Imaging of onset and progression of Alzheimer's disease with voxel-compression mapping of serial magnetic resonance images

CCADD: An online webserver for Alzheimer's disease detection from brain MRI

Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.

Protein Oxidation in Aging and Alzheimer's Disease Brain

Proteins are essential molecules that play crucial roles in maintaining cellular homeostasis and carrying out biological functions such as catalyzing biochemical reactions, structural proteins, immune response, etc. However, proteins also are highly susceptible to damage by reactive oxygen species (ROS) and reactive nitrogen species (RNS). In this review, we summarize the role of protein oxidation in normal aging and Alzheimer's disease (AD). The major emphasis of this review article is on the carbonylation and nitration of proteins in AD and mild cognitive impairment (MCI). The oxidatively modified proteins showed a strong correlation with the reported changes in brain structure, carbohydrate metabolism, synaptic transmission, cellular energetics, etc., of both MCI and AD brains compared to the controls. Some proteins were found to be common targets of oxidation and were observed during the early stages of AD, suggesting that those changes might be critical in the onset of symptoms and/or formation of the pathological hallmarks of AD. Further studies are required to fully elucidate the role of protein oxidation and nitration in the progression and pathogenesis of AD.

Pathway-specific polygenic scores for Alzheimer's disease are associated with changes in brain structure in younger and older adults

Genome-wide association studies have identified multiple Alzheimer's disease risk loci with small effect sizes. Polygenic risk scores, which aggregate these variants, are associated with grey matter structural changes. However, genome-wide scores do not allow mechanistic interpretations. The present study explored associations between disease pathway-specific scores and grey matter structure in younger and older adults. Data from two separate population cohorts were used as follows: the Avon Longitudinal Study of Parents and Children, mean age 19.8, and UK Biobank, mean age 64.4 (combined n = 18 689). Alzheimer's polygenic risk scores were computed using the largest genome-wide association study of clinically assessed Alzheimer's to date. Relationships between subcortical volumes and cortical thickness, pathway-specific scores and genome-wide scores were examined. Increased pathway-specific scores were associated with reduced cortical thickness in both the younger and older cohorts. For example, the reverse cholesterol transport pathway score showed evidence of association with lower left middle temporal cortex thickness in the younger Avon participants (P = 0.034; beta = -0.013, CI -0.025, -0.001) and in the older UK Biobank participants (P = 0.019; beta = -0.003, CI -0.005, -4.56 × 10-4). Pathway scores were associated with smaller subcortical volumes, such as smaller hippocampal volume, in UK Biobank older adults. There was also evidence of positive association between subcortical volumes in Avon younger adults. For example, the tau protein-binding pathway score was negatively associated with left hippocampal volume in UK Biobank (P = 8.35 × 10-05; beta = -11.392, CI -17.066, -5.718) and positively associated with hippocampal volume in the Avon study (P = 0.040; beta = 51.952, CI 2.445, 101.460). The immune response score had a distinct pattern of association, being only associated with reduced thickness in the right posterior cingulate in older and younger adults (P = 0.011; beta = -0.003, CI -0.005, -0.001 in UK Biobank; P = 0.034; beta = -0.016, CI -0.031, -0.001 in the Avon study). The immune response score was associated with smaller subcortical volumes in the older adults, but not younger adults. The disease pathway scores showed greater evidence of association with imaging phenotypes than the genome-wide score. This suggests that pathway-specific polygenic methods may allow progress towards a mechanistic understanding of structural changes linked to polygenic risk in pre-clinical Alzheimer's disease. Pathway-specific profiling could further define pathophysiology in individuals, moving towards precision medicine in Alzheimer's disease.

Declarative Learning, Priming, and Procedural Learning Performances comparing Individuals with Amnestic Mild Cognitive Impairment, and Cognitively Unimpaired Older Adults

OBJECTIVE

While declarative learning is dependent on the hippocampus, procedural learning and repetition priming can operate independently from the hippocampus, making them potential targets for behavioral interventions that utilize non-declarative memory systems to compensate for the declarative learning deficits associated with hippocampal insult. Few studies have assessed procedural learning and repetition priming in individuals with amnestic mild cognitive impairment (aMCI).

METHOD

This study offers an overview across declarative, conceptual repetition priming, and procedural learning tasks by providing between-group effect sizes and Bayes Factors (BFs) comparing individuals with aMCI and controls. Seventy-six individuals with aMCI and 83 cognitively unimpaired controls were assessed. We hypothesized to see the largest differences between individuals with aMCI and controls on declarative learning, followed by conceptual repetition priming, with the smallest differences on procedural learning.

RESULTS

Consistent with our hypotheses, we found large differences between groups with supporting BFs on declarative learning. For conceptual repetition priming, we found a small-to-moderate between-group effect size and a non-conclusive BF somewhat in favor of a difference between groups. We found more variable but overall trivial differences on procedural learning tasks, with inconclusive BFs, in line with expectations.

CONCLUSIONS

The current results suggest that conceptual repetition priming does not remain intact in individuals with aMCI while procedural learning may remain intact. While additional studies are needed, our results contribute to the evidence-base that suggests that procedural learning may remain spared in aMCI and helps inform behavioral interventions that aim to utilize procedural learning in this population.

Focused ultrasound–mediated blood-brain barrier opening in Alzheimer’s disease: long-term safety, imaging, and cognitive outcomes

OBJECTIVE

MRI-guided low-intensity focused ultrasound (FUS) has been shown to reversibly open the blood-brain barrier (BBB), with the potential to deliver therapeutic agents noninvasively to target brain regions in patients with Alzheimer’s disease (AD) and other neurodegenerative conditions. Previously, the authors reported the short-term safety and feasibility of FUS BBB opening of the hippocampus and entorhinal cortex (EC) in patients with AD. Given the need to treat larger brain regions beyond the hippocampus and EC, brain volumes and locations treated with FUS have now expanded. To evaluate any potential adverse consequences of BBB opening on disease progression, the authors report safety, imaging, and clinical outcomes among participants with mild AD at 6–12 months after FUS treatment targeted to the hippocampus, frontal lobe, and parietal lobe.

METHODS

In this open-label trial, participants with mild AD underwent MRI-guided FUS sonication to open the BBB in β-amyloid positive regions of the hippocampus, EC, frontal lobe, and parietal lobe. Participants underwent 3 separate FUS treatment sessions performed 2 weeks apart. Outcome assessments included safety, imaging, neurological, cognitive, and florbetaben β-amyloid PET.

RESULTS

Ten participants (range 55–76 years old) completed 30 separate FUS treatments at 2 participating institutions, with 6–12 months of follow-up. All participants had immediate BBB opening after FUS and BBB closure within 24–48 hours. All FUS treatments were well tolerated, with no serious adverse events related to the procedure. All 10 participants had a minimum of 6 months of follow-up, and 7 participants had a follow-up out to 1 year. Changes in the Alzheimer’s Disease Assessment Scale–cognitive and Mini-Mental State Examination scores were comparable to those in controls from the Alzheimer’s Disease Neuroimaging Initiative. PET scans demonstrated an average β-amyloid plaque of 14% in the Centiloid scale in the FUS-treated regions.

CONCLUSIONS

This study is the largest cohort of participants with mild AD who received FUS treatment, and has the longest follow-up to date. Safety was demonstrated in conjunction with reversible and repeated BBB opening in multiple cortical and deep brain locations, with a concomitant reduction of β-amyloid. There was no apparent cognitive worsening beyond expectations up to 1 year after FUS treatment, suggesting that the BBB opening treatment in multiple brain regions did not adversely influence AD progression. Further studies are needed to determine the clinical significance of these findings. FUS offers a unique opportunity to decrease amyloid plaque burden as well as the potential to deliver targeted therapeutics to multiple brain regions in patients with neurodegenerative disorders.

