Diffusion tensor imaging in early Alzheimer's disease

Demystifying the Role of Neuroinflammatory Mediators as Biomarkers for Diagnosis, Prognosis, and Treatment of Alzheimer's Disease: A Review

Neuroinflammatory mediators play a pivotal role in the pathogenesis of Alzheimer's Disease (AD), influencing its onset, progression, and severity. The precise mechanisms behind AD are still not fully understood, leading current treatments to focus mainly on managing symptoms rather than preventing or curing the condition. The amyloid and tau hypotheses are the most widely accepted explanations for AD pathology; however, they do not completely account for the neuronal degeneration observed in AD. Growing evidence underscores the crucial role of neuroinflammation in the pathology of AD. The neuroinflammatory hypothesis presents a promising new approach to understanding the mechanisms driving AD. This review examines the importance of neuroinflammatory biomarkers in the diagnosis, prognosis, and treatment of AD. It delves into the mechanisms underlying neuroinflammation in AD, highlighting the involvement of various mediators such as cytokines, chemokines, and ROS. Additionally, this review discusses the potential of neuroinflammatory biomarkers as diagnostic tools, prognostic indicators, and therapeutic targets for AD management. By understanding the intricate interplay between neuroinflammation and AD pathology, this review aims to help in the development of efficient diagnostic and treatment plans to fight this debilitating neurological condition. Furthermore, it elaborates recent advancements in neuroimaging techniques and biofluid analysis for the identification and monitoring of neuroinflammatory biomarkers in AD patients.

Characterization of local white matter microstructural alterations in Alzheimer's disease: A reproducible study

Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD. Diffusion tensor imaging data from a public dataset (53 patients with AD and 70 healthy controls [HCs]) and a clinical dataset (27 patients with AD and 19 HCs) were included for mutual validation. Whole-brain tractograms were automatically subdivided into 800 clusters through the automatic fiber clustering approach. Then, 100 segments were divided along the clusters, and the diffusion properties of each segment were calculated. Results showed that patients with AD had significantly lower fraction anisotropy (FA) and significantly higher mean diffusivity (MD) in some regions of the fiber clusters in the cingulum bundle, uncinate fasciculus, external capsule, and corpus callosum than HCs. Importantly, these changes were reproducible across the two datasets. Correlation analysis revealed a positive correlation between FA and Mini-Mental State Examination (MMSE) scores and a negative correlation between MD and MMSE in these clusters. The accuracy of the constructed classifier reached 89.76% with an area under the curve of 0.93. This finding indicates that this study can effectively identify local WM microstructural changes in AD and provides new insight into the analysis and diagnosis of WM abnormalities in patients with AD.

Normal-Tension Glaucoma and Potential Clinical Links to Alzheimer's Disease

Glaucoma is a group of optic neuropathies and the world's leading cause of irreversible blindness. Normal-tension glaucoma (NTG) is a subtype of glaucoma that is characterized by a typical pattern of peripheral retinal loss, in which the patient's intraocular pressure (IOP) is considered within the normal range (<21 mmHg). Currently, the only targetable risk factor for glaucoma is lowering IOP, and patients with NTG continue to experience visual field loss after IOP-lowering treatments. This demonstrates the need for a better understanding of the pathogenesis of NTG and underlying mechanisms leading to neurodegeneration. Recent studies have found significant connections between NTG and cerebral manifestations, suggesting NTG as a neurodegenerative disease beyond the eye. Gaining a better understanding of NTG can potentially provide new Alzheimer's Disease diagnostics capabilities. This review identifies the epidemiology, current biomarkers, altered fluid dynamics, and cerebral and ocular manifestations to examine connections and discrepancies between the mechanisms of NTG and Alzheimer's Disease.

Microstructural integrity of the locus coeruleus and its tracts reflect noradrenergic degeneration in Alzheimer's disease and Parkinson's disease

BACKGROUND

Degeneration of the locus coeruleus (LC) noradrenergic system contributes to clinical symptoms in Alzheimer's disease (AD) and Parkinson's disease (PD). Diffusion magnetic resonance imaging (MRI) has the potential to evaluate the integrity of the LC noradrenergic system. The aim of the current study was to determine whether the diffusion MRI-measured integrity of the LC and its tracts are sensitive to noradrenergic degeneration in AD and PD.

METHODS

Post-mortem in situ T1-weighted and multi-shell diffusion MRI was performed for 9 AD, 14 PD, and 8 control brain donors. Fractional anisotropy (FA) and mean diffusivity were derived from the LC, and from tracts between the LC and the anterior cingulate cortex, the dorsolateral prefrontal cortex (DLPFC), the primary motor cortex (M1) or the hippocampus. Brain tissue sections of the LC and cortical regions were obtained and immunostained for dopamine-beta hydroxylase (DBH) to quantify noradrenergic cell density and fiber load. Group comparisons and correlations between outcome measures were performed using linear regression and partial correlations.

RESULTS

The AD and PD cases showed loss of LC noradrenergic cells and fibers. In the cortex, the AD cases showed increased DBH + immunoreactivity in the DLPFC compared to PD cases and controls, while PD cases showed reduced DBH + immunoreactivity in the M1 compared to controls. Higher FA within the LC was found for AD, which was correlated with loss of noradrenergic cells and fibers in the LC. Increased FA of the LC-DLPFC tract was correlated with LC noradrenergic fiber loss in the combined AD and control group, whereas the increased FA of the LC-M1 tract was correlated with LC noradrenergic neuronal loss in the combined PD and control group. The tract alterations were not correlated with cortical DBH + immunoreactivity.

CONCLUSIONS

In AD and PD, the diffusion MRI-detected alterations within the LC and its tracts to the DLPFC and the M1 were associated with local noradrenergic neuronal loss within the LC, rather than noradrenergic changes in the cortex.

An extensible hierarchical graph convolutional network for early Alzheimer's disease identification

BACKGROUND AND OBJECTIVE

For early identification of Alzheimer's disease (AD) based on multi-modal magnetic resonance imaging (MRI) data, it is important to make comprehensive use of image features and non-image information to analyze the gray matter atrophy and the structural/functional connectivity abnormalities for different courses of AD.

METHODS

In this study, we propose an extensible hierarchical graph convolutional network (EH-GCN) for early AD identification. Based on the extracted image features from multi-modal MRI data using the presented multi-branch residual network (ResNet), the brain regions-of-interests (ROIs) based GCN is built to extract structural and functional connectivity features between different ROIs of the brain. In order to further improve the performance of AD identification, an optimized spatial GCN is proposed as convolution operator in the population-based GCN to avoid rebuilding the graph network and take advantage of relationships between subjects. Finally, the proposed EH-GCN is built by embedding the image features and internal brain connectivity features into the spatial population-based GCN, which provides an extensible way to improve early AD identification performance by adding imaging features and non-image information from multi-modal data.

