MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD

Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease

Alzheimer's disease (AD) remains a significant global health challenge, affecting millions worldwide and imposing substantial burdens on healthcare systems. Advances in artificial intelligence (AI), particularly in deep learning and machine learning, have revolutionized neuroimaging-based AD diagnosis. However, the complexity and lack of interpretability of these models limit their clinical applicability. Explainable Artificial Intelligence (XAI) addresses this challenge by providing insights into model decision-making, enhancing transparency, and fostering trust in AI-driven diagnostics. This review explores the role of XAI in AD neuroimaging, highlighting key techniques such as SHAP, LIME, Grad-CAM, and Layer-wise Relevance Propagation (LRP). We examine their applications in identifying critical biomarkers, tracking disease progression, and distinguishing AD stages using various imaging modalities, including MRI and PET. Additionally, we discuss current challenges, including dataset limitations, regulatory concerns, and standardization issues, and propose future research directions to improve XAI's integration into clinical practice. By bridging the gap between AI and clinical interpretability, XAI holds the potential to refine AD diagnostics, personalize treatment strategies, and advance neuroimaging-based research.

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Pathogenesis, diagnostics, and therapeutics for Alzheimer's disease: Breaking the memory barrier

Alzheimer's disease (AD) is the most common cause of dementia and accounts for 60-70 % of all cases. It affects millions of people worldwide. AD poses a substantial economic burden on societies and healthcare systems. AD is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. As the prevalence of AD continues to increase, understanding its pathogenesis, improving diagnostic methods, and developing effective therapeutics have become paramount. This comprehensive review delves into the intricate mechanisms underlying AD, explores the current state of diagnostic techniques, and examines emerging therapeutic strategies. By revealing the complexities of AD, this review aims to contribute to the growing body of knowledge surrounding this devastating disease.

Targeting muscarinic receptors for the treatment of alcohol use disorders: Opportunities and hurdles for clinical development

Emerging evidence suggests muscarinic acetylcholine receptors represent novel targets to treat alcohol use disorder. In this review, we draw from literature across medicinal chemistry, molecular biology, addiction and learning/cognition fields to interrogate the proposition for muscarinic receptor ligands in treating various aspects of alcohol use disorder, including cognitive dysfunction, motivation to consume alcohol and relapse. In support of this proposition, we describe cholinergic dysfunction in the pathophysiology of alcohol use disorder at a network level, including alcohol-induced adaptations present in both human post-mortem brains and reverse-translated rodent models. Preclinical behavioural pharmacology implicates specific muscarinic receptors, in particular, M4 and M5 receptors, as potential therapeutic targets worthy of further interrogation. We detail how these receptors can be selectively targeted in vivo by the use of subtype-selective allosteric modulators, a strategy that overcomes the issue of targeting a highly conserved orthosteric site bound by acetylcholine. Finally, we highlight the intense pharma interest in allosteric modulators of muscarinic receptors for other indications that provide an opportunity for repurposing into the alcohol use disorder space and provide some currently unanswered questions as a roadmap for future investigation.

Landmark-based spherical quasi-conformal mapping for hippocampal surface registration

Background

The cognitive decline induced by Alzheimer's disease (AD) is closely related to changes in hippocampal structure captured by magnetic resonance imaging (MRI). To accurately analyze the morphological changes of the hippocampus induced by AD, it is necessary to establish a one-to-one surface correspondence to compare the morphological measurements across different hippocampal surfaces. However, most existing landmark-based registration methods cannot satisfy both landmark matching and diffeomorphism under large deformations. To address these challenges, we propose a landmark-based spherical registration method via quasi-conformal mapping to establish a one-to-one correspondence between different hippocampal surfaces.

Methods

In our approach, we use the eigen-graph of the hippocampal surface to extract the intrinsic and unified landmarks of all the hippocampal surfaces and then realize the parameterization process from the hippocampal surface to a unit sphere according to the barycentric coordinate theory and the triangular mesh optimization algorithm. Finally, through the local stereographic projection, the alignment of the landmarks is achieved based on the quasi-conformal mapping on a two-dimensional (2D) plane under the constraints of Beltrami coefficients which can effectively control the topology distortion.

Results

We verified the proposed registration method on real hippocampus data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and created AD and normal control (NC) groups. Our registration algorithm achieved an area distortion index (ADI) of 0.4362e-4±0.7800e-5 in the AD group and 0.5671e-4±0.602e-5 in the NC group, and it achieved an angle distortion index (Eangle) of 0.6407±0.0258 in the AD group and 0.6271±0.0194 in the NC group. The accuracy of support vector machine (SVM) classification for the AD vs. NC groups based on the morphological features extracted from the registered hippocampal surfaces reached 94.2%.

Conclusions

This landmark-based spherical quasi-conformal mapping for hippocampal surface registration algorithm can maintain precise alignment of the landmarks and bijectivity in the presence of large deformation.

Considerations for biomarker strategies in clinical trials investigating tau-targeting therapeutics for Alzheimer's disease

The use of biomarker-led clinical trial designs has been transformative for investigating amyloid-targeting therapies for Alzheimer's disease (AD). The designs have ensured the correct selection of patients on these trials, supported target engagement and have been used to support claims of disease modification and clinical efficacy. Ultimately, this has recently led to approval of disease-modifying, amyloid-targeting therapies for AD; something that should be noted for clinical trials investigating tau-targeting therapies for AD. There is a clear overlap of the purpose of biomarker use at each stage of clinical development between amyloid-targeting and tau-targeting clinical trials. However, there are differences within the potential context of use and interpretation for some biomarkers in particular measurements of amyloid and utility of soluble, phosphorylated tau biomarkers. Given the complexities of tau in health and disease, it is paramount that therapies target disease-relevant tau and, in parallel, appropriate assays of target engagement are developed. Tau positron emission tomography, fluid biomarkers reflecting tau pathology and downstream measures of neurodegeneration will be important both for participant recruitment and for monitoring disease-modification in tau-targeting clinical trials. Bespoke design of biomarker strategies and interpretations for different modalities and tau-based targets should also be considered.

