate Alzheimer's disease with an accuracy of 92% and 76%, sensitivity of 94% and 82%, and area under the curve of 96% and 92% on OASIS-3 and independent ADNI-2 test data, respectively.

CONCLUSIONS

Brain-on-Cloud shows to be a reliable, lightweight and easily-reproducible framework for automatic diagnosis of Alzheimer's disease from 3D structural magnetic resonance whole-brain scans, performing well without segmenting the brain into its portions. Preserving the brain anatomy, its application and diagnostic ability can be extended to other cognitive disorders. Due to its cloud nature, computational lightness and fast execution, it can also be applied in real-time diagnostic scenarios providing prompt clinical decision support.

Spousal caregiving, widowhood, and cognition: A systematic review and a biopsychosocial framework for understanding the relationship between interpersonal losses and dementia risk in older adulthood

Accumulating research suggests that stressful life events, especially those that threaten close intimate bonds, are associated with an increased risk of dementia. Grieving the loss of a spouse, whether in the form of caregiving or after the death, ranks among 'life's most significant stressors', evoking intense psychological and physiological distress. Despite numerous studies reporting elevated dementia risk or poorer cognition among spousal caregivers and widow(er)s compared to controls, no review has summarized findings across cognitive outcomes (i.e., dementia incidence, cognitive impairment rates, cognitive performance) or proposed a theoretical model for understanding the links between partner loss and abnormal cognitive decline. The current systematic review summarizes findings across 64 empirical studies. Overall, both cross-sectional and longitudinal studies revealed an adverse association between partner loss and cognitive outcomes. In turn, we propose a biopsychosocial model of cognitive decline that explains how caregiving and bereavement may position some to develop cognitive impairment or Alzheimer's disease and related dementias. More longitudinal studies that focus on the biopsychosocial context of caregivers and widow(er)s are needed.

Characterizing Network Selectiveness to the Dynamic Spreading of Neuropathological Events in Alzheimer’s Disease

Background: Mounting evidence shows that the neuropathological burdens manifest preference in affecting brain regions during the dynamic progression of Alzheimer’s disease (AD). Since the distinct brain regions are physically wired by white matter fibers, it is reasonable to hypothesize the differential spreading pattern of neuropathological burdens may underlie the wiring topology, which can be characterized using neuroimaging and network science technologies. Objective: To study the dynamic spreading patterns of neuropathological events in AD. Methods: We first examine whether hub nodes with high connectivity in the brain network (assemble of white matter wirings) are susceptible to a higher level of pathological burdens than other regions that are less involved in the process of information exchange in the network. Moreover, we propose a novel linear mixed-effect model to characterize the multi-factorial spreading process of neuropathological burdens from hub nodes to non-hub nodes, where age, sex, and APOE4 indicators are considered as confounders. We apply our statistical model to the longitudinal neuroimaging data of amyloid-PET and tau-PET, respectively. Results: Our meta-data analysis results show that 1) AD differentially affects hub nodes with a significantly higher level of pathology, and 2) the longitudinal increase of neuropathological burdens on non-hub nodes is strongly correlated with the connectome distance to hub nodes rather than the spatial proximity. Conclusion: The spreading pathway of AD neuropathological burdens might start from hub regions and propagate through the white matter fibers in a prion-like manner.

Amyloid Burden in Alzheimer's Disease Patients Is Associated with Alterations in Circadian Rhythm

Background and Purpose

In this study we evaluated the relationship between amyloid-beta (Aβ) deposition and 3 aspects of sleep quality in a group of clinically diagnosed Alzheimer's disease (AD) patients.

Methods

We used self-report questionnaires to assess the quality of sleep using 3 previously established surveys: the Glasgow Sleep Effort Scale (GSES), the Pittsburgh Sleep Quality Index (PSQI), and the Morningness-Eveningness Questionnaire (MEQ). These questionnaires focused on the sleep effort, sleep efficiency, and circadian rhythm patterns of each participant. Also, we evaluated the regional distribution of Aβ in the brain by amyloid positron emission tomography-computed tomography (PET-CT) standardized uptake value ratios (SUVRs) in healthy normal (HN), mild cognitive impairment (MCI), and AD dementia groups. The MCI and AD dementia groups were combined to form the group with cognitive impairment due to AD (CIAD).

Results

GSES and MEQ scores differed significantly between the HN, MCI, and AD dementia groups (p<0.037), whereas PSQI scores were similar across the groups (p=0.129). GSES and MEQ scores also differed between the HN and CIAD groups (p<0.018). Circadian rhythm scores positively correlated with amyloid PET-CT SUVR in posterior cingulate cortices (p<0.049).

Conclusions

Sleep effort and abnormal shifts in circadian rhythm were more significant in the CIAD group than in the HN group. At the same time, HN subjects had minimal sleep disturbance, irrespective of clinical status. Thus, alterations in circadian rhythm may be indicative of neurodegeneration due to Aβ deposition.

Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment

White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer's disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called "Director Field Analysis" (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer's Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto-occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.

Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling

BACKGROUND

Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans.

OBJECTIVE

This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk.

METHODS

Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class.

RESULTS

Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters.

CONCLUSION

We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.

