Cortical thickness atrophy in the transentorhinal cortex in mild cognitive impairment

Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections

This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.

Amidst an amygdala renaissance in Alzheimer's disease

The amygdala was highlighted as an early site for neurofibrillary tau tangle pathology in Alzheimer's disease in the seminal 1991 article by Braak and Braak. This knowledge has, however, only received traction recently with advances in imaging and image analysis techniques. Here, we provide a cross-disciplinary overview of pathology and neuroimaging studies on the amygdala. These studies provide strong support for an early role of the amygdala in Alzheimer's disease and the utility of imaging biomarkers of the amygdala in detecting early changes and predicting decline in cognitive functions and neuropsychiatric symptoms in early stages. We summarize the animal literature on connectivity of the amygdala, demonstrating that amygdala nuclei that show the earliest and strongest accumulation of neurofibrillary tangle pathology are those that are connected to brain regions that also show early neurofibrillary tangle accumulation. Additionally, we propose an alternative pathway of neurofibrillary tangle spreading within the medial temporal lobe between the amygdala and the anterior hippocampus. The proposed existence of this pathway is strengthened by novel experimental data on human functional connectivity. Finally, we summarize the functional roles of the amygdala, highlighting the correspondence between neurofibrillary tangle accumulation and symptomatic profiles in Alzheimer's disease. In summary, these findings provide a new impetus for studying the amygdala in Alzheimer's disease and a unique perspective to guide further study on neurofibrillary tangle spreading and the occurrence of neuropsychiatric symptoms in Alzheimer's disease.

MR-based spatiotemporal anisotropic atrophy evaluation of hippocampus in Alzheimer's disease progression by multiscale skeletal representation

Increasing evidence has shown a higher sensitivity of Alzheimer's disease (AD) progression by local hippocampal atrophy rather than the whole volume. However, existing morphological methods based on subfield-volume or surface in imaging studies are not capable to describe the comprehensive process of hippocampal atrophy as sensitive as histological findings. To map histological distinctive measurements onto medical magnetic resonance (MR) images, we propose a multiscale skeletal representation (m-s-rep) to quantify focal hippocampal atrophy during AD progression in longitudinal cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The m-s-rep captures large-to-small-scale hippocampal morphology by spoke interpolation over label projection on skeletal models. To enhance morphological correspondence within subjects, we align the longitudinal m-s-reps by surface-based transformations from baseline to subsequent timepoints. Cross-sectional and longitudinal measurements derived from m-s-rep are statistically analyzed to comprehensively evaluate the bilateral hippocampal atrophy. Our findings reveal that during the early AD progression, atrophy primarily affects the lateral-medial extent of the hippocampus, with a difference of 1.8 mm in lateral-medial width in 2 years preceding conversion (p < .001), and the medial head exhibits a maximum difference of 3.05%/year in local atrophy rate (p = .011) compared to controls. Moreover, progressive mild cognitive impairment (pMCI) exhibits more severe and widespread atrophy in the head and body compared to stable mild cognitive impairment (sMCI), with a maximum difference of 1.21 mm in thickness in the medial head 1 year preceding conversion (p = .012). In summary, our proposed method can quantitatively measure the hippocampal morphological changes on 3T MR images, potentially assisting the pre-diagnosis and prognosis of AD.

A Correlative Relationship Between Heart Failure and Cognitive Impairment: A Narrative Review

Heart failure (HF) is a chronic condition affecting millions of people worldwide. While the cardinal manifestations of HF are related to the cardiovascular system, it has become progressively evident that mild cognitive impairment (MCI) is also a significant complication of the disease. In fact, a significant number of patients with HF may experience MCI, which can manifest as deficits in attention, memory, executive function, and processing speed. The mechanisms responsible for cognitive dysfunction in HF are intricate and multifactorial. Possible factors contributing to this condition include decreased cerebral blood flow, thrombogenicity associated with HF, systemic inflammatory conditions, and proteotoxicity. MCI in HF has significant clinical implications, as it is linked to poorer quality of life, increased morbidity and mortality, and higher healthcare costs. Additionally, MCI can disrupt self-care behaviors, adherence to medication, and decision-making abilities, all of which are crucial for effectively managing HF. However, there is currently no gold standard diagnostic tool and follow-up strategy for MCI in HF patients. There is limited knowledge on the prevention and treatment of MCI. In conclusion, MCI is a common and clinically important complication of HF. Considering the substantial influence of MCI on patient outcomes, it is imperative for healthcare providers to be cognizant of this issue and integrate cognitive screening and management strategies into the care of HF patients.