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus.

AIMS

We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC).

METHODS

We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification.

RESULTS

By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances.

CONCLUSIONS

The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

OBJECTIVE

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD.

METHODS

We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy.

RESULTS

We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set.

CONCLUSION

The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

Regional cerebral blood flow and brain atrophy in mild cognitive impairment and Alzheimer's disease

Objectives

A decline in the regional cerebral blood flow (CBF) is proposed to be one of the initial changes in the Alzheimer's disease process. To date, there are limited data on the correlation between CBF decline and gray matter atrophy in mild cognitive impairment (MCI) and AD patients. to investigate the association between CBF with the gray matter structural parameters such as cortical volume, surface area, and thickness in AD, MCI, and healthy controls (HC).

Methods

Data from three groups of participants including 39 HC, 82 MCI, and 28 AD subjects were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI). One-way ANOVA and linear regression were used to compare data and find a correlation between structural parameters such as cortical volume, surface area, and thickness and CBF which measured by arterial spin labeling (ASL)-MRI.

Results

Our findings revealed a widespread significant correlation between the CBF and structural parameters in temporal, frontal, parietal, occipital, precentral gyrus, pericalcarine cortex, entorhinal cortex, supramarginal gyrus, fusiform, precuneus, and pallidum.

Conclusion

CBF decline may be a useful biomarker for MCI and AD and accurately reflect the structural changes related to AD. According to the present results, CBF decline, as measured by ASL-MRI, is correlated with lower measures of structural parameters in AD responsible regions. It means that CBF decline may reflect AD-associated atrophy across disease progression and is also used as an early biomarker for AD and MCI diagnosis.

Clinical Feasibility of Automated Brain Tissue and Myelin Volumetry of Normal Brian Using Synthetic Magnetic Resonance Imaging With Fast Imaging Protocol: A Single-Center Pilot Study

OBJECTIVE

This study aimed to investigate the clinical feasibility of synthetic magnetic resonance imaging (MRI) with fast imaging protocol for automated brain tissue and myelin volumetry in healthy volunteers at 3.0-T MRI.

METHODS

Thirty-four healthy volunteers were scanned using synthetic MRI with 3 sets of scan parameters: groups Fast (FAS; 2 minutes, 29 seconds), Routine (ROU; 4 minutes, 7 seconds), and Research (RES; 7 minutes, 46 seconds). White matter (WM), gray matter (GM), cerebrospinal fluid (CSF), non-WM/GM/CSF (NoN), brain parenchymal volume (BPV), intracranial volume (ICV), and myelin volume (MYV) were compared between 3 groups. Linear correlation analysis was performed for measured volumes of groups FAS and ROU versus group RES.

RESULTS

Significant differences were found in all the measured brain tissue volumes between groups FAS and ROU (P < 0.001), FAS and RES (P < 0.05), and ROU and RES (P < 0.05), except for NoN between groups ROU and RES (P = 0.0673), ICV between groups FAS and ROU (P = 0.2552), and ICV between groups FAS and RES (P = 0.4898). The intergroup coefficients of variation were 4.36% for WM, 6.39% for GM, 10.14% for CSF, 67.5% for NoN, 1.21% for BPV, 0.08% for ICV, and 5.88% for MYV. Strong linear correlation was demonstrated for WM, GM, CSF, BPV, ICV, and MYV (R = 0.9230-1.131) between FAS versus RES, and ROU versus RES.

CONCLUSIONS

Using synthetic MRI with fast imaging protocol can change the measured brain tissue volumes of volunteers. It is necessary to use consistent acquisition protocols for comparing or following up cases quantitatively.

Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimers Disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimers Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker

BACKGROUND

A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes.

OBJECTIVE

We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61).

METHODS

Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs.

RESULTS

The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC.

CONCLUSION

The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.

Human disease biomarker panels through systems biology

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient's biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

T2 heterogeneity as an in vivo marker of microstructural integrity in medial temporal lobe subfields in ageing and mild cognitive impairment

A better understanding of early brain changes that precede loss of independence in diseases like Alzheimer's disease (AD) is critical for development of disease-modifying therapies. Quantitative MRI, such as T2 relaxometry, can identify microstructural changes relevant to early stages of pathology. Recent evidence suggests heterogeneity of T2 may be a more informative MRI measure of early pathology than absolute T2. Here we test whether T2 markers of brain integrity precede the volume changes we know are present in established AD and whether such changes are most marked in medial temporal lobe (MTL) subfields known to be most affected early in AD. We show that T2 heterogeneity was greater in people with mild cognitive impairment (MCI; n = 49) compared to healthy older controls (n = 99) in all MTL subfields, but this increase was greatest in MTL cortices, and smallest in dentate gyrus. This reflects the spatio-temporal progression of neurodegeneration in AD. T2 heterogeneity in CA1-3 and entorhinal cortex and volume of entorhinal cortex showed some ability to predict cognitive decline, where absolute T2 could not, however further studies are required to verify this result. Increases in T2 heterogeneity in MTL cortices may reflect localised pathological change and may present as one of the earliest detectible brain changes prior to atrophy. Finally, we describe a mechanism by which memory, as measured by accuracy and reaction time on a paired associate learning task, deteriorates with age. Age-related memory deficits were explained in part by lower subfield volumes, which in turn were directly associated with greater T2 heterogeneity. We propose that tissue with high T2 heterogeneity represents extant tissue at risk of permanent damage but with the potential for therapeutic rescue. This has implications for early detection of neurodegenerative diseases and the study of brain-behaviour relationships.

Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images

An effective presymptomatic diagnosis and treatment of Alzheimer's disease (AD) would have enormous public health benefits. Sparse coding (SC) has shown strong potential for longitudinal brain image analysis in preclinical AD research. However, the traditional SC computation is time-consuming and does not explore the feature correlations that are consistent over the time. In addition, longitudinal brain image cohorts usually contain incomplete image data and clinical labels. To address these challenges, we propose a novel two-stage Multi-Resemblance Multi-Target Low-Rank Coding (MMLC) method, which encourages that sparse codes of neighboring longitudinal time points are resemblant to each other, favors sparse code low-rankness to reduce the computational cost and is resilient to both source and target data incompleteness. In stage one, we propose an online multi-resemblant low-rank SC method to utilize the common and task-specific dictionaries in different time points to immune to incomplete source data and capture the longitudinal correlation. In stage two, supported by a rigorous theoretical analysis, we develop a multi-target learning method to address the missing clinical label issue. To solve such a multi-task low-rank sparse optimization problem, we propose multi-task stochastic coordinate coding with a sequence of closed-form update steps which reduces the computational costs guaranteed by a theoretical convergence proof. We apply MMLC on a publicly available neuroimaging cohort to predict two clinical measures and compare it with six other methods. Our experimental results show our proposed method achieves superior results on both computational efficiency and predictive accuracy and has great potential to assist the AD prevention.

Predicting future cognitive decline with hyperbolic stochastic coding

Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.

Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data

The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.

Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.

A Novel Computational Proxy for Characterizing Cognitive Reserve in Alzheimer's Disease

BACKGROUND

Although the abnormal depositions of amyloid plaques and neurofibrillary tangles are the hallmark of Alzheimer's disease (AD), converging evidence shows that the individual's neurodegeneration trajectory is regulated by the brain's capability to maintain normal cognition.

OBJECTIVE

The concept of cognitive reserve has been introduced into the field of neuroscience, acting as a moderating factor for explaining the paradoxical relationship between the burden of AD pathology and the clinical outcome. It is of high demand to quantify the degree of conceptual cognitive reserve on an individual basis.

METHODS

We propose a novel statistical model to quantify an individual's cognitive reserve against neuropathological burdens, where the predictors include demographic data (such as age and gender), socioeconomic factors (such as education and occupation), cerebrospinal fluid biomarkers, and AD-related polygenetic risk score. We conceptualize cognitive reserve as a joint product of AD pathology and socioeconomic factors where their interaction manifests a significant role in counteracting the progression of AD in our statistical model.

RESULTS

We apply our statistical models to re-investigate the moderated neurodegeneration trajectory by considering cognitive reserve, where we have discovered that 1) high education individuals have significantly higher reserve against the neuropathology than the low education group; however, 2) the cognitive decline in the high education group is significantly faster than low education individuals after the level of pathological burden increases beyond the tipping point.

CONCLUSION

We propose a computational proxy of cognitive reserve that can be used in clinical routine to assess the progression of AD.

Computing Univariate Neurodegenerative Biomarkers with Volumetric Optimal Transportation: A Pilot Study

Changes in cognitive performance due to neurodegenerative diseases such as Alzheimer's disease (AD) are closely correlated to the brain structure alteration. A univariate and personalized neurodegenerative biomarker with strong statistical power based on magnetic resonance imaging (MRI) will benefit clinical diagnosis and prognosis of neurodegenerative diseases. However, few biomarkers of this type have been developed, especially those that are robust to image noise and applicable to clinical analyses. In this paper, we introduce a variational framework to compute optimal transportation (OT) on brain structural MRI volumes and develop a univariate neuroimaging index based on OT to quantify neurodegenerative alterations. Specifically, we compute the OT from each image to a template and measure the Wasserstein distance between them. The obtained Wasserstein distance, Wasserstein Index (WI) for short to specify the distance to a template, is concise, informative and robust to random noise. Comparing to the popular linear programming-based OT computation method, our framework makes use of Newton's method, which makes it possible to compute WI in large-scale datasets. Experimental results, on 314 subjects (140 Aβ + AD and 174 Aβ- normal controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset, provide preliminary evidence that the proposed WI is correlated with a clinical cognitive measure (the Mini-Mental State Examination (MMSE) score), and it is able to identify group difference and achieve a good classification accuracy, outperforming two other popular univariate indices including hippocampal volume and entorhinal cortex thickness. The current pilot work suggests the application of WI as a potential univariate neurodegenerative biomarker.

T2 heterogeneity: a novel marker of microstructural integrity associated with cognitive decline in people with mild cognitive impairment

BACKGROUND

Early Alzheimer's disease (AD) diagnosis is vital for development of disease-modifying therapies. Prior to significant brain tissue atrophy, several microstructural changes take place as a result of Alzheimer's pathology. These include deposition of amyloid, tau and iron, as well as altered water homeostasis in tissue and some cell death. T2 relaxation time, a quantitative MRI measure, is sensitive to these changes and may be a useful non-invasive, early marker of tissue integrity which could predict conversion to dementia. We propose that different microstructural changes affect T2 in opposing ways, such that average 'midpoint' measures of T2 are less sensitive than measuring distribution width (heterogeneity). T2 heterogeneity in the brain may present a sensitive early marker of AD pathology.

METHODS

In this cohort study, we tested 97 healthy older controls, 49 people with mild cognitive impairment (MCI) and 10 with a clinical diagnosis of AD. All participants underwent structural MRI including a multi-echo sequence for quantitative T2 assessment. Cognitive change over 1 year was assessed in 20 participants with MCI. T2 distributions were modelled in the hippocampus and thalamus using log-logistic distribution giving measures of log-median value (midpoint; T2μ) and distribution width (heterogeneity; T2σ).

RESULTS

We show an increase in T2 heterogeneity (T2σ; p < .0001) in MCI compared to healthy controls, which was not seen with midpoint (T2μ; p = .149) in the hippocampus and thalamus. Hippocampal T2 heterogeneity predicted cognitive decline over 1 year in MCI participants (p = .018), but midpoint (p = .132) and volume (p = .315) did not. Age affects T2, but the effects described here are significant even after correcting for age.