A Personalized Computer-Aided Diagnosis System for Mild Cognitive Impairment (MCI) Using Structural MRI (sMRI)

Alzheimer's disease (AD) is a neurodegenerative disorder that targets the central nervous system (CNS). Statistics show that more than five million people in America face this disease. Several factors hinder diagnosis at an early stage, in particular, the divergence of 10-15 years between the onset of the underlying neuropathological changes and patients becoming symptomatic. This study surveyed patients with mild cognitive impairment (MCI), who were at risk of conversion to AD, with a local/regional-based computer-aided diagnosis system. The described system allowed for visualization of the disorder's effect on cerebral cortical regions individually. The CAD system consists of four steps: (1) preprocess the scans and extract the cortex, (2) reconstruct the cortex and extract shape-based features, (3) fuse the extracted features, and (4) perform two levels of diagnosis: cortical region-based followed by global. The experimental results showed an encouraging performance of the proposed system when compared with related work, with a maximum accuracy of 86.30%, specificity 88.33%, and sensitivity 84.88%. Behavioral and cognitive correlations identified brain regions involved in language, executive function/cognition, and memory in MCI subjects, which regions are also involved in the neuropathology of AD.

Diagnostic accuracy of the UDS 3.0 neuropsychological battery in a cohort with Alzheimer's disease in Colombia

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease that causes a gradual loss of cognitive functions and limits daily activities performance. Early diagnosis of AD is essential to start timely treatment. This study aimed to validate the Uniform Data Set neuropsychological battery version 3.0 (UDS 3.0) in a Colombian cohort.

METHODS

This study is a cross-sectional type, consecutive, incidental, with 143 persons, divided into two groups: 48 diagnosed AD cases and 95 healthy controls, between the ages of 50 and 80+, and between 1 and 19+ years of education.

RESULTS

The results indicate differences between the control group and the AD group in most battery tests. A significant correlation was found between the Montreal Cognitive Assessment (MoCA), Multilingual Naming Test (MINT), Craft Story, Benson Figure Test, P-word and F-word Phonemic Fluency Test, and their respective reference tests. Cutoff points were found based on the Youden index for each sub-test. The results indicate that all sub-tests are above the reference line of the ROC curve.

CONCLUSION

The use of the UDS 3.0 in Colombia would help improving clinical diagnostic routes because of its high accuracy and high correlation with tests that measure general impairment; it has good sensitivity and specificity, and it can be a useful tool for AD.

Pharmacological intervention in a transgenic mouse model improves Alzheimer's-associated pathological phenotype: Involvement of proteasome activation

Alzheimer's disease (AD) is the most common form of dementia worldwide, characterized by a progressive decline in a variety of cognitive and non-cognitive functions. The amyloid beta protein cascade hypothesis places the formation of amyloid beta protein aggregates on the first position in the complex pathological cascade leading to neurodegeneration, and therefore AD might be considered to be a protein-misfolding disease. The Ubiquitin Proteasome System (UPS), being the primary protein degradation mechanism with a fundamental role in the maintenance of proteostasis, has been identified as a putative therapeutic target to delay and/or to decelerate the progression of neurodegenerative disorders that are characterized by accumulated/aggregated proteins. The purpose of this study was to test if the activation of proteasome in vivo can alleviate AD pathology. Specifically by using two compounds with complementary modes of proteasome activation and documented antioxidant and redox regulating properties in the 5xFAD transgenic mice model of AD, we ameliorated a number of AD related deficits. Shortly after proteasome activation we detected significantly reduced amyloid-beta load correlated with improved motor functions, reduced anxiety and frailty level. Essentially, to our knowledge this is the first report to demonstrate a dual activation of the proteasome and its downstream effects. In conclusion, these findings open up new directions for future therapeutic potential of proteasome-mediated proteolysis enhancement.

Longitudinal changes in EEG power, sleep cycles and behaviour in a tau model of neurodegeneration

Background

Disturbed sleep is associated with cognitive decline in neurodegenerative diseases such as Alzheimer’s disease (AD) and frontotemporal dementia (FTD). The progressive sequence of how neurodegeneration affects aspects of sleep architecture in conjunction with behavioural changes is not well understood.

Methods

We investigated changes in sleep architecture, spectral power and circadian rhythmicity in the tet-off rTg4510 mouse overexpressing human P301L tau within the same subjects over time. Doxycycline-induced transgene-suppressed rTg4510 mice, tTa carriers and wild-type mice were used as comparators. Spectral power and sleep stages were measured from within the home cage environment using EEG electrodes. In addition, locomotor activity and performance during a T-maze task were measured.

Results

Spectral power in the delta and theta bands showed a time-dependent decrease in rTg4510 mice compared to all other groups. After the initial changes in spectral power, wake during the dark period increased whereas NREM and number of REM sleep bouts decreased in rTg4510 compared to wild-type mice. Home cage locomotor activity in the dark phase significantly increased in rTg4510 compared to wild-type mice by 40 weeks of age. Peak-to-peak circadian rhythm amplitude and performance in the T-maze was impaired throughout the experiment independent of time. At 46 weeks, rTG4510 mice had significant degeneration in the hippocampus and cortex whereas doxycycline-treated rTG4510 mice were protected. Pathology significantly correlated with sleep and EEG outcomes, in addition to locomotor and cognitive measures.

Conclusions

We show that reduced EEG spectral power precedes reductions in sleep and home cage locomotor activity in a mouse model of tauopathy. The data shows increasing mutant tau changes sleep architecture, EEG properties, behaviour and cognition, which suggest tau-related effects on sleep architecture in patients with neurodegenerative diseases.

Deep residual learning for neuroimaging: An application to predict progression to Alzheimer's disease

BACKGROUND

The unparalleled performance of deep learning approaches in generic image processing has motivated its extension to neuroimaging data. These approaches learn abstract neuroanatomical and functional brain alterations that could enable exceptional performance in classification of brain disorders, predicting disease progression, and localizing brain abnormalities.

NEW METHOD

This work investigates the suitability of a modified form of deep residual neural networks (ResNet) for studying neuroimaging data in the specific application of predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Prediction was conducted first by training the deep models using MCI individuals only, followed by a domain transfer learning version that additionally trained on AD and controls. We also demonstrate a network occlusion based method to localize abnormalities.

