Magnetic Resonance Imaging to Detect Early Molecular and Cellular Changes in Alzheimer's Disease

Exploring the potential of mindfulness-based therapy in the prevention and treatment of neurodegenerative diseases based on molecular mechanism studies

Neurodegenerative diseases (ND) have received increasing attention due to their irreversibility, but there is still no means to completely cure ND in clinical practice. Mindfulness therapy (MT), including Qigong, Tai Chi, meditation, and yoga, etc., has become an effective complementary treatment modality in solving clinical and subclinical problems due to its advantages of low side effects, less pain, and easy acceptance by patients. MT is primarily used to treat mental and emotional disorders. In recent years, evidence has shown that MT has a certain therapeutic effect on ND with a potential molecular basis. In this review, we summarize the pathogenesis and risk factors of Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS), relating to telomerase activity, epigenetics, stress, and the pro-inflammatory transcription factor nuclear factor kappa B (NF-κB) mediated inflammatory response, and analyze the molecular mechanism basis of MT to prevent and treat ND, to provide possible explanations for the potential of MT treatments for ND.

Smoking is associated with lower brain volume and cognitive differences: A large population analysis based on the UK Biobank

The evidence about the association of smoking with both brain structure and cognitive functions remains inconsistent. Using structural magnetic resonance imaging from the UK Biobank (n = 33,293), we examined the relationships between smoking status, dosage, and abstinence with total and 166 regional brain gray matter volumes (GMV). The relationships between the smoking parameters with cognitive function, and whether this relationship was mediated by brain structure, were then investigated. Smoking was associated with lower total and regional GMV, with the extent depending on the frequency of smoking and on whether smoking had ceased: active regular smokers had the lowest GMV (Cohen's d = -0.362), and former light smokers had a slightly smaller GMV (Cohen's d = -0.060). The smaller GMV in smokers was most evident in the thalamus. Higher lifetime exposure (i.e., pack-years) was associated with lower total GMV (β = -311.84, p = 8.35 × 10-36). In those who ceased smoking, the duration of abstinence was associated with a larger total GMV (β = 139.57, p = 2.36 × 10-08). It was further found that reduced cognitive function was associated with smoker parameters and that the associations were partially mediated by brain structure. This is the largest scale investigation we know of smoking and brain structure, and these results are likely to be robust. The findings are of associations between brain structure and smoking, and in the future, it will be important to assess whether brain structure influences smoking status, or whether smoking influences brain structure, or both.

Class activation attention transfer neural networks for MCI conversion prediction

Accurate prediction of the trajectory of Alzheimer's disease (AD) from an early stage is of substantial value for treatment and planning to delay the onset of AD. We propose a novel attention transfer method to train a 3D convolutional neural network to predict which patients with mild cognitive impairment (MCI) will progress to AD within 3 years. A model is first trained on a separate but related source task (task we are transferring information from) to automatically learn regions of interest (ROI) from a given image. Next we train a model to simultaneously classify progressive MCI (pMCI) and stable MCI (sMCI) (the target task we want to solve) and the ROIs learned from the source task. The predicted ROIs are then used to focus the model's attention on certain areas of the brain when classifying pMCI versus sMCI. Thus, in contrast to traditional transfer learning, we transfer attention maps instead of transferring model weights from a source task to the target classification task. Our Method outperformed all methods tested including traditional transfer learning and methods that used expert knowledge to define ROI. Furthermore, the attention map transferred from the source task highlights known Alzheimer's pathology.

Neural Network in the Analysis of the MR Signal as an Image Segmentation Tool for the Determination of T(1) and T(2) Relaxation Times with Application to Cancer Cell Culture

Artificial intelligence has been entering medical research. Today, manufacturers of diagnostic instruments are including algorithms based on neural networks. Neural networks are quickly entering all branches of medical research and beyond. Analyzing the PubMed database from the last 5 years (2017 to 2021), we see that the number of responses to the query "neural network in medicine" exceeds 10,500 papers. Deep learning algorithms are of particular importance in oncology. This paper presents the use of neural networks to analyze the magnetic resonance imaging (MRI) images used to determine MRI relaxometry of the samples. Relaxometry is becoming an increasingly common tool in diagnostics. The aim of this work was to optimize the processing time of DICOM images by using a neural network implemented in the MATLAB package by The MathWorks with the patternnet function. The application of a neural network helps to eliminate spaces in which there are no objects with characteristics matching the phenomenon of longitudinal or transverse MRI relaxation. The result of this work is the elimination of aerated spaces in MRI images. The whole algorithm was implemented as an application in the MATLAB package.

