Combining hippocampal volume metrics to better understand Alzheimer's disease progression in at-risk individuals

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

Background

Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease.

Objective

This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance.

Methods

Metal exposure history, whole blood metal, and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume, cortical thickness) and diffusion tensor imaging [mean (MD), axial (AD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, correlation, multiple linear, Bayesian kernel machine regression, and mediation analyses were employed to examine effects of single- or mixed-metal predictor(s) and their interactions on MTL structural and neuropsychological metrics; and on the path from metal exposure to neuropsychological consequences.

Results

Compared to controls, exposed subjects had higher blood Cu, Fe, K, Mn, Pb, Se, and Zn levels (p's<0.026) and poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Exposed subjects displayed higher MD, AD, and RD in all MTL ROIs (p's<0.040) and lower FA in entorhinal and parahippocampal cortices (p's<0.033), but not morphological differences. Long-term mixed-metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p=0.023). Higher whole blood Mn and Cu predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007) without overall metal mixture or interaction effects.

Discussion

Mixed metal exposure predicted MTL structural and neuropsychological features that are similar to Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate the path for environmental exposure to Alzheimer's disease-related health outcomes.

Detection and treatment of Alzheimer's disease in its preclinical stage

Longitudinal multimodal biomarker studies reveal that the continuum of Alzheimer's disease (AD) includes a long latent phase, referred to as preclinical AD, which precedes the onset of symptoms by decades. Treatment during the preclinical AD phase offers an optimal opportunity for slowing the progression of disease. However, trial design in this population is complex. In this Review, we discuss the recent advances in accurate plasma measurements, new recruitment approaches, sensitive cognitive instruments and self-reported outcomes that have facilitated the successful launch of multiple phase 3 trials for preclinical AD. The recent success of anti-amyloid immunotherapy trials in symptomatic AD has increased the enthusiasm for testing this strategy at the earliest feasible stage. We provide an outlook for standard screening of amyloid accumulation at the preclinical stage in clinically normal individuals, during which effective therapy to delay or prevent cognitive decline can be initiated.

Leveraging the regulatory framework to facilitate drug development in Parkinson's disease

There is an exigent need for disease-modifying and symptomatic treatment approaches for Parkinson's disease. A better understanding of Parkinson's disease pathophysiology and new insights in genetics has opened exciting new venues for pharmacological treatment targets. There are, however, many challenges on the path from discovery to drug approval. These challenges revolve around appropriate endpoint selection, the lack of accurate biomarkers, challenges with diagnostic accuracy, and other challenges commonly encountered by drug developers. The regulatory health authorities, however, have provided tools to provide guidance for drug development and to assist with these challenges. The main goal of the Critical Path for Parkinson's Consortium, a nonprofit public-private partnership part of the Critical Path Institute, is to advance these so-called drug development tools for Parkinson's disease trials. The focus of this chapter will be on how the health regulators' tools were successfully leveraged to facilitate drug development in Parkinson's disease and other neurodegenerative diseases.

The Molecular Effects of Environmental Enrichment on Alzheimer's Disease

Environmental enrichment (EE) is an environmental paradigm encompassing sensory, cognitive, and physical stimulation at a heightened level. Previous studies have reported the beneficial effects of EE in the brain, particularly in the hippocampus. EE improves cognitive function as well as ameliorates depressive and anxiety-like behaviors, making it a potentially effective neuroprotective strategy against neurodegenerative diseases such as Alzheimer's disease (AD). Here, we summarize the current evidence for EE as a neuroprotective strategy as well as the potential molecular pathways that can explain the effects of EE from a biochemical perspective using animal models. The effectiveness of EE in enhancing brain activity against neurodegeneration is explored with a view to differences present in early and late life EE exposure, with its potential application in human being discussed. We discuss EE as one of the non pharmacological approaches in preventing or delaying the onset of AD for future research.

Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.

Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data

Alzheimer's disease (AD) is the most prevalent form of dementia. The accurate diagnosis of AD, especially in the early phases is very important for timely intervention. It has been suggested that brain atrophy, as measured with structural magnetic resonance imaging (sMRI), can be an efficacy marker of neurodegeneration. While classification methods have been successful in diagnosis of AD, the performance of such methods have been very poor in diagnosis of those in early stages of mild cognitive impairment (EMCI). Therefore, in this study we investigated whether optimisation based on evolutionary algorithms (EA) can be an effective tool in diagnosis of EMCI as compared to cognitively normal participants (CNs). Structural MRI data for patients with EMCI (n = 54) and CN participants (n = 56) was extracted from Alzheimer's disease Neuroimaging Initiative (ADNI). Using three automatic brain segmentation methods, we extracted volumetric parameters as input to the optimisation algorithms. Our method achieved classification accuracy of greater than 93%. This accuracy level is higher than the previously suggested methods of classification of CN and EMCI using a single- or multiple modalities of imaging data. Our results show that with an effective optimisation method, a single modality of biomarkers can be enough to achieve a high classification accuracy.