RESULTS

The implemented framework captured non-linear features that successfully predicted AD progression and also conformed to the spectrum of various clinical scores. In a repeated cross-validated setup, the learnt predictive models showed highly similar peak activations that corresponded to previous AD reports.

COMPARISON WITH EXISTING METHODS

The implemented architecture achieved a significant performance improvement over the classical support vector machine and the stacked autoencoder frameworks (p <  0.005), numerically better than state-of-the-art performance using sMRI data alone (> 7% than the second-best performing method) and within 1% of the state-of-the-art performance considering learning using multiple neuroimaging modalities as well.

CONCLUSIONS

The explored frameworks reflected the high potential of deep learning architectures in learning subtle predictive features and utility in critical applications such as predicting and understanding disease progression.

Brain structure changes over time in normal and mildly impaired aged persons

Structural brain changes in aging are known to occur even in the absence of dementia, but the magnitudes and regions involved vary between studies. To further characterize these changes, we analyzed paired MRI images acquired with identical protocols and scanner over a median 5.8-year interval. The normal study group comprised 78 elders (25M 53F, baseline age range 70–78 years) who underwent an annual standardized expert assessment of cognition and health and who maintained normal cognition for the duration of the study. We found a longitudinal grey matter (GM) loss rate of 2.56 ± 0.07 ml/year (0.20 ± 0.04%/year) and a cerebrospinal fluid (CSF) expansion rate of 2.97 ± 0.07 ml/year (0.22 ± 0.04%/year). Hippocampal volume loss rate was higher than the GM and CSF global rates, 0.0114 ± 0.0004 ml/year (0.49 ± 0.04%/year). Regions of greatest GM loss were posterior inferior frontal lobe, medial parietal lobe and dorsal cerebellum. Rates of GM loss and CSF expansion were on the low end of the range of other published values, perhaps due to the relatively good health of the elder volunteers in this study. An additional smaller group of 6 subjects diagnosed with MCI at baseline were followed as well, and comparisons were made with the normal group in terms of both global and regional GM loss and CSF expansion rates. An increased rate of GM loss was found in the hippocampus bilaterally for the MCI group.

Degenerative Amnesia for Past Public Events: An Attempt to Measure Storage and Retrieval

A still unsettled issue of amnesia concerns the differential contributions to recall impairment of the underlying retrieval and storage abilities. The aim of the present study was to disentangle and to measure such roles in the recall of past public events comparing patients with degenerative amnesia and healthy elderly. The experiment included 44 healthy elderly and two groups of participants with degenerative amnesia, namely 17 patients with amnestic mild cognitive impairment and 22 mild Alzheimer's disease patients. Recall of famous past public events was assessed by means of a 52-item questionnaire standardized for the Italian population. A latent-variable approach was adopted in order to infer the contributions of retrieval and storage to the recall performances. A stochastic model was adopted, which in a previous study of recall of recent and remote past public events in healthy elderly succeeded to prove reduced retrieval efficiency for more recent events. The results of the present study suggest that retrieval is more fragile than storage in all three experimental groups. A storage impairment turned out only in the Alzheimer's disease group, where it was limited to more recent memories. In view of the combined roles of the hippocampus and cortex in past memory processing, our results are consistent with the hypothesis that the degenerative process primarily impairs the strategic memory search. However, the sufficiency criterion of the adopted Markov model fell short of significance. Due to this statistical shortcoming, our conclusions, though consistent with the clinical predictions, are to be taken as provisional.

The Role of Brain Magnetic Resonance Imaging (MRI) as an Early Detector of Cognitive Impairment

Background:

Along with the increase of the health and prosperity level will affect the life expectancy in Indonesia, there has also been an increase in degenerative disease cases. One of the problems arises is cognitive impairment. The mild version of this impairment is often associated with the increase risk that will eventually lead to dementia. Therefore, early detection of this impairment is necessary.

Objective:

This study is aimed at proving the correlation between Fazekas scale on brain MRI and MoCA-Ina score in defining the degree of cognitive impairment.

Methods:

This study employed observational analytic design and cross sectional study for its data collection method. The Fazekas scale on brain MRI of 32 patients was read by 3 radiologist, while the MoCA-Ina scoring was done by a competent neurologist. Both tests were done double blindly. Later on, the correlation between Fazekas scale and MoCA-Ina score would be assessed using Spearman Correlation.

Results:

Statistical calculation conducted using Spearman Correlation reveals that the coefficient is -0.519 with significant score (P) 0.002, which is smaller than α: 0.05. Therefore, it can be concluded that there is a strong negative correlation between Fazekas scale and MoCA-Ina score.

Conclusion:

Fazekas scale evaluation on brain MRI is necessary to be performed as it helps predicting the decline of one's cognitive function, so that an early therapy can be acted upon to prevent dementia in the future.

Non-linear registration improves statistical power to detect hippocampal atrophy in aging and dementia

OBJECTIVE

To compare the performance of different methods for determining hippocampal atrophy rates using longitudinal MRI scans in aging and Alzheimer's disease (AD).

BACKGROUND

Quantifying hippocampal atrophy caused by neurodegenerative diseases is important to follow the course of the disease. In dementia, the efficacy of new therapies can be partially assessed by measuring their effect on hippocampal atrophy. In radiotherapy, the quantification of radiation-induced hippocampal volume loss is of interest to quantify radiation damage. We evaluated plausibility, reproducibility and sensitivity of eight commonly used methods to determine hippocampal atrophy rates using test-retest scans.

