Aging and Alzheimer's Disease: Multimodal Investigation of Image-derived Biomarkers

Aging is an inevitable process in life and the primary risk factor for most neurodegenerative diseases including Alzheimer’s disease (AD), the most common form of dementia. As life expectancy continues to increase, AD prevalence is expected to rise. AD-related pathological processes unfold decades before the emergence of clinical signs of cognitive decline and involve brain changes such as atrophy, accumulation of amyloid-beta plaques and tau neurofibrillary tangles (NFT), synaptic and neuronal loss, demyelination, and iron accumulation that would eventually lead to cognitive impairment. Here, to assess brain myelin and iron content in vivo, quantitative MRI (qMRI) maps like magnetization transfer saturation (MTsat), Effective transverse relaxation rate (R2*), and proton density (PD) were used. And synaptic density was measured using the total volume distribution map (Vt) of [F18] UCB-H PET images. In this thesis, we examined the simultaneous occurrence of these brain changes in aging and AD, identifying significant differences in the hippocampus and amygdala. Demyelination emerged as a key distinguishing factor between AD and healthy groups. The effects of age on various brain characteristics were re-evaluated in a multivariate model, with proton density being the most age-related factor in healthy aging. Finally, we attempted to examine the association of cognitive performance and the rate of cognitive decline with qMRI maps and GM and WM volume. The univariate regression analyses at baseline revealed correlations between different cognitive scores and brain tissue properties within the cerebellum, hippocampus, middle temporal, and medial orbitofrontal cortex. Moreover, the multivariate analysis shows that cognitive performance was related to combined tissue properties in the middle frontal gyrus, insula, and cerebellum. There were only a few results for the rate of cognitive decline, with univariate correlations within the left fusiform between longitudinal relaxation rate (R1) maps in GM and attention and memory decline. To conclude, our findings shed light on the complex relationships between changes in aging and AD brains. Furthermore, we emphasize the importance of multivariate analysis for detecting subtle microstructural changes associated with aging that may motivate interventions to mitigate cognitive decline in older adults.Aging and Alzheimer’s Disease: Multimodal Investigation of Image-derived Biomarkers3. Good health and well-bein

Investigating demyelination, iron accumulation, and synaptic loss in Alzheimer’s disease using multimodal imaging techniques

Alzheimer’s disease (AD), the most common type of dementia, is associated with neuronal death and synaptic loss [1], [2]. Pathological aggregation of amyloid-beta and tau protein are key elements of AD pathophysiology. Myelin loss and iron accumulation in the brain are also fundamental features of aging and dementia [3], [4], but are less frequently investigated. Quantitative MRI (qMRI) enables us to determine the brain tissue parameters such as magnetization transfer (MT) and effective transverse relaxation (R2*), which leads to the detection of microstructural tissue-related alterations in aging and neurodegenerative diseases [5]. Here we investigate the association of neurodegeneration (as indexed by loss of synaptic density), increased iron accumulation, and decreased myelination in Alzheimer's disease in cohorts of 24 amyloid-positive patients (AD, 11 males and 13 females) and 19 healthy controls (HC, 9 males, and 10 females). All participants underwent a multiparameter qMRI protocol, which was processed to generate probability maps for MTsat and R2* [5]. Synaptic density was evaluated by the total volume distribution (Vt)maps, representing the distribution of the [18F] UCB-H PET radiotracer in the brain [6]. The data is organized according to the Brain Imaging Data Structure (BIDS) [7]. MRI data processing was performed in MATLAB (The MathWorks Inc., Natick, MA, USA) using the SPM12 framework (www.fil.ion.ucl.ac.uk/spm) and the hMRI toolbox [8] after modifications to make MR data compatible with the BIDS format [9]. Each multi-parameter map presents a different tissue-related (semi-)quantitative property, and therefore the qMRI maps have specific units. Therefore, all maps were z-transformed to ensure the comparability of the maps in a multivariate model. Then, we used General Linear Model (GLM) to test the groups against each other using age and sex as the covariates. Also, a multivariate GLM (mGLM) was performed on all modalities using the MSPM toolbox (https://github.com/LREN-CHUV/MSPM) to test differences in groups controlling for the age and sex of the participants [10]. Univariate group analysis of MTsat data resulted in a significant difference at the cluster level in the right hippocampus with p_cluster<0.05 FWE corrected and p_voxel<.001 uncorrected as cluster forming threshold (Figure1.A). In contrast, the same analysis for R2* modality reveals no difference between the groups. PET_Vt maps showed a difference between AD and HC at p_voxel<0.05 (FWE corrected) in the right amygdala and hippocampus (Figure1.B), which agrees with previously reported results in [6]. See table.1 for more information. Multimodal analysis combining R2*, MTsat, and PET_Vt shows a bilateral difference in hippocampus between patients and healthy controls for voxel-wise analysis with corrected FWE P-voxel < 0.05 (Figure1.C). The canonical analysis suggests that AD patients had combined decreased myelination, decreased synaptic density, and increased iron in the hippocampus compared to controls. To conclude, in the case of AD, there is an interaction between neuropathological risk factors, therefore, to restrain the true multivariate nature of the data and better control for the false positive rate, one should use the multivariate model over multiple univariate models