Higher cortical thickness/volume in Alzheimer's-related regions: protective factor or risk factor?

Some evidence suggests a biphasic pattern of changes in cortical thickness wherein higher, rather than lower, thickness is associated with very early Alzheimer's disease (AD) pathology. We examined whether integrating information from AD brain signatures based on mean diffusivity (MD) can aid in the interpretation of cortical thickness/volume as a risk factor for future AD-related changes. Participants were 572 men in the Vietnam Era Twin Study of Aging who were cognitively unimpaired at baseline (mean age = 56 years; range = 51-60). Individuals with both high thickness/volume signatures and high MD signatures at baseline had lower cortical thickness/volume in AD signature regions and lower episodic memory performance 12 years later compared to those with high thickness/volume and low MD signatures at baseline. Groups did not differ in level of young adult cognitive reserve. Our findings are in line with a biphasic model in which increased cortical thickness may precede future decline and establish the value of examining cortical MD alongside cortical thickness to identify subgroups with differential risk for poorer brain and cognitive outcomes.

Excellent Interrater Reliability for Manual Segmentation of the Medial Perirhinal Cortex

Objective: Evaluation of interrater reliability for manual segmentation of brain structures that are affected first by neurofibrillary tau pathology in Alzheimer's disease. Method: Medial perirhinal cortex, lateral perirhinal cortex, and entorhinal cortex were manually segmented by two raters on structural magnetic resonance images of 44 adults (20 men; mean age = 69.2 ± 10.4 years). Intraclass correlation coefficients (ICC) of cortical thickness and volumes were calculated. Results: Very high ICC values of manual segmentation for the cortical thickness of all regions (0.953-0.986) and consistently lower ICC values for volume estimates of the medial and lateral perirhinal cortex (0.705-0.874). Conclusions: The applied manual segmentation protocol allows different raters to achieve remarkably similar cortical thickness estimates for regions of the parahippocampal gyrus. In addition, the results suggest a preference for cortical thickness over volume as a reliable measure of atrophy, especially for regions affected by collateral sulcus variability (i.e., medial and lateral perirhinal cortex). The results provide a basis for future automated segmentation and collection of normative data.

Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

Dissection and culturing of adult lateral entorhinal cortex layer II neurons from APP/PS1 Alzheimer model mice

BACKGROUND

Primary neuronal cultures enable cell-biological studies of Alzheimer's disease (AD), albeit typically non-neuron-specific. The first cortical neurons affected in AD reside in layer II of the lateralmost part of the entorhinal cortex, and they undergo early accumulation of intracellular amyloid-β, form subsequent tau pathology, and start degenerating pre-symptomatically. These vulnerable entorhinal neurons uniquely express the glycoprotein reelin and provide selective inputs to the hippocampal memory system. Gaining a more direct access to study these neurons is therefore highly relevant.

NEW METHOD

We demonstrate a methodological approach for dissection and long-term culturing of adult lateral entorhinal layer II-neurons from AD-model mice.

RESULTS

We maintain adult dissected lateralmost entorhinal layer II-neurons beyond two months in culture. We show that they express neuronal markers, and that they are electrophysiologically active by 15 days in vitro and continuing beyond 2 months.

COMPARISON WITH EXISTING METHODS

Primary neurons are typically harvested from embryonic or early postnatal brains because such neurons are easier to culture compared to adult neurons. Methods to culture adult primary neurons have been reported, however, to our knowledge, culturing of adult entorhinal neuron-type specific primary neurons from AD-model animals have not been reported.

CONCLUSIONS

Our methodological approach offers a window to study initial pathological changes in the AD disease-cascade. This includes the study of proteinopathy, single-neuron changes, and network-level dysfunction.

Lowering levels of reelin in entorhinal cortex layer II-neurons results in lowered levels of intracellular amyloid-β