PET Imaging of Dementia: Update 2022

ABSTRACT

PET imaging plays an essential role in achieving earlier and more specific diagnoses of dementia syndromes, important for clinical prognostication and optimal medical management. This has become especially vital with the recent development of pathology-specific disease-modifying therapy for Alzheimer disease, which will continue to evolve and require methods to select appropriate treatment candidates. Techniques that began as research tools such as amyloid and tau PET have now entered clinical use, making nuclear medicine physicians and radiologists essential members of the care team. This review discusses recent changes in the understanding of dementia and examines the roles of nuclear medicine imaging in clinical practice. Within this framework, multiple cases will be shown to illustrate a systematic approach of FDG PET interpretation and integration of PET imaging of specific molecular pathology including dopamine transporters, amyloid, and tau. The approach presented here incorporates contemporary understanding of both common and uncommon dementia syndromes, intended as an updated practical guide to assist with the sophisticated interpretation of nuclear medicine examinations in the context of this rapidly and continually developing area of imaging.

Sex and gender differences in dementia

The dementia landscape has undergone a striking paradigm shift. The advances in understanding of neurodegeneration and proteinopathies has changed our approach to patients with cognitive impairment. Firstly, it has recently been shown that the various proteinopathies that are the cause of the dementia begin to build up long before the appearance of any obvious symptoms. This has cemented the idea that there is an urgency in diagnosis as it occurs very late in the pathophysiology of these diseases. Secondly, that accurate diagnosis is required to deliver targeted therapies, that is precision medicine. With this latter point, the realization that various factors of a person need to be considered as they may impact the presentation and progression of disease has risen to the forefront. Two of these factors aside from race and age are biological sex and gender (social construct), as both can have tremendous impact on manifestation of disease. This chapter will cover what is known and remains to be known on the interaction of sex and gender with some of the major causes of dementia.

Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion

Progressive brain atrophy is a key neuropathological hallmark of Alzheimer's disease (AD) dementia. However, atrophy patterns along the progression of AD dementia are diffuse and variable and are often missed by univariate methods. Consequently, identifying the major regional atrophy patterns underlying AD dementia progression is challenging. In the current study, we propose a method that evaluates the degree to which specific regional atrophy patterns are predictive of AD dementia progression, while holding all other atrophy changes constant using a total sample of 334 subjects. We first trained a dense convolutional neural network model to differentiate individuals with mild cognitive impairment (MCI) who progress to AD dementia versus those with a stable MCI diagnosis. Then, we retested the model multiple times, each time occluding different regions of interest (ROIs) from the model's testing set's input. We also validated this approach by occluding ROIs based on Braak's staging scheme. We found that the hippocampus, fusiform, and inferior temporal gyri were the strongest predictors of AD dementia progression, in agreement with established staging models. We also found that occlusion of limbic ROIs defined according to Braak stage III had the largest impact on the performance of the model. Our predictive model reveals the major regional patterns of atrophy predictive of AD dementia progression. These results highlight the potential for early diagnosis and stratification of individuals with prodromal AD dementia based on patterns of cortical atrophy, prior to interventional clinical trials.

Dissecting the clinical heterogeneity of early-onset Alzheimer's disease

Early-onset Alzheimer's disease (EOAD) is a rare but particularly devastating form of AD. Though notable for its high degree of clinical heterogeneity, EOAD is defined by the same neuropathological hallmarks underlying the more common, late-onset form of AD. In this review, we describe the various clinical syndromes associated with EOAD, including the typical amnestic phenotype as well as atypical variants affecting visuospatial, language, executive, behavioral, and motor functions. We go on to highlight advances in fluid biomarker research and describe how molecular, structural, and functional neuroimaging can be used not only to improve EOAD diagnostic acumen but also enhance our understanding of fundamental pathobiological changes occurring years (and even decades) before the onset of symptoms. In addition, we discuss genetic variation underlying EOAD, including pathogenic variants responsible for the well-known mendelian forms of EOAD as well as variants that may increase risk for the much more common forms of EOAD that are either considered to be sporadic or lack a clear autosomal-dominant inheritance pattern. Intriguingly, specific pathogenic variants in PRNP and MAPT-genes which are more commonly associated with other neurodegenerative diseases-may provide unexpectedly important insights into the formation of AD tau pathology. Genetic analysis of the atypical clinical syndromes associated with EOAD will continue to be challenging given their rarity, but integration of fluid biomarker data, multimodal imaging, and various 'omics techniques and their application to the study of large, multicenter cohorts will enable future discoveries of fundamental mechanisms underlying the development of EOAD and its varied clinical presentations.

Multiple-region grey matter atrophy as a predictor for the development of dementia in a community: the Hisayama Study

OBJECTIVE

To assess the association of regional grey matter atrophy with dementia risk in a general older Japanese population.

METHODS

We followed 1158 dementia-free Japanese residents aged ≥65 years for 5.0 years. Regional grey matter volume (GMV) at baseline was estimated by applying voxel-based morphometry methods. The GMV-to-total brain volume ratio (GMV/TBV) was calculated, and its association with dementia risk was estimated using Cox proportional hazard models. We assessed whether the predictive ability of a model based on known dementia risk factors could be improved by adding the total number of regions with grey matter atrophy among dementia-related brain regions, where the cut-off value for grey matter atrophy in each region was determined by receiver operating characteristic curves.

RESULTS

During the follow-up, 113 participants developed all-cause dementia, including 83 with Alzheimer's disease (AD). Lower GMV/TBV of the medial temporal lobe, insula, hippocampus and amygdala were significantly/marginally associated with higher risk of all-cause dementia and AD (all p for trend ≤0.08). The risks of all-cause dementia and AD increased significantly with increasing total number of brain regions exhibiting grey matter atrophy (both p for trend <0.01). Adding the total number of regions with grey matter atrophy into a model consisting of known risk factors significantly improved the predictive ability for AD (Harrell's c-statistics: 0.765-0.802; p=0.02).

CONCLUSIONS

Our findings suggest that the total number of regions with grey matter atrophy among the medial temporal lobe, insula, hippocampus and amygdala is a significant predictor for developing dementia, especially AD, in the general older population.

Detecting amyloid positivity in early Alzheimer's disease using combinations of plasma Aβ42/Aβ40 and p-tau

INTRODUCTION

We studied usefulness of combining blood amyloid beta (Aβ)42/Aβ40, phosphorylated tau (p-tau)217, and neurofilament light (NfL) to detect abnormal brain Aβ deposition in different stages of early Alzheimer's disease (AD).

METHODS

Plasma biomarkers were measured using mass spectrometry (Aβ42/Aβ40) and immunoassays (p-tau217 and NfL) in cognitively unimpaired individuals (CU, N = 591) and patients with mild cognitive impairment (MCI, N = 304) from two independent cohorts (BioFINDER-1, BioFINDER-2).

RESULTS

In CU, a combination of plasma Aβ42/Aβ40 and p-tau217 detected abnormal brain Aβ status with area under the curve (AUC) of 0.83 to 0.86. In MCI, the models including p-tau217 alone or Aβ42/Aβ40 and p-tau217 had similar AUCs (0.86-0.88); however, the latter showed improved model fit. The models were implemented in an online application providing individualized risk assessments (https://brainapps.shinyapps.io/PredictABplasma/).

DISCUSSION

A combination of plasma Aβ42/Aβ40 and p-tau217 discriminated Aβ status with relatively high accuracy, whereas p-tau217 showed strongest associations with Aβ pathology in MCI but not in CU.