MATERIALS AND METHODS

Manual, FSL-FIRST, FreeSurfer, multi-atlas segmentation (MALF) and non-linear registration methods (Elastix, NiftyReg, ANTs and MIRTK) were used to determine hippocampal atrophy rates on longitudinal T1-weighted MRI from the ADNI database. Appropriate parameters for the non-linear registration methods were determined using a small training dataset (N = 16) in which two-year hippocampal atrophy was measured using test-retest scans of 8 subjects with low and 8 subjects with high atrophy rates. On a larger dataset of 20 controls, 40 mild cognitive impairment (MCI) and 20  AD patients, one-year hippocampal atrophy rates were measured. A repeated measures ANOVA analysis was performed to determine differences between controls, MCI and AD patients. For each method we calculated effect sizes and the required sample sizes to detect one-year volume change between controls and MCI (NCTRL_MCI) and between controls and AD (NCTRL_AD). Finally, reproducibility of hippocampal atrophy rates was assessed using within-session rescans and expressed as an average distance measure DAve, which expresses the difference in atrophy rate, averaged over all subjects. The same DAve was used to determine the agreement between different methods.

RESULTS

Except for MALF, all methods detected a significant group difference between CTRL and AD, but none could find a significant difference between the CTRL and MCI. FreeSurfer and MIRTK required the lowest sample sizes (FreeSurfer: NCTRL_MCI = 115, NCTRL_AD = 17 with DAve = 3.26%; MIRTK: NCTRL_MCI = 97, NCTRL_AD = 11 with DAve = 3.76%), while ANTs was most reproducible (NCTRL_MCI = 162, NCTRL_AD = 37 with DAve = 1.06%), followed by Elastix (NCTRL_MCI = 226, NCTRL_AD = 15 with DAve = 1.78%) and NiftyReg (NCTRL_MCI = 193, NCTRL_AD = 14 with DAve = 2.11%). Manually measured hippocampal atrophy rates required largest sample sizes to detect volume change and were poorly reproduced (NCTRL_MCI = 452, NCTRL_AD = 87 with DAve = 12.39%). Atrophy rates of non-linear registration methods also agreed best with each other.

DISCUSSION AND CONCLUSION

Non-linear registration methods were most consistent in determining hippocampal atrophy and because of their better reproducibility, methods, such as ANTs, Elastix and NiftyReg, are preferred for determining hippocampal atrophy rates on longitudinal MRI. Since performances of non-linear registration methods are well comparable, the preferred method would mostly depend on computational efficiency.

Natriuretic Peptides in Post-mortem Brain Tissue and Cerebrospinal Fluid of Non-demented Humans and Alzheimer's Disease Patients

Animal studies suggest the involvement of natriuretic peptides (NP) in several brain functions that are known to be disturbed during Alzheimer's disease (AD). However, it remains unclear whether such findings extend to humans. In this study, we aimed to: (1) map the gene expression and localization of NP and their receptors (NPR) in human post-mortem brain tissue; (2) compare the relative amounts of NP and NPR between the brain tissue of AD patients and non-demented controls, and (3) compare the relative amounts of NP between the cerebrospinal fluid (CSF) of AD patients and non-demented controls. Using the publicly available Allen Human Brain Atlas dataset, we mapped the gene expression of NP and NPR in healthy humans. Using immunohistochemistry, we visualized the localization of NP and NPR in the frontal cortex of AD patients (n = 10, mean age 85.8 ± 6.2 years) and non-demented controls (mean age = 80.2 ± 9.1 years). Using Western blotting and ELISA, we quantified the relative amounts of NP and NPR in the brain tissue and CSF of these AD patients and non-demented controls. Our results showed that NP and NPR genes were ubiquitously expressed throughout the brain in healthy humans. NP and NPR were present in various cellular structures including in neurons, astrocyte-like structures, and cerebral vessels in both AD patients and non-demented controls. Furthermore, we found higher amounts of NPR type-A in the brain of AD patients (p = 0.045) and lower amounts of NP type-B in the CSF of AD patients (p = 0.029). In conclusion, this study shows the abundance of NP and NPR in the brain of humans suggesting involvement of NP in various brain functions. In addition, our findings suggest alterations of NP levels in the brain of AD patients. The role of NP in the development and progression of AD remains to be elucidated.

Convergent Results from Neuropsychology and from Neuroimaging in Patients with Mild Cognitive Impairment

BACKGROUND/AIMS

To investigate the correspondence between neuropsychological single measures and variation in fludeoxyglucose positron emission tomography (FDG PET) glucose metabolism and magnetic resonance imaging (MRI) cortical thickness in mild cognitive impairment (MCI) patients.

METHODS

Forty-two elderly controls and 73 MCI subjects underwent FDG PET and MRI scanning. Backward regression analyses with PET and MRI regions were used as dependent variables, while Rey Auditory Verbal Memory Test (RAVLT) recall, Trail Making Test B (TMT B), and a composite test score (RAVLT learning and immediate recall, TMT A, COWAT, and letter-number sequencing) were used as predictor variables.

RESULTS

The composite score predicted variation in cortical metabolism; supplementary analyses showed that TMT B was significantly correlated with PET metabolism as well. RAVLT and TMT B were significant predictors of variation in MRI cortical thickness.

CONCLUSION

Our results indicate that RAVLT and TMT B are sensitive to variation in Alzheimer disease neuroimaging markers.

Recovery from Proactive Semantic Interference and MRI Volume: A Replication and Extension Study

BACKGROUND

The rise in incidence of Alzheimer's disease (AD) has led to efforts to advance early detection of the disease during its preclinical stages. To achieve this, the field needs to develop more sensitive cognitive tests that relate to biological markers of disease pathology. Failure to recover from proactive interference (frPSI) is one such cognitive marker that is associated with volumetric reductions in the hippocampus, precuneus, and other AD-prone regions, and to amyloid load in the brain.