Projection neurons in the anteriolateral part of entorhinal cortex layer II are the predominant cortical site for hyper-phosphorylation of tau and formation of neurofibrillary tangles in prodromal Alzheimer's disease. A majority of layer II projection neurons in anteriolateral entorhinal cortex are unique among cortical excitatory neurons by expressing the protein reelin. In prodromal Alzheimer's disease, these reelin-expressing neurons are prone to accumulate intracellular amyloid-β, which is mimicked in a rat model that replicates the spatio-temporal cascade of the disease. Two important findings in relation to this are that reelin-signalling downregulates tau phosphorylation, and that oligomeric amyloid-β interferes with reelin-signalling. Taking advantage of this rat model, we used proximity ligation assay to assess whether reelin and intracellular amyloid-β directly interact during early, pre-plaque stages in anteriolateral entorhinal cortex layer II reelin-expressing neurons. We next made a viral vector delivering micro-RNA against reelin, along with a control vector, and infected reelin-expressing anteriolateral entorhinal cortex layer II-neurons to test whether reelin levels affect levels of intracellular amyloid-β and/or amyloid precursor protein. We analysed 25.548 neurons from 24 animals, which results in three important findings. First, in reelin-expressing anteriolateral entorhinal cortex layer II-neurons, reelin and intracellular amyloid-β engage in a direct protein-protein interaction. Second, injecting micro-RNA against reelin lowers reelin levels in these neurons, amounting to an effect size of 1.3-4.5 (Bayesian estimation of Cohen's d effect size, 95% credible interval). This causes a concomitant reduction of intracellular amyloid-β ranging across three levels of aggregation, including a reduction of Aβ42 monomers/dimers amounting to an effect size of 0.5-3.1, a reduction of Aβ prefibrils amounting to an effect size of 1.1-3.5 and a reduction of protofibrils amounting to an effect size of 0.05-2.1. Analysing these data using Bayesian estimation of mutual information furthermore reveals that levels of amyloid-β are dependent on levels of reelin. Third, the reduction of intracellular amyloid-β occurs without any substantial associated changes in levels of amyloid precursor protein. We conclude that reelin and amyloid-β directly interact at the intracellular level in the uniquely reelin-expressing projection neurons in anteriolateral entorhinal cortex layer II, where levels of amyloid-β are dependent on levels of reelin. Since amyloid-β is known to impair reelin-signalling causing upregulated phosphorylation of tau, our findings are likely relevant to the vulnerability for neurofibrillary tangle-formation of this entorhinal neuronal population.

From sMRI to task-fMRI: A unified geometric deep learning framework for cross-modal brain anatomo-functional mapping

Achieving predictions of brain functional activation patterns/task-fMRI maps from its underlying anatomy is an important yet challenging problem. Once successful, it will not only open up new ways to understand how brain anatomy influences functional organization of the brain, but also provide new technical support for the clinical use of anatomical information to guide the localization of cortical functional areas. However, due to the non-Euclidean complex architecture of brain anatomy and the inherent low signal-to-noise ratio (SNR) properties of fMRI signals, the key challenge in building such a cross-modal brain anatomo-functional mapping is how to effectively learn the context-aware information of brain anatomy and overcome the interference of noise-containing task-fMRI labels on the learning process. In this work, we propose a Unified Geometric Deep Learning framework (BrainUGDL) to perform the cross-modal brain anatomo-functional mapping task. Considering that both global and local structures of brain anatomy have an impact on brain functions from their respective perspectives, we innovatively propose the novel Global Graph Encoding (GGE) unit and Local Graph Attention (LGA) unit embedded into two parallel branches, focusing on learning the high-level global and local context information, respectively. Specifically, GGE learns the global context information of each mesh vertex by building and encoding global interactions, and LGA learns the local context information of each mesh vertex by selectively aggregating patch structure enhanced features from its spatial neighbors. The information learnt from the two branches is then fused to form a comprehensive representation of brain anatomical features for final brain function predictions. To address the inevitable measurement noise in task-fMRI labels, we further elaborate a novel uncertainty-filtered learning mechanism, which enables BrainUGDL to realize revised learning from the noise-containing labels through the estimated uncertainty. Experiments across seven open task-fMRI datasets from human connectome project (HCP) demonstrate the superiority of BrainUGDL. To our best knowledge, our proposed BrainUGDL is the first to achieve the prediction of individual task-fMRI maps solely based on brain sMRI data.

Projective Diffeomorphic Mapping of Molecular Digital Pathology with Tissue MRI

Reconstructing dense 3D anatomical coordinates from 2D projective measurements has become a central problem in digital pathology for both animal models and human studies. Here we describe Projective Large Deformation Diffeomorphic Metric Mapping (LDDMM), a technique which projects diffeomorphic mappings of dense human magnetic resonance imaging (MRI) atlases at tissue scales onto sparse measurements at micrometre scales associated with histological and more general optical imaging modalities. We solve the problem of dense mapping surjectively onto histological sections by incorporating technologies for crossing modalities that use nonlinear scattering transforms to represent multiple radiomic-like textures at micron scales, together with a Gaussian mixture-model framework for modelling tears and distortions associated to each section. We highlight the significance of our method through incorporation of neuropathological measures and MRI, of relevance to the development of biomarkers for Alzheimer's disease and one instance of the integration of imaging data across the scales of clinical imaging and digital pathology.