When Does Alzheimer's Disease Really Start? The Role of Biomarkers

(Appeared originally in Int J Mol Sci 2019, 20 5536).

Polyglutamine Ataxias: Our Current Molecular Understanding and What the Future Holds for Antisense Therapies

Polyglutamine (polyQ) ataxias are a heterogenous group of neurological disorders all caused by an expanded CAG trinucleotide repeat located in the coding region of each unique causative gene. To date, polyQ ataxias encompass six disorders: spinocerebellar ataxia types 1, 2, 3, 6, 7, and 17 and account for a larger group of disorders simply known as polyglutamine disorders, which also includes Huntington's disease. These diseases are typically characterised by progressive ataxia, speech and swallowing difficulties, lack of coordination and gait, and are unfortunately fatal in nature, with the exception of SCA6. All the polyQ spinocerebellar ataxias have a hallmark feature of neuronal aggregations and share many common pathogenic mechanisms, such as mitochondrial dysfunction, impaired proteasomal function, and autophagy impairment. Currently, therapeutic options are limited, with no available treatments that slow or halt disease progression. Here, we discuss the common molecular and clinical presentations of polyQ spinocerebellar ataxias. We will also discuss the promising antisense oligonucleotide therapeutics being developed as treatments for these devastating diseases. With recent advancements and therapeutic approvals of various antisense therapies, it is envisioned that some of the studies reviewed may progress into clinical trials and beyond.

Going round in circles-The Papez circuit in Alzheimer's disease

The hippocampus is regarded as the pivotal structure for episodic memory symptoms associated with Alzheimer's disease (AD) pathophysiology. However, what is often overlooked is that the hippocampus is 'only' one part of a network of memory critical regions, the Papez circuit. Other Papez circuit regions are often regarded as less relevant for AD as they are thought to sit 'downstream' of the hippocampus. However, this notion is oversimplistic, and increasing evidence suggests that other Papez regions might be affected before or concurrently with the hippocampus. In addition, AD research has mostly focused on episodic memory deficits, whereas spatial navigation processes are also subserved by the Papez circuit with increasing evidence supporting its valuable potential as a diagnostic measure of incipient AD pathophysiology. In the current review, we take a step forward analysing recent evidence on the structural and functional integrity of the Papez circuit across AD disease stages. Specifically, we will review the integrity of specific Papez regions from at-genetic-risk (APOE4 carriers), to mild cognitive impairment (MCI), to dementia stage of sporadic AD and autosomal dominant AD (ADAD). We related those changes to episodic memory and spatial navigation/orientation deficits in AD. Finally, we provide an overview of how the Papez circuit is affected in AD diseases and their specific symptomology contributions. This overview strengthened the need for moving away from a hippocampal-centric view to a network approach on how the whole Papez circuit is affected in AD and contributes to its symptomology, informing future research and clinical approaches.

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR.

Visual Performance and Cortical Atrophy in Vision-Related Brain Regions Differ Between Older Adults with (or at Risk for) Alzheimer's Disease

BACKGROUND

Visual impairment is associated with deficits in cognitive function and risk for cognitive decline and Alzheimer's disease (AD).

OBJECTIVE

The purpose of this study was to characterize the degree of visual impairment and explore the association thereof with cortical atrophy in brain regions associated with visual processing in individuals with (or at risk for) AD.

METHODS

Using the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) dataset, we analyzed vision and brain imaging data from three diagnostic groups: individuals with subjective cognitive decline (SCD; N = 35), mild cognitive impairment (MCI; N = 74), and mild AD (N = 30). We used ANCOVAs to determine whether performance on reading acuity and contrast sensitivity tests differed across diagnostic groups. Hierarchical regression analyses were applied to determine whether visual performance predicted gray matter volume for vision-related regions of interest above and beyond group membership.

RESULTS

The AD group performed significantly worse on reading acuity (F(2,138) = 4.12, p <  0.01, ω 2 = 0.04) compared to the SCD group and on contrast sensitivity (F(2,138) = 7.6, p <  0.01, ω 2 = 0.09) compared to the SCD and MCI groups, which did not differ from each other. Visual performance was associated with volume in some vision-related structures beyond clinical diagnosis.

CONCLUSION

Our findings demonstrate poor visual performance in AD and that both group membership and visual performance are predictors of cortical pathology, consistent with the idea that atrophy in visual areas and pathways contributes to the functional vision deficits observed in AD.

Genetic Overlap Between Alzheimer's Disease and Depression Mapped Onto the Brain

Background: Alzheimer’s disease (AD) and depression are debilitating brain disorders that are often comorbid. Shared brain mechanisms have been implicated, yet findings are inconsistent, reflecting the complexity of the underlying pathophysiology. As both disorders are (partly) heritable, characterising their genetic overlap may provide aetiological clues. While previous studies have indicated negligible genetic correlations, this study aims to expose the genetic overlap that may remain hidden due to mixed directions of effects.

Methods: We applied Gaussian mixture modelling, through MiXeR, and conjunctional false discovery rate (cFDR) analysis, through pleioFDR, to genome-wide association study (GWAS) summary statistics of AD (n = 79,145) and depression (n = 450,619). The effects of identified overlapping loci on AD and depression were tested in 403,029 participants of the UK Biobank (UKB) (mean age 57.21, 52.0% female), and mapped onto brain morphology in 30,699 individuals with brain MRI data.

Results: MiXer estimated 98 causal genetic variants overlapping between the 2 disorders, with 0.44 concordant directions of effects. Through pleioFDR, we identified a SNP in the TMEM106B gene, which was significantly associated with AD (B = −0.002, p = 9.1 × 10^–4) and depression (B = 0.007, p = 3.2 × 10^–9) in the UKB. This SNP was also associated with several regions of the corpus callosum volume anterior (B > 0.024, p < 8.6 × 10^–4), third ventricle volume ventricle (B = −0.025, p = 5.0 × 10^–6), and inferior temporal gyrus surface area (B = 0.017, p = 5.3 × 10^–4).

Discussion: Our results indicate there is substantial genetic overlap, with mixed directions of effects, between AD and depression. These findings illustrate the value of biostatistical tools that capture such overlap, providing insight into the genetic architectures of these disorders.

Neuroimaging Spectrum at Pre-, Early, and Late Symptomatic Stages of SCA17 Mice

Spinocerebellar ataxia (SCA) is a hereditary neurodegenerative disease. We have generated SCA17 transgenic mice bearing human TBP with 109 CAG repeats under the Purkinje cell-specific L7/pcp2 promoter. These mice recapitulate the patients' phenotypes and are suitable for the study of the SCA17 pathomechanism. Magnetic resonance imaging (MRI) and immunostainings were performed to identify the neuroimaging spectrum during disease progression. The results indicate that despite an overall normal appearance at birth, postnatal brain damage takes place rapidly in SCA17. Cerebellar atrophy, fourth-ventricle enlargement, and reduced cerebellar N-acetylaspartate levels were detected at the presymptomatic stage, when the mice were juvenile. The aberrations, which included reductions in body weight; cerebral size; striatal size; and the mean, radial, and axial diffusivities of the cerebellum, became more salient as the disease progressed to the old, late-symptomatic stage. Phosphorylated H2A histone family, member X (γH2AX) immunostaining revealed that the cerebellum underwent severe cell senescence in the old stage while the striatum appeared relatively unaffected by aging. Morphometric analysis indicated that the cerebellar atrophy occurred in all subregions with aging. The data establish that the SCA17 mouse brain appears normal at birth but becomes aberrant at the presymptomatic/juvenile stage. More widespread deficits add to the pathological spectrum at the old stage. The study provides information for the expression and expansion of L7/pcp2 promoter and implies the disease progression of SCA17 patients.