OBJECTIVE

The current study attempted to replicate and extend our previous findings that frPSI is a sensitive marker of early AD, and related to a unique pattern of volumetric loss in AD prone areas.

METHODS

Three different memory measures were examined relative to volumetric loss and cortical thickness among 45 participants with amnestic mild cognitive impairment.

RESULTS

frPSI was uniquely associated with reduced volumes in the hippocampus (r = 0.50) precuneus (r = 0.41), and other AD prone regions, replicating previous findings. Strong associations between frPSI and lower entorhinal cortex volumes and cortical thickness (r≥0.60) and precuneus (r = 0.50) were also observed.

CONCLUSION

Unique and strong associations between volumetric reductions and frPSI as observed by Loewenstein and colleagues were replicated. Together with cortical thickness findings, these results indicate that frPSI is worthy of further study as a sensitive and early cognitive marker of AD.

Classification of Alzheimer's and MCI Patients from Semantically Parcelled PET Images: A Comparison between AV45 and FDG-PET

Early identification of dementia in the early or late stages of mild cognitive impairment (MCI) is crucial for a timely diagnosis and slowing down the progression of Alzheimer's disease (AD). Positron emission tomography (PET) is considered a highly powerful diagnostic biomarker, but few approaches investigated the efficacy of focusing on localized PET-active areas for classification purposes. In this work, we propose a pipeline using learned features from semantically labelled PET images to perform group classification. A deformable multimodal PET-MRI registration method is employed to fuse an annotated MNI template to each patient-specific PET scan, generating a fully labelled volume from which 10 common regions of interest used for AD diagnosis are extracted. The method was evaluated on 660 subjects from the ADNI database, yielding a classification accuracy of 91.2% for AD versus NC when using random forests combining features from cross-sectional and follow-up exams. A considerable improvement in the early versus late MCI classification accuracy was achieved using FDG-PET compared to the AV-45 compound, yielding a 72.5% rate. The pipeline demonstrates the potential of exploiting longitudinal multiregion PET features to improve cognitive assessment.

Recovery from Proactive Semantic Interference in Mild Cognitive Impairment and Normal Aging: Relationship to Atrophy in Brain Regions Vulnerable to Alzheimer's Disease

BACKGROUND

There is growing evidence that proactive semantic interference (PSI) and failure to recover from PSI may represent early features of Alzheimer's disease (AD).

OBJECTIVE

This study investigated the association between PSI, recovery from PSI, and reduced MRI volumes in AD signature regions among cognitively impaired and unimpaired older adults.

METHODS

Performance on the LASSI-L (a novel test of PSI and recovery from PSI) and regional brain volumetric measures were compared between 38 cognitively normal (CN) elders and 29 older participants with amnestic mild cognitive impairment (MCI). The relationship between MRI measures and performance on the LASSI-L as well as traditional memory and non-memory cognitive measures was also evaluated in both diagnostic groups.

RESULTS

Relative to traditional neuropsychological measures, MCI patients' failure to recover from PSI was associated with reduced volumes in the hippocampus (rs = 0.48), precuneus (rs = 0.50); rostral middle frontal lobules (rs = 0.54); inferior temporal lobules (rs = 0.49), superior parietal lobules (rs = 0.47), temporal pole (rs = 0.44), and increased dilatation of the inferior lateral ventricle (rs = -0.49). For CN elders, only increased inferior lateral ventricular size was associated with vulnerability to PSI (rs = -0.49), the failure to recover from PSI (rs = -0.57), and delayed recall on the Hopkins Verbal Learning Test-Revised (rs = -0.48).

DISCUSSION

LASSI-L indices eliciting failure to recover from PSI were more highly associated with more MRI regional biomarkers of AD than other traditional cognitive measures. These results as well as recent amyloid imaging studies with otherwise cognitively normal subjects, suggest that recovery from PSI may be a sensitive marker of preclinical AD and deserves further investigation.

A novel cognitive assessment paradigm to detect Pre-mild cognitive impairment (PreMCI) and the relationship to biological markers of Alzheimer's disease

OBJECTIVE

A number of older adults obtain normal scores on formal cognitive tests, but present clinical concerns that raise suspicion of cognitive decline. Despite not meeting full criteria for Mild Cognitive Impairment (MCI), these PreMCI states confer risk for progression to Alzheimer's disease (AD). This investigation addressed a pressing need to identify cognitive measures that are sensitive to PreMCI and are associated with brain biomarkers of neurodegeneration.

METHOD

Participants included 49 older adults with a clinical history suggestive of cognitive decline but normal scores on an array of neuropsychological measures, thus not meeting formal criteria for MCI. The performance of these PreMCI participants were compared to 117 cognitively normal (CN) elders on the LASSI-L, a cognitive stress test that uniquely assesses the failure to recover from proactive semantic interference effects (frPSI). Finally, a subset of these individuals had volumetric analyses based on MRI scans.

RESULTS

PreMCI participants evidenced greater LASSI- L deficits, particularly with regards to frPSI and delayed recall, relative to the CN group. No differences on MRI measures were observed. Controlling for false discovery rate (FDR), frPSI was uniquely related to increased dilatation of the inferior lateral ventricle and decreased MRI volumes in the hippocampus, precuneus, superior parietal region, and other AD prone areas. In contrast, other LASSI-L indices and standard memory tests were not related to volumetric findings.

CONCLUSIONS

Despite equivalent performance on traditional memory measures, the frPSI distinguished between PreMCI and CN elders and was associated with reductions in brain volume in numerous AD-relevant brain regions.