AD Resemblance Atrophy Index of Brain Magnetic Resonance Imaging in Predicting the Progression of Mild Cognitive Impairment Carrying Apolipoprotein E-ε4 Allele

Background and Objective

Early identification is important for timely Alzheimer’s disease (AD) treatment. Apolipoprotein E ε4 allele (APOE-ε4) is an important genetic risk factor for sporadic AD. The AD-Resemblance Atrophy Index (RAI)—a structural magnetic resonance imaging-derived composite index—was found to predict the risk of progression from mild cognitive impairment (MCI) to AD. Therefore, we investigated whether the AD-RAI can predict cognitive decline and progression to AD in patients with MCI carrying APOE ε4.

Methods

We included 733 participants with MCI from the Alzheimer’s Disease Neuroimaging Initiative Database (ADNI). Their APOE genotypes, cognitive performance, and levels of AD-RAI were assessed at baseline and follow-up. Linear regression models were used to test the correlations between the AD-RAI and baseline cognitive measures, and linear mixed models with random intercepts and slopes were applied to investigate whether AD-RAI and APOE-ε4 can predict the level of cognitive decline. Cox proportional risk regression models were used to test the association of AD-RAI and APOE status with the progression from MCI to AD.

Results

The baseline AD-RAI was higher in the MCI converted to AD group than in the MCI stable group (P < 0.001). The AD-RAI was significantly correlated with cognition, and had a synergistic effect with APOE-ε4 to predict the rate of cognitive decline. The AD-RAI predicted the risk and timing of MCI progression to AD. Based on the MCI population carrying APOE-ε4, the median time to progression from MCI to AD was 24 months if the AD-RAI > 0.5, while the median time to progression from MCI to AD was 96 months for patients with an AD-RAI ≤ 0.5.

Conclusion

The AD-RAI can predict the risk of progression to AD in people with MCI carrying APOE ε4, is strongly correlated with cognition, and can predict cognitive decline.

Apolipoprotein ε4 modifies obesity-related atrophy in the hippocampal formation of cognitively healthy adults

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Age-related inverted U-shaped curve of hippocampal myelin/neurite packing.

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Reduced hippocampal myelin/neurite packing and size/complexity in obesity.

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APOE modifies the effects of obesity on hippocampal size/complexity.

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Age-related slowing of spatial navigation but no risk effects on cognition.

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CA/DG predict episodic memory and subiculum predicts spatial navigation performance.

Characterizing age- and risk-related hippocampal vulnerabilities may inform about the neural underpinnings of cognitive decline. We studied the impact of three risk-factors, Apolipoprotein (APOE)-ε4, a family history of dementia, and central obesity, on the CA1, CA2/3, dentate gyrus and subiculum of 158 cognitively healthy adults (38-71 years). Subfields were labelled with the Automatic Segmentation of Hippocampal Subfields and FreeSurfer (version 6) protocols. Volumetric and microstructural measurements from quantitative magnetization transfer and Neurite Orientation Density and Dispersion Imaging were extracted for each subfield and reduced to three principal components capturing apparent myelin/neurite packing, size/complexity, and metabolism. Aging was associated with an inverse U-shaped curve on myelin/neurite packing and affected all subfields. Obesity led to reductions in myelin/neurite packing and size/complexity regardless of APOE and family history of dementia status. However, amongst individuals with a healthy Waist-Hip-Ratio, APOE ε4 carriers showed lower size/complexity than non-carriers. Segmentation protocol type did not affect this risk pattern. These findings reveal interactive effects between APOE and central obesity on the hippocampal formation of cognitively healthy adults.

Retinal Microvascular Alterations as the Biomarkers for Alzheimer Disease: Are We There Yet?