Obesity and Oxidative Stress in Older Adults At Risk for Dementia: A Magnetic Resonance Spectroscopy Study

OBJECTIVE

This study aimed to investigate the relationship between obesity and oxidative stress in older adults at risk for dementia. It also aimed to explore the influence of physical activity on the relationship between obesity and oxidative stress in this at risk cohort.

METHODS

Older adults at risk for dementia underwent comprehensive medical, neuropsychological, and psychiatric assessment. At risk was defined as participants with subjective or mild cognitive impairment. Glutathione was assessed by magnetic resonance spectroscopy in the left hippocampus and the anterior and posterior cingulate cortex. Body mass index (BMI) was calculated and classified as healthy (BMI <25 kg/m2) or overweight/obese (BMI ≥25 kg/m2).

RESULTS

Sixty-five older adults (mean age=66.2 y) were included for analysis. The overweight/obese group had significantly greater glutathione in the hippocampus compared with the healthy weight group (t=-2.76, P=0.008). No significant difference in glutathione was observed between groups in the anterior or posterior cingulate. In the overweight/obese group, a higher BMI was associated with a diabetes diagnosis and lower total time engaging in physical activity (r=-0.36, P=0.025), however, glutathione did not correlate with activity levels across groups.

CONCLUSION

This study demonstrates that changes in in vivo markers of oxidative stress are present in overweight/obese older adults at risk for dementia. Future research should explore the relationship with diabetes and the longitudinal relationship between BMI and oxidative stress, and response to therapeutic interventions.

A longitudinal observation of brain structure between AD and FTLD

INTRODUCTION

Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) are the leading causes of dementia. To better understand the disease development of cognitive function and anatomical structure in AD and FTLD, we analyzed the changes in brain volume by MRI and the psychological test results. Here, we report a dynamic observation of brain structure.

METHODS

Thirteen patients diagnosed with probable AD by the 2011 NIA-AA criteria and eight FTLD patients diagnosed by the FTLD criteria underwent MRI at baseline. All subjects were rescanned after 5 months to 3 years of follow-up. The anatomic changes on T1-weighted imaging of each subject were measured, and the separate changes in the two groups and the differences in the changes between AD and FTLD were analyzed.

RESULTS

In AD patients, the anterior and posterior horns of the lateral ventricle and lateral fissure enlarged progressively (p < 0.001). The volume of the regions, including the medial and lateral temporal lobe, especially the parahippocampal gyrus, and the frontal lobe decreased significantly as the disease progressed (p < 0.001). Additionally, the volume of white matter in the frontal, parietal, temporal lobe and cerebellum decreased in a relatively symmetric pattern (p < 0.001). In FTLD patients, the anterior horn of the lateral ventricle, lateral fissure, cerebral longitudinal fissure, external space of the orbitofrontal cortex, and mesencephalon surrounding the cisterna were enlarged (p < 0.005), while regions including the left frontal lobe, anterior cingulate cortex, basal ganglia (especially the left basal ganglia), left lateral temporal lobe and inferior cerebellar vermis decreased as the disease progressed (p < 0.005). Regarding the differences between AD and FTLD, atrophy of the frontal lobe and bilateral basal ganglia was more significant in FTLD than in AD (p < 0.01). In addition, enlargements of the anterior horn of the lateral ventricle, left lateral fissure and interpeduncular cistern were more significant in FTLD patients than in AD patients (p < 0.01).

CONCLUSIONS

These findings suggest that AD and FTLD have distinctly different atrophy patterns: AD patients show diffuse atrophy while FTLD patients show an asymmetrical focal atrophy pattern, which might explain the relatively better and longer preservation of daily living function in FTLD patients.

Association of mid-life serum lipid levels with late-life brain volumes: The atherosclerosis risk in communities neurocognitive study (ARICNCS)

BACKGROUND

Limited information exists regarding the association between midlife lipid levels and late-life total and regional brain volumes.

METHODS

We studied 1872 participants in the longitudinal community-based Atherosclerosis Risk in Communities Neurocognitive Study. Serum lipid levels were measured in 1987-1989 (mean age, 53 ± 5 years). Participants underwent 3T brain MRI scans in 2011-2013. Brain volumes were measured using FreeSurfer image analysis software. Linear regression models were used to assess the associations between serum lipids and brain volumes modeled in standard deviation (SD) units, adjusting for potential confounders.

RESULTS

In adjusted analyses, one SD higher low-density lipoprotein cholesterol (LDL) levels were associated with larger total brain volumes (β 0.033, 95% CI 0.006-0.060) as well as larger volumes of the temporal (β 0.038, 95% CI 0.003-0.074) and parietal lobes (β 0.044, 95% CI 0.009-0.07) and Alzheimer disease-related region (β 0.048, 95% CI 0.048-0.085). Higher triglyceride levels were associated with smaller total brain volumes (β -0.033, 95% CI -0.060, -0.007). The associations between LDL levels and brain volumes were modified by age (P for interaction <0.001), with higher LDL levels associated with larger total and regional brain volumes only among adults >53 years at baseline, and were attenuated after application of weights to account for informative attrition, although associations with the parietal and Alzheimer's disease-related region remained significant. High-density lipoprotein cholesterol was not associated with brain volumes.

CONCLUSION

Higher LDL levels in late midlife were associated with larger brain volumes later in life, while higher triglyceride levels were associated with smaller brain volumes. These associations were driven by adults >53 years at baseline.

Dysfunctional proteins in neuropsychiatric disorders: From neurodegeneration to autism spectrum disorders

Despite fundamental differences in disease course and outcomes, neurodevelopmental (autism spectrum disorders - ASD) and neurodegenerative disorders (Alzheimer's disease - AD and Parkinson's disease - PD) present surprising, common traits in their molecular pathomechanisms. Uncontrolled oligomerization and aggregation of amyloid β (Aβ), microtubule-associated protein (MAP) tau, or α-synuclein (α-syn) contribute to synaptic impairment and the ensuing neuronal death in both AD and PD. Likewise, the pathogenesis of ASD may be attributed, at least in part, to synaptic dysfunction; attention has also been recently paid to irregularities in the metabolism and function of the Aβ precursor protein (APP), tau, or α-syn. Commonly affected elements include signaling pathways that regulate cellular metabolism and survival such as insulin/insulin-like growth factor (IGF) - PI3 kinase - Akt - mammalian target of rapamycin (mTOR), and a number of key synaptic proteins critically involved in neuronal communication. Understanding how these shared pathomechanism elements operate in different conditions may help identify common targets and therapeutic approaches.

Iron stored in ferritin is chemically reduced in the presence of aggregating Aβ(1-42)

Atypical low-oxidation-state iron phases in Alzheimer's disease (AD) pathology are implicated in disease pathogenesis, as they may promote elevated redox activity and convey toxicity. However, the origin of low-oxidation-state iron and the pathways responsible for its formation and evolution remain unresolved. Here we investigate the interaction of the AD peptide β-amyloid (Aβ) with the iron storage protein ferritin, to establish whether interactions between these two species are a potential source of low-oxidation-state iron in AD. Using X-ray spectromicroscopy and electron microscopy we found that the co-aggregation of Aβ and ferritin resulted in the conversion of ferritin's inert ferric core into more reactive low-oxidation-states. Such findings strongly implicate Aβ in the altered iron handling and increased oxidative stress observed in AD pathogenesis. These amyloid-associated iron phases have biomarker potential to assist with disease diagnosis and staging, and may act as targets for therapies designed to lower oxidative stress in AD tissue.