Molecular Computational Anatomy: Unifying the Particle to Tissue Continuum via Measure Representations of the Brain

OBJECTIVE

The objective of this research is to unify the molecular representations of spatial transcriptomics and cellular scale histology with the tissue scales of computational anatomy for brain mapping.

IMPACT STATEMENT

We present a unified representation theory for brain mapping based on geometric varifold measures of the microscale deterministic structure and function with the statistical ensembles of the spatially aggregated tissue scales.

INTRODUCTION

Mapping across coordinate systems in computational anatomy allows us to understand structural and functional properties of the brain at the millimeter scale. New measurement technologies in digital pathology and spatial transcriptomics allow us to measure the brain molecule by molecule and cell by cell based on protein and transcriptomic functional identity. We currently have no mathematical representations for integrating consistently the tissue limits with the molecular particle descriptions. The formalism derived here demonstrates the methodology for transitioning consistently from the molecular scale of quantized particles-using mathematical structures as first introduced by Dirac as the class of generalized functions-to the tissue scales with methods originally introduced by Euler for fluids.

METHODS

We introduce two mathematical methods based on notions of generalized functions and statistical mechanics. We use geometric varifolds, a product measure on space and function, to represent functional states at the micro-scales-electrophysiology, molecular histology-integrated with a Boltzmann-like program to pass from deterministic particle descriptions to empirical probabilities on the functional states at the tissue scales.

RESULTS

Our space-function varifold representation provides a recipe for traversing from molecular to tissue scales in terms of a cascade of linear space scaling composed with nonlinear functional feature mapping. Following the cascade implies every scale is a geometric measure so that a universal family of measure norms can be introduced which quantifies the geodesic connection between brains in the orbit independent of the probing technology, whether it be RNA identities, Tau or amyloid histology, spike trains, or dense MR imagery.

CONCLUSIONS

We demonstrate a unified brain mapping theory for molecular and tissue scales based on geometric measure representations. We call the consistent aggregation of tissue scales from particle and cellular scales, molecular computational anatomy.

Mechanistic Modeling of Longitudinal Shape Changes: Equations of Motion and Inverse Problems

This paper examines a longitudinal shape evolution model in which a three-dimensional volume progresses through a family of elastic equilibria in response to the time-derivative of an internal force, or yank, with an additional regularization to ensure diffeomorphic transformations. We consider two different models of yank and address the long time existence and uniqueness of solutions for the equations of motion in both models. In addition, we derive sufficient conditions for the existence of an optimal yank that best describes the change from an observed initial volume to an observed volume at a later time. The main motivation for this work is the understanding of processes such as growth and atrophy in anatomical structures, where the yank could be roughly interpreted as a metabolic event triggering morphological changes. We provide preliminary results on simple examples to illustrate, under this model, the retrievability of some attributes of such events.

Entorhinal Subfield Vulnerability to Neurofibrillary Tangles in Aging and the Preclinical Stage of Alzheimer's Disease

BACKGROUND

Neurofibrillary tangle (NFT) accumulation in the entorhinal cortex (EC) precedes the transformation from cognitive controls to mild cognitive impairment and Alzheimer's disease (AD). While tauopathy has been described in the EC before, the order and degree to which the individual subfields within the EC are engulfed by NFTs in aging and the preclinical AD stage is unknown.

OBJECTIVE

We aimed to investigate substructures within the EC to map the populations of cortical neurons most vulnerable to tau pathology in aging and the preclinical AD stage.

METHODS

We characterized phosphorylated tau (CP13) in 10 cases at eight well-defined anterior-posterior levels and assessed NFT density within the eight entorhinal subfields (described by Insausti and colleagues) at the preclinical stages of AD. We validated with immunohistochemistry and labeled the NFT density ratings on ex vivo MRIs. We measured subfield cortical thickness and reconstructed the labels as three-dimensional isosurfaces, resulting in anatomically comprehensive, histopathologically validated tau "heat maps."

RESULTS

We found the lateral EC subfields ELc, ECL, and ECs (lateral portion) to have the highest tau density in semi-quantitative scores and quantitative measurements. We observed significant stepwise higher tau from anterior to posterior levels (p < 0.001). We report an age-dependent anatomically-specific vulnerability, with all cases showing posterior tau pathology, yet older individuals displaying an additional anterior tau burden. Finally, cortical thickness of each subfield negatively correlated with respective tau scores (p < 0.05).