BACKGROUND

Alzheimer disease (AD) is a heterogeneous and multifactorial disorder with an insidious onset and slowly progressive disease course. To date, there are no effective treatments, but biomarkers for early diagnosis and monitoring of disease progression offer a promising first step in developing and testing potential interventions. Cerebral vascular imaging biomarkers to assess the contributions of vascular dysfunction to AD are strongly recommended to be integrated into the current amyloid-β (Aβ) [A], tau [T], and neurodegeneration [(N)]-the "AT(N)" biomarker system for clinical research. However, the methodology is expensive and often requires invasive procedures to document cerebral vascular dysfunction. The retina has been used as a surrogate to study cerebral vascular changes. There is growing interest in the identification of retinal microvascular changes as a safe, easily accessible, low cost, and time-efficient approach to enhancing our understanding of the vascular pathogenesis associated with AD.

EVIDENCE ACQUISITION

A systemic review of the literature was performed regarding retinal vascular changes in AD and its prodromal stages, focusing on functional and structural changes of large retinal vessels (vessels visible on fundus photographs) and microvasculature (precapillary arterioles, capillary, and postcapillary venules) that are invisible on fundus photographs.

RESULTS

Static and dynamic retinal microvascular alterations such as retinal arterial wall motion, blood flow rate, and microvascular network density were reported in AD, mild cognitive impairment, and even in the preclinical stages of the disease. The data are somewhat controversial and inconsistent among the articles reviewed and were obtained based on cross-sectional studies that used different patient cohorts, equipment, techniques, and analysis methods.

CONCLUSIONS

Retinal microvascular alterations exist across the AD spectrum. Further large scale, within-subject longitudinal studies using standardized imaging and analytical methods may advance our knowledge concerning vascular contributions to the pathogenesis of AD.

A Transient Survival Model of Alteration of Electrophysiological Properties Due to Amyloid Beta Toxicity Based on SH-SY5Y Cell Line

BACKGROUND

Accumulation of toxic strands of amyloid beta (AB), which cause neurofibrillary tangles and, ultimately, cell death, is suspected to be the main culprit behind clinical symptoms of Alzheimer's disease. Although the mechanism of cell death due to AB accumulation is well known, the intermediate phase between the start of accumulation and cell death is less known and investigated, partially due to technical challenges in identifying partially affected cells.

OBJECTIVE

First, we aimed to establish an in vitro model that would show resilience against AB toxicity. Then we used morphological, molecular and electrophysiological assays to investigate how the characteristics of the surviving cells changed after AB toxicity.

METHODS

To investigate this phase, we used differentiation of SH-SY5Y neuroblastoma stem cells by Retinoic Acid (RA) and Brain Derived Neurotrophic Factor (BDNF) to establish an in vitro model which would be able to demonstrate various levels of resistance to AB toxicity. We utilized fluorescent microscopy and whole cell patch clamp recordings to investigate behavior of the model.

RESULTS

We observed significantly higher morphological resilience against AB toxicity in cells which were differentiated by both Retinoic Acid and Brain Derived Neurotrophic Factor compared to Retinoic Acid only. However, the electrophysiological properties of the Retinoic Acid + Brain-Derived Neurotrophic Factor differentiated cells were significantly altered after AB treatment.

CONCLUSION

We established a transient survival model for AB toxicity and observed the effects of AB on transmembrane currents of differentiated neurons.

Dementia and Major Neurocognitive Disorders: Some Lessons Learned One Century after the first Alois Alzheimer's Clinical Notes

Over 100 years ago, Alois Alzheimer presented the clinical signs and symptoms of what has been later called "Alzheimer Dementia" in a young woman whose name was Augustine Deter [...].

Altered Macular Microvasculature in Mild Cognitive Impairment and Alzheimer Disease

BACKGROUND

The goal of the present study was to analyze the macular microvacular network in mild cognitive impirment (MCI) and Alzheimer disease (AD).

METHODS

Twelve patients with AD and 19 patients with MCI were recruited together with 21 cognitively normal controls with a similar range of ages. Optical coherence tomography angiography was used to image the retinal microvascular network at the macular region, including retinal vascular network (RVN), superficial vascular plexus (SVP), and deep vascular plexus (DVP). Fractal analysis (box counting, Dbox) representing the microvascular density was performed in different annular zones and quadrantal sectors. The macular ganglion cell-inner plexiform layer (GC-IPL) thickness was measured using Zeiss OCT. The relationship between the retinal microvasculature and clinical manifestations was analyzed.