Reduced Dynamic Complexity of BOLD Signals Differentiates Mild Cognitive Impairment From Normal Aging

Mild cognitive impairment (MCI) is characterized as a transitional phase between cognitive decline associated with normal aging and Alzheimer’s disease (AD). Resting-state functional magnetic resonance imaging (fMRI) measuring blood oxygenation level-dependent (BOLD) signals provides complementary information considered essential for understanding disease progression. Previous studies suggested that multi-scale entropy (MSE) analysis quantifying the complexity of BOLD signals is a novel and promising method for investigating neurodegeneration associated with cognitive decline in different stages of MCI. Therefore, the current study used MSE to explore the changes in the complexity of resting-state brain BOLD signals in patients with early MCI (EMCI) and late MCI (LMCI). We recruited 345 participants’ data from the Alzheimer’s Disease Neuroimaging Initiative database, including 176 normal control (NC) subjects, 87 patients with EMCI and 82 patients with LMCI. We observed a significant reduction of brain signal complexity toward regularity in the left fusiform gyrus region in the EMCI group and in the rostral anterior cingulate cortex in the LMCI group. Our results extend prior work by revealing that significant reductions of brain BOLD signal complexity can be detected in different stages of MCI independent of age, sex and regional atrophy. Notably, the reduction of BOLD signal complexity in the rostral anterior cingulate cortex was significantly associated with greater risk of progression to AD. The present study thus identified MSE as a potential imaging biomarker for the early diagnosis of pre-clinical Alzheimer’s disease and provides further insights into the neuropathology of cognitive decline in prodromal AD.

Change in NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) Level and Risk of Dementia in Multi-Ethnic Study of Atherosclerosis (MESA)

Cross-sectionally measured NT-proBNP (N-terminal pro-B-type natriuretic peptide) is related to incident dementia. However, data linking changes in NT-proBNP to risk of future dementia are lacking. We aimed to examine the association of change in NT-proBNP over 3.2 years with incident dementia. We included 4563 participants in MESA (Multi-Ethnic Study of Atherosclerosis) prospective cohort who were free of cardiovascular disease at enrollment, had NT-proBNP level measured at MESA exams 1 (baseline, 2000-2002) and 3 (2004-2005), and had no diagnosis of dementia before exam 3. The association of change in NT-proBNP level between MESA exams 1 through 3 and all-cause hospitalized dementia (by International Classification of Diseases, Ninth Revision, codes) after MESA exam 3 (2004-2005) through 2015 was assessed using competing-risks Cox proportional hazard regression analysis. During 45 522 person-years of follow-up, 223 dementia cases were documented. Increase in log-NT-proBNP from MESA exams 1 through 3 was positively associated with incidence of dementia (multivariable hazard ratio, 1.28 [95% CI, 1.001-1.64]; P=0.049). An increase of at least 25% in NT-proBNP level from MESA exam 1 through 3 was associated with a 55% (P=0.02) increase in the risk of dementia in multivariable analysis. Addition of temporal NT-proBNP change to a model including risk factors and baseline NT-proBNP improved the prediction of dementia (Harrell C statistic from 0.85 to 0.87, P=0.049). Increase in NT-proBNP is independently associated with future all-cause hospitalized dementia and offers a moderately better predictive performance for risk of dementia compared with risk factors and baseline NT-proBNP. Clinical Trial Registration- URL: https://www.clinicaltrials.gov. Unique identifier: NCT00005487.

When Does Alzheimer's Disease Really Start? The Role of Biomarkers

While Alzheimer’s disease (AD) classical diagnostic criteria rely on clinical data from a stablished symptomatic disease, newer criteria aim to identify the disease in its earlier stages. For that, they incorporated the use of AD’s specific biomarkers to reach a diagnosis, including the identification of Aβ and tau depositions, glucose hypometabolism, and cerebral atrophy. These biomarkers created a new concept of the disease, in which AD’s main pathological processes have already taken place decades before we can clinically diagnose the first symptoms. Therefore, AD is now considered a dynamic disease with a gradual progression, and dementia is its final stage. With that in mind, new models were proposed, considering the orderly increment of biomarkers and the disease as a continuum, or the variable time needed for the disease’s progression. In 2011, the National Institute on Aging and the Alzheimer’s Association (NIA-AA) created separate diagnostic recommendations for each stage of the disease continuum—preclinical, mild cognitive impairment, and dementia. However, new scientific advances have led them to create a unifying research framework in 2018 that, although not intended for clinical use as of yet, is a step toward shifting the focus from the clinical symptoms to the biological alterations and toward changing the future diagnostic and treatment possibilities. This review aims to discuss the role of biomarkers in the onset of AD.

Visualizing Alzheimer's disease progression in low dimensional manifolds

While tomographic neuroimaging data is information rich, objective, and with high sensitivity in the study of brain diseases such as Alzheimer's disease (AD), its direct use in clinical practice and in regulated clinical trial (CT) still has many challenges. Taking CT as an example, unless the relevant policy and the perception of the primary outcome measures change, the need to construct univariate indices (out of the 3-D imaging data) to serve as CT's primary outcome measures will remain the focus of active research. More relevant to this current study, an overall global index that summarizes multiple complicated features from neuroimages should be developed in order to provide high diagnostic accuracy and sensitivity in tracking AD progression over time in clinical setting. Such index should also be practically intuitive and logically explainable to patients and their families. In this research, we propose a new visualization tool, derived from the manifold-based nonlinear dimension reduction of brain MRI features, to track AD progression over time. In specific, we investigate the locally linear embedding (LLE) method using a dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI), which includes the longitudinal MRIs from 562 subjects. About 20% of them progressed to the next stage of dementia. Using only the baseline data of cognitively unimpaired (CU) and AD subjects, LLE reduces the feature dimension to two and a subject's AD progression path can be plotted in this low dimensional LLE feature space. In addition, the likelihood of being categorized to AD is indicated by color. This LLE map is a new data visualization tool that can assist in tracking AD progression over time.

NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease

In 2011, the National Institute on Aging and Alzheimer’s Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer’s disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer’s Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer’s Association Research Framework, Alzheimer’s disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six-stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker-based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker-based research should not be considered a template for all research into age-related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β-amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.

Blood amyloid-β oligomerization associated with neurodegeneration of Alzheimer's disease

Introduction

Oligomeric amyloid-ß is a major toxic species associated with Alzheimer’s disease pathogenesis. Methods used to measure oligomeric amyloid-β in the blood have increased in number in recent years. The Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ) is a specific method to measure oligomerization tendencies in the blood. The objective of this study was to determine the association between amyloid-ß oligomerization in the plasma and structural changes of the brain.

Methods

We studied 162 subjects composed of 92 community-based normal healthy subjects, 17 with subjective cognitive decline, 14 with mild cognitive impairment and 39 with Alzheimer’s disease dementia. All subjects underwent MDS-OAβ and three-dimensional T1 magnetic resonance imaging. To determine the structural changes of the brain that are statistically correlated with MDS-OAβ level, we used voxel-based morphometry with corrections for age and total intracranial volume covariates.

Results

We found brain volume reduction in the bilateral temporal, amygdala, parahippocampal and lower parietal lobe and left cingulate and precuneus regions (family-wise error, p < 0.05). Reduction was also found in white matter in proximity to the left temporal and bilateral lower parietal lobes and posterior corpus callosum (family-wise error, p < 0.05). Brain volume increment was not observed in any regions within grey or white matter.

Discussion

Findings suggest that substantial correlation exists between amyloid ß oligomerization in the blood and brain volume reduction in the form of Alzheimer’s disease despite of uncertainty in the casual relationship.