CONCLUSION

Our findings indicate that posterior-lateral subfields within the EC are the most vulnerable to early NFTs and atrophy in aging and preclinical AD.

Structural connectivity-based segmentation of the human entorhinal cortex

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected with presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected with distal CA1 and proximal subiculum (dCA1pSub) and lateral orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in the previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC.

From Picoscale Pathology to Decascale Disease: Image Registration with a Scattering Transform and Varifolds for Manipulating Multiscale Data

Advances in neuroimaging have yielded extensive variety in the scale and type of data available. Effective integration of such data promises deeper understanding of anatomy and disease-with consequences for both diagnosis and treatment. Often catered to particular datatypes or scales, current computational tools and mathematical frameworks remain inadequate for simultaneously registering these multiple modes of "images" and statistically analyzing the ensuing menagerie of data. Here, we present (1) a registration algorithm using a "scattering transform" to align high and low resolution images and (2) a varifold-based modeling framework to compute 3D spatial statistics of multiscale data. We use our methods to quantify microscopic tau pathology across macroscopic 3D regions of the medial temporal lobe to address a major challenge in the diagnosis of Alzheimer's Disease-the reliance on invasive methods to detect microscopic pathology.

Longitudinal 18F-MK-6240 tau tangles accumulation follows Braak stages

Tracking longitudinal tau tangles accumulation across the Alzheimer's disease continuum is crucial to better understand the natural history of tau pathology and for clinical trials. Although the available first-generation tau PET tracers detect tau accumulation in symptomatic individuals, their nanomolar affinity offers limited sensitivity to detect early tau accumulation in asymptomatic subjects. Here, we hypothesized the novel sub-nanomolar affinity tau tangles tracer [18F]MK-6240 can detect longitudinal tau accumulation in asymptomatic and symptomatic subjects. We studied 125 living individuals (65 cognitively unimpaired elderly amyloid-β negative, 22 cognitively unimpaired elderly amyloid-β positive, 21 mild cognitive impairment amyloid-β positive, 17 Alzheimer's disease dementia amyloid-β positive) with baseline amyloid-β [18F]AZD4694 PET and baseline and follow-up tau [18F]MK-6240 PET. [18F]MK-6240 standardized uptake value ratio (SUVR) was calculated at 90-110 min after tracer injection and used the cerebellar crus I as the reference region. In addition, we assessed in vivo [18F]MK-6240 SUVR and postmortem phosphorylated tau pathology in two Alzheimer's disease dementia participants who deceased after the PET scans. We found that cognitively unimpaired amyloid-β negative individuals had significant longitudinal tau accumulation confined to PET Braak-like stage I (3.9%) and II (2.8%) areas. Cognitively unimpaired amyloid-β positive showed greater tau accumulation in Braak-like stage I (8.9%), compared to later Braak stages. Mild cognitive impairment and Alzheimer's dementia amyloid-β positive patients showed tau accumulation in Braak III-VI, but not in Braak I-II regions. Cognitively impaired amyloid-β positive individuals that were Braak II-IV at baseline showed 4.6-7.5% annual increase in tau accumulation in Braak III-IV regions, whereas cognitively impaired amyloid-β positive Braak V-VI at baseline had 8.3-10.7% annual increase in Braak V-VI regions. Neuropathological assessments confirmed the PET-based Braak stages V-VI observed in the two brain donors. Our results suggest that [18F]MK-6240 SUVR is able to detect longitudinal tau accumulation in asymptomatic and symptomatic Alzheimer's disease. The highest magnitude of [18F]MK-6240 SUVR accumulation moved from medial temporal to sensorimotor cortex across the disease clinical spectrum. Trials using [18F]MK-6240 SUVR in cognitively unimpaired would be required to use regions-of-interest corresponding to early Braak stages, whereas trials in cognitively impaired would benefit from using regions-of-interest in late Braak stages. Anti-tau trials should take into consideration individuals' baseline PET Braak-like stage to minimize the variability introduced by the hierarchical accumulation of tau tangles in the human brain. Finally, our postmortem findings supported [18F]MK-6240 SUVR as a biomarker to stage tau pathology in Alzheimer's disease patients.