RESULTS

Patients with AD had lower densities of RVN, SVP, and DVP in the annulus, from 0.6 to 2.5 mm in diameter (P < 0.05) in comparison with controls. Patients with MCI had lower density of DVP in the superior nasal quadrant (P < 0.05) than that of the controls. There were no significant differences of GC-IPL thickness among groups (P > 0.05). There was a trend of vascular density loss from control to MCI then AD (P < 0.05). Retinal microvascular density of DVP was correlated with GC-IPL thickness (P < 0.05) in patients with AD, but not in patients with MCI and controls.

CONCLUSIONS

Patients with AD had less density of retinal microvascular networks than controls. Our findings suggest the presence of retinal microvascular dysfunction in AD.

Systems modeling of white matter microstructural abnormalities in Alzheimer's disease

INTRODUCTION

Microstructural abnormalities in white matter (WM) are often reported in Alzheimer's disease (AD). However, it is unclear which brain regions have the strongest WM changes in presymptomatic AD and what biological processes underlie WM abnormality during disease progression.

METHODS

We developed a systems biology framework to integrate matched diffusion tensor imaging (DTI), genetic and transcriptomic data to investigate regional vulnerability to AD and identify genetic risk factors and gene subnetworks underlying WM abnormality in AD.

RESULTS

We quantified regional WM abnormality and identified most vulnerable brain regions. A SNP rs2203712 in CELF1 was most significantly associated with several DTI-derived features in the hippocampus, the top ranked brain region. An immune response gene subnetwork in the blood was most correlated with DTI features across all the brain regions.

DISCUSSION

Incorporation of image analysis with gene network analysis enhances our understanding of disease progression and facilitates identification of novel therapeutic strategies for AD.

Multifunctional nano-enabled delivery systems in Alzheimer's disease management

This review discusses the recent advances in multifunctional nano-enabled delivery systems (NDS) for Alzheimer's disease management, including multitherapeutics, multimodal imaging-guided diagnostics, and theranostics.

Near-infrared Fluorescence Ocular Imaging (NIRFOI) of Alzheimer's Disease

PURPOSE

Near-infrared fluorescence (NIRF) imaging has been widely used in preclinical studies; however, its low tissue penetration represents a daunting problem for translational clinical imaging of neurodegenerative diseases. The retina is known as an extension of the central nerve system (CNS), and it is widely considered as a window to the brain. Therefore, the retina can be considered as an alternative organ for investigating neurodegenerative diseases, and an eye represents an ideal NIRF imaging organ, due to its minimal opacity.

PROCEDURES

NIRF ocular imaging (NIRFOI), for the first time, was explored for imaging of Alzheimer's disease (AD) via utilizing "smart" fluorescent probes CRANAD-X (X = - 2, - 3, - 30, - 58, and - 102) for amyloid beta (Aβ), and CRANAD-61 for reactive oxygen species (ROS). Mice were intravenously injected the fluorescence dyes and images from the eyes were captured with an IVIS imaging system at different time points.

RESULTS

All of the tested NIRF probes could be used to differentiate transgenic AD mice and WT mice, and NIRFOI could provide much higher sensitivity for imaging Aβs than NIRF brain imaging did. Our data suggested that NIRFOI could capture the imaging signals from both soluble and insoluble Aβ species. Moreover, we demonstrated that NIRFOI with CRANAD-102 could be used to monitor the therapeutic effects of BACE-1 inhibitor LY2811376. Compared to NIRF brain imaging, NIRFOI provided a larger change of Aβ levels before and after LY2811376 treatment. In addition, we demonstrated that CRANAD-61 could be used to image reactive oxygen species in the eyes.

CONCLUSION

The large detection margin by NIRFOI is very important for both diagnosis and therapy response monitoring. Compared to fluorescence microscopic imaging, NIRFOI captures signals with a wide angle (large field of view (FOV)) and can be used to detect soluble Aβs. We believe that NIRFOI has remarkable translational potential for future human studies and can be a potential imaging technology for fast, cheap, accessible, and reliable screening of AD in the future.