Electronic supplementary material

The online version of this article (10.1186/s13195-019-0499-7) contains supplementary material, which is available to authorized users.

Registration-based methods applied to serial high-resolution T1-weighted magnetic resonance imaging for the assessment of brain volume changes in anorexia nervosa of the restricting type

We aimed to determine whether variation in the body mass index (BMI)—a marker of anorexia nervosa (AN) severity—is associated with brain volume changes longitudinally estimated using registration-based methods on serial high-resolution T1-weighted magnetic resonance images (MRI). Fifteen female patients (mean age = 21 years; standard deviation [SD] = 5.7; range: 15–33 years) with the diagnosis of AN of the restricting type (AN-r)—according to the Diagnostic and Statistic Manual of Mental Disorders, 5^th edition criteria—underwent T1-weighted MRI at baseline and after a mean follow-up period of 11 months (SD = 6.4). We used the brain boundary shift integral (BSI) and the ventricular BSI (VBSI) to estimate volume changes after registering voxels of follow-up onto baseline MRI. Very significant and strong correlations were found between BMI variation and the brain BSI, as well as between BMI variation and the VBSI. After adjustment for age at onset, duration of illness, and the BMI rate of change before baseline MRI, the statistical significance of both associations persisted. Registration-based methods on serial MRI represent an additional tool to estimate AN severity, because they provide measures of brain volume change strongly associated with BMI variation.

Cortical microstructure in young onset Alzheimer's disease using neurite orientation dispersion and density imaging

Alzheimer's disease (AD) is associated with extensive alterations in grey matter microstructure, but our ability to quantify this in vivo is limited. Neurite orientation dispersion and density imaging (NODDI) is a multi-shell diffusion MRI technique that estimates neuritic microstructure in the form of orientation dispersion and neurite density indices (ODI/NDI). Mean values for cortical thickness, ODI, and NDI were extracted from predefined regions of interest in the cortical grey matter of 38 patients with young onset AD and 22 healthy controls. Five cortical regions associated with early atrophy in AD (entorhinal cortex, inferior temporal gyrus, middle temporal gyrus, fusiform gyrus, and precuneus) and one region relatively spared from atrophy in AD (precentral gyrus) were investigated. ODI, NDI, and cortical thickness values were compared between controls and patients for each region, and their associations with MMSE score were assessed. NDI values of all regions were significantly lower in patients. Cortical thickness measurements were significantly lower in patients in regions associated with early atrophy in AD, but not in the precentral gyrus. Decreased ODI was evident in patients in the inferior and middle temporal gyri, fusiform gyrus, and precuneus. The majority of AD-related decreases in cortical ODI and NDI persisted following adjustment for cortical thickness, as well as each other. There was evidence in the patient group that cortical NDI was associated with MMSE performance. These data suggest distinct differences in cortical NDI and ODI occur in AD and these metrics provide pathologically relevant information beyond that of cortical thinning.

MIDAS: Regionally linear multivariate discriminative statistical mapping

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data.

Cognitive reserve and cortical thickness in preclinical Alzheimer's disease

This study examined whether cognitive reserve (CR) alters the relationship between magnetic resonance imaging (MRI) measures of cortical thickness and risk of progression from normal cognition to the onset of clinical symptoms associated with mild cognitive impairment (MCI). The analyses included 232 participants from the BIOCARD study. Participants were cognitively normal and largely middle aged (M age = 56.5) at their baseline MRI scan. After an average of 11.8 years of longitudinal follow-up, 48 have developed clinical symptoms of MCI or dementia (M time from baseline to clinical symptom onset = 7.0 years). Mean thickness was measured over eight 'AD vulnerable' cortical regions, and cognitive reserve was indexed by a composite score consisting of years of education, reading, and vocabulary measures. Using Cox regression models, CR and cortical thickness were each independently associated with risk of clinical symptom onset within 7 years of baseline, suggesting that the neuronal injury occurring proximal to symptom onset has a direct association with clinical outcomes, regardless of CR. In contrast, there was a significant interaction between CR and mean cortical thickness for risk of progression more than 7 years from baseline, suggesting that individuals with high CR are better able to compensate for cortical thinning that is beginning to occur at the very earliest phase of AD.

Pathophysiology in the comorbidity of Bipolar Disorder and Alzheimer's Disease: pharmacological and stem cell approaches

Neuropsychiatric disorders involve various pathological mechanisms, resulting in neurodegeneration and brain atrophy. Neurodevelopmental processes have shown to be critical for the progression of those disorders, which are based on genetic and epigenetic mechanisms as well as on extrinsic factors. We review here common mechanisms underlying the comorbidity of Bipolar Disorders and Alzheimer's Disease, such as aberrant neurogenesis and neurotoxicity, reporting current therapeutic approaches. The understanding of these mechanisms precedes stem cell-based strategies as a new therapeutic possibility for treatment and prevention of Bipolar and Alzheimer's Disease progression. Taking into account the difficulty of studying the molecular basis of disease progression directly in patients, we also discuss the importance of stem cells for effective drug screening, modeling and treating psychiatric diseases, once in vitro differentiation of patient-induced pluripotent stem cells provides relevant information about embryonic origins, intracellular pathways and molecular mechanisms.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

BACKGROUND

Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL).

METHODS

We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients.

RESULTS

Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei.

CONCLUSION

The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.

A specific pattern of gray matter atrophy in Alzheimer's disease with depression

Considering the high incidence of depressive symptoms in Alzheimer's disease (AD), we conducted a large-sample study to investigate the pattern of gray matter (GM) abnormalities that differentiates depressive from non-depressive AD patients. We included 201 AD patients who underwent MRI assessment and categorized them into depressive and non-depressive subgroups based on the Geriatric Depression Scale (GDS; cut-off score: ≤9). We performed whole-brain voxel-based morphometry analysis in 173 patients after MRI quality control and used between-group comparisons and regression analysis models to analyze the volumetric data controlling for nuisance variables. Depressive AD patients had extensive GM volume loss mainly in the paracentral region, specifically in post- and pre-central gyrus, supplementary motor areas and thalamus compared to non-depressive patients. Similar findings were obtained for the group of 173 patients using regression analysis and GDS score as predictor variable. We provided the first clear demonstration of a unique pattern of GM atrophy that characterizes AD patients with depression which is consistent with regions implicated in the phenomenon of psychomotor retardation that characterizes depression.

Comparison of In Vivo and Ex Vivo MRI for the Detection of Structural Abnormalities in a Mouse Model of Tauopathy

With increasingly large numbers of mouse models of human disease dedicated to MRI studies, compromises between in vivo and ex vivo MRI must be fully understood in order to inform the choice of imaging methodology. We investigate the application of high resolution in vivo and ex vivo MRI, in combination with tensor-based morphometry (TBM), to uncover morphological differences in the rTg4510 mouse model of tauopathy. The rTg4510 mouse also offers a novel paradigm by which the overexpression of mutant tau can be regulated by the administration of doxycycline, providing us with a platform on which to investigate more subtle alterations in morphology with morphometry. Both in vivo and ex vivo MRI allowed the detection of widespread bilateral patterns of atrophy in the rTg4510 mouse brain relative to wild-type controls. Regions of volume loss aligned with neuronal loss and pathological tau accumulation demonstrated by immunohistochemistry. When we sought to investigate more subtle structural alterations in the rTg4510 mice relative to a subset of doxycycline-treated rTg4510 mice, ex vivo imaging enabled the detection of more regions of morphological brain changes. The disadvantages of ex vivo MRI may however mitigate this increase in sensitivity: we observed a 10% global shrinkage in brain volume of the post-mortem tissues due to formalin fixation, which was most notable in the cerebellum and olfactory bulbs. However, many central brain regions were not adversely affected by the fixation protocol, perhaps due to our “in-skull” preparation. The disparity between our TBM findings from in vivo and ex vivo MRI underlines the importance of appropriate study design, given the trade-off between these two imaging approaches. We support the utility of in vivo MRI for morphological phenotyping of mouse models of disease; however, for subtler phenotypes, ex vivo offers enhanced sensitivity to discrete morphological changes.