RESULTS

Experiments are performed on two datasets, which illustrate the effectiveness of the extracted structural/functional connectivity features and the high computational efficiency of the proposed method. The classification accuracy of AD vs NC, AD vs MCI and MCI vs NC classification tasks reaches 88.71%, 82.71% and 79.68% respectively. The extracted connectivity features between ROIs indicate that functional abnormalities are earlier than gray matter atrophy and abnormalities of structural connections, which is consistent with the clinical manifestations. The proposed method allows for the addition of other modal image features and non-image information from multi-modal data to continuously improve the performance of clinical data analysis.

CONCLUSIONS

The proposed method can help us comprehensively analyze the role of gray matter atrophy, the damage of white matter nerve fiber tracts and the degradation of functional connectivity for different courses of AD, which could be useful for further extraction of clinical biomarkers for early AD identification.

Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis

Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.

Reversing Alzheimer's disease dementia with clemastine, fingolimod, or rolipram, plus anti-amyloid therapy

A few anti-amyloid trials offer a slight possibility of preventing progression of cognitive loss, but none has reversed the process. A possible reason is that amyloid may be necessary but insufficient in the pathogenesis of AD, and other causal factors may need addressing in addition to amyloid. It is argued here that drugs addressing myelination and synaptogenesis are the optimum partners for anti-amyloid drugs, since there is much evidence that early in the process that leads to AD, both neural circuits and synaptic activity are dysfunctional. Evidence to support this argument is presented. Evidence is also presented that clemastine, fingolimod, and rolipram, benefit both myelination and synaptogenesis. It is suggested that a regimen that includes one of them plus an anti-amyloid drug, could reverse AD.

Age-related differences in white matter microstructure measured by advanced diffusion MRI in healthy older adults at risk for Alzheimer's disease

Neurite orientation dispersion and density imaging (NODDI) is an advanced diffusion imaging technique, which can detect more distinct microstructural features compared to conventional Diffusion Tensor Imaging (DTI). NODDI allows the signal to be divided into multiple water compartments and derive measures for orientation dispersion index (ODI), neurite density index (NDI) and volume fraction of isotropic diffusion compartment (FISO). This study aimed to investigate which diffusion metric-fractional anisotropy (FA), mean diffusivity (MD), NDI, ODI, or FISO-is most influenced by aging and reflects cognitive function in a population of healthy older adults at risk for Alzheimer's disease (AD). Age was significantly associated with all but one diffusion parameters and regions of interest. NDI and MD in the cingulate region adjacent to the cingulate cortex showed a significant association with a composite measure of Executive Function and was proven to partially mediate the relationship between aging and Executive Function decline. These results suggest that both DTI and NODDI parameters are sensitive to age-related differences in white matter regions vulnerable to aging, particularly among older adults at risk for AD.

Diffusion tension imaging is a good tool for assessing patients with dementia and behavioral problems and discriminating them from other dementia patients

Background

Dementia is one of the leading public health concerns as the world’s population ages. Although Alzheimer’s disease (AD) is the most common dementia diagnosis among older patients, some patients have additional behavioral symptoms. It is therefore important to provide an exact diagnosis, both to provide the best possible treatment for patients and to facilitate better understanding.

Purpose

To investigate whether magnetic resonance imaging (MRI) with fractional anisotropy (FA) can accurately find patients with behavioral symptoms within a group of AD patients.

Material and Methods

Forty-five patients from the geriatric outpatient clinic were recruited consecutively to form a group of patients with AD and behavioral symptoms (AD + BS) and a control group of 50 patients with established AD. All patients had a full assessment for dementia to establish the diagnosis according to ICD-10. MRI included 3D anatomical recordings for morphometric measurements, DTI for fiber tracking, and quantitative assessment of regional white matter integrity. The DTI analyses included computing of the diffusion tensor and its derived FA index.

Results

We found a significant difference in FA values between the patient groups’ frontal lobes. The FA was greater in the study group in both left (0.39 vs 0.09, p < 0.05) and right (0.40 vs 0.16, p < 0.05) frontal lobes.

Conclusion

MRI with FA will find damage in frontal tracts and may be used as a diagnostic tool and be considered a robust tool for the recognizing different types of dementia in the future.

Bridging the Gap Between Morphometric Similarity Mapping and Gene Transcription in Alzheimer's Disease

Disruptions in brain connectivity have been widely reported in Alzheimer’s disease (AD). Morphometric similarity (MS) mapping provides a new way of estimating structural connectivity by interregional correlation of T1WI- and DTI-derived parameters within individual brains. Here, we aimed to identify AD-related MS changing patterns and genes related to the changes and further explored the molecular and cellular mechanism underlying MS changes in AD. Both 3D-T1WI and DTI data of 106 AD patients and 106 well-matched healthy elderly individuals from the ADNI database were included in our study. Cortical regions with significantly decreased MS were found in the temporal and parietal cortex, increased MS was found in the frontal cortex and variant changes were found in the occipital cortex in AD patients. Mean MS in regions with significantly changed MS was positively or negatively associated with memory function. Negative MS-related genes were significantly downregulated in AD, specifically enriched in neurons, and participated in biological processes, with the most significant term being synaptic transmission. This study revealed AD-related cortical MS changes associated with memory function. Linking gene expression to cortical MS changes may provide a possible molecular and cellular substrate for MS abnormality and cognitive decline in AD.

Vascular health and diffusion properties of normal appearing white matter in midlife

In this study, we perform a region of interest diffusion tensor imaging and advanced diffusion complexity analysis of normal appearing white matter to determine the impact of vascular health on these diffusivity metrics in midlife adults. 77 participants (26 black, 35 female) at year 30 visit in the Coronary Artery Risk Development in Young Adults longitudinal study were scanned with an advanced diffusion-weighted imaging and fluid-attenuated inversion recovery protocol. Fractional anisotropy and non-linear diffusion complexity measures were estimated. Cumulative measures across 30 years (9 study visits) of systolic blood pressure, body mass index, glucose, smoking and cholesterol were calculated as the area under the curve from baseline up to year 30 examination. Partial correlation analyses assessed the association between cumulative vascular health measures and normal appearing white matter diffusion metrics in these participants. Midlife normal appearing white matter diffusion properties were significantly associated (P < 0.05) with cumulative exposure to vascular risk factors from young adulthood over the 30-year time period. Higher cumulative systolic blood pressure exposure was associated with increased complexity and decreased fractional anisotropy. Higher cumulative body mass index exposure was associated with decreased fractional anisotropy. Additionally, in the normal appearing white matter of black participants (P < 0.05), who exhibited a higher cumulative vascular risk exposure, fractional anisotropy was lower and complexity was higher in comparison to normal appearing white matter in white participants. Higher burden of vascular risk factor exposure from young adulthood to midlife is associated with changes in the diffusion properties of normal appearing white matter in midlife. These changes which may reflect axonal disruption, increased inflammation and/or increased glial proliferation, were primarily observed in both anterior and posterior normal appearing white matter regions of the corpus callosum. These results suggest that microstructural changes in normal appearing white matter are sensitive to vascular health during young adulthood and are possibly therapeutic targets in interventions focused on preserving white matter health across life.