Classification of MRI and psychological testing data based on support vector machine

Alzheimer's disease (AD) is a progressive, and often fatal, brain disease that causes neurodegeneration, resulting in memory loss as well as other cognitive and behavioral problems. Here, we propose a novel multimodal method combining independent components from MRI measures and clinical assessments to distinguish Alzheimer's patients or mild cognitive impairment (MCI) subjects from healthy elderly controls. 70 AD subjects (mean age: 77.15 ± 6.2 years), 98 MCI subjects (mean age: 76.91 ± 5.7 years), and 150 HC subjects (mean age: 75.69 ± 3.8 years) were analyzed. Our method includes the following steps: pre-processing, estimating the number of independent components from the MR image data, extracting effective voxels for classification, and classification using a support vector machine (SVM)-based classifier. As a result, with regards to classifying AD from healthy controls, we achieved a classification accuracy of 97.7%, sensitivity of 99.2%, and specificity of 96.7%; for differentiating MCI from healthy controls, we achieved a classification accuracy of 87.8%, a sensitivity of 86.0%, and a specificity of 89.6; these results are better than those obtained with clinical measurements alone (accuracy of 79.5%, sensitivity of 74.0%, and specificity of 85.1%). We found that (1) both AD patients and MCI subjects showed brain tissue loss, but the volumes of gray matter loss in MCI subjects was far less, supporting the notion that MCI is a prodromal stage of AD; and (2) combining gray matter features from MRI and three commonly used measures of mental status, cognitive function improved classification accuracy, sensitivity, and specificity compared with classification using only independent components or clinical measurements.

Cortical Atrophy is Associated with Accelerated Cognitive Decline in Mild Cognitive Impairment with Subsyndromal Depression

OBJECTIVES

To investigate the association between cognitive decline and cortical atrophy in individuals with mild cognitive impairment (MCI) and chronic subsyndromal symptoms of depression (SSD) over a 4-year period.

DESIGN

Prospective cohort study.

SETTING

Multicenter, clinic-based.

PARTICIPANTS

Within the Alzheimer's Disease Neuroimaging Initiative repository, the Neuropsychiatric Inventory was used to identify individuals with MCI and stable endorsement (SSD group N = 32) or no endorsement (non-SSD group N = 69) of depressive symptoms across time points.

MEASUREMENTS

Repeated measures of cognitive outcomes, cortical atrophy, and their associations were evaluated with mixed effects models adjusting for age, education, sex, and APOE genotype.

RESULTS

The SSD group demonstrated accelerated decline on measures of global cognition (Alzheimer Disease Assessment Scale; df = 421, t = 2.242, p = 0.025), memory (Wechsler Memory Scale-Revised Logical Memory II; df = 244, t = -2.525, p = 0.011), information processing speed (Trail Making Test Parts A [df = 421, t = 2.376, p = 0.018] and B [df = 421, t = 2.533, p = 0.012]), and semantic fluency (Category Fluency; df = 424, t = -2.418, p = 0.016), as well as accelerated frontal lobe (df = 341, t = -2.648, p = 0.008) and anterior cingulate (df = 341, t = -3.786, p < 0.001) atrophy. No group differences were observed for rate of decline on measures of attention, learning, and confrontation naming or for rate of atrophy in any other regions. Accelerated frontal lobe and anterior cingulate atrophy was associated with cognitive decline on measures of global cognition, information processing speed, and semantic fluency (all p < 0.05), but not memory.

CONCLUSIONS

Individuals with chronic SSD may represent an MCI subgroup that is highly vulnerable to accelerated cognitive decline, an effect that may be governed by frontal lobe and anterior cingulate atrophy.

Neuroticism, depression, and anxiety traits exacerbate the state of cognitive impairment and hippocampal vulnerability to Alzheimer's disease

Introduction

Certain personality traits are associated with higher risk of Alzheimer's disease, similar to cognitive impairment. The identification of biological markers associated with personality in mild cognitive impairment could advance the early detection of Alzheimer's disease.

Methods

We used hierarchical multivariate linear models to quantify the interaction between personality traits, state of cognitive impairment, and MRI biomarkers (gray matter brain volume, gray matter mean water diffusion) in the medial temporal lobe (MTL).

Results

Over and above a main effect of cognitive state, the multivariate linear model showed significant interaction between cognitive state and personality traits predicting MTL abnormality. The interaction effect was mainly driven by neuroticism and its facets (anxiety, depression, and stress) and was associated with right-left asymmetry and an anterior to posterior gradient in the MTL.

Discussion

Our results support the hypothesis that personality traits can alter the vulnerability and pathoplasticity of disease and therefore modulate related biomarker expression.

Effects of mild cognitive impairment on emotional scene memory

Young and older adults experience benefits in attention and memory for emotional compared to neutral information, but this memory benefit is greatly diminished in Alzheimer's disease (AD). Little is known about whether this impairment arises early or late in the time course between healthy aging and AD. This study compared memory for positive, negative, and neutral items with neutral backgrounds between patients with mild cognitive impairment (MCI) and healthy older adults. We also used a divided attention condition in older adults as a possible model for the deficits observed in MCI patients. Results showed a similar pattern of selective memory for emotional items while forgetting their backgrounds in older adults and MCI patients, but MCI patients had poorer memory overall. Dividing attention during encoding disproportionately reduced memory for backgrounds (versus items) relative to a full attention condition. Participants performing in the lower half on the divided attention task qualitatively and quantitatively mirrored the results in MCI patients. Exploratory analyses comparing lower- and higher-performing MCI patients showed that only higher-performing MCI patients had the characteristic scene memory pattern observed in healthy older adults. Together, these results suggest that the effects of emotion on memory are relatively well preserved for patients with MCI, although emotional memory patterns may start to be altered once memory deficits become more pronounced.