Multivariate Age-related Analysis of Variance in quantitative MRI maps: Widespread age-related differences revisited

AbstractThis study utilized multivariate ANOVA analysis to investigate age-related microstructural changes in the brain tissues driven primarily by myelin, iron, and water content. Voxel-wise analyses were performed on gray matter (GM) and white matter (WM), in addition to region of interest (ROI) analyses. The multivariate approach identified brain regions showing coordinated alterations in multiple tissue properties and demonstrated bidirectional correlations between age and all examined modalities in various brain regions, including the caudate nucleus, putamen, insula, cerebellum, lingual gyri, hippocampus, and olfactory bulb. The multivariate model was more sensitive than univariate analyses as evidenced by detecting a larger number of significant voxels within clusters in the supplementary motor area, frontal cortex, hippocampus, amygdala, occipital cortex, and cerebellum bilaterally. The examination of normalized, smoothed, and z-transformed maps within the ROIs revealed age-dependent differences in myelin, iron, and water content. These findings contribute to our understanding of age-related brain differences and provide insights into the underlying mechanisms of aging. The study emphasizes the importance of multivariate analysis for detecting subtle microstructural changes associated with aging that may motivate interventions to mitigate cognitive decline in older adults

Using Machine Learning to Estimate Some Anisotropy Indices, Application to Brownian Textures and Breast Images

editorial reviewedIn this paper, we analyze image textures with help of anisotropic fractional Brownian fields. We also use some anisotropy indices characterizing the anisotropy of these textures. Multi-oriented quadratic variations form the basis of mentioned indices. Anisotropy indices are invariant to some image transforms. Furthermore they can be estimated from the observed data. An application of these indices, combining with a measure of texture roughness, is in lesion detection in mammograms

A Different Olfactory Perception in Anosmic Patients: Evidence from Functional MRI

Olfactory system is a vital sensory system in mammals, giving them the ability to connect with their environment. Anosmia, or the complete loss of olfaction ability, which could be caused by injuries, is an interesting topic for inspectors with the aim of diagnosing patients. Sniffing test is currently utilized to examine if an individual is suffering from anosmia; however, functional Magnetic Resonance Imaging (fMRI) provides unique information about the structure and function of the different areas of the human brain, and therefore this noninvasive method could be used as a tool to locate the olfactory-related regions of the brain. In this study, by recruiting 31 healthy and anosmic individuals, we investigated the neural BOLD responses in the olfactory cortices following two odor stimuli, rose and eucalyptus, by using a 3T MR scanner. Comparing the two groups, we observed a network of brain areas being more active in the normal individuals when smelling the odors. In addition, a number of brain areas also showed an activation decline during the odor stimuli, which is hypothesized as a resource allocation deactivation. This study illustrated alterations in the brain activity between the normal individuals and anosmic patients when smelling odors, and could potentially help for a better anosmia diagnosis in the future

Multimodal imaging of microstructural cerebral changes and loss of synaptic density in Alzheimer's disease

