In vivo parahippocampal white matter pathology as a biomarker of disease progression to Alzheimer's disease

Seeking the Amygdala: Novel Use of Diffusion Tensor Imaging to Delineate the Basolateral Amygdala

The amygdaloid complex, including the basolateral nucleus (BLA), contributes crucially to emotional and cognitive brain functions, and is a major target of research in both humans and rodents. However, delineating structural amygdala plasticity in both normal and disease-related contexts using neuroimaging has been hampered by the difficulty of unequivocally identifying the boundaries of the BLA. This challenge is a result of the poor contrast between BLA and the surrounding gray matter, including other amygdala nuclei. Here, we describe a novel diffusion tensor imaging (DTI) approach to enhance contrast, enabling the optimal identification of BLA in the rodent brain from magnetic resonance (MR) images. We employed this methodology together with a slice-shifting approach to accurately measure BLA volumes. We then validated the results by direct comparison to both histological and cellular-identity (parvalbumin)-based conventional techniques for defining BLA in the same brains used for MRI. We also confirmed BLA connectivity targets using DTI-based tractography. The novel approach enables the accurate and reliable delineation of BLA. Because this nucleus is involved in and changed by developmental, degenerative and adaptive processes, the instruments provided here should be highly useful to a broad range of neuroimaging studies. Finally, the principles used here are readily applicable to numerous brain regions and across species.

Neuroanatomical and neurocognitive changes associated with subjective cognitive decline

Introduction

Subjective Cognitive Decline (SCD) can progress to mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and thus may represent a preclinical stage of the AD continuum. However, evidence about structural changes observed in the brain during SCD remains inconsistent.

Materials and methods

This cross-sectional study aimed to evaluate, in subjects recruited from the CompAS project, neurocognitive and neurostructural differences between a group of forty-nine control subjects and forty-nine individuals who met the diagnostic criteria for SCD and exhibited high levels of subjective cognitive complaints (SCCs). Structural magnetic resonance imaging was used to compare neuroanatomical differences in brain volume and cortical thickness between both groups.

Results

Relative to the control group, the SCD group displayed structural changes involving frontal, parietal, and medial temporal lobe regions of critical importance in AD etiology and functionally related to several cognitive domains, including executive control, attention, memory, and language.

Conclusion

Despite the absence of clinical deficits, SCD may constitute a preclinical entity with a similar (although subtle) pattern of neuroanatomical changes to that observed in individuals with amnestic MCI or AD dementia.

Brain Atrophy and Clinical Characterization of Adults With Mild Cognitive Impairment and Different Cerebrospinal Fluid Biomarker Profiles According to the AT(N) Research Framework of Alzheimer’s Disease

Introduction

This study aimed to evaluate, in adults with mild cognitive impairment (MCI), the brain atrophy that may distinguish between three AT(N) biomarker-based profiles, and to determine its clinical value.

Methods

Structural MRI (sMRI) was employed to evaluate the volume and cortical thickness differences in MCI patients with different AT(N) profiles, namely, A−T−(N)−: normal AD biomarkers; A+T−(N)−: AD pathologic change; and A+T+(N)+: prodromal AD. Sensitivity and specificity of these changes were also estimated.

Results

An initial atrophy in medial temporal lobe (MTL) areas was found in the A+T−(N)− and A+T+(N)+ groups, spreading toward the parietal and frontal regions in A+T+(N)+ patients. These structural changes allowed distinguishing AT(N) profiles within the AD continuum; however, the profiles and their pattern of neurodegeneration were unsuccessful to determine the current clinical status.

Conclusion

sMRI is useful in the determination of the specific brain structural changes of AT(N) profiles along the AD continuum, allowing differentiation between MCI adults with or without pathological AD biomarkers.

Cerebellar White Matter Disruption in Alzheimer's Disease Patients: A Diffusion Tensor Imaging Study

The cognitive role of the cerebellum has recently gained much attention, and its pivotal role in Alzheimer's disease (AD) has now been widely recognized. Diffusion tensor imaging (DTI) has been used to evaluate the disruption of the microstructural milieu in AD, and though several white matter (WM) tracts such as corpus callosum, inferior and superior longitudinal fasciculus, cingulum, fornix, and uncinate fasciculus have been evaluated in AD, data on cerebellar WM tracts are currently lacking. We performed a tractography-based DTI reconstruction of the middle cerebellar peduncle (MCP), and the left and right superior cerebellar peduncles separately (SCPL and SCPR) and addressed the differences in fractional anisotropy (FA), axial diffusivity (Dax), radial diffusivity (RD), and mean diffusivity (MD) in the three tracts between 50 patients with AD and 25 healthy subjects. We found that AD patients showed a lower FA and a higher RD compared to healthy subjects in MCP, SCPL, and SCPR. Moreover, higher MD was found in SCPR and SCPL and higher Dax in SCPL. This result is important as it challenges the traditional view that WM bundles in the cerebellum are unaffected in AD and might identify new targets for therapeutic interventions.

Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer's Disease

Identifying biomarkers that can assess the risk of developing Alzheimer's Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

A White Matter Connection of Schizophrenia and Alzheimer's Disease

Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer's disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter "regional vulnerability index" (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen's d = 0.44, P = 1·10-5, N = 173 patients/230 control) and replication (Cohen's d = 0.78, P = 9·10-7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r = .21 and .29, P < .01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients.

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.

Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification

High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.

Longitudinal accrual of neocortical amyloid burden is associated with microstructural changes of the fornix in cognitively normal adults

The fornix and parahippocampal cingulum are 2 major limbic tracts in the core memory network of the hippocampus. Although these fiber tracts are known to degrade with Alzheimer's disease (AD), little is known about their vulnerability in the asymptomatic phase of AD. In this longitudinal study of cognitively normal adults, we assessed amyloid-beta (Aβ) plaques using positron emission tomography and white matter microstructure using diffusion tensor imaging. We found that an increase of neocortical Aβ burden over time was associated with an increase of radial diffusivity in the fornix but not in the parahippocampal cingulum. The effect of increasing neocortical Aβ burden on the fornix remained significant after controlling for baseline measures, head motion, global brain atrophy, regional Aβ burden in the hippocampus, or microstructural changes in the global white matter. In addition, microstructural changes in the fornix were not associated with decline of episodic memory or other cognitive abilities. Our findings suggest that microstructural changes in the fornix may be an early sign in the asymptomatic phase of AD.

Discriminative multi-task feature selection for multi-modality classification of Alzheimer's disease

Recently, multi-task based feature selection methods have been used in multi-modality based classification of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, in traditional multi-task feature selection methods, some useful discriminative information among subjects is usually not well mined for further improving the subsequent classification performance. Accordingly, in this paper, we propose a discriminative multi-task feature selection method to select the most discriminative features for multi-modality based classification of AD/MCI. Specifically, for each modality, we train a linear regression model using the corresponding modality of data, and further enforce the group-sparsity regularization on weights of those regression models for joint selection of common features across multiple modalities. Furthermore, we propose a discriminative regularization term based on the intra-class and inter-class Laplacian matrices to better use the discriminative information among subjects. To evaluate our proposed method, we perform extensive experiments on 202 subjects, including 51 AD patients, 99 MCI patients, and 52 healthy controls (HC), from the baseline MRI and FDG-PET image data of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The experimental results show that our proposed method not only improves the classification performance, but also has potential to discover the disease-related biomarkers useful for diagnosis of disease, along with the comparison to several state-of-the-art methods for multi-modality based AD/MCI classification.

Hippocampal and entorhinal structures in subjective memory impairment: a combined MRI volumetric and DTI study

BACKGROUND

Subjective memory impairment (SMI) is common among older adults. Increasing evidence suggests that SMI is a risk factor for future cognitive decline, as well as for mild cognitive impairment and dementia. Medial temporal lobe structures, including the hippocampus and entorhinal cortex, are affected in the early stages of Alzheimer's disease. The current study examined the gray matter (GM) volume and microstructural changes of hippocampal and entorhinal regions in individuals with SMI, compared with elderly control participants without memory complaints.

METHODS

A total of 45 participants (mean age: 70.31 ± 6.07 years) took part in the study, including 18 participants with SMI and 27 elderly controls without memory complaints. We compared the GM volume and diffusion tensor imaging (DTI) measures in the hippocampal and entorhinal regions between SMI and control groups.

RESULTS

Individuals with SMI had lower entorhinal cortical volumes than control participants, but no differences in hippocampal volume were found between groups. In addition, SMI patients exhibited DTI changes (lower fractional anisotropy (FA) and higher mean diffusivity in SMI) in the hippocampal body and entorhinal white matter compared with controls. Combining entorhinal cortical volume and FA in the hippocampal body improved the accuracy of classification between SMI and control groups.

CONCLUSIONS

These findings suggest that the entorhinal region exhibits macrostructural as well as microstructural changes in individuals with SMI, whereas the hippocampus exhibits only microstructural alterations.