Evaluation of dysphagia risk, nutritional status and caloric intake in elderly patients with Alzheimer's

Objective

to evaluate the risk of dysphagia and its relationship with the stage of Alzheimer's Disease, as well as the relationship between the risk of dysphagia and nutritional status and caloric intake in elderly people with Alzheimer's disease.

Methods

the sample consisted of 30 subjects of both genders with probable Alzheimer's disease. The stage of the disease, nutritional status, energy intake, and risk of dysphagia were assessed.

Results

it was found that increased risk of dysphagia is associated with the advance in the stages of Alzheimer's disease and that even patients in the early stages of disease have a slight risk of developing dysphagia. No association was found between nutritional status and the risk of dysphagia. High levels of inadequate intake of micronutrients were also verified in the patients.

Conclusion

an association between dysphagia and the development of Alzheimer's disease was found. The results indicate the need to monitor the presence of dysphagia and the micronutrient intake in patients with Alzheimer's disease.

Hippocampal Volumetry as a Biomarker for Dementia in People with Low Education

Background/Aims

To evaluate the relationship between hippocampal volume and cognitive decline in patients with dementia due to probable Alzheimer's disease (AD), amnestic mild cognitive impairment (aMCI) and education, and the possible relationship between cognitive reserve and education in this population.

Methods

From February 2013 to October 2015, 76 patients (25 men, 51 women) were classified according to the NIA-AA diagnostic criteria. We used two 3.0-tesla MRI scanners and performed manual hippocampal volumetry.

Results

Twenty-six patients were found to have AD, 20 aMCI and 30 had normal aging (NA). The mean normalized hippocampal volume in age-, sex- and education (years)-matched subjects was 2.38 ± 0.51 cm³ in AD (p < 0.001), 2.91 ± 0.78 cm³ in aMCI (p = 0.019) and 3.07 ± 0.76 cm³ in NA.

Conclusion

Psychometric test (MMSE and MoCA) scores had a good to strong positive correlation with statistically significant differences in the entire population and healthy subjects but not among dementia patients and lower educational level groups. The patients with low education had greater hippocampal volumes, which is in line with the cognitive reserve theory; lower-educated individuals can tolerate less neuropathology and will thus show less atrophy at a similar level of cognitive performance than higher-educated subjects.

Dance Experience and Associations with Cortical Gray Matter Thickness in the Aging Population

INTRODUCTION

We investigated the effect dance experience may have on cortical gray matter thickness and cognitive performance in elderly participants with and without mild cognitive impairment (MCI).

METHODS

39 cognitively normal and 48 MCI elderly participants completed a questionnaire regarding their lifetime experience with music, dance, and song. Participants identified themselves as either dancers or nondancers. All participants received structural 1.5-tesla MRI scans and detailed clinical and neuropsychological evaluations. An advanced 3D cortical mapping technique was then applied to calculate cortical thickness.

RESULTS

Despite having a trend-level significantly thinner cortex, dancers performed better in cognitive tasks involving learning and memory, such as the California Verbal Learning Test-II (CVLT-II) short delay free recall (p = 0.004), the CVLT-II long delay free recall (p = 0.003), and the CVLT-II learning over trials 1-5 (p = 0.001).

DISCUSSION

Together, these results suggest that dance may result in an enhancement of cognitive reserve in aging, which may help avert or delay MCI.

Chronic Depressive Symptomatology in Mild Cognitive Impairment Is Associated with Frontal Atrophy Rate which Hastens Conversion to Alzheimer Dementia

OBJECTIVE

Investigate the association of chronic depressive symptomatology (chrDS) with cortical atrophy rates and conversion to Alzheimer dementia (AD) over 3 years in mild cognitive impairment (MCI).

METHODS

In a multicenter, clinic-based study, MCI elderly participants were selected from the Alzheimer's Disease Neuroimaging Initiative repository, based on availability of both serial structural magnetic resonance imaging and chrDS endorsed on three depression-related items from the Neuropsychiatric Inventory Questionnaire (chrDS N = 32 or no depressive symptoms N = 62) throughout follow-up. Clinical and laboratory investigations were performed every 6 months during the first 2 years and yearly thereafter (median follow-up: 3 years; interquartile range: 1.5-4.0 years). Cortical atrophy rates in 16 predefined frontotemporoparietal regions affected in major depression and AD and the rate of incident AD at follow-up.

RESULTS

ChrDS in a single domain amnestic MCI sample were associated with accelerated cortical atrophy in the frontal lobe and anterior cingulate but not with atrophy rates in temporomedial or other AD-affected regions. During follow-up, 38 participants (42.7%) developed AD. Participants with chrDS had 60% shorter conversion time to AD than those without depressive symptoms. This association remained significant in survival models adjusted for temporomedial atrophy rates and showed the same trend in models adjusted for frontal cortical atrophy rate, which all increased the risk of AD.

CONCLUSION

Our results suggest that chrDS associated with progressive atrophy of frontal regions may represent an additional risk factor for conversion to dementia in MCI as opposite to representing typical prodromal AD symptomatology.

Blood and CSF biomarkers in brain subcortical ischemic vascular disease: Involved pathways and clinical applicability

Vascular dementia is the second most common type of dementia after Alzheimer’s disease (AD). Subcortical ischemic vascular disease refers to a form of vascular cognitive impairment characterized by the presence of diffuse white matter hyperintensities (WMHs) and multiple lacunar infarcts. These neuroimaging findings are mainly caused by cerebral small-vessel disease (cSVD) and relate to aging and cognitive impairment, but they can also be silent and highly prevalent in otherwise healthy individuals. We aimed to review studies on blood and cerebrospinal fluid (CSF) markers related to the presence of WMHs and lacunar infarcts that have been conducted in the past in large population-based studies and in high-risk selected patients (such as those with vascular risk factors, vascular cognitive impairment, or AD). Relevant associations with the presence and progression of cSVD have been described in the blood for markers related to inflammatory processes, endothelial damage and coagulation/fibrinolysis processes, etc. Also, different combinations of CSF markers might help to differentiate between etiologic types of dementia. In the future, to translate these findings into clinical practice and use biomarkers to early diagnosis and monitoring vascular cognitive impairment would require the replication of candidate markers in large-scale, multicenter, and prospectively designed studies.