CONCLUSIONS

We show that T2 heterogeneity can identify subtle changes in microstructural integrity of brain tissue in MCI and predict cognitive decline over a year. We describe a new model that considers the competing effects of factors that both increase and decrease T2. These two opposing forces suggest that previous conclusions based on T2 midpoint may have obscured the true potential of T2 as a marker of subtle neuropathology. We propose that T2 heterogeneity reflects microstructural integrity with potential to be a widely used early biomarker of conditions such as AD.

Cortical thickness computation by solving tetrahedron-based harmonic field

Cortical thickness computation in magnetic resonance imaging (MRI) is an important method to study the brain morphological changes induced by neurodegenerative diseases. This paper presents an algorithm of thickness measurement based on a volumetric Laplacian operator (VLO), which is able to capture accurately the geometric information of brain images. The proposed algorithm is a novel three-step method: 1) The rule of parity and the shrinkage strategy are combined to detect and fix the intersection error regions between the cortical surface meshes separated by FreeSurfer software and the tetrahedral mesh is constructed which reflects the original morphological features of the cerebral cortex, 2) VLO and finite element method are combined to compute the temperature distribution in the cerebral cortex under the Dirichlet boundary conditions, and 3) the thermal gradient line is determined based on the constructed local isothermal surfaces and linear geometric interpolation results. Combined with half-face data storage structure, the cortical thickness can be computed accurately and effectively from the length of each gradient line. With the obtained thickness, we set experiments to study the group differences among groups of Alzheimer's disease (AD, N = 110), mild cognitive impairment (MCI, N = 101) and healthy control people (CTL, N = 128) by statistical analysis. The results show that the q-value associated with the group differences is 0.0458 between AD and CTL, 0.0371 between MCI and CTL, and 0.0044 between AD and MCI. Practical tests demonstrate that the algorithm of thickness measurement has high efficiency and is generic to be applied to various biological structures that have internal and external surfaces.

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images

BACKGROUND

Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

OBJECTIVE

A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN.

METHODS

First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog).

RESULTS

We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods.

CONCLUSION

Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

Association of Brain Magnetic Resonance Imaging Signs With Cognitive Outcomes in Persons With Nonimpaired Cognition and Mild Cognitive Impairment

IMPORTANCE

Brain atrophy and vascular lesions contribute to dementia and mild cognitive impairment (MCI) in clinical referral populations. Prospective evidence in older general populations is limited.

OBJECTIVE

To evaluate which magnetic resonance imaging (MRI) signs are independent risk factors for dementia and MCI.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cohort study included 1553 participants sampled from the Atherosclerosis Risk in Communities Study who had brain MRI scans and were dementia free during visit 5 (June 2011 to September 2013). Participants' cognitive status was evaluated through visit 6 (June 2016 to December 2017).

EXPOSURES

Brain regional volumes, microhemorrhages, white matter hyperintensity (WMH) volumes, and infarcts measured on 3-T MRI.

MAIN OUTCOMES AND MEASURES

Cognitive status (dementia, MCI, or nonimpaired cognition) was determined from in-person evaluations. Dementia among participants who missed visit 6 was identified via dementia surveillance and hospital discharge or death certificate codes. Cox proportional hazards models were used to evaluate the risk of dementia in 3 populations: dementia-free participants (N = 1553), participants with nonimpaired cognition (n = 1014), and participants with MCI (n = 539). Complementary log-log models were used for risk of MCI among participants with nonimpaired cognition who also attended visit 6 (n = 767). Models were adjusted for demographic variables, apolipoprotein E ε4 alleles, vascular risk factors, depressive symptoms, and heart failure.

RESULTS

Overall, 212 incident dementia cases were identified among 1553 participants (mean [SD] age at visit 5, 76 [5.2] years; 946 [60.9%] women; 436 [28.1%] African American) with a median (interquartile range) follow-up period of 4.9 (4.3-5.2) years. Significant risk factors of dementia included lower volumes in the Alzheimer disease (AD) signature region, including hippocampus, entorhinal cortex, and surrounding structures (hazard ratio [HR] per 1-SD decrease, 2.40; 95% CI, 1.89-3.04), lobar microhemorrhages (HR, 1.90; 95% CI, 1.30-2.77), higher volumes of WMH (HR per 1-SD increase, 1.44; 95% CI, 1.23-1.69), and lacunar infarcts (HR, 1.66; 95% CI, 1.20-2.31). The AD signature region volume was also consistently associated with both MCI and progression from MCI to dementia, while subcortical microhemorrhages and infarcts contributed less to the progression from MCI to dementia.

CONCLUSIONS AND RELEVANCE

In this study, lower AD signature region volumes, brain microhemorrhages, higher WMH volumes, and infarcts were risk factors associated with dementia in older community-based residents. Vascular changes were more important in the development of MCI than in its progression to dementia, while AD-related signs were important in both stages.

Applying surface-based hippocampal morphometry to study APOE-E4 allele dose effects in cognitively unimpaired subjects

Apolipoprotein E (APOE) e4 is the major genetic risk factor for late-onset Alzheimer's disease (AD). The dose-dependent impact of this allele on hippocampal volumes has been documented, but its influence on general hippocampal morphology in cognitively unimpaired individuals is still elusive. Capitalizing on the study of a large number of cognitively unimpaired late middle aged and older adults with two, one and no APOE-e4 alleles, the current study aims to characterize the ability of our automated surface-based hippocampal morphometry algorithm to distinguish between these three levels of genetic risk for AD and demonstrate its superiority to a commonly used hippocampal volume measurement. We examined the APOE-e4 dose effect on cross-sectional hippocampal morphology analysis in a magnetic resonance imaging (MRI) database of 117 cognitively unimpaired subjects aged between 50 and 85 years (mean = 57.4, SD = 6.3), including 36 heterozygotes (e3/e4), 37 homozygotes (e4/e4) and 44 non-carriers (e3/e3). The proposed automated framework includes hippocampal surface segmentation and reconstruction, higher-order hippocampal surface correspondence computation, and hippocampal surface deformation analysis with multivariate statistics. In our experiments, the surface-based method identified APOE-e4 dose effects on the left hippocampal morphology. Compared to the widely-used hippocampal volume measure, our hippocampal morphometry statistics showed greater statistical power by distinguishing cognitively unimpaired subjects with two, one, and no APOE-e4 alleles. Our findings mirrored previous studies showing that APOE-e4 has a dose effect on the acceleration of brain structure deformities. The results indicated that the proposed surface-based hippocampal morphometry measure is a potential preclinical AD imaging biomarker for cognitively unimpaired individuals.