White matter hyperintensity topography in Alzheimer's disease and links to cognition

Introduction

White matter hyperintensities (WMH) are often described in Alzheimer's disease (AD), but their topography and specific relationships with cognition remain unclear.

Methods

Regional WMH were estimated in 54 cognitively impaired amyloid beta–positive AD (Aβpos‐AD), compared to 40 cognitively unimpaired amyloid beta–negative older controls (Aβneg‐controls) matched for vascular risk factors. The cross‐sectional association between regional WMH volume and cognition was assessed within each group, controlling for cerebral amyloid burden, global cortical atrophy, and hippocampal atrophy.

Results

WMH volume was larger in Aβpos‐AD compared to Aβneg‐controls in all regions, with the greatest changes in the splenium of the corpus callosum (S‐CC). In Aβpos‐AD patients, larger total and regional WMH volume, especially in the S‐CC, was strongly associated with decreased cognition.

Discussion

WMH specifically contribute to lower cognition in AD, independently from amyloid deposition and atrophy. This study emphasizes the clinical relevance of WMH in AD, especially posterior WMH, and most notably S‐CC WMH.

Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer's disease

Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer's disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.

Alterations and Associations Between Magnetic Susceptibility of the Basal Ganglia and Diffusion Properties in Alzheimer's Disease

This study adopted diffusion tensor imaging to detect alterations in the diffusion parameters of the white matter fiber in Alzheimer's disease (AD) and used quantitative susceptibility mapping to detect changes in magnetic susceptibility. However, whether the changes of susceptibility values due to excessive iron in the basal ganglia have correlations with the alterations of the diffusion properties of the white matter in patients with AD are still unknown. We aim to investigate the correlations among magnetic susceptibility values of the basal ganglia, diffusion indexes of the white matter, and cognitive function in patients with AD. Thirty patients with AD and nineteen healthy controls (HCs) were recruited. Diffusion indexes of the whole brain were detected using tract-based spatial statistics. The caudate nucleus, putamen, and globus pallidus were selected as regions of interest, and their magnetic susceptibility values were measured. Compared with HCs, patients with AD showed that there were significantly increased axial diffusivity (AxD) in the internal capsule, superior corona radiata (SCR), and right anterior corona radiata (ACR); increased radial diffusivity (RD) in the right anterior limb of the internal capsule, ACR, and genu of the corpus callosum (GCC); and decreased fractional anisotropy (FA) in the right ACR and GCC. The alterations of RD values, FA values, and susceptibility values of the right caudate nucleus in patients with AD were correlated with cognitive scores. Besides, AxD values in the right internal capsule, ACR, and SCR were positively correlated with the magnetic susceptibility values of the right caudate nucleus in patients with AD. Our findings revealed that the magnetic susceptibility of the caudate nucleus may be an MRI-based biomarker of the cognitive dysfunction of AD and abnormal excessive iron distribution in the basal ganglia had adverse effects on the diffusion properties of the white matter.

Bridging the Gap Between Fluid Biomarkers for Alzheimer's Disease, Model Systems, and Patients

Alzheimer’s disease (AD) is a debilitating neurodegenerative disease characterized by the accumulation of two proteins in fibrillar form: amyloid-β (Aβ) and tau. Despite decades of intensive research, we cannot yet pinpoint the exact cause of the disease or unequivocally determine the exact mechanism(s) underlying its progression. This confounds early diagnosis and treatment of the disease. Cerebrospinal fluid (CSF) biomarkers, which can reveal ongoing biochemical changes in the brain, can help monitor developing AD pathology prior to clinical diagnosis. Here we review preclinical and clinical investigations of commonly used biomarkers in animals and patients with AD, which can bridge translation from model systems into the clinic. The core AD biomarkers have been found to translate well across species, whereas biomarkers of neuroinflammation translate to a lesser extent. Nevertheless, there is no absolute equivalence between biomarkers in human AD patients and those examined in preclinical models in terms of revealing key pathological hallmarks of the disease. In this review, we provide an overview of current but also novel AD biomarkers and how they relate to key constituents of the pathological cascade, highlighting confounding factors and pitfalls in interpretation, and also provide recommendations for standardized procedures during sample collection to enhance the translational validity of preclinical AD models.

Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Diffusion tensor imaging of the corpus callosum in healthy aging: Investigating higher order polynomial regression modelling

Previous diffusion tensor imaging (DTI) studies confirmed the vulnerability of corpus callosum (CC) fibers to aging. However, most studies employed lower order regressions to study the relationship between age and white matter microstructure. The present study investigated whether higher order polynomial regression modelling can better describe the relationship between age and CC DTI metrics compared to lower order models in 140 healthy participants (ages 18-85). The CC was found to be non-uniformly affected by aging, with accelerated and earlier degradation occurring in anterior portion; callosal volume, fiber count, fiber length, mean fibers per voxel, and FA decreased with age while mean, axial, and radial diffusivities increased. Half of the parameters studied also displayed significant age-sex interaction or intracranial volume effects. Higher order models were chosen as the best fit, based on Bayesian Information Criterion minimization, in 16 out of 23 significant cases when describing the relationship between DTI measurements and age. Higher order model fits provided different estimations of aging trajectory peaks and decline onsets than lower order models; however, a likelihood ratio test found that higher order regressions generally did not fit the data significantly better than lower order polynomial or linear models. The results contrast the modelling approaches and highlight the importance of using higher order polynomial regression modelling when investigating associations between age and CC white matter microstructure.

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.

Machine Learning and DWI Brain Communicability Networks for Alzheimer’s Disease Detection

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer’s disease (AD) diagnosis through the use of diffusion weighted imaging (DWI) data. When combined with tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicability metric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions.

Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging

The main symptom of patients with Alzheimer’s disease is cognitive dysfunction. Alzheimer’s disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer’s disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer’s disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer’s disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3–0.5 in patients with normal cognition and 0–0.2 in those developing Alzheimer’s disease. Moreover, in the other four regions, the range increased to 0.45–0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer’s disease; however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer’s Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

Fiber-tract localized diffusion coefficients highlight patterns of white matter disruption induced by proximity to glioma

Gliomas account for 26.5% of all primary central nervous system tumors. Recent studies have used diffusion tensor imaging (DTI) to extract white matter fibers and the diffusion coefficients derived from MR processing to provide useful, non-invasive insights into the extent of tumor invasion, axonal integrity, and gross differentiation of glioma from metastasis. Here, we extend this work by examining whether a tract-based analysis can improve non-invasive localization of tumor impact on white matter integrity. This study retrospectively analyzed preoperative magnetic resonance sequences highlighting contrast enhancement and DTI scans of 13 subjects that were biopsy-confirmed to have either high or low-grade glioma. We reconstructed the corticospinal tract and superior longitudinal fasciculus by applying atlas-based regions of interest to fibers derived from whole-brain deterministic streamline tractography. Within-subject comparison of hemispheric diffusion coefficients (e.g., fractional anisotropy and mean diffusivity) indicated higher levels of white matter degradation in the ipsilesional hemisphere. Novel application of along-tract analyses revealed that tracts traversing the tumor region showed significant white matter degradation compared to the contralesional hemisphere and ipsilesional tracts displaced by the tumor.