Magnetic Nanoparticles Applications for Amyloidosis Study and Detection: A Review

Magnetic nanoparticles (MNPs) have great potential in biomedical and clinical applications because of their many unique properties. This contribution provides an overview of the MNPs mainly used in the field of amyloid diseases. The first part discusses their use in understanding the amyloid mechanisms of fibrillation, with emphasis on their ability to control aggregation of amyloidogenic proteins. The second part deals with the functionalization by various moieties of numerous MNPs’ surfaces (molecules, peptides, antibody fragments, or whole antibodies of MNPs) for the detection and the quantification of amyloid aggregates. The last part of this review focuses on the use of MNPs for magnetic-resonance-based amyloid imaging in biomedical fields, with particular attention to the application of gadolinium-based paramagnetic nanoparticles (AGuIX), which have been recently developed. Biocompatible AGuIX nanoparticles show favorable characteristics for in vivo use, such as nanometric and straightforward functionalization. Their properties have enabled their application in MRI. Here, we report that AGuIX nanoparticles grafted with the Pittsburgh compound B can actively target amyloid aggregates in the brain, beyond the blood–brain barrier, and remain the first step in observing amyloid plaques in a mouse model of Alzheimer’s disease.

T2 Relaxometry and Diffusion Tensor Indices of the Hippocampus and Entorhinal Cortex Improve Sensitivity and Specificity of MRI to Detect Amnestic Mild Cognitive Impairment and Alzheimer's Disease Dementia

BACKGROUND

Quantitative T2 and diffusion MRI indices inform about tissue state and microstructure, both of which may be affected by pathology before tissue atrophy.

PURPOSE

To evaluate the capability of both volumetric and quantitative MRI (qMRI) of the hippocampus and entorhinal cortex (EC) for classification of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease dementia (ADD).

STUDY TYPE

Retrospective cross-sectional study.

POPULATION

Consecutive cohorts of healthy age-matched controls (n = 62), aMCI patients (n = 25), and ADD patients (n = 14).

FIELD STRENGTH/SEQUENCE

3T using T1-weighted imaging, T2-weighted imaging, T2 relaxometry and diffusion tensor imaging (DTI).

ASSESSMENT

Montreal Cognitive Assessment and paired associate learning tests for cognitive state. Hippocampal subfield volumes by the automated segmentation of hippocampal subfields system from structural brain images. T2 relaxation time and DTI indices quantified for hippocampal subfields. The fraction of voxels with high T2 values (>20 ms above subfield median) was calculated and regionalized for hippocampus and EC.

STATISTICAL TESTS

Support vector machine and receiver operating characteristic analyses from cognitive and MRI data.

RESULTS

qMRI classified aMCI and ADD with excellent sensitivity (79.0% and 94.5%, respectively) and specificity (85.6% and 86.1%, respectively), superior to volumes alone (70.0% and 84.5% for respective sensitivities; 82.2 and 91.1 for respective specificities) and similar to cognitive tests (61.7% and 87.5% for respective sensitivities; 88.2% and 90.7% for respective specificities). Regions of high T2 are dispersed throughout each hippocampal subfield in aMCI and ADD with higher concentration than controls, and was most pronounced in the EC. No other individual qMRI marker than EC volume can separate aMCI from ADD, however. DATA CONCLUSION: qMRI markers of hippocampal and entorhinal tissue states are sensitive and specific classifiers of aMCI and ADD. They may serve as markers of a neurodegenerative state preceding volume loss.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:445-455.

Quantitative sodium MR imaging: A review of its evolving role in medicine

Sodium magnetic resonance (MR) imaging in humans has promised metabolic information that can improve medical management in important diseases. This technology has yet to find a role in clinical practice, lagging proton MR imaging by decades. This review covers the literature that demonstrates that this delay is explained by initial challenges of low sensitivity at low magnetic fields and the limited performance of gradients and electronics available in the 1980s. These constraints were removed by the introduction of 3T and now ultrahigh (≥7T) magnetic field scanners with superior gradients and electronics for proton MR imaging. New projection pulse sequence designs have greatly improved sodium acquisition efficiency. The increased field strength has provided the expected increased sensitivity to achieve resolutions acceptable for metabolic interpretation even in small target tissues. Consistency of quantification of the sodium MR image to provide metabolic parametric maps has been demonstrated by several different pulse sequences and calibration procedures. The vital roles of sodium ion in membrane transport and the extracellular matrix will be reviewed to indicate the broad opportunities that now exist for clinical sodium MR imaging. The final challenge is for the technology to be supplied on clinical ≥3T scanners.