Early Cerebellar Network Shifting in Spinocerebellar Ataxia Type 6

Spinocerebellar ataxia 6 (SCA6), an autosomal dominant degenerative disease, is characterized by diplopia, gait ataxia, and incoordination due to severe progressive degeneration of Purkinje cells in the vestibulo- and spinocerebellum. Ocular motor deficits are common, including difficulty fixating on moving objects, nystagmus and disruption of smooth pursuit movements. In presymptomatic SCA6, there are alterations in saccades and smooth-pursuit movements. We sought to assess functional and structural changes in cerebellar connectivity associated with a visual task, hypothesizing that gradual changes would parallel disease progression. We acquired functional magnetic resonance imaging and diffusion tensor imaging data during a passive smooth-pursuit task in 14 SCA6 patients, representing a range of disease duration and severity, and performed a cross-sectional comparison of cerebellar networks compared with healthy controls. We identified a shift in activation from vermis in presymptomatic individuals to lateral cerebellum in moderate-to-severe cases. Concomitantly, effective connectivity between regions of cerebral cortex and cerebellum was at its highest in moderate cases, and disappeared in severe cases. Finally, we noted structural differences in the cerebral and cerebellar peduncles. These unique results, spanning both functional and structural domains, highlight widespread changes in SCA6 and compensatory mechanisms associated with cerebellar physiology that could be utilized in developing new therapies.

Impact of SORL1 genetic variations on MRI markers in non-demented elders

The sorting protein-related receptor 1 (SORL1 or LR11) gene has been verified to play an important role in the pathologic process of β-amyloid (Aβ) formation and trafficking in Alzheimer's Disease (AD) by plenty of cytological and molecular biological studies. But there were few studies investigated the association of SORL1 gene and neurodegeneration features from a rather macroscopic perspective. In the present study, we explored the effect of SORL1 genotypes on AD-related brain atrophy. We recruited 812 individuals with both baseline and two-year follow-up information from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and applied multiple linear regression models to examine the association between eight single nucleotide polymorphisms (SNPs) and neuroimaging phenotypes. Finally, four SNPs (rs11219350, rs2298813, rs3781836, rs3824968) showed trend of association with the volume of hippocampus and parahippocampal gyrus but failed to survive the false discovery rate (FDR) correction. Only rs1784933 and rs753780 showed significant association with right parahippocampal gyrus. According to our findings, SORL1 variations influence the atrophy of specific AD-related brain structures, which suggested the potential role of SORL1 in the neurodegeneration of cognitive related regions.

Hippocampal diffusion tensor imaging microstructural changes in vascular dementia

To explore microstructural integrity of hippocampus in vascular dementia (VD) using DTI. Twenty-five individuals with VD, without magnetic resonance imaging (MRI) evidence of gray matter pathology, and 25 matched healthy control (HC) individuals underwent a 3T MRI protocol including T2, FLAIR, and PD in the axial plane, 3D whole-brain T1-weighted with an isotropic resolution of 1 mm, and DTI acquired using 64 diffusion sensitizing directions, b value of 1,500 s/mm(2), 65 axial slices, isotropic resolution of 1.8 mm. Images were processed to obtain indices of microstructural variations of bilateral hippocampi. Mean diffusivity (MD) in the hippocampus of patients with VD was significantly increased (p < 0.05) bilaterally with respect to that of the group of HC examinees. In VD group left hippocampal MD (10(-6 )× mm(2)/s) was 833.4 ± 92.8; in HC group left MD was 699.8 ± 56. In VD group, right hippocampal MD was 859.1 ± 69.8; in HC group right MD was 730.4 ± 40.2. No group differences were found in hippocampal FA. DTI shows microstructural hippocampal damage in VD in patients with normal appearing gray matter structures on conventional MRI, indicating the need for further research on the link between VD and AD.

The Electrophysiological Phenomenon of Alzheimer's Disease: A Psychopathology Theory

The current understanding of Alzheimer's disease (AD) is based on the Aβ and tau pathology and the resulting neuropathological changes, which are associated with manifested clinical symptoms. However, electrophysiological brain changes may provide a more expansive understanding of AD. Hence, the objective of this systematic review is to propose a theory about the electrophysiological phenomenon of Alzheimer's disease (EPAD). The review of literature resulted from an extensive search of PubMed and MEDLINE databases. One-hundred articles were purposively selected. They provided an understanding of the concepts establishing the theory of EPAD (neuropathological changes, neurochemical changes, metabolic changes, and electrophysiological brain changes). Changes in the electrophysiology of the brain are foundational to the association or interaction of the concepts. Building on Berger's Psychophysical Model, it is evident that electrophysiological brain changes occur and affect cortical areas to generate or manifest symptoms from onset and across the stages of AD, which may be prior to pathological changes. Therefore, the interaction of the concepts demonstrates how the psychopathology results from affected electrophysiology of the brain. The theory of the EPAD provides a theoretical foundation for appropriate measurements of AD without dependence on neuropathological changes. Future research is warranted to further test this theory. Ultimately, this theory contributes to existing knowledge because it shows how electrophysiological changes are useful in understanding the risk and progression of AD across the stages.