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Applied surface-based hippocampal morphometry on cognitively unimpaired subjects.

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Our study identified APOE-e4 dose effects on cognitively unimpaired subjects.

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Surface-based hippocampal morphometry outperformed the hippocampal volume measure.

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Surface-based hippocampal morphometry may be a potential preclinical AD biomarker.

MR approaches in neurodegenerative disorders

Neurodegenerative disease is the umbrella term which refers to a range of clinical conditions causing degeneration of neurons within the central nervous system leading to loss of brain function and eventual death. The most prevalent of these is Alzheimer's disease (AD), which affects approximately 50 million people worldwide and is predicted to reach 75 million by 2030. Neurodegenerative diseases can only be fully diagnosed at post mortem by neuropathological assessment of the type and distribution of protein deposits which characterise each different condition, but there is a clear role for imaging technologies in aiding patient diagnoses in life. Magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques have been applied to study these conditions for many years. In this review, we consider the range of MR-based measurements and describe the findings in AD, but also contrast these with the second most common dementia, dementia with Lewy bodies (DLB). The most definitive observation is the major structural brain changes seen in AD using conventional T1-weighted (T1w) MRI, where medial temporal lobe structures are notably atrophied in most symptomatic patients with AD, but often preserved in DLB. Indeed these findings are sufficiently robust to have been incorporated into clinical diagnostic criteria. Diffusion tensor imaging (DTI) reveals widespread changes in tissue microstructure, with increased mean diffusivity and decreased fractional anisotropy reflecting the degeneration of the white matter structures. There are suggestions that there are subtle differences between AD and DLB populations. At the metabolic level, atrophy-corrected MRS demonstrates reduced density of healthy neurons in brain areas with altered perfusion and in regions known to show higher deposits of pathogenic proteins. As studies have moved from patients with advanced disease and clear dysfunction to patients with earlier presentation such as with mild cognitive impairment (MCI), which in some represents the first signs of their ensuing dementia, the ability of MRI to detect differences has been weaker and further work is still required, ideally in much larger cohorts than previously studied. The vast majority of imaging research in dementia populations has been univariate with respect to the MR-derived parameters considered. To date, none of these measurements has uniquely replicated the patterns of tissue involvement seen by neuropathology, and the ability of MR techniques to deliver a non-invasive diagnosis eludes us. Future opportunities may lie in combining MR and nuclear medicine approaches (position emission tomography, PET) to provide a more complete view of structural and metabolic changes. Such developments will require multi-variate analyses, possibly combined with artificial intelligence or deep learning algorithms, to enhance our ability to combine the array of image-derived information, genetic, gender and lifestyle factors.

Inter-observer variation of hippocampus delineation in hippocampal avoidance prophylactic cranial irradiation

BACKGROUND

Hippocampal avoidance prophylactic cranial irradiation (HA-PCI) techniques have been developed to reduce radiation damage to the hippocampus. An inter-observer hippocampus delineation analysis was performed and the influence of the delineation variability on dose to the hippocampus was studied.

MATERIALS AND METHODS

For five patients, seven observers delineated both hippocampi on brain MRI. The intra-class correlation (ICC) with absolute agreement and the generalized conformity index (CI_gen) were computed. Median surfaces over all observers' delineations were created for each patient and regional outlining differences were analysed. HA-PCI dose plans were made from the median surfaces and we investigated whether dose constraints in the hippocampus could be met for all delineations.

RESULTS

The ICC for the left and right hippocampus was 0.56 and 0.69, respectively, while the CI_gen ranged from 0.55 to 0.70. The posterior and anterior-medial hippocampal regions had most variation with SDs ranging from approximately 1 to 2.5 mm. The mean dose (D_mean) constraint was met for all delineations, but for the dose received by 1% of the hippocampal volume (D_1%) violations were observed.

CONCLUSION

The relatively low ICC and CI_gen indicate that delineation variability among observers for both left and right hippocampus was large. The posterior and anterior-medial border have the largest delineation inaccuracy. The hippocampus D_mean constraint was not violated.

HIPPOCAMPUS MORPHOMETRY STUDY ON PATHOLOGY-CONFIRMED ALZHEIMER'S DISEASE PATIENTS WITH SURFACE MULTIVARIATE MORPHOMETRY STATISTICS

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques (Aβ) in human brains. Most of prior brain imaging researchers used the clinical symptom based diagnosis without the confirmation of imaging or fluid Aβ information. In this work, we study hippocampus morphometry on a cohort consisting of Aβ positive AD (N = 151) and matched Aβ negative cognitively unimpaired subjects (N = 271) with Aβ positivity determined via florbetapir PET. The brain images are obtained from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI). We compute our surface multivariate morphometry statistics from segmented hippocampus structure in structural MR images. With these features, we find statistically significant difference by using Hotelling's T² tests. Meanwhile, we apply a patch-based analysis of sparse coding system for binary group classification and achieve an accuracy rate of 90.48%. Our results demonstrate that our surface multivariate morphometry statistics (MMS) perform better than traditional hippocampal volume measures in classification and it may be applied as a potential biomarker for distinguishing dementia due to AD from age matched normal aging individuals.

Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease

Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very high and generally less than 90%. The method of random support vector machine cluster was proposed to classify AD and HC in this paper. From the Alzheimer's Disease Neuroimaging Initiative database, the subjects including 25 AD individuals and 35 HC individuals were obtained. The classification accuracy could reach to 94.44% in the results. Furthermore, the method could also be used for feature selection and the accuracy could be maintained at the level of 94.44%. In addition, we could also find out abnormal brain regions (inferior frontal gyrus, superior frontal gyrus, precentral gyrus and cingulate cortex). It is worth noting that the proposed random support vector machine cluster could be a new insight to help the diagnosis of AD.