Raman Spectrometric Detection Methods for Early and Non-Invasive Diagnosis of Alzheimer's Disease

The continuous increasing rate of patients suffering of Alzheimer's disease (AD) worldwide requires the adoption of novel techniques for non-invasive early diagnosis and monitoring of the disease. Here we review the various Raman spectroscopic techniques, including Fourier Transform-Raman spectroscopy, surface-enhanced Raman scattering spectroscopy, coherent anti-Stokes Raman scattering spectroscopy, and confocal Raman microspectroscopy, that could be used for the diagnosis of AD. These techniques have shown the potential to detect AD biomarkers, such as the amyloid-β peptide and the tau protein, or the neurotransmitters involved in the disease (e.g., Glutamate and γ-Aminobutyric acid), or the typical structural alterations in specific brain areas. The possibility to detect the specific biomarkers in liquid biopsies and to obtain high resolution 3D microscope images of the affected area make the Raman spectroscopy a valuable ally in the early diagnosis and monitoring of AD.

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer's disease

BACKGROUND

Animal models of Alzheimer's disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present.

METHODS

Cognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats.

RESULTS

The results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes.

CONCLUSIONS

In this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.

Impaired retinal microcirculation in patients with Alzheimer's disease

The goal of this study was to determine the retinal blood flow rate (BFR) and blood flow velocity (BFV) of pre-capillary arterioles and post-capillary venules in patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Forty patients (20 AD and 20 MCI) and 21 cognitively normal (CN) controls with a similar age range (± 5 yrs) were recruited. A retinal function imager (RFI) was used to measure BFRs and BFVs of arterioles and venules in the macular region. The thickness of the ganglion cell-inner plexiform layer (GCIPL) was measured using Zeiss Cirrus optical coherence tomography. Macular BFRs in AD group were 2.64 ± 0.20 nl/s (mean ± standard deviation) in arterioles and 2.23 ± 0.19 nl/s in venules, which were significantly lower than in MCI and CN groups (P < 0.05). In addition, BFRs in MCI were lower than in CN in both arterioles and venules (P < 0.05). The BFV of the arterioles was 3.20 ± 1.07 mm/s in AD patients, which was significantly lower than in CN controls (3.91 ± 0.77 mm/s, P = 0.01). The thicknesses of GCIPL in patients with AD and MCI were significantly lower than in CN controls (P < 0.05). Neither BFV nor BFR in arterioles and venules was related to age, GCIPL thickness, mini mental state examination (MMSE) score and disease duration in patients with AD and MCI (P > 0.05). The lower BFR in both arterioles and venules in AD and MCI patients together with the loss of GCIPL were evident, indicating the impairment of the two components in the neurovascular-hemodynamic system, which may play a role in disease progression.

A new data analysis approach for measuring longitudinal changes of metabolism in cognitively normal elderly adults

Introduction

Previously, we discussed several critical barriers in including [¹⁸F] fluorodeoxyglucose positron emission tomography ([¹⁸F]FDG-PET) imaging of preclinical Alzheimer’s disease (AD) subjects. These factors included the reference region selection and intensity normalization of PET images and the within- and across-subject variability of affected brain regions. In this study, we utilized a novel FDG-PET analysis, the regional FDG time correlation coefficient, rFTC, that can address and resolve these barriers and provide a more sensitive way of monitoring longitudinal changes in metabolism of cognitively normal elderly adults. The rFTC analysis captures the within-subject similarities between baseline and follow-up regional radiotracer distributions.

Methods

The rFTC trajectories of 27 cognitively normal subjects were calculated to identify 1) trajectories of rFTC decline in individual cognitively normal subjects; 2) how these trajectories correlate with the subjects’ cognitive test scores, baseline cerebrospinal fluid (CSF) levels of amyloid beta (Aβ), and apolipoprotein E4 (APOE-E4) status; and 3) whether similar trajectories are observed in regional/composite standardized uptake value ratio (SUVR) values.