Association of white matter microstructural integrity with cognition and dementia

Late-life measures of white matter (WM) microstructural integrity may predict cognitive status, cognitive decline, and incident mild cognitive impairment (MCI) or dementia. We considered participants of the Atherosclerosis Risk in Communities study who underwent cognitive assessment and neuroimaging in 2011-2013 and were followed through 2016-2017 (n = 1775 for analyses of prevalent MCI and dementia, baseline cognitive performance, and longitudinal cognitive change and n = 889 for analyses of incident MCI, dementia, or death). Cross-sectionally, both overall WM fractional anisotropy and overall WM mean diffusivity were strongly associated with baseline cognitive performance and risk of prevalent MCI or dementia. Longitudinally, greater overall WM mean diffusivity was associated with accelerated cognitive decline, as well as incident MCI, incident dementia, and mortality, but WM fractional anisotropy was not robustly associated with cognitive change or incident cognitive impairment. Both cross-sectional and longitudinal associations were attenuated after additionally adjusting for likely downstream pathologic changes. Increased WM mean diffusivity may provide an early indication of dementia pathogenesis.

Development of a transcallosal tractography template and its application to dementia

Understanding the architecture of transcallosal connections would allow for more specific assessments of neurodegeneration across many fields of neuroscience, neurology, and psychiatry. To map these connections, we conducted probabilistic tractography in 100 Human Connectome Project subjects in 32 cortical areas using novel post-processing algorithms to create a spatially precise Trancallosal Tract Template (TCATT). We found robust transcallosal tracts in all 32 regions, and a topographical analysis in the corpus callosum largely agreed with well-established subdivisions of the corpus callosum. We then obtained diffusion MRI data from a cohort of patients with Alzheimer's disease (AD) and another with progressive supranuclear palsy (PSP) and used a two-compartment model to calculate free-water corrected fractional anisotropy (FA_T) and free-water (FW) within the TCATT. These metrics were used to determine between-group differences and to determine which subset of tracts was best associated with cognitive function (Montreal Cognitive Assessment (MoCA)). In AD, we found robust between-group differences in FW (31/32 TCATT tracts) in the absence of between-group differences in FA_T. FW in the inferior temporal gyrus TCATT tract was most associated with MoCA scores in AD. In PSP, there were widespread differences in both FA_T and FW, and MoCA was predicted by FA_T in the inferior frontal pars triangularis, preSMA, and medial frontal gyrus TCATT tracts as well as FW in the inferior frontal pars opercularis TCATT tract. The TCATT improves spatial localization of corpus callosum measurements to enhance the evaluation of treatment effects, as well as the monitoring of brain microstructure in relation to cognitive dysfunction and disease progression. Here, we have shown its direct relevance in capturing between-group differences and associating it with the MoCA in AD and PSP.

Neuroimaging Biomarkers for Alzheimer's Disease

Currently, over five million Americans suffer with Alzheimer's disease (AD). In the absence of a cure, this number could increase to 13.8 million by 2050. A critical goal of biomedical research is to establish indicators of AD during the preclinical stage (i.e. biomarkers) allowing for early diagnosis and intervention. Numerous advances have been made in developing biomarkers for AD using neuroimaging approaches. These approaches offer tremendous versatility in terms of targeting distinct age-related and pathophysiological mechanisms such as structural decline (e.g. volumetry, cortical thinning), functional decline (e.g. fMRI activity, network correlations), connectivity decline (e.g. diffusion anisotropy), and pathological aggregates (e.g. amyloid and tau PET). In this review, we survey the state of the literature on neuroimaging approaches to developing novel biomarkers for the amnestic form of AD, with an emphasis on combining approaches into multimodal biomarkers. We also discuss emerging methods including imaging epigenetics, neuroinflammation, and synaptic integrity using PET tracers. Finally, we review the complementary information that neuroimaging biomarkers provide, which highlights the potential utility of composite biomarkers as suitable outcome measures for proof-of-concept clinical trials with experimental therapeutics.

Perivascular space fluid contributes to diffusion tensor imaging changes in white matter

Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from non-parenchymal fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.

Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer's disease

Introduction

Although increased mean diffusivity of the white matter has been repeatedly linked to Alzheimer’s disease pathology, the underlying mechanism is not known.

Methods

Here, we used ADNI-3 multishell diffusion magnetic resonance imaging data to separate the diffusion signal of the parenchyma from less hindered fluid pools within the white matter such as perivascular space fluid and fluid-filled cavities.

Results

We found that the source of the pathological increase of the mean diffusivity is the increased nonparenchymal fluid, often found in lacunes and perivascular spaces. In this cohort, the cognitive decline was significantly associated with the fluid increase and not with the microstructural changes of the white matter parenchyma itself. The white matter fluid increase was dominantly observed in the sagittal stratum and anterior thalamic radiation.

Discussion

These findings are positive steps toward understanding the pathophysiology of white matter alteration and its role in the cognitive decline.

Association of longitudinal white matter degeneration and cerebrospinal fluid biomarkers of neurodegeneration, inflammation and Alzheimer's disease in late-middle-aged adults

Alzheimer's disease (AD) is characterized by substantial neurodegeneration, including both cortical atrophy and loss of underlying white matter fiber tracts. Understanding longitudinal alterations to white matter may provide new insights into trajectories of brain change in both healthy aging and AD, and fluid biomarkers may be particularly useful in this effort. To examine this, 151 late-middle-aged participants enriched with risk for AD with at least one lumbar puncture and two diffusion tensor imaging (DTI) scans were selected for analysis from two large observational and longitudinally followed cohorts. Cerebrospinal fluid (CSF) was assayed for biomarkers of AD-specific pathology (phosphorylated-tau/Aβ42 ratio), axonal degeneration (neurofilament light chain protein, NFL), dendritic degeneration (neurogranin), and inflammation (chitinase-3-like protein 1, YKL-40). Linear mixed effects models were performed to test the hypothesis that biomarkers for AD, neurodegeneration, and inflammation, or two-year change in those biomarkers, would be associated with worse white matter health overall and/or progressively worsening white matter health over time. At baseline in the cingulum, phosphorylated-tau/Aβ42 was associated with higher mean diffusivity (MD) overall (intercept) and YKL-40 was associated with increases in MD over time. Two-year change in neurogranin was associated with higher mean diffusivity and lower fractional anisotropy overall (intercepts) across white matter in the entire brain and in the cingulum. These findings suggest that biomarkers for AD, neurodegeneration, and inflammation are potentially important indicators of declining white matter health in a cognitively healthy, late-middle-aged cohort.