Studying Alzheimer disease, Parkinson disease, and amyotrophic lateral sclerosis with 7-T magnetic resonance

Ultra-high-field (UHF) magnetic resonance (MR) scanners, that is, equipment operating at static magnetic field of 7 tesla (7 T) and above, enable the acquisition of data with greatly improved signal-to-noise ratio with respect to conventional MR systems (e.g., scanners operating at 1.5 T and 3 T). The change in tissue relaxation times at UHF offers the opportunity to improve tissue contrast and depict features that were previously inaccessible. These potential advantages come, however, at a cost: in the majority of UHF-MR clinical protocols, potential drawbacks may include signal inhomogeneity, geometrical distortions, artifacts introduced by patient respiration, cardiac cycle, and motion. This article reviews the 7 T MR literature reporting the recent studies on the most widespread neurodegenerative diseases: Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.

Cortical Thickness and Hippocampal Volume in Vascular and Non-vascular Depressed Patients

Background: Reduced cortical thickness and hippocampal volume are prevalent markers of late life depression as well as mild cognitive impairment (MCI) but are conspicuously absent in the vascular depression (VD) literature. The present study aimed to determine differences in cortical thickness and hippocampal volume between VD and non-VD patients. Methods: Participants were enrolled in an 8-week open treatment antidepressant trial. Forty-one depressed individuals aged 50 and older underwent brain magnetic resonance imaging at baseline and were classified as VD or non-VD. Cortical thickness values for the left and right entorhinal, parahippocampal, and precuneal cortices, as well as left and right hippocampal volume, were linearly regressed on VD status to determine mean differences between VD and non-VD. Covariates included site, age, sex, and mean thickness or intracranial volume. Results: No statistical differences were found between VD and non-VD patients in cortical thickness of the bilateral precuneal, entorhinal, or parahippocampal cortices, or hippocampal volume (p > 0.001). Conclusions: The absence of statistical differences in gray matter between VD and non-VD patients raises several diagnostic, etiological, and developmental possibilities, namely that VD may not be connected with other late-life psychiatric illnesses such as MCI or dementia and that vascular disease may not be a common etiological risk factor for depression and dementia. Larger datasets, prospective longitudinal studies, and cognitively intact controls are needed to further address these types of questions.

Structural Changes in Thalamic Nuclei Across Prodromal and Clinical Alzheimer's Disease

BACKGROUND

Increasing evidence suggests that thalamic nuclei may atrophy in Alzheimer's disease (AD). We hypothesized that there will be significant atrophy of limbic thalamic nuclei associated with declining memory and cognition across the AD continuum.

OBJECTIVE

The objective of this work was to characterize volume differences in thalamic nuclei in subjects with early and late mild cognitive impairment (MCI) as well as AD when compared to healthy control (HC) subjects using a novel MRI-based thalamic segmentation technique (THOMAS).

METHODS

MPRAGE data from the ADNI database were used in this study (n = 540). Healthy control (n = 125), early MCI (n = 212), late MCI (n = 114), and AD subjects (n = 89) were selected, and their MRI data were parcellated to determine the volumes of 11 thalamic nuclei for each subject. Volumes across the different clinical subgroups were compared using ANCOVA.

RESULTS

There were significant differences in thalamic nuclei volumes between HC, late MCI, and AD subjects. The anteroventral, mediodorsal, pulvinar, medial geniculate, and centromedian nuclei were significantly smaller in subjects with late MCI and AD when compared to HC subjects. Furthermore, the mediodorsal, pulvinar, and medial geniculate nuclei were significantly smaller in early MCI when compared to HC subjects.

CONCLUSION

This work highlights nucleus specific atrophy within the thalamus in subjects with early and late MCI and AD. This is consistent with the hypothesis that memory and cognitive changes in AD are mediated by damage to a large-scale integrated neural network that extends beyond the medial temporal lobes.

Re-emphasizing early Alzheimer's disease pathology starting in select entorhinal neurons, with a special focus on mitophagy