Breakdown of the blood-brain barrier: A mediator of increased Alzheimer's risk in patients with metabolic disorders?

Metabolic disorders (MDs), including type 1 and 2 diabetes and chronic obesity, are among the faster growing diseases globally and are a primary risk factor for Alzheimer's disease (AD). The term "type-3 diabetes" has been proposed for AD due to the interrelated cellular, metabolic, and immune features shared by diabetes, insulin resistance (IR), and the cognitive impairment and neurodegeneration found in AD. Patients with MDs and/or AD commonly exhibit altered glucose homeostasis and IR; systemic chronic inflammation encompassing all of the periphery, blood-brain barrier (BBB), and central nervous system; pathological vascular remodeling; and increased BBB permeability that allows transfusion of neurotoxic molecules from the blood to the brain. This review summarizes the components of the BBB, mechanisms through which MDs alter BBB permeability via immune and metabolic pathways, the contribution of BBB dysfunction to the manifestation and progression of AD, and current avenues of therapeutic research that address BBB permeability. In addition, issues with the translational applicability of current animal models of AD regarding BBB dysfunction and proposals for future directions of research that address the relationship between MDs, BBB dysfunction, and AD are discussed.

Diagnostic Performance of Automated MRI Volumetry by icobrain dm for Alzheimer's Disease in a Clinical Setting: A REMEMBER Study

Background:

Magnetic resonance imaging (MRI) has become important in the diagnostic work-up of neurodegenerative diseases. icobrain dm, a CE-labeled and FDA-cleared automated brain volumetry software, has shown potential in differentiating cognitively healthy controls (HC) from Alzheimer’s disease (AD) dementia (ADD) patients in selected research cohorts.

Objective:

This study examines the diagnostic value of icobrain dm for AD in routine clinical practice, including a comparison to the widely used FreeSurfer software, and investigates if combined brain volumes contribute to establish an AD diagnosis.

Methods:

The study population included HC (n = 90), subjective cognitive decline (SCD, n = 93), mild cognitive impairment (MCI, n = 357), and ADD (n = 280) patients. Through automated volumetric analyses of global, cortical, and subcortical brain structures on clinical brain MRI T1w (n = 820) images from a retrospective, multi-center study (REMEMBER), icobrain dm’s (v.4.4.0) ability to differentiate disease stages via ROC analysis was compared to FreeSurfer (v.6.0). Stepwise backward regression models were constructed to investigate if combined brain volumes can differentiate between AD stages.

Results:

icobrain dm outperformed FreeSurfer in processing time (15–30 min versus 9–32 h), robustness (0 versus 67 failures), and diagnostic performance for whole brain, hippocampal volumes, and lateral ventricles between HC and ADD patients. Stepwise backward regression showed improved diagnostic accuracy for pairwise group differentiations, with highest performance obtained for distinguishing HC from ADD (AUC = 0.914; Specificity 83.0%; Sensitivity 86.3%).

Conclusion:

Automated volumetry has a diagnostic value for ADD diagnosis in routine clinical practice. Our findings indicate that combined brain volumes improve diagnostic accuracy, using real-world imaging data from a clinical setting.

Gauging the role and impact of drug interactions and repurposing in neurodegenerative disorders

Neurodegenerative diseases (ND) are of vast origin which are characterized by gradual progressive loss of neurons in the brain region. ND can be classified according to the clinical symptoms present (e.g. Cognitive decline, hyperkinetic, and hypokinetic movements disorder) or by the pathological protein deposited (e.g., Amyloid, tau, Alpha-synuclein, TDP-43). Alzheimer's disease preceded by Parkinson's is the most prevalent form of ND world-wide. Multiple factors like aging, genetic mutations, environmental factors, gut microbiota, blood-brain barrier microvascular complication, etc. may increase the predisposition towards ND. Genetic mutation is a major contributor in increasing the susceptibility towards ND, the concept of one disease-one gene is obsolete and now multiple genes are considered to be involved in causing one particular disease. Also, the involvement of multiple pathological mechanisms like oxidative stress, neuroinflammation, mitochondrial dysfunction, etc. contributes to the complexity and makes them difficult to be treated by traditional mono-targeted ligands. In this aspect, the Poly-pharmacological drug approach which targets multiple pathological pathways at the same time provides the best way to treat such complex networked CNS diseases. In this review, we have provided an overview of ND and their pathological origin, along with a brief description of various genes associated with multiple diseases like Alzheimer's, Parkinson's, Multiple sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), Huntington's and a comprehensive detail about the Poly-pharmacology approach (MTDLs and Fixed-dose combinations) along with their merits over the traditional single-targeted drug is provided. This review also provides insights into current repurposing strategies along with its regulatory considerations.