The importance of endpoint selection: How effective does a drug need to be for success in a clinical trial of a possible Alzheimer's disease treatment?

To date, Alzheimer’s disease (AD) clinical trials have been largely unsuccessful. Failures have been attributed to a number of factors including ineffective drugs, inadequate targets, and poor trial design, of which the choice of endpoint is crucial. Using data from the Alzheimer’s Disease Neuroimaging Initiative, we have calculated the minimum detectable effect size (MDES) in change from baseline of a range of measures over time, and in different diagnostic groups along the AD development trajectory. The Functional Activities Questionnaire score had the smallest MDES for a single endpoint where an effect of 27% could be detected within 3 years in participants with Late Mild Cognitive Impairment (LMCI) at baseline, closely followed by the Clinical Dementia Rating Sum of Boxes (CDRSB) score at 28% after 2 years in the same group. Composite measures were even more successful than single endpoints with an MDES of 21% in 3 years. Using alternative cognitive, imaging, functional, or composite endpoints, and recruiting patients that have LMCI could improve the success rate of AD clinical trials.

The anteroposterior and primary-to-posterior limbic ratios as MRI-derived volumetric markers of Alzheimer's disease

BACKGROUND/AIMS

Alzheimer's disease (AD) shows a characteristic pattern of brain atrophy, with predominant involvement of posterior limbic structures, and relative preservation of rostral limbic and primary cortical regions. We aimed to investigate the diagnostic utility of two gray matter volume ratios based on this pattern, and to develop a fully automated method to calculate them from unprocessed MRI files.

PATIENTS AND METHODS

Cross-sectional study of 118 subjects from the ADNI database, including normal controls and patients with mild cognitive impairment (MCI) and AD. Clinical variables and 3T T1-weighted MRI files were analyzed. Regional gray matter and total intracranial volumes were calculated with a shell script (gm_extractor) based on FSL. Anteroposterior and primary-to-posterior limbic ratios (APL and PPL) were calculated from these values. Diagnostic utility of variables was tested in logistic regression models using Bayesian model averaging for variable selection. External validity was evaluated with bootstrap sampling and a test set of 60 subjects.

RESULTS

gm_extractor showed high test-retest reliability and high concurrent validity with FSL's FIRST. Volumetric measurements agreed with the expected anatomical pattern associated with AD. APL and PPL ratios were significantly different between groups, and were selected instead of hippocampal and entorhinal volumes to differentiate normal from MCI or cognitively impaired (MCI plus AD) subjects.

CONCLUSION

APL and PPL ratios may be useful components of models aimed to differentiate normal subjects from patients with MCI or AD. These values, and other gray matter volumes, may be reliably calculated with gm_extractor.

Longitudinal whole-brain atrophy and ventricular enlargement in nondemented Parkinson's disease

We investigated whole-brain atrophy and ventricular enlargement over 18 months in nondemented Parkinson's disease (PD) and examined their associations with clinical measures and baseline CSF markers. PD subjects (n = 100) were classified at baseline into those with mild cognitive impairment (MCI; PD-MCI, n = 36) and no cognitive impairment (PD-NC, n = 64). Percentage of whole-brain volume change (PBVC) and ventricular expansion over 18 months were assessed with FSL-SIENA and ventricular enlargement (VIENA) respectively. PD-MCI showed increased global atrophy (-1.1% ± 0.8%) and ventricular enlargement (6.9 % ± 5.2%) compared with both PD-NC (PBVC: -0.4 ± 0.5, p < 0.01; VIENA: 2.1% ± 4.3%, p < 0.01) and healthy controls. In a subset of 35 PD subjects, CSF levels of tau, and Aβ42/Aβ40 ratio were correlated with PBVC and ventricular enlargement respectively. The sample size required to demonstrate a 20% reduction in PBVC and VIENA was approximately 1/15th of that required to detect equivalent changes in cognitive decline. These findings suggest that longitudinal MRI measurements have potential to serve as surrogate markers to complement clinical assessments for future disease-modifying trials in PD.

Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease

Previous studies have demonstrated altered brain activity in Alzheimer's disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from ¹⁸F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from ¹⁸F FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid β_1–42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD.

Towards a Holistic Cortical Thickness Descriptor: Heat Kernel-Based Grey Matter Morphology Signatures

In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.

Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations

PURPOSE

Precise and reproducible hippocampus outlining is important to quantify hippocampal atrophy caused by neurodegenerative diseases and to spare the hippocampus in whole brain radiation therapy when performing prophylactic cranial irradiation or treating brain metastases. This study aimed to quantify systematic differences between methods by comparing regional volume and outline reproducibility of manual, FSL-FIRST and FreeSurfer hippocampus segmentations.

MATERIALS AND METHODS

This study used a dataset from ADNI (Alzheimer's Disease Neuroimaging Initiative), including 20 healthy controls, 40 patients with mild cognitive impairment (MCI), and 20 patients with Alzheimer's disease (AD). For each subject back-to-back (BTB) T1-weighted 3D MPRAGE images were acquired at time-point baseline (BL) and 12 months later (M12). Hippocampi segmentations of all methods were converted into triangulated meshes, regional volumes were extracted and regional Jaccard indices were computed between the hippocampi meshes of paired BTB scans to evaluate reproducibility. Regional volumes and Jaccard indices were modelled as a function of group (G), method (M), hemisphere (H), time-point (T), region (R) and interactions.

RESULTS

For the volume data the model selection procedure yielded the following significant main effects G, M, H, T and R and interaction effects G-R and M-R. The same model was found for the BTB scans. For all methods volumes reduces with the severity of disease. Significant fixed effects for the regional Jaccard index data were M, R and the interaction M-R. For all methods the middle region was most reproducible, independent of diagnostic group. FSL-FIRST was most and FreeSurfer least reproducible.