Neuropsychological Markers of Medial Perirhinal and Entorhinal Cortex Functioning are Impaired Twelve Years Preceding Diagnosis of Alzheimer's Dementia

Neurofibrillary pathology in Alzheimer's dementia (AD) is associated with cognitive impairments and cortical thinning, and begins in medial perirhinal cortex (mPRC) before entering entorhinal cortex (ERC). Thus, mPRC dysfunction (e.g., semantic object memory impairments) may predate or accompany ERC (i.e., episodic memory) dysfunction in the preclinical course of typical AD. We developed formulae estimating mPRC and ERC integrity (i.e., cortical thickness) using common neuropsychological tests in 31 healthy individuals and 58 early AD patients. These formulae estimated the longitudinal courses of mPRC and ERC functioning in independent groups of 28 optimally healthy individuals who developed AD (NC-AD) over 2.8-13.4 years and 28 pairwise-matched, stable, healthy individuals (NC-NC). Mixed models demonstrated significantly worse NC-AD than NC-NC estimated mPRC and ERC functioning at the earliest observation, 12 years preceding diagnosis, and a significant decline 4 years preceding the AD diagnosis. These findings demonstrate that specific neuropsychological impairments occur early in the course of preclinical AD and that tasks measuring mPRC functioning may serve as additional, powerful markers of preclinical AD.

Correlations of Behavioral Deficits with Brain Pathology Assessed through Longitudinal MRI and Histopathology in the HdhQ150/Q150 Mouse Model of Huntington's Disease

A variety of mouse models have been developed that express mutant huntingtin (mHTT) leading to aggregates and inclusions that model the molecular pathology observed in Huntington's disease. Here we show that although homozygous HdhQ150 knock-in mice developed motor impairments (rotarod, locomotor activity, grip strength) by 36 weeks of age, cognitive dysfunction (swimming T maze, fear conditioning, odor discrimination, social interaction) was not evident by 94 weeks. Concomitant to behavioral assessments, T2-weighted MRI volume measurements indicated a slower striatal growth with a significant difference between wild type (WT) and HdhQ150 mice being present even at 15 weeks. Indeed, MRI indicated significant volumetric changes prior to the emergence of the "clinical horizon" of motor impairments at 36 weeks of age. A striatal decrease of 27% was observed over 94 weeks with cortex (12%) and hippocampus (21%) also indicating significant atrophy. A hypothesis-free analysis using tensor-based morphometry highlighted further regions undergoing atrophy by contrasting brain growth and regional neurodegeneration. Histology revealed the widespread presence of mHTT aggregates and cellular inclusions. However, there was little evidence of correlations between these outcome measures, potentially indicating that other factors are important in the causal cascade linking the molecular pathology to the emergence of behavioral impairments. In conclusion, the HdhQ150 mouse model replicates many aspects of the human condition, including an extended pre-manifest period prior to the emergence of motor impairments.

Clinical Aspects of Alzheimer's Disease

Alzheimer's disease is the most common form of dementia accounting for 50-60% of all dementia cases. This chapter briefly reviews the history of Alzheimer's disease and provides an overview of the clinical syndromes associated with Alzheimer pathology and their associated neuroimaging findings. This chapter also reviews the neuropathology and genetics of Alzheimer's disease and concludes by discussing current work undertaken to identify suitable in vivo biomarkers for the disease.

Folate and Vitamin B12-Related Biomarkers in Relation to Brain Volumes

AIM

We investigated cross-sectional associations between circulating homocysteine, folate, biomarkers of vitamin B₁₂ status and brain volumes. We furthermore compared brain volumes of participants who received daily folic acid and vitamin B₁₂ supplementation with participants who did not.

METHODS

Participants of the B-PROOF study (n = 2919) were assigned to 400 µg folic acid and 500 µg vitamin B₁₂, or a placebo. After two years of intervention, T₁-weighted magnetic resonance imaging (MRI) scans were made in a random subsample (n = 218) to obtain grey and white matter volume, and total brain volume (TBV). Plasma homocysteine, serum folate, vitamin B₁₂, holotranscobalamin, and methylmalonic acid concentrations were measured.

RESULTS

Multiple linear regression analyses showed inverse associations between plasma homocysteine with TBV (β = -0.91, 95% CI -1.85-0.03; p = 0.06) and between serum folate and TBV (β = -0.20, 95% CI -0.38, -0.02; p = 0.03). No significant associations were observed for serum vitamin B₁₂ and holotranscobalamin. Fully adjusted ANCOVA models showed that the group that received B-vitamins had a lower TBV (adjusted mean 1064, 95% CI 1058-1069 mL) than the non-supplemented group (1072, 95% CI 1067-1078 mL, p = 0.03).

CONCLUSIONS

Results were contradictory, with higher Hcy levels associated with lower TBV, but also with higher folate levels associated with lower TBV. In addition, the lack of a baseline measurement withholds us from giving recommendations on whether folic acid and vitamin B₁₂ supplementation will be beneficial above and beyond normal dietary intake for brain health.

Simultaneous effects on parvalbumin-positive interneuron and dopaminergic system development in a transgenic rat model for sporadic schizophrenia

To date, unequivocal neuroanatomical features have been demonstrated neither for sporadic nor for familial schizophrenia. Here, we investigated the neuroanatomical changes in a transgenic rat model for a subset of sporadic chronic mental illness (CMI), which modestly overexpresses human full-length, non-mutant Disrupted-in-Schizophrenia 1 (DISC1), and for which aberrant dopamine homeostasis consistent with some schizophrenia phenotypes has previously been reported. Neuroanatomical analysis revealed a reduced density of dopaminergic neurons in the substantia nigra and reduced dopaminergic fibres in the striatum. Parvalbumin-positive interneuron occurrence in the somatosensory cortex was shifted from layers II/III to V/VI, and the number of calbindin-positive interneurons was slightly decreased. Reduced corpus callosum thickness confirmed trend-level observations from in vivo MRI and voxel-wise tensor based morphometry. These neuroanatomical changes help explain functional phenotypes of this animal model, some of which resemble changes observed in human schizophrenia post mortem brain tissues. Our findings also demonstrate how a single molecular factor, DISC1 overexpression or misassembly, can account for a variety of seemingly unrelated morphological phenotypes and thus provides a possible unifying explanation for similar findings observed in sporadic schizophrenia patients. Our anatomical investigation of a defined model for sporadic mental illness enables a clearer definition of neuroanatomical changes associated with subsets of human sporadic schizophrenia.

Dementia: What is it All About?

Dementia is an increasingly frequent clinical syndrome that is characterised by deficits in multiple cognitive domains, changes in behaviour and functional deterioration. Alzheimer's disease, together with vascular dementia, account for 2/3 of all cases of dementia. Other less frequent causes of dementia include dementia with Lewy bodies and frontotemporal dementia. This paper reviews the clinical, pathophysiological and neuroimaging aspects of these four prevalent causes of dementia.