The entorhinal-hippocampal system contains distinct networks subserving declarative memory. This system is selectively vulnerable to changes of ageing and pathological processes. The entorhinal cortex (EC) is a pivotal component of this memory system since it serves as the interface between the neocortex and the hippocampus. EC is heavily affected by the proteinopathies of Alzheimer's disease (AD). These appear in a stereotypical spatiotemporal manner and include increased levels of intracellular amyloid-beta Aβ (iAβ), parenchymal deposition of Aβ plaques, and neurofibrillary tangles (NFTs) containing abnormally processed Tau. Increased levels of iAβ and the formation of NFTs are seen very early on in a population of neurons belonging to EC layer II (EC LII), and recent evidence leads us to believe that this population is made up of highly energy-demanding reelin-positive (RE+) projection neurons. Mitochondria are fundamental to the energy supply, metabolism, and plasticity of neurons. Evidence from AD postmortem brain tissues supports the notion that mitochondrial dysfunction is one of the initial pathological events in AD, and this is likely to take place in the vulnerable RE + EC LII neurons. Here we review and discuss these notions, anchored to the anatomy of AD, and formulate a hypothesis attempting to explain the vulnerability of RE + EC LII neurons to the formation of NFTs. We attempt to link impaired mitochondrial clearance to iAβ and signaling involving both apolipoprotein 4 and reelin, and argue for their relevance to the formation of NFTs specifically in RE + EC LII neurons during the prodromal stages of AD. We believe future studies on these interactions holds promise to advance our understanding of AD etiology and provide new ideas for drug development.

18F-MK-6240 PET for early and late detection of neurofibrillary tangles

Braak stages of tau neurofibrillary tangle accumulation have been incorporated in the criteria for the neuropathological diagnosis of Alzheimer's disease. It is expected that Braak staging using brain imaging can stratify living individuals according to their individual patterns of tau deposition, which may prove crucial for clinical trials and practice. However, previous studies using the first-generation tau PET agents have shown a low sensitivity to detect tau pathology in areas corresponding to early Braak histopathological stages (∼20% of cognitively unimpaired elderly with tau deposition in regions corresponding to Braak I-II), in contrast to ∼80-90% reported in post-mortem cohorts. Here, we tested whether the novel high affinity tau tangles tracer 18F-MK-6240 can better identify individuals in the early stages of tau accumulation. To this end, we studied 301 individuals (30 cognitively unimpaired young, 138 cognitively unimpaired elderly, 67 with mild cognitive impairment, 54 with Alzheimer's disease dementia, and 12 with frontotemporal dementia) with amyloid-β 18F-NAV4694, tau 18F-MK-6240, MRI, and clinical assessments. 18F-MK-6240 standardized uptake value ratio images were acquired at 90-110 min after the tracer injection. 18F-MK-6240 discriminated Alzheimer's disease dementia from mild cognitive impairment and frontotemporal dementia with high accuracy (∼85-100%). 18F-MK-6240 recapitulated topographical patterns consistent with the six hierarchical stages proposed by Braak in 98% of our population. Cognition and amyloid-β status explained most of the Braak stages variance (P < 0.0001, R2 = 0.75). No single region of interest standardized uptake value ratio accurately segregated individuals into the six topographic Braak stages. Sixty-eight per cent of the cognitively unimpaired elderly amyloid-β-positive and 37% of the cognitively unimpaired elderly amyloid-β-negative subjects displayed tau deposition, at least in the transentorhinal cortex (Braak I). Tau deposition solely in the transentorhinal cortex was associated with an elevated prevalence of amyloid-β, neurodegeneration, and cognitive impairment (P < 0.0001). 18F-MK-6240 deposition in regions corresponding to Braak IV-VI was associated with the highest prevalence of neurodegeneration, whereas in Braak V-VI regions with the highest prevalence of cognitive impairment. Our results suggest that the hierarchical six-stage Braak model using 18F-MK-6240 imaging provides an index of early and late tau accumulation as well as disease stage in preclinical and symptomatic individuals. Tau PET Braak staging using high affinity tracers has the potential to be incorporated in the diagnosis of living patients with Alzheimer's disease in the near future.

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation. In this paper, we propose DL+DiReCT, a method using high-quality deep learning-based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface-based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT-based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan-rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect-sizes for group-wise differences of healthy controls compared to individuals with dementia were highest with the deep learning-based segmentation. DL+DiReCT is a promising combination of a deep learning-based method with a traditional registration technique to detect subtle changes in cortical thickness.