DISCUSSION/CONCLUSION

A novel method to perform detailed analysis of subtle differences in hippocampus segmentation is proposed. The method showed that hippocampal segmentation reproducibility was best for FSL-FIRST and worst for Freesurfer. We also found systematic regional differences in hippocampal segmentation between different methods reinforcing the need of adopting harmonized protocols.

Alzheimer's Disease Cerebrospinal Fluid and Neuroimaging Biomarkers: Diagnostic Accuracy and Relationship to Drug Efficacy

Widely researched Alzheimer's disease (AD) biomarkers include in vivo brain imaging with PET and MRI, imaging of amyloid plaques, and biochemical assays of Aβ 1 - 42, total tau, and phosphorylated tau (p-tau-181) in cerebrospinal fluid (CSF). In this review, we critically evaluate these biomarkers and discuss their clinical utility for the differential diagnosis of AD. Current AD biomarker tests are either highly invasive (requiring CSF collection) or expensive and labor-intensive (neuroimaging), making them unsuitable for use in the primary care, clinical office-based setting, or to assess drug efficacy in clinical trials. In addition, CSF and neuroimaging biomarkers continue to face challenges in achieving required sensitivity and specificity and minimizing center-to-center variability (for CSF-Aβ 1 - 42 biomarkers CV = 26.5% ; http://www.alzforum.org/news/conference-coverage/paris-standardization-hurdle-spinal-fluid-imaging-markers). Although potentially useful for selecting patient populations for inclusion in AD clinical trials, the utility of CSF biomarkers and neuroimaging techniques as surrogate endpoints of drug efficacy needs to be validated. Recent trials of β- and γ-secretase inhibitors and Aβ immunization-based therapies in AD showed no significant cognitive improvements, despite changes in CSF and neuroimaging biomarkers. As we learn more about the dysfunctional cellular and molecular signaling processes that occur in AD, and how these processes are manifested in tissues outside of the brain, new peripheral biomarkers may also be validated as non-invasive tests to diagnose preclinical and clinical AD.

Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study

The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T² test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.

MORPHOMETRIC ANALYSIS OF HIPPOCAMPUS AND LATERAL VENTRICLE REVEALS REGIONAL DIFFERENCE BETWEEN COGNITIVELY STABLE AND DECLINING PERSONS

Alzheimers disease (AD) is a progressive neurodegenerative disease most prevalent in the elderly. Distinguishing disease-related memory decline from normal age-related memory decline has been clinically difficult due to the subtlety of cognitive change during the preclinical stage of AD. In contrast, sensitive biomarkers derived from in vivo neuroimaging data could improve the early identification of AD. In this study, we employed a morphometric analysis in the hippocampus and lateral ventricle. A novel group-wise template-based segmentation algorithm was developed for ventricular segmentation. Further, surface multivariate tensor-based morphometry and radial distance on each surface point were computed. Using Hotellings T² test, we found significant morphometric differences in both hippocampus and lateral ventricle between stable and clinically declining subjects. The left hemisphere was more severely affected than the right during this early disease stage. Hippocampal and ventricular morphometry has significant potential as an imaging biomarker for onset prediction and early diagnosis of AD.

Biochemical and Radiological Markers of Alzheimer's Disease Progression

Alzheimer's disease (AD) is a neurodegenerative, inevitably progressive disease with a rate of cognitive, functional, and behavioral decline that varies highly from patient to patient. Although several clinical predictors of AD progression have been identified, to our mind in clinical practice there is a lack of a reliable biomarker that enables one to stratify the risk of deterioration. Identification of biomarkers that allow the monitoring of AD progression could change the way physicians and caregivers make treatment decisions. This review summarizes the results of studies on potential biochemical and radiological markers related to AD progression.

Different patterns of cortical gray matter loss over time in behavioral variant frontotemporal dementia and Alzheimer's disease

We examined patterns of cortical thickness loss and cognitive decline over time in 19 patients with Alzheimer's disease (AD), 10 with behavioral variant frontotemporal dementia (bvFTD), and 34 controls with a mean interval of 2.1 ± 0.4 years. We measured vertexwise and regional cortical thickness changes of 6 lobar regions of interest between groups with the longitudinal FreeSurfer pipeline. Compared with controls, AD and bvFTD had a steeper rate of cognitive decline and showed faster cortical thinning per year. Decrease of thickness over time was highest in AD and generalized throughout the whole brain, most pronounced posteriorly, whereas bvFTD patients had a more selective loss in frontal cortex and in anterior parts of the temporal lobes. In a direct comparison, AD patients showed faster cortical thinning in the insula, temporal, and parietal regions, whereas bvFTD patients only showed faster cortical thinning in the orbitofrontal gyrus. Decline of cognitive performances was in line with cortical thinning and deteriorated the most in AD patients.

MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials

The goal of this work was to assess statistical power to detect treatment effects in Alzheimer’s disease (AD) clinical trials using magnetic resonance imaging (MRI)–derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer’s Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid−/ApoE4− group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.

Neuroprotective Effects of Cistanches Herba Therapy on Patients with Moderate Alzheimer's Disease

Cistanches Herba (CH) is thought to be a “Yang-invigorating” material in traditional Chinese medicine. We evaluated neuroprotective effects of Cistanches Herba on Alzheimer's disease (AD) patients. Moderate AD participants were divided into 3 groups: Cistanches Herba capsule (CH, n = 10), Donepezil tablet (DON, n = 8), and control group without treatment (n = 6). We assessed efficacy by MMSE and ADAS-cog, and investigated the volume changes of hippocampus by 1.5 T MRI scans. Protein, mRNA levels, and secretions of total-tau (T-tau), tumor necrosis factor-α (TNF-α), and interleukin- (IL) 1β (IL-1β) in cerebrospinal fluid (CSF) were detected by Western blot, RT-PCR, and ELISA. The scores showed statistical difference after 48 weeks of treatment compared to control group. Meanwhile, volume changes of hippocampus were slight in drug treatment groups but distinct in control group; the levels of T-tau, TNF-α, and IL-1β were decreased compared to those in control group. Cistanches Herba could improve cognitive and independent living ability of moderate AD patients, slow down volume changes of hippocampus, and reduce the levels of T-tau, TNF-α, and IL-1β. It suggested that Cistanches Herba had potential neuroprotective effects for moderate AD.