Multiple neuropathological changes are involved in Alzheimer's disease (AD). AD hallmark biomarkers are amyloid-beta, tau pathology, and neuronal and synaptic loss. Other possible brain tissue-related biomarkers, such as iron and myelin content in the brain, are less frequently studied. Thanks to quantitative MRI (qMRI), tissue parameters such as magnetization transfer (MT), effective transverse relaxation (R2*), and proton density (PD) can be determined quantitatively, enabling the detection of microstructural tissue-related alterations in aging and neurodegenerative diseases. The current study investigated the co-occurrence of neurodegeneration (as measured with synaptic density), increased iron content, and decreased myelin content in Alzheimer's disease. The study involved 24 amyloid-positive patients (AD, 11 males) and 19 healthy controls (HC, 9 males). All participants underwent a multi-parameter mapping MRI protocol, from which quantitative maps for MTsat and R2* were estimated. Synaptic density was indexed by the total volume distribution map (Vt) derived from [18F] UCB-H PET imaging. First, groups were compared with univariate statistical analyses applied to R2*, MTsat, and Vt maps. Then multivariate General Linear Model (mGLM) was used to detect the co-occurrence of changes in R2*, MTsat, and Vt at the voxel level. Univariate GLM analysis of R2* showed no significant difference between the two groups. In contrast, the same analysis for MTsat resulted in a significant between-group difference in the right hippocampus at the cluster level with a corrected threshold (P-value < .05). The mGLM analysis revealed a significant difference in both right and left hippocampus between the AD and HC groups, as well as in the left precuneus, right middle frontal, and left superior orbitofrontal gyrus when all three modalities were present, suggesting these regions as the most affected despite the diverse changes of myelin, iron, and synapse degeneration in AD. Here, the mGLM is introduced as an alternative for multiple comparisons between different modalities, as it reduces the risk of false positives due to the multiplicity of the tests while informing about the co-occurrence of neuropathological processes in dementia.V

Multimodal imaging of microstructural cerebral changes and loss of synaptic density in Alzheimer’s disease

Background: Multiple neuropathological changes are involved in Alzheimer’s disease (AD) progression. The hallmark biomarkers are amyloid-beta, tau pathology, neuronal and synaptic loss. Other potential biomarkers, such as the level of iron and myelin content in the brain, have not been thoroughly studied. Nevertheless, these can be estimated in vivo thanks to tissue magnetic resonance (MR) properties measured through quantitative MR imaging (qMRI) techniques. Aim: We aimed to assess the co-occurrence of neurodegeneration (as measured with synaptic density), increased iron content and decreased myelin content in Alzheimer’s disease. Method: Data include 24 amyloid-positive Alzheimers patients (AD-11/13 males/females) and 19 healthy controls (HC-9/11 males/females). They underwent a multiparameter qMRI protocol used to generate quantitative maps sensitive to microstructural changes in myelin, iron deposits, and water content in grey matter (GM). Synaptic density was indexed by [18F]UCB-H-PET imaging using the distribution volume density (VT) maps. First, we applied univariate statistical analyses to investigate variation between AD and HC groups for each modality individually. Then, a multivariate GLM approach was used to compare the two groups pooling all modalities. Results/Conclusions: In GM univariate analyses, there was no significant difference between the AD and HC groups in any map at corrected statistical threshold. Conversely, the multivariate analysis on GM, combining MT, R2s, and synaptic density, provided significant group differences (FWEcorr P-value < 0.05) see figure 1. These variations are observed in the right amygdala (at voxel level) and in 5 distinct clusters covering the bilateral anterior hippocampal structures. These show that patients with AD present convergence of neuropathological changes in the hippocampal area, suggesting that different pathological mechanisms co-exist in areas known to harbor early-stage neuronal death

Multimodal imaging of microstructural cerebral alterations and loss of synaptic density in Alzheimer's disease.

peer reviewedMultiple neuropathological events are involved in Alzheimer's disease (AD). The current study investigated the concurrence of neurodegeneration, increased iron content, atrophy, and demyelination in AD. Quantitative multiparameter magnetic resonance imaging (MRI) maps providing neuroimaging biomarkers for myelination and iron content along with synaptic density measurements using [18F] UCB-H PET were acquired in 24 AD and 19 Healthy controls (19 males). The whole brain voxel-wise group comparison revealed demyelination in the right hippocampus, while no significant iron content difference was detected. Bilateral atrophy and synaptic density loss were observed in the hippocampus and amygdala. The multivariate GLM (mGLM) analysis shows a bilateral difference in the hippocampus and amygdala, right pallidum, left fusiform and temporal lobe suggesting that these regions are the most affected despite the diverse differences in brain tissue properties in AD. Demyelination was identified as the most affecting factor in the observed differences. Here, the mGLM is introduced as an alternative for multiple comparisons between different modalities, reducing the risk of false positives while informing about the co-occurrence of neuropathological processes in AD

A comparison on the magnitude and complex-valued methods to detect the brain activation, application to functional MRI

editorial reviewedIn functional MRI studies, the significant brain activation is determined using the magnitude-only time series framework after the image reconstruction. Although this method is very popular, the information of the phase part is ignored. Most recently, a novel strategy for determining the significant brain activation is proposed which considers the magnitude and phase parts of the data simultaneously. This approach affects the quality of reconstructed image and the power of the statistical tests. In this study, the significant brain activation achieved using the complex-and magnitude-valued approaches were evaluated