Biospectroscopic Imaging Provides Evidence of Hippocampal Zn Deficiency and Decreased Lipid Unsaturation in an Accelerated Aging Mouse Model

Western society is facing a health epidemic due to the increasing incidence of dementia in aging populations, and there are still few effective diagnostic methods, minimal treatment options, and no cure. Aging is the greatest risk factor for memory loss that occurs during the natural aging process, as well as being the greatest risk factor for neurodegenerative disease such as Alzheimer's disease. Greater understanding of the biochemical pathways that drive a healthy aging brain toward dementia (pathological aging or Alzheimer's disease), is required to accelerate the development of improved diagnostics and therapies. Unfortunately, many animal models of dementia model chronic amyloid precursor protein overexpression, which although highly relevant to mechanisms of amyloidosis and familial Alzheimer's disease, does not model well dementia during the natural aging process. A promising animal model reported to model mechanisms of accelerated natural aging and memory impairments, is the senescence accelerated murine prone strain 8 (SAMP8), which has been adopted by many research group to study the biochemical transitions that occur during brain aging. A limitation to traditional methods of biochemical characterization is that many important biochemical and elemental markers (lipid saturation, lactate, transition metals) cannot be imaged at meso- or microspatial resolution. Therefore, in this investigation, we report the first multimodal biospectroscopic characterization of the SAMP8 model, and have identified important biochemical and elemental alterations, and colocalizations, between 4 month old SAMP8 mice and the relevant control (SAMR1) mice. Specifically, we demonstrate direct evidence of Zn deficiency within specific subregions of the hippocampal CA3 sector, which colocalize with decreased lipid unsaturation. Our findings also revealed colocalization of decreased lipid unsaturation and increased lactate in the corpus callosum white matter, adjacent to the hippocampus. Such findings may have important implication for future research aimed at elucidating specific biochemical pathways for therapeutic intervention.

Protective role of rosmarinic acid on amyloid beta 42-induced echoic memory decline: Implication of oxidative stress and cholinergic impairment

In the present study, we examined whether rosmarinic acid (RA) reverses amyloid β (Aβ) induced reductions in antioxidant defense, lipid peroxidation, cholinergic damage as well as the central auditory deficits. For this purpose, Wistar rats were randomly divided into four groups; Sham(S), Sham + RA (SR), Aβ42 peptide (Aβ) and Aβ42 peptide + RA (AβR) groups. Rat model of Alzheimer was established by bilateral injection of Aβ42 peptide (2,2 nmol/10 μl) into the lateral ventricles. RA (50 mg/kg, daily) was administered orally by gavage for 14 days after intracerebroventricular injection. At the end of the experimental period, we recorded the auditory event related potentials (AERPs) and mismatch negativity (MMN) response to assess auditory functions followed by histological and biochemical analysis. Aβ42 injection led to a significant increase in the levels of thiobarbituric acid reactive substances (TBARS) and 4-Hydroxy-2-nonenal (4-HNE) but decreased the activity of antioxidant enzymes (SOD, CAT, GSH-Px) and glutathione levels. Moreover, Aβ42 injection resulted in a reduction in the acetylcholine content and acetylcholine esterase activity. RA treatment prevented the observed alterations in the AβR group. Furthermore, RA attenuated the increased Aβ staining and astrocyte activation. We also found that Aβ42 injection decreased the MMN response and theta power/coherence of AERPs, suggesting an impairing effect on auditory discrimination and echoic memory processes. RA treatment reversed the Aβ42 related alterations in AERP parameters. In conclusion, our study demonstrates that RA prevented Aβ-induced antioxidant-oxidant imbalance and cholinergic damage, which may contribute to the improvement of neural network dynamics of auditory processes in this rat model.

In Vivo Detection of Gray Matter Neuropathology in the 3xTg Mouse Model of Alzheimer's Disease with Diffusion Tensor Imaging

A diagnosis of Alzheimer's disease (AD), a neurodegenerative disorder accompanied by severe functional and cognitive decline, is based on clinical findings, with final confirmation of the disease at autopsy by the presence of amyloid-β (Aβ) plaques and neurofibrillary tangles. Given that microstructural brain alterations occur years prior to clinical symptoms, efforts to detect brain changes early could significantly enhance our ability to diagnose AD sooner. Diffusion tensor imaging (DTI), a type of MRI that characterizes the magnitude, orientation, and anisotropy of the diffusion of water in tissues, has been used to infer neuropathological changes in vivo. Its utility in AD, however, is still under investigation. The current study used DTI to examine brain regions susceptible to AD-related pathology; the cerebral cortex, entorhinal cortex, and hippocampus, in 12-14-month-old 3xTg AD mice that possess both Aβ plaques and neurofibrillary tangles. Mean diffusivity did not differ between 3xTg and control mice in any region. Decreased fractional anisotropy (p < 0.01) and axial diffusivity (p < 0.05) were detected only in the hippocampus, in which both congophilic Aβ plaques and hyperphosphorylated tau accumulation, consistent with neurofibrillary tangle formation, were detected. Pathological tau accumulation was seen in the cortex. The entorhinal cortex was largely spared from AD-related neuropathology. This is the first study to demonstrate DTI abnormalities in gray matter in a mouse model of AD in which both pathological hallmarks are present, suggesting the feasibility of DTI as a non-invasive means of detecting brain pathology in vivo in early-stage AD.

The Association Between Retinal Neuronal Layer and Brain Structure is Disrupted in Patients with Cognitive Impairment and Alzheimer's Disease

Both healthy and pathological aging due to Alzheimer's disease (AD) are associated with decreased brain grey matter volume (GMV) and disrupted white matter (WM) microstructure. Thinner macular ganglion cell-inner plexiform layer (GC-IPL) measured by spectral-domain optical coherence tomography has been reported in patients with AD and mild cognitive impairment. Emerging evidence suggested a link between thinner GC-IPL and lower GMV in subjects with no dementia using region-of-interest-based approach. However, it remains unknown whether GC-IPL thickness is associated with brain WM microstructure and how such association differed between normal and cognitively impaired subjects. Here, for subjects with no cognitive impairment (NCI), thinner GC-IPL was associated with lower WM microstructure integrity in the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, corticospinal tracts, anterior thalamic radiation, and cingulum regions, while it was weakly associated with lower GMV in visual cortex and cerebellum. Nevertheless, these retina-brain associations were disrupted in the presence of cognitive impairment. Correlations between GMV and GC-IPL were lost in patients with cognitive impairment but no dementia (CIND) and AD patients. GC-IPL was related to WM microstructural disruption in similar regions with decreased significance. In contrast, lower WM microstructure integrity in the fornix showed a trend of correlation with thinner GC-IPL in both CIND and AD but not NCI. Collectively, our findings suggest the possible physiological retina-brain relationship in healthy aging, which might be disrupted by disease-induced changes in patients with cognitive impairment. Longitudinal study with larger patient sample should follow to confirm the disease mechanism behind these retina-brain relationship changes.

Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining

Recursive social decision-making requires the use of flexible, context-sensitive long-term strategies for negotiation. To succeed in social bargaining, participants' own perspectives must be dynamically integrated with those of interactors to maximize self-benefits and adapt to the other's preferences, respectively. This is a prerequisite to develop a successful long-term self-other integration strategy. While such form of strategic interaction is critical to social decision-making, little is known about its neurocognitive correlates. To bridge this gap, we analysed social bargaining behaviour in relation to its structural neural correlates, ongoing brain dynamics (oscillations and related source space), and functional connectivity signatures in healthy subjects and patients offering contrastive lesion models of neurodegeneration and focal stroke: behavioural variant frontotemporal dementia, Alzheimer's disease, and frontal lesions. All groups showed preserved basic bargaining indexes. However, impaired self-other integration strategy was found in patients with behavioural variant frontotemporal dementia and frontal lesions, suggesting that social bargaining critically depends on the integrity of prefrontal regions. Also, associations between behavioural performance and data from voxel-based morphometry and voxel-based lesion-symptom mapping revealed a critical role of prefrontal regions in value integration and strategic decisions for self-other integration strategy. Furthermore, as shown by measures of brain dynamics and related sources during the task, the self-other integration strategy was predicted by brain anticipatory activity (alpha/beta oscillations with sources in frontotemporal regions) associated with expectations about others' decisions. This pattern was reduced in all clinical groups, with greater impairments in behavioural variant frontotemporal dementia and frontal lesions than Alzheimer's disease. Finally, connectivity analysis from functional magnetic resonance imaging evidenced a fronto-temporo-parietal network involved in successful self-other integration strategy, with selective compromise of long-distance connections in frontal disorders. In sum, this work provides unprecedented evidence of convergent behavioural and neurocognitive signatures of strategic social bargaining in different lesion models. Our findings offer new insights into the critical roles of prefrontal hubs and associated temporo-parietal networks for strategic social negotiation.

Effectiveness of diffusion tensor imaging in differentiating early-stage subcortical ischemic vascular disease, Alzheimer's disease and normal ageing

Objective

To describe and compare diffusion tensor imaging (DTI) parameters between patients with subcortical ischemic vascular disease (SIVD) and Alzheimer’s disease (AD) diagnosed using structuralized neuropsychiatric assessments, and investigate potential neuronal substrates related to cognitive performance.

Methods

Thirty-five patients with SIVD, 40 patients with AD, and 33 cognitively normal control (NC) subjects matched by age and education level were consecutively recruited and underwent cognitive function assessments and DTI examinations. Comparisons among these three subgroups with regards to cognitive performance and DTI parameters including fractional anisotropy (FA) and mean diffusivity (MD) values were performed. Partial correlation analysis after controlling for age and education was used to evaluate associations between cognitive performance and DTI parameters.

Results

With regards to cognitive performance, the patients with SIVD had lower total scores in frontal assessment battery (FAB) compared to those with AD (p < 0.05) in the context of comparable Mini-Mental Status Examination and Cognitive Abilities Screening Instrument scores. With regards to DTI parameters, there were more regions of significant differences in FA among these three subgroups compared with MD. Compared with NC group, the patients with SIVD had significant global reductions in FA (p < 0.001 ~ 0.05), while significant reductions in FA among the patients with AD were regionally confined within the left superior longitudinal fasciculus, genu and splenium of the corpus callosum, and bilateral forceps major, and the anterior thalamic radiation, uncinate fasciculus, and cingulum of the left side (p < 0.01 ~ 0.05). Analysis of FA values within the left forceps major, left anterior thalamic radiation, and genu of the corpus callosum revealed a 71.8% overall correct classification (p < 0.001) with sensitivity of 69.4%, specificity of 73.8%, positive predictive value of 69.4%, and negative predictive value of 73.8% in discriminating patients with SIVD from those with AD. In combined analysis of the patients with SIVD and AD (n = 75), the total FAB score was positively correlated with FA within the bilateral forceps minor, genu of the corpus callosum, left forceps major, left uncinate fasciculus, and right inferior longitudinal fasciculus (p = 0.001 ~ 0.038), and inversely correlated with MD within the right superior longitudinal fasciculus, genu and body of the corpus callosum, bilateral forceps minor, right uncinate fasciculus, and right inferior longitudinal fasciculus (p = 0.003 ~ 0.040)

Conclusions

Our findings suggest the effectiveness of DTI measurements in distinguishing patients with early-stage AD from those with SIVD, with discernible changes in spatial distribution and magnitude of significance of the DTI parameters. Strategic FA assessments provided the most robust discriminative power to differentiate SIVD from AD, and FAB may serve as an additional cognitive marker. We also identified the neuronal substrates responsible for FAB performance.

Seed Location Impacts Whole-Brain Structural Network Comparisons between Healthy Elderly and Individuals with Alzheimer's Disease

Whole-brain networks derived from diffusion tensor imaging (DTI) data require the identification of seed and target regions of interest (ROIs) to assess connectivity patterns. This study investigated how initiating tracts from gray matter (GM) or white matter (WM) seed ROIs impacts (1) structural networks constructed from DTI data from healthy elderly (control) and individuals with Alzheimer’s disease (AD) and (2) between-group comparisons using these networks. DTI datasets were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Deterministic tractography was used to build two whole-brain networks for each subject; one in which tracts were initiated from WM ROIs and another in which they were initiated from GM ROIs. With respect to the first goal, in both groups, WM-seeded networks had approximately 400 more connections and stronger connections (as measured by number of streamlines per connection) than GM-seeded networks, but shared 94% of the connections found in the GM-seed networks. With respect to the second goal, between-group comparisons revealed a stronger subnetwork (as measured by number of streamlines per connection) in controls compared to AD using both WM-seeded and GM-seeded networks. The comparison using WM-seeded networks produced a larger (i.e., a greater number of connections) and more significant subnetwork in controls versus AD. Global, local, and nodal efficiency were greater in controls compared to AD, and between-group comparisons of these measures using WM-seeded networks had larger effect sizes than those using GM-seeded networks. These findings affirm that seed location significantly affects the ability to detect between-group differences in structural networks.