Mnemonic discrimination relates to perforant path integrity: An ultra-high resolution diffusion tensor imaging study

Pattern separation describes the orthogonalization of similar inputs into unique, non-overlapping representations. This computational process is thought to serve memory by reducing interference and to be mediated by the dentate gyrus of the hippocampus. Using ultra-high in-plane resolution diffusion tensor imaging (hrDTI) in older adults, we previously demonstrated that integrity of the perforant path, which provides input to the dentate gyrus from entorhinal cortex, was associated with mnemonic discrimination, a behavioral outcome designed to load on pattern separation. The current hrDTI study assessed the specificity of this perforant path integrity-mnemonic discrimination relationship relative to other cognitive constructs (identified using a factor analysis) and white matter tracts (hippocampal cingulum, fornix, corpus callosum) in 112 healthy adults (20-87 years). Results revealed age-related declines in integrity of the perforant path and other medial temporal lobe (MTL) tracts (hippocampal cingulum, fornix). Controlling for global effects of brain aging, perforant path integrity related only to the factor that captured mnemonic discrimination performance. Comparable integrity-mnemonic discrimination relationships were also observed for the hippocampal cingulum and fornix. Thus, whereas perforant path integrity specifically relates to mnemonic discrimination, mnemonic discrimination may be mediated by a broader MTL network.

Advances in imaging-genetic relationships for Alzheimer's disease: clinical implications

Alzheimer's disease (AD) is the most common cause of dementia and represents a major public health problem. From a clinical perspective, AD is devastating to patients and their families. The genetic approach to the study of dementia undoubtedly continues to provide a significant contribution to understanding the pathogenesis, diagnosis and therapeutic perspectives, but also raises important ethical implications. With advances in new technology, including genetics and PET/MRI scanning, the role of genetic studies and neuroimaging is being redefined as an aid in the clinical diagnosis of AD, and also in presymptomatic evaluation. Here, we review some of the issues related to the neuroimaging-genetic relationship in AD with a possible clinical implication as a preclinical biomarker for dementia and also for tracking disease progression.

Manifold regularized multitask feature learning for multimodality disease classification

Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single task, and use group-sparsity regularizer to capture the intrinsic relatedness among multiple tasks (i.e., modalities) and jointly select the common features from multiple tasks. Furthermore, we introduce a new manifold-based Laplacian regularizer to preserve the data distribution information from each task. Finally, we use the multikernel support vector machine method to fuse multimodality data for eventual classification. Conversely, we also extend our method to the semisupervised setting, where only partial data are labeled. We evaluate our method using the baseline magnetic resonance imaging (MRI), fluorodeoxyglucose positron emission tomography (FDG-PET), and cerebrospinal fluid (CSF) data of subjects from AD neuroimaging initiative database. The experimental results demonstrate that our proposed method can not only achieve improved classification performance, but also help to discover the disease-related brain regions useful for disease diagnosis.

Soluble amyloid beta levels are elevated in the white matter of Alzheimer's patients, independent of cortical plaque severity

Alzheimer’s disease (AD) is the most common neurodegenerative disease and the leading cause of dementia. In addition to grey matter pathology, white matter changes are now recognized as an important pathological feature in the emergence of the disease. Despite growing recognition of the importance of white matter abnormalities in the pathogenesis of AD, the causes of white matter degeneration are still unknown. While multiple studies propose Wallerian-like degeneration as the source of white matter change, others suggest that primary white matter pathology may be due, at least in part, to other mechanisms, including local effects of toxic Aβ peptides. In the current study, we investigated levels of soluble amyloid-beta (Aβ) in white matter of AD patients (n=12) compared with controls (n=10). Fresh frozen white matter samples were obtained from anterior (Brodmann area 9) and posterior (Brodmann area 1, 2 and 3) areas of post-mortem AD and control brains. ELISA was used to examine levels of soluble Aβ -42 and Aβ -40. Total cortical neuritic plaque severity rating was derived from individual ratings in the following areas of cortex: mid-frontal, superior temporal, pre-central, inferior parietal, hippocampus (CA1), subiculum, entorhinal cortex, transentorhinal cortex, inferior temporal, amygdala and basal forebrain. Compared with controls, AD samples had higher white matter levels of both soluble Aβ -42 and Aβ -40. While no regional white matter differences were found in Aβ -40, Aβ -42 levels were higher in anterior regions than in posterior regions across both groups. After statistically controlling for total cortical neuritic plaque severity, differences in both soluble Aβ -42 and Aβ -40 between the groups remained, suggesting that white matter Aβ peptides accumulate independent of overall grey matter fibrillar amyloid pathology and are not simply a reflection of overall amyloid burden. These results shed light on one potential mechanism through which white matter degeneration may occur in AD. Given that white matter degeneration may be an early marker of disease, preceding grey matter atrophy, understanding the mechanisms and risk factors that may lead to white matter loss could help to identify those at high risk and to intervene earlier in the pathogenic process.