Longitudinal atrophy in early Braak regions in preclinical Alzheimer's disease

A major focus of Alzheimer's disease (AD) research has been finding sensitive outcome measures to disease progression in preclinical AD, as intervention studies begin to target this population. We hypothesize that tailored measures of longitudinal change of the medial temporal lobe (MTL) subregions (the sites of earliest cortical tangle pathology) are more sensitive to disease progression in preclinical AD compared to standard cognitive and plasma NfL measures. Longitudinal T1-weighted MRI of 337 participants were included, divided into amyloid-β negative (Aβ-) controls, cerebral spinal fluid p-tau positive (T+) and negative (T-) preclinical AD (Aβ+ controls), and early prodromal AD. Anterior/posterior hippocampus, entorhinal cortex, Brodmann areas (BA) 35 and 36, and parahippocampal cortex were segmented in baseline MRI using a novel pipeline. Unbiased change rates of subregions were estimated using MRI scans within a 2-year-follow-up period. Experimental results showed that longitudinal atrophy rates of all MTL subregions were significantly higher for T+ preclinical AD and early prodromal AD than controls, but not for T- preclinical AD. Posterior hippocampus and BA35 demonstrated the largest group differences among hippocampus and MTL cortex respectively. None of the cross-sectional MTL measures, longitudinal cognitive measures (PACC, ADAS-Cog) and cross-sectional or longitudinal plasma NfL reached significance in preclinical AD. In conclusion, longitudinal atrophy measurements reflect active neurodegeneration and thus are more directly linked to active disease progression than cross-sectional measurements. Moreover, accelerated atrophy in preclinical AD seems to occur only in the presence of concomitant tau pathology. The proposed longitudinal measurements may serve as efficient outcome measures in clinical trials.

Entorhinal and Transentorhinal Atrophy in Preclinical Alzheimer's Disease

This study examines the atrophy patterns in the entorhinal and transentorhinal cortices of subjects that converted from normal cognition to mild cognitive impairment. The regions were manually segmented from 3T MRI, then corrected for variability in boundary definition over time using an automated approach called longitudinal diffeomorphometry. Cortical thickness was calculated by deforming the gray matter-white matter boundary surface to the pial surface using an approach called normal geodesic flow. The surface was parcellated based on four atlases using large deformation diffeomorphic metric mapping. Average cortical thickness was calculated for (1) manually-defined entorhinal cortex, and (2) manually-defined transentorhinal cortex. Group-wise difference analysis was applied to determine where atrophy occurred, and change point analysis was applied to determine when atrophy started to occur. The results showed that by the time a diagnosis of mild cognitive impairment is made, the transentorhinal cortex and entorhinal cortex was up to 0.6 mm thinner than a control with normal cognition. A change point in atrophy rate was detected in the transentorhinal cortex 9–14 years prior to a diagnosis of mild cognitive impairment, and in the entorhinal cortex 8–11 years prior. The findings are consistent with autopsy findings that demonstrate neuronal changes in the transentorhinal cortex before the entorhinal cortex.

Imaging biomarkers in neurodegeneration: current and future practices

There is an increasing role for biological markers (biomarkers) in the understanding and diagnosis of neurodegenerative disorders. The application of imaging biomarkers specifically for the in vivo investigation of neurodegenerative disorders has increased substantially over the past decades and continues to provide further benefits both to the diagnosis and understanding of these diseases. This review forms part of a series of articles which stem from the University College London/University of Gothenburg course "Biomarkers in neurodegenerative diseases". In this review, we focus on neuroimaging, specifically positron emission tomography (PET) and magnetic resonance imaging (MRI), giving an overview of the current established practices clinically and in research as well as new techniques being developed. We will also discuss the use of machine learning (ML) techniques within these fields to provide additional insights to early diagnosis and multimodal analysis.

Diffeomorphic Registration With Intensity Transformation and Missing Data: Application to 3D Digital Pathology of Alzheimer's Disease

This paper examines the problem of diffeomorphic image registration in the presence of differing image intensity profiles and sparsely sampled, missing, or damaged tissue. Our motivation comes from the problem of aligning 3D brain MRI with 100-micron isotropic resolution to histology sections at 1 × 1 × 1,000-micron resolution with multiple varying stains. We pose registration as a penalized Bayesian estimation, exploiting statistical models of image formation where the target images are modeled as sparse and noisy observations of the atlas. In this injective setting, there is no assumption of symmetry between atlas and target. Cross-modality image matching is achieved by jointly estimating polynomial transformations of the atlas intensity. Missing data is accommodated via a multiple atlas selection procedure where several atlas images may be of homogeneous intensity and correspond to "background" or "artifact." The two concepts are combined within an Expectation-Maximization algorithm, where atlas selection posteriors and deformation parameters are updated iteratively and polynomial coefficients are computed in closed form. We validate our method with simulated images, examples from neuropathology, and a standard benchmarking dataset. Finally, we apply it to reconstructing digital pathology and MRI in standard atlas coordinates. By using a standard convolutional neural network to detect tau tangles in histology slices, this registration method enabled us to quantify the 3D density distribution of tauopathy throughout the medial temporal lobe of an Alzheimer's disease postmortem specimen.