Dementias

This chapter will focus on the descriptive, analytic, and intervention-oriented epidemiology of dementia and its most frequent etiologic type due to Alzheimer's disease. The chapter opens with a brief presentation of the concept of dementia, followed by the presentation of dementia of the Alzheimer type (DAT), including natural history, clinical manifestation, neuropathology, medical prognosis, and management. Further, the chapter presents the prevalence and incidence of dementia, with special consideration of secular trends in prevalence and incidence of DAT, and prognosis of the socioeconomic impact of dementia. Thereafter the main risk factors for DAT are covered. The chapter also addresses the results of ongoing therapeutic and preventive intervention trials for DAT. Finally, the future challenges of the epidemiology of dementia with a focus on the impact of the new diagnostic criteria for neurocognitive disorders, as well as the development of biomarkers for DAT and other types of dementia, will be briefly discussed.

Fronto-Striatal Atrophy in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease

Behavioral variant frontotemporal dementia (bvFTD) has only recently been associated with significant striatal atrophy, whereas the striatum appears to be relatively preserved in Alzheimer’s disease (AD). Considering the critical role the striatum has in cognition and behavior, striatal degeneration, together with frontal atrophy, could be responsible of some characteristic symptoms in bvFTD and emerges therefore as promising novel diagnostic biomarker to distinguish bvFTD and AD. Previous studies have, however, only taken either cortical or striatal atrophy into account when comparing the two diseases. In this study, we establish for the first time a profile of fronto-striatal atrophy in 23 bvFTD and 29 AD patients at presentation, based on the structural connectivity of striatal and cortical regions. Patients are compared to 50 healthy controls by using a novel probabilistic connectivity atlas, which defines striatal regions by their cortical white-matter connectivity, allowing us to explore the degeneration of the frontal and striatal regions that are functionally linked. Comparisons with controls revealed that bvFTD showed substantial fronto-striatal atrophy affecting the ventral as well as anterior and posterior dorso-lateral prefrontal cortices and the related striatal subregions. In contrast, AD showed few fronto-striatal atrophy, despite having significant posterior dorso-lateral prefrontal degeneration. Direct comparison between bvFTD and AD revealed significantly more atrophy in the ventral striatal–ventromedial prefrontal cortex regions in bvFTD. Consequently, deficits in ventral fronto-striatal regions emerge as promising novel and efficient diagnosis biomarker for bvFTD. Future investigations into the contributions of these fronto-striatal loops on bvFTD symptomology are needed to develop simple diagnostic and disease tracking algorithms.

Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease

Alzheimer's disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brain's connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The model's rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimer's disease assessment.

Integrated cortical structural marker for Alzheimer's disease

In this article, we propose an approach to integrate cortical morphology measures for improving the discrimination of individuals with and without very mild Alzheimer's disease (AD). FreeSurfer was applied to scans collected from 83 participants with very mild AD and 124 cognitively normal individuals. We generated cortex thickness, white matter convexity (aka "sulcal depth"), and white matter surface metric distortion measures on a normalized surface atlas in this first study to integrate high resolution gray matter thickness and white matter surface geometric measures in identifying very mild AD. Principal component analysis was applied to each individual structural measure to generate eigenvectors. Discrimination power based on individual and combined measures are compared, based on stepwise logistic regression and 10-fold cross-validation. Global AD likelihood index and surface-based likelihood maps were also generated. Our results show complementary patterns on the cortical surface between thickness, which reflects gray matter atrophy, convexity, which reflects white matter sulcal depth changes and metric distortion, which reflects white matter surface area changes. The classifier integrating all 3 types of surface measures significantly improved classification performance compared with classification based on single measures. The principal component analysis-based approach provides a framework for achieving high discrimination power by integrating high-dimensional data, and this method could be very powerful in future studies for early diagnosis of diseases that are known to be associated with abnormal gyral and sulcal patterns.

Description and classification of normal and pathological aging processes based on brain magnetic resonance imaging morphology measures

We present a discrete compactness (DC) index, together with a classification scheme, based both on the size and shape features extracted from brain volumes, to determine different aging stages: healthy controls (HC), mild cognitive impairment (MCI), and Alzheimer's disease (AD). A set of 30 brain magnetic resonance imaging (MRI) volumes for each group was segmented and two indices were measured for several structures: three-dimensional DC and normalized volumes (NVs). The discrimination power of these indices was determined by means of the area under the curve (AUC) of the receiver operating characteristic, where the proposed compactness index showed an average AUC of 0.7 for HC versus MCI comparison, 0.9 for HC versus AD separation, and 0.75 for MCI versus AD groups. In all cases, this index outperformed the discrimination capability of the NV. Using selected features from the set of DC and NV measures, three support vector machines were optimized and validated for the pairwise separation of the three classes. Our analysis shows classification rates of up to 98.3% between HC and AD, 85% between HC and MCI, and 93.3% for MCI and AD separation. These results outperform those reported in the literature and demonstrate the viability of the proposed morphological indices to classify different aging stages.

Structural imaging biomarkers of Alzheimer's disease: predicting disease progression

Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.