Multimodal Magnetic Resonance Imaging in Alzheimer's Disease Patients at Prodromal Stage

One objective of modern neuroimaging is to identify markers that can aid in diagnosis, monitor disease progression, and impact long-term drug analysis. In this study, physiopathological modifications in seven subcortical structures of patients with mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) were characterized by simultaneously measuring quantitative magnetic resonance parameters that are sensitive to complementary tissue characteristics (e.g., volume atrophy, shape changes, microstructural damage, and iron deposition). Fourteen MCI patients and fourteen matched, healthy subjects underwent 3T-magnetic resonance imaging with whole-brain, T1-weighted, T2^*-weighted, and diffusion-tensor imaging scans. Volume, shape, mean R2^*, mean diffusivity (MD), and mean fractional anisotropy (FA) in the thalamus, hippocampus, putamen, amygdala, caudate nucleus, pallidum, and accumbens were compared between MCI patients and healthy subjects. Comparisons were then performed using voxel-based analyses of R2^*, MD, FA maps, and voxel-based morphometry to determine which subregions showed the greatest difference for each parameter. With respect to the micro- and macro-structural patterns of damage, our results suggest that different and distinct physiopathological processes are present in the prodromal phase of AD. MCI patients had significant atrophy and microstructural changes within their hippocampi and amygdalae, which are known to be affected in the prodromal stage of AD. This suggests that the amygdala is affected in the same, direct physiopathological process as the hippocampus. Conversely, atrophy alone was observed within the thalamus and putamen, which are not directly involved in AD pathogenesis. This latter result may reflect another mechanism, whereby atrophy is linked to indirect physiopathological processes.

Feeling, learning from and being aware of inner states: interoceptive dimensions in neurodegeneration and stroke

Interoception is a complex process encompassing multiple dimensions, such as accuracy, learning and awareness. Here, we examined whether each of those dimensions relies on specialized neural regions distributed throughout the vast interoceptive network. To this end, we obtained relevant measures of cardiac interoception in healthy subjects and patients offering contrastive lesion models of neurodegeneration and focal brain damage: behavioural variant fronto-temporal dementia (bvFTD), Alzheimer's disease (AD) and fronto-insular stroke. Neural correlates of the three dimensions were examined through structural and functional resting-state imaging, and online measurements of the heart-evoked potential (HEP). The three patient groups presented deficits in interoceptive accuracy, associated with insular damage, connectivity alterations and abnormal HEP modulations. Interoceptive learning was differentially impaired in AD patients, evidencing a key role of memory networks in this skill. Interoceptive awareness results showed that bvFTD and AD patients overestimated their performance; this pattern was related to abnormalities in anterior regions and associated networks sub-serving metacognitive processes, and probably linked to well-established insight deficits in dementia. Our findings indicate how damage to specific hubs in a broad fronto-temporo-insular network differentially compromises interoceptive dimensions, and how such disturbances affect widespread connections beyond those critical hubs. This is the first study in which a multiple lesion model reveals fine-grained alterations of body sensing, offering new theoretical insights into neuroanatomical foundations of interoceptive dimensions.This article is part of the themed issue 'Interoception beyond homeostasis: affect, cognition and mental health'.

Correlates of virtual navigation performance in older adults

Despite considerable evidence for deleterious effects of aging on place learning and memory, less is known about the trajectory and the putative neural mechanisms of these decrements. The virtual Morris water task (vMWT) is a human analog of a nonhuman spatial navigation task. The present study investigated longitudinal changes in place learning in 51 healthy, nondemented adults (age 30-83 years) who completed the vMWT and a neuropsychological battery at 2 time-points (interval = ∼8 years). We also assessed cross-sectional associations between vMWT and brain structure, biochemical integrity, and standardized neuropsychological measures in a subset of 22 individuals who underwent magnetic resonance imaging at follow-up. Despite no longitudinal decrement in vMWT performance, there were cross-sectional age differences on the vMWT favoring younger adults. Negative associations were observed between vMWT latency and gray matter volumes in the right hippocampus, bilateral thalamus, and right medial orbitofrontal cortex and between vMWT latency and white matter fractional anisotropy in the bilateral uncinate fasciculus. Collectively, these results suggest a pattern of differences in the structural integrity of regions supporting successful navigation even in the absence of longitudinal performance decrements.

Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural Network Pathology: Toward Clinical Applications

OBJECTIVES

The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition.

METHODS

We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls).

RESULTS

Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited.

CONCLUSIONS

Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.

Mild Cognitive Impairment is Associated With White Matter Integrity Changes in Late-Myelinating Regions Within the Corpus Callosum

Degenerative brain changes in Alzheimer's disease may occur in reverse order of normal brain development based on the retrogenesis model. This study tested whether evidence of reverse myelination was observed in mild cognitive impairment (MCI) using a data-driven analytic approach based on life span developmental data. Whole-brain high-resolution diffusion tensor imaging scans were obtained for 31 patients with MCI and 79 demographically matched healthy older adults. Comparisons across corpus callosum (CC) regions of interest (ROIs) showed decreased fractional anisotropy (FA) in the body but not in the genu or splenium; early-, middle-, and late-myelinating ROIs restricted to the CC revealed decreased FA in late- but not early- or middle-myelinating ROIs. Voxelwise group differences revealed areas of lower FA in MCI, but whole-brain differences were equally distributed across early-, middle-, and late-myelinating regions. Overall, results within the CC support the retrogenesis model, although caution is needed when generalizing these results beyond the CC.

Multimodality Imaging Approach in Alzheimer disease. Part I: Structural MRI, Functional MRI, Diffusion Tensor Imaging and Magnetization Transfer Imaging

The authors make a complete review of the potential clinical applications of traditional and novel magnetic resonance imaging (MRI) techniques in the evaluation of patients with Alzheimer's disease, including structural MRI, functional MRI, diffusion tension imaging and magnetization transfer imaging.

A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging

The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface, considering the anatomical correspondence between the DTI and the T1-weighted image. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. Here we introduce a Surface Guided Diffusion Mapping (SGDM) method to compensate for such geometric distortions. We compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. We also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The CSF probability values provided by the SGDM method were lower than those produced by the NN and Linear methods. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the NN and Linear methods assigned the MD value in the CSF region to the cortical midsurface (GM region). Our results suggest that the SGDM method is an effective way to correct such mapping errors.

Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: a meta-analysis of the region of interest structural imaging studies

Individual structural neuroimaging studies of the corpus callosum (CC) in Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the region of interest (ROI) analysis have yielded inconsistent findings. The aim of this study was to conduct a meta-analysis of structural imaging studies using ROI technique to measure the CC midsagittal area changes in patients with AD or MCI. Databases of PubMed, the Cochrane Library, the ISI Web of Science, and Science Direct from inception to June 2014 were searched with key words "corpus callosum" or "callosal", plus "Alzheimer's disease" or "mild cognitive impairment". Twenty-three studies with 603 patients with AD, 146 with MCI, and 638 healthy controls were included in this meta-analysis. Effect size was used to measure the difference between patients with AD or MCI and healthy controls. Significant callosal atrophy was found in MCI patients with an effect size of -0.36 (95% CI, -0.57 to -0.14; P = 0.001). The degree of the CC atrophy in mild AD was less severe than that in moderate AD with a mean effect size -0.69 (95% CI, -0.89 to -0.49) versus -0.92 (95% CI, -1.16 to -0.69), respectively. Comparing with healthy controls, patients with MCI had atrophy in the anterior portion of the CC (i.e., rostrum and genu). In contrast, patients with AD had atrophy in both anterior and posterior portions (i.e., splenium). These results suggest that callosal atrophy may be related to the degree of cognitive decline in patients with MCI and AD, and it may be used as a biomarker for patients with cognitive deficit even before meeting the criteria for AD.