Results

While some of the subjects maintained a stable rFTC trajectory, other subjects had declining and fluctuating rFTC values. We found that the rFTC decline was significantly higher in APOE-E4 carriers compared to noncarriers (p=0.04). We also found a marginally significant association between rFTC decline and cognitive decline measured by Alzheimer’s Disease Assessment Scale – cognitive subscale (ADAS_cog) decline (0.05). In comparison to the rFTC trajectories, the composite region of interest (ROI) SUVR trajectories did not change in any of the subjects. No individual/composite ROI SUVR changes contributed significantly to explaining changes in ADAS_cog, conversion to mild cognitive impairment (MCI), or any general changes in clinical symptoms.

Conclusion

The rFTC decline may serve as a new biomarker of early metabolic changes before the MCI stage.

Evaluating aged mice in three touchscreen tests that differ in visual demands: Impaired cognitive function and impaired visual abilities

Normal aging is often accompanied by reductions in cognitive abilities as well as impairments in visual acuity in men and mice. In preclinical models of human cognition this concomitance can make it difficult to assess the relative contributions of declined vision and cognitive ability on behavioral measures of cognition. To assess the influence of age on cognition and the impact of visual decline on the performance of touchscreen-based behavioral paradigms in mice, aged (11, 12, 16, 17, 19 and 21 months old) male C57BL/6J mice were compared to young (3 or 4 months old) male C57BL/6J mice using three tests of cognition as well as an assessment of visual acuity. Performance of a Visual Discrimination, Spatial Reversal, and an Automated Search Task were all affected by age. However, there was no relationship between reduced visual acuity and the observed performance impairments. Moreover, the visual acuity of animals with profound cognitive impairments overlapped with those showing normal cognitive ability. Despite the potential confound of impaired visual ability, it appears that the touchscreen approach might be particularly effective in studying age-related cognitive decline. This approach will increase the utility of aged mice as a model of decreased cognitive flexibility and may be particularly important for the study of age-related disorders such as Alzheimer's disease.

Multimodal imaging Gd-nanoparticles functionalized with Pittsburgh compound B or a nanobody for amyloid plaques targeting

AIM

Gadolinium-based nanoparticles were functionalized with either the Pittsburgh compound B or a nanobody (B10AP) in order to create multimodal tools for an early diagnosis of amyloidoses.

MATERIALS & METHODS

The ability of the functionalized nanoparticles to target amyloid fibrils made of β-amyloid peptide, amylin or Val30Met-mutated transthyretin formed in vitro or from pathological tissues was investigated by a range of spectroscopic and biophysics techniques including fluorescence microscopy.

RESULTS

Nanoparticles functionalized by both probes efficiently interacted with the three types of amyloid fibrils, with K_D values in 10 micromolar and 10 nanomolar range for, respectively, Pittsburgh compound B and B10AP nanoparticles. Moreover, they allowed the detection of amyloid deposits on pathological tissues.

CONCLUSION

Such functionalized nanoparticles could represent promising flexible and multimodal imaging tools for the early diagnostic of amyloid diseases, in other words, Alzheimer's disease, Type 2 diabetes mellitus and the familial amyloidotic polyneuropathy.

Unified model of brain tissue microstructure dynamically binds diffusion and osmosis with extracellular space geometry

We present a universal model of brain tissue microstructure that dynamically links osmosis and diffusion with geometrical parameters of brain extracellular space (ECS). Our model robustly describes and predicts the nonlinear time dependency of tortuosity (λ=sqrt[D/D^{*}]) changes with very high precision in various media with uniform and nonuniform osmolarity distribution, as demonstrated by previously published experimental data (D = free diffusion coefficient, D^{*} = effective diffusion coefficient). To construct this model, we first developed a multiscale technique for computationally effective modeling of osmolarity in the brain tissue. Osmolarity differences across cell membranes lead to changes in the ECS dynamics. The evolution of the underlying dynamics is then captured by a level set method. Subsequently, using a homogenization technique, we derived a coarse-grained model with parameters that are explicitly related to the geometry of cells and their associated ECS. Our modeling results in very accurate analytical approximation of tortuosity based on time, space, osmolarity differences across cell membranes, and water permeability of cell membranes. Our model provides a unique platform for studying ECS dynamics not only in physiologic conditions such as sleep-wake cycles and aging but also in pathologic conditions such as stroke, seizure, and neoplasia, as well as in predictive pharmacokinetic modeling such as predicting medication biodistribution and efficacy and novel biomolecule development and testing.