Cortical thickness in relation to clinical symptom onset in preclinical AD

Mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia are preceded by a phase of disease, referred to as ‘preclinical AD’, during which cognitively normal individuals have evidence of AD pathology in the absence of clinical impairment. This study examined whether a magnetic resonance imaging (MRI) measure of cortical thickness in brain regions, collectively known as ‘AD vulnerable’ regions, predicted the time to onset of clinical symptoms associated with MCI and whether cortical thickness was similarly predictive of clinical symptom onset within 7 years post baseline versus progression at a later point in time. These analyses included 240 participants from the BIOCARD study, a cohort of longitudinally followed individuals who were cognitively normal at the time of their MRI (mean age = 56 years). Participants have been followed for up to 18 years (M follow-up = 11.8 years) and 50 participants with MRIs at baseline have developed MCI or dementia over time (mean time to clinical symptom onset = 7 years). Cortical thickness in AD vulnerable regions was based on the mean thickness of eight cortical regions. Using Cox regression models, we found that lower mean cortical thickness was associated with an increased risk of progression from normal cognition to clinical symptom onset within 7 years of baseline (p = 0.03), but not with progression > 7 years from baseline (p = 0.30). Lower cortical thickness was also associated with higher levels of phosphorylated tau, measured in cerebrospinal fluid at baseline. These results suggest that cortical thinning in AD vulnerable regions is detectable in cognitively normal individuals several years prior to the onset of clinical symptoms that are a harbinger of a diagnosis of MCI, and that the changes are more likely to be evident in the years proximal to clinical symptom onset, consistent with hypothetical AD biomarker models.

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Examined cortical thickness in relation to onset of clinical symptoms of MCI.

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Cortical thickness was associated with clinical symptom onset within 7 years.

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Cortical thickness was associated with levels of CSF p-tau, but not CSF amyloid.

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Changes in cortical thickness may be evident during preclinical AD.

HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework

Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseases on an individual basis. The driving assumption behind the vast majority of the existing methodologies is that a single imaging pattern can distinguish between healthy and diseased populations, or between two subgroups of patients (e.g., progressors vs. non-progressors). This assumption effectively ignores the ample evidence for the heterogeneous nature of brain diseases. Neurodegenerative, neuropsychiatric and neurodevelopmental disorders are largely characterized by high clinical heterogeneity, which likely stems in part from underlying neuroanatomical heterogeneity of various pathologies. Detecting and characterizing heterogeneity may deepen our understanding of disease mechanisms and lead to patient-specific treatments. However, few approaches tackle disease subtype discovery in a principled machine learning framework. To address this challenge, we present a novel non-linear learning algorithm for simultaneous binary classification and subtype identification, termed HYDRA (Heterogeneity through Discriminative Analysis). Neuroanatomical subtypes are effectively captured by multiple linear hyperplanes, which form a convex polytope that separates two groups (e.g., healthy controls from pathologic samples); each face of this polytope effectively defines a disease subtype. We validated HYDRA on simulated and clinical data. In the latter case, we applied the proposed method independently to the imaging and genetic datasets of the Alzheimer's Disease Neuroimaging Initiative (ADNI 1) study. The imaging dataset consisted of T1-weighted volumetric magnetic resonance images of 123 AD patients and 177 controls. The genetic dataset consisted of single nucleotide polymorphism information of 103 AD patients and 139 controls. We identified 3 reproducible subtypes of atrophy in AD relative to controls: (1) diffuse and extensive atrophy, (2) precuneus and extensive temporal lobe atrophy, as well some prefrontal atrophy, (3) atrophy pattern very much confined to the hippocampus and the medial temporal lobe. The genetics dataset yielded two subtypes of AD characterized mainly by the presence/absence of the apolipoprotein E (APOE) ε4 genotype, but also involving differential presence of risk alleles of CD2AP, SPON1 and LOC39095 SNPs that were associated with differences in the respective patterns of brain atrophy, especially in the precuneus. The results demonstrate the potential of the proposed approach to map disease heterogeneity in neuroimaging and genetic studies.

Alzheimer's disease or Alzheimer's syndrome?: a longitudinal computed tomography neuroradiological follow-up study of 56 cases diagnosed clinically as Alzheimer's disease

BACKGROUND

Some 200 patients, including those with Alzheimer's disease and other types of dementia, stay year-round in Yokohama - Houyuu Hospital. They undergo computed tomography (CT) neuroradiological examination at least once or twice a year. For this study, the accumulative data, including clinical and neuroradiological, were analyzed.

METHODS

Differential diagnoses of Alzheimer's disease were performed in accordance with the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association criteria. The 56 patients (15 men, 41 women) included in this study underwent in-hospital observation on average for 4.4 years (range: 1-10 years). The patients were classified into four groups according to the age of disease onset. The CT findings were summarized for each group and then compared among the groups to determine if there were any differences related to age of onset and, if so, to identify and analyze them.

RESULTS

(1) The duration of deceased cases' total clinical course (in years) compared among the four groups. In general, the degree of dementia was more severe among those with earlier disease onset. (2) In cases admitted within 2 years from onset (n =14), the suspected initiating focus of cortical atrophy occurred in the frontal lobe (n = 6), the temporal lobe (n = 6), or the fronto-temporal lobes (n = 2). (3) Although CT findings generally showed that the more severe cases had earlier onset, serial CT examinations in each case showed widely different pathologies in degree, nature and manner of progression, regardless of group classification. (4) The earliest sites of brain atrophy, sites of its severest involvement within the brain, and neuroradiological development of the cerebral cortex pathology in combination with hemispheric white matter, lateral ventricles, and third ventricles varied among the four groups and between case within each group. Alzheimer's disease could not be subclassified simply by the age of clinical onset.

CONCLUSION

Cases of so-called Alzheimer's disease, as observed through continued clinical follow-up and serial CT examinations, appear so diverse in symptomatology and radiological pathomorphology that it is difficult to consider them a single nosological entity. The pathology of Alzheimer's disease has to be reconsidered in accordance with the variety observed in the sequential development of neuroradiological findings. The pathology must be reconstructed in terms of topographical dimensions and chronological developments. The diagnosis of Alzheimer's disease appears to be not so simple based on any conventional diagnostic operational standards.

Brain glutathione levels--a novel biomarker for mild cognitive impairment and Alzheimer's disease

BACKGROUND

Extant data from in vivo animal models and postmortem studies indicate that Alzheimer's disease (AD) pathology is associated with reduction of the brain antioxidant glutathione (GSH), yet direct clinical evidence has been lacking. In this study, we investigated GSH modulation in the brain with AD and assessed the diagnostic potential of GSH estimation in hippocampi (HP) and frontal cortices (FC) as a biomarker for AD and its prodromal stage, mild cognitive impairment (MCI).

METHODS

Brain GSH levels were measured in HP of 21 AD, 22 MCI, and 21 healthy old controls (HC) and FC of 19 AD, 19 MCI, and 28 HC with in vivo proton magnetic resonance spectroscopy. The association between GSH levels and clinical measures of AD progression was tested. Linear regression models were used to determine the best combination of GSH estimation in these brain regions for discrimination between AD, MCI, and HC.

RESULTS

AD-dependent reduction of GSH was observed in both HP and FC (p < .001). Furthermore, GSH reduction in these regions correlated with decline in cognitive functions. Receiver operator characteristics analyses evidenced that hippocampal GSH robustly discriminates between MCI and healthy controls with 87.5% sensitivity, 100% specificity, and positive and negative likelihood ratios of 8.76/.13, whereas cortical GSH differentiates MCI and AD with 91.7% sensitivity, 100% specificity, and positive and negative likelihood ratios of 9.17/.08.

CONCLUSIONS

The present study provides compelling in vivo evidence that estimation of GSH levels in specific brain regions with magnetic resonance spectroscopy constitutes a clinically relevant biomarker for MCI and AD.