Neuropsychological and neuroimaging characteristics of amnestic mild cognitive impairment subtypes: a selective overview

Alzheimer's disease (AD) is a progressive age-related neurodegenerative disease. Amnestic mild cognitive impairment (aMCI) is considered to represent early AD. Various aMCI clinical subtypes have been identified as either single domain (SD) or multidomain (MD). The various subtypes represent heterogeneous syndrome, indicating the different probability of progression to AD. Understanding the heterogeneous concept of aMCI can help to construct potential biomarkers to monitor the progression of aMCI to AD. This review provides an overview of various neuroimaging measures for subtypes of aMCI. Focusing on neuropsychological, structural, and functional neuroimaging findings, we found that aMCI showed differences in clinical progression and the abnormalities in MD-aMCI were distributed across temporal, frontal, and parietal cortices, which is similar to AD. This is also compatible with the notion that MD-aMCI is a transition stage between SD-aMCI and AD. Our review provided a framework for the diagnosis of clinical subtypes of aMCI and early detection and intervention of the progression from aMCI to AD.

Independent component analysis-based identification of covariance patterns of microstructural white matter damage in Alzheimer's disease

The existing DTI studies have suggested that white matter damage constitutes an important part of the neurodegenerative changes in Alzheimer’s disease (AD). The present study aimed to identify the regional covariance patterns of microstructural white matter changes associated with AD. In this study, we applied a multivariate analysis approach, independent component analysis (ICA), to identify covariance patterns of microstructural white matter damage based on fractional anisotropy (FA) skeletonised images from DTI data in 39 AD patients and 41 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative database. The multivariate ICA decomposed the subject-dimension concatenated FA data into a mixing coefficient matrix and a source matrix. Twenty-eight independent components (ICs) were extracted, and a two sample t-test on each column of the corresponding mixing coefficient matrix revealed significant AD/HC differences in ICA weights for 7 ICs. The covariant FA changes primarily involved the bilateral corona radiata, the superior longitudinal fasciculus, the cingulum, the hippocampal commissure, and the corpus callosum in AD patients compared to HCs. Our findings identified covariant white matter damage associated with AD based on DTI in combination with multivariate ICA, potentially expanding our understanding of the neuropathological mechanisms of AD.

Using structural and diffusion magnetic resonance imaging to differentiate the dementias

Dementia is one of the major causes of personal, societal and financial dependence in older people and in today's ageing society there is a pressing need for early and accurate markers of cognitive decline. There are several subtypes of dementia but the four most common are Alzheimer's disease, Lewy body dementia, vascular dementia and frontotemporal dementia. These disorders can only be diagnosed at autopsy, and ante-mortem assessments of "probable dementia (e.g. of Alzheimer type)" are traditionally driven by clinical symptoms of cognitive or behavioural deficits. However, owing to the overlapping nature of symptoms and age of onset, a significant proportion of dementia cases remain incorrectly diagnosed. Misdiagnosis can have an extensive impact, both at the level of the individual, who may not be offered the appropriate treatment, and on a wider scale, by influencing the entry of patients into relevant clinical trials. Magnetic resonance imaging (MRI) may help to improve diagnosis by providing non-invasive and detailed disease-specific markers of cognitive decline. MRI-derived measurements of grey and white matter structural integrity are potential surrogate markers of disease progression, and may also provide valuable diagnostic information. This review summarises the latest evidence on the use of structural and diffusion MRI in differentiating between the four major dementia subtypes.

Fornix White Matter is Correlated with Resting-State Functional Connectivity of the Thalamus and Hippocampus in Healthy Aging but Not in Mild Cognitive Impairment - A Preliminary Study

In this study, we wished to examine the relationship between the structural connectivity of the fornix, a white matter (WM) tract in the limbic system, which is affected in amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease, and the resting-state functional connectivity (FC) of two key related subcortical structures, the thalamus, and hippocampus. Twenty-two older healthy controls (HC) and 18 older adults with aMCI underwent multi-modal MRI scanning. The fornix was reconstructed using constrained-spherical deconvolution-based tractography. The FC between the thalamus and hippocampus was calculated using a region-of-interest approach from which the mean time series were exacted and correlated. Diffusion tensor imaging measures of the WM microstructure of the fornix were correlated against the Fisher Z correlation values from the FC analysis. There was no difference between the groups in the fornix WM measures, nor in the resting-state FC of the thalamus and hippocampus. We did however find that the relationship between functional and structural connectivity differed significantly between the groups. In the HCs, there was a significant positive association between linear diffusion (CL) in the fornix and the FC of the thalamus and hippocampus, however, there was no relationship between these measures in the aMCI group. These preliminary findings suggest that in aMCI, the relationship between the functional and structural connectivity of regions of the limbic system may be significantly altered compared to healthy ageing. The combined use of diffusion weighted imaging and functional MRI may advance our understanding of neural network changes in aMCI, and elucidate subtle changes in the relationship between structural and functional brain networks.

Tract Based Spatial Statistic Reveals No Differences in White Matter Microstructural Organization between Carriers and Non-Carriers of the APOE ɛ4 and ɛ2 Alleles in Young Healthy Adolescents

The apolipoprotein E (APOE) ɛ4 allele is the best established genetic risk factor for Alzheimer's disease (AD) and has been previously associated with alterations in structural gray matter and changes in functional brain activity in healthy middle-aged individuals and older non-demented subjects. In order to determine the neural mechanism by which APOE polymorphisms affect white matter (WM) structure, we investigated the diffusion characteristics of WM tracts in carriers and non-carriers of the APOE ɛ4 and ɛ2 alleles using an unbiased whole brain analysis technique (Tract Based Spatial Statistics) in a healthy young adolescent (14 years) cohort. A large sample of healthy young adolescents (n = 575) were selected from the European neuroimaging-genetics IMAGEN study with available APOE status and accompanying diffusion imaging data. MR Diffusion data was acquired on 3T systems using 32 diffusion-weighted (DW) directions and 4 non-DW volumes (b-value = 1,300 s/mm² and isotropic resolution of 2.4×2.4×2.4  mm). No significant differences in WM structure were found in diffusion indices between carriers and non-carriers of the APOE ɛ4 and ɛ2 alleles, and dose-dependent effects of these variants were not established, suggesting that differences in WM structure are not modulated by the APOE polymorphism. In conclusion, our results suggest that microstructural properties of WM structure are not associated with the APOE ɛ4 and ɛ2 alleles in young adolescence, suggesting that the neural effects of these variants are not evident in 14-year-olds and may only develop later in life.