Multiparametric imaging hippocampal neurodegeneration and functional connectivity with simultaneous PET/MRI in Alzheimer's disease

Purpose

The objective of this study is to investigate the hippocampal neurodegeneration and its associated aberrant functions in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients using simultaneous PET/MRI.

Methods

Forty-two cognitively normal controls (NC), 38 MCI, and 22 AD patients were enrolled in this study. All subjects underwent ¹⁸F-FDG PET/functional MRI (fMRI) and high-resolution T1-weighted MRI scans on a hybrid GE Signa PET/MRI scanner. Neurodegeneration in hippocampus and its subregions was quantified by regional gray matter volume and ¹⁸F-FDG standardized uptake value ratio (SUVR) relative to cerebellum. An iterative reblurred Van Cittert iteration method was used for voxelwise partial volume correction on ¹⁸F-FDG PET images. Regional gray matter volume was estimated from voxel-based morphometric analysis with MRI. fMRI data were analyzed after slice time correction and head motion correction using statistical parametric mapping (SPM12) with DPARSF toolbox. The regions of interest including hippocampus, cornu ammonis (CA1), CA2/3/dentate gyrus (DG), and subiculum were defined in the standard MNI space.

Results

Patient groups had reduced SUVR, gray matter volume, and functional connectivity compared to NC in CA1, CA2/3/DG, and subiculum (AD < MCI < NC). There was a linear correlation between the left CA2/3DG gray matter volume and ¹⁸F-FDG SUVR in AD patients (P < 0.001, r = 0.737). Significant correlation was also found between left CA2/3/DG-superior medial frontal gyrus functional connectivity and left CA2/3/DG hypometabolism in patients with AD. The functional connectivity of right CA1-precuneus in patients with MCI and right subiculum-superior frontal gyrus in patients with AD was positively correlated with mini mental status examination scores (P < 0.05).

Conclusion

Our findings demonstrate that the associations existed at subregional hippocampal level between the functional connectivity measured by fMRI and neurodegeneration measured by structural MRI and ¹⁸F-FDG PET. Our results may provide a basis for precision neuroimaging of hippocampus in AD.

Mechanical property alterations across the cerebral cortex due to Alzheimer's disease

Alzheimer's disease is a personally devastating neurodegenerative disorder and a major public health concern. There is an urgent need for medical imaging techniques that better characterize the early stages and monitor the progression of the disease. Magnetic resonance elastography (MRE) is a relatively new and highly sensitive MRI technique that can non-invasively assess tissue microstructural integrity via measurement of brain viscoelastic mechanical properties. For the first time, we use high-resolution MRE methods to conduct a voxel-wise MRE investigation and state-of-the-art post hoc region of interest analysis of the viscoelastic properties of the cerebral cortex in patients with Alzheimer's disease (N = 11) compared with cognitively healthy older adults (N = 12). We replicated previous findings that have reported significant volume and stiffness reductions at the whole-brain level. Significant reductions in volume were also observed in Alzheimer's disease when white matter, cortical grey matter and subcortical grey matter compartments were considered separately; lower stiffness was also observed in white matter and cortical grey matter, but not in subcortical grey matter. Voxel-based morphometry of both cortical and subcortical grey matter revealed localized reductions in volume due to Alzheimer's disease in the hippocampus, fusiform, middle, superior temporal gyri and precuneus. Similarly, voxel-based MRE identified lower stiffness in the middle and superior temporal gyri and precuneus, although the spatial distribution of these effects was not identical to the pattern of volume reduction. Notably, MRE additionally identified stiffness deficits in the operculum and precentral gyrus located within the frontal lobe; regions that did not undergo volume loss identified through voxel-based morphometry. Voxel-based-morphometry and voxel-based MRE results were confirmed by a complementary post hoc region-of-interest approach in native space where the viscoelastic changes remained significant even after statistically controlling for regional volumes. The pattern of reduction in cortical stiffness observed in Alzheimer's disease patients raises the possibility that MRE may provide unique insights regarding the neural mechanisms which underlie the development and progression of the disease. The measured mechanical property changes that we have observed warrant further exploration to investigate the diagnostic usefulness of MRE in cases of Alzheimer's disease and other dementias.