Structural Brain Changes in Early-Onset Alzheimer's Disease Subjects Using the LONI Pipeline Environment

BACKGROUND AND PURPOSE

This study investigates 36 subjects aged 55-65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to expand our knowledge of early-onset (EO) Alzheimer's Disease (EO-AD) using neuroimaging biomarkers.

METHODS

Nine of the subjects had EO-AD, and 27 had EO mild cognitive impairment (EO-MCI). The structural ADNI data were parcellated using BrainParser, and the 15 most discriminating neuroimaging markers between the two cohorts were extracted using the Global Shape Analysis (GSA) Pipeline workflow. Then the Local Shape Analysis (LSA) Pipeline workflow was used to conduct local (per-vertex) post-hoc statistical analyses of the shape differences based on the participants' diagnoses (EO-MCI+EO-AD). Tensor-based Morphometry (TBM) and multivariate regression models were used to identify the significance of the structural brain differences based on the participants' diagnoses.

RESULTS

The significant between-group regional differences using GSA were found in 15 neuroimaging markers. The results of the LSA analysis workflow were based on the subject diagnosis, age, years of education, apolipoprotein E (ε4), Mini-Mental State Examination, visiting times, and logical memory as regressors. All the variables had significant effects on the regional shape measures. Some of these effects survived the false discovery rate (FDR) correction. Similarly, the TBM analysis showed significant effects on the Jacobian displacement vector fields, but these effects were reduced after FDR correction.

CONCLUSIONS

These results may explain some of the differences between EO-AD and EO-MCI, and some of the characteristics of the EO cognitive impairment subjects.

A novel cortical thickness estimation method based on volumetric Laplace-Beltrami operator and heat kernel

Cortical thickness estimation in magnetic resonance imaging (MRI) is an important technique for research on brain development and neurodegenerative diseases. This paper presents a heat kernel based cortical thickness estimation algorithm, which is driven by the graph spectrum and the heat kernel theory, to capture the gray matter geometry information from the in vivo brain magnetic resonance (MR) images. First, we construct a tetrahedral mesh that matches the MR images and reflects the inherent geometric characteristics. Second, the harmonic field is computed by the volumetric Laplace-Beltrami operator and the direction of the steamline is obtained by tracing the maximum heat transfer probability based on the heat kernel diffusion. Thereby we can calculate the cortical thickness information between the point on the pial and white matter surfaces. The new method relies on intrinsic brain geometry structure and the computation is robust and accurate. To validate our algorithm, we apply it to study the thickness differences associated with Alzheimer's disease (AD) and mild cognitive impairment (MCI) on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our preliminary experimental results on 151 subjects (51 AD, 45 MCI, 55 controls) show that the new algorithm may successfully detect statistically significant difference among patients of AD, MCI and healthy control subjects. Our computational framework is efficient and very general. It has the potential to be used for thickness estimation on any biological structures with clearly defined inner and outer surfaces.

Studying ventricular abnormalities in mild cognitive impairment with hyperbolic Ricci flow and tensor-based morphometry

Mild Cognitive Impairment (MCI) is a transitional stage between normal aging and dementia and people with MCI are at high risk of progression to dementia. MCI is attracting increasing attention, as it offers an opportunity to target the disease process during an early symptomatic stage. Structural magnetic resonance imaging (MRI) measures have been the mainstay of Alzheimer's disease (AD) imaging research, however, ventricular morphometry analysis remains challenging because of its complicated topological structure. Here we describe a novel ventricular morphometry system based on the hyperbolic Ricci flow method and tensor-based morphometry (TBM) statistics. Unlike prior ventricular surface parameterization methods, hyperbolic conformal parameterization is angle-preserving and does not have any singularities. Our system generates a one-to-one diffeomorphic mapping between ventricular surfaces with consistent boundary matching conditions. The TBM statistics encode a great deal of surface deformation information that could be inaccessible or overlooked by other methods. We applied our system to the baseline MRI scans of a set of MCI subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI: 71 MCI converters vs. 62 MCI stable). Although the combined ventricular area and volume features did not differ between the two groups, our fine-grained surface analysis revealed significant differences in the ventricular regions close to the temporal lobe and posterior cingulate, structures that are affected early in AD. Significant correlations were also detected between ventricular morphometry, neuropsychological measures, and a previously described imaging index based on fluorodeoxyglucose positron emission tomography (FDG-PET) scans. This novel ventricular morphometry method may offer a new and more sensitive approach to study preclinical and early symptomatic stage AD.

Imaging endpoints for clinical trials in Alzheimer's disease

As the need to develop a successful disease-modifying treatment for Alzheimer's disease (AD) becomes more urgent, imaging is increasingly used in therapeutic trials. We provide an overview of how the different imaging modalities are used in AD studies and the current regulatory guidelines for their use in clinical trials as endpoints. We review the current literature for results of imaging endpoints of efficacy and safety in published clinical trials. We start with trials in mild to moderate AD, where imaging (largely magnetic resonance imaging (MRI)) has long played a role in inclusion and exclusion criteria; more recently, MRI has been used to identify adverse events and to measure rates of brain atrophy. The advent of amyloid imaging using positron emission tomography has led to trials incorporating amyloid measurements as endpoints and incidentally to the recognition of the high proportion of amyloid-negative individuals that may be recruited into these trials. Ongoing and planned trials now commonly include multimodality imaging: amyloid positron emission tomography, MRI and other modalities. At the same time, the failure of recent large profile trials in mild to moderate AD together with the realisation that there is a long prodromal period to AD has driven a push to move studies to earlier in the disease. Imaging has particularly important roles, alongside other biomarkers, in assessing efficacy because conventional clinical outcomes may have limited ability to detect treatment effects in these early stages.