When Alzheimer's is LATE: Why Does it Matter?

Recent therapeutic advances provide heightened motivation for accurate diagnosis of the underlying biologic causes of dementia. This review focuses on the importance of clinical recognition of limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE affects approximately one-quarter of older adults and produces an amnestic syndrome that is commonly mistaken for Alzheimer's disease (AD). Although AD and LATE often co-occur in the same patients, these diseases differ in the protein aggregates driving neuropathology (Aß amyloid/tau vs TDP-43). This review discusses signs and symptoms, relevant diagnostic testing, and potential treatment implications for LATE that may be helpful for physicians, patients, and families. ANN NEUROL 2023;94:211-222.

Tau-neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum.

INTRODUCTION: Variability in relationship of tau-based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non-specific nature of N, modulated by non-AD co-pathologies, age-related changes, and resilience factors. METHODS: We used regional T-N residual patterns to partition 184 patients within the Alzheimer's continuum into data-driven groups. These were compared with groups from 159 non-AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T-N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS: AD groups displayed spatial T-N mismatch patterns resembling neurodegeneration patterns in non-AD groups, similarly associated with non-AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T-N mismatch correlated with TDP-43 co-pathology. DISCUSSION: T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability in AD.

ACAT1/SOAT1 Blockade Suppresses LPS-Mediated Neuroinflammation by Modulating the Fate of Toll-like Receptor 4 in Microglia.

Cholesterol is stored as cholesteryl esters by the enzymes acyl-CoA:cholesterol acyltransferases/sterol O:acyltransferases (ACATs/SOATs). ACAT1 blockade (A1B) ameliorates the pro-inflammatory responses of macrophages to lipopolysaccharides (LPS) and cholesterol loading. However, the mediators involved in transmitting the effects of A1B in immune cells is unknown. Microglial Acat1/Soat1 expression is elevated in many neurodegenerative diseases and in acute neuroinflammation. We evaluated LPS-induced neuroinflammation experiments in control vs. myeloid-specific Acat1/Soat1 knockout mice. We also evaluated LPS-induced neuroinflammation in microglial N9 cells with and without pre-treatment with K-604, a selective ACAT1 inhibitor. Biochemical and microscopy assays were used to monitor the fate of Toll-Like Receptor 4 (TLR4), the receptor at the plasma membrane and the endosomal membrane that mediates pro-inflammatory signaling cascades. In the hippocampus and cortex, results revealed that Acat1/Soat1 inactivation in myeloid cell lineage markedly attenuated LPS-induced activation of pro-inflammatory response genes. Studies in microglial N9 cells showed that pre-incubation with K-604 significantly reduced the LPS-induced pro-inflammatory responses. Further studies showed that K-604 decreased the total TLR4 protein content by increasing TLR4 endocytosis, thus enhancing the trafficking of TLR4 to the lysosomes for degradation. We concluded that A1B alters the intracellular fate of TLR4 and suppresses its pro-inflammatory signaling cascade in response to LPS.

Tau-Atrophy Variability Reveals Phenotypic Heterogeneity in Alzheimer's Disease.

OBJECTIVE: Tau neurofibrillary tangles (T) are the primary driver of downstream neurodegeneration (N) and subsequent cognitive impairment in Alzheimer's disease (AD). However, there is substantial variability in the T-N relationship - manifested in higher or lower atrophy than expected for level of tau in a given brain region. The goal of this study was to determine if region-based quantitation of this variability allows for identification of underlying modulatory factors, including polypathology. METHODS: Cortical thickness (N) and 18 F-Flortaucipir SUVR (T) were computed in 104 gray matter regions from a cohort of cognitively-impaired, amyloid-positive (A+) individuals. Region-specific residuals from a robust linear fit between SUVR and cortical thickness were computed as a surrogate for T-N mismatch. A summary T-N mismatch metric defined using residuals were correlated with demographic and imaging-based modulatory factors, and to partition the cohort into data-driven subgroups. RESULTS: The summary T-N mismatch metric correlated with underlying factors such as age and burden of white matter hyperintensity lesions. Data-driven subgroups based on clustering of residuals appear to represent different biologically relevant phenotypes, with groups showing distinct spatial patterns of higher or lower atrophy than expected. INTERPRETATION: These data support the notion that a measure of deviation from a normative relationship between tau burden and neurodegeneration across brain regions in individuals on the AD continuum captures variability due to multiple underlying factors, and can reveal phenotypes, which if validated, may help identify possible contributors to neurodegeneration in addition to tau, which may ultimately be useful for cohort selection in clinical trials. ANN NEUROL 2021;90:751-762.

Limbic-Predominant Age-Related TDP-43 Encephalopathy: LATE-Breaking Updates in Clinicopathologic Features and Biomarkers.

PURPOSE OF REVIEW: Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a recently defined neurodegenerative disease characterized by amnestic phenotype and pathological inclusions of TAR DNA-binding protein 43 (TDP-43). LATE is distinct from rarer forms of TDP-43 diseases such as frontotemporal lobar degeneration with TDP-43 but is also a common copathology with Alzheimer's disease (AD) and cerebrovascular disease and accelerates cognitive decline. LATE contributes to clinicopathologic heterogeneity in neurodegenerative diseases, so it is imperative to distinguish LATE from other etiologies. RECENT FINDINGS: Novel biomarkers for LATE are being developed with magnetic resonance imaging (MRI) and positron emission tomography (PET). When cooccurring with AD, LATE exhibits identifiable patterns of limbic-predominant atrophy on MRI and hypometabolism on 18F-fluorodeoxyglucose PET that are greater than expected relative to levels of local AD pathology. Efforts are being made to develop TDP-43-specific radiotracers, molecularly specific biofluid measures, and genomic predictors of TDP-43. LATE is a highly prevalent neurodegenerative disease distinct from previously characterized cognitive disorders.

Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.

Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision support tool to augment radiologist performance. We utilized previously developed AI systems that combine convolutional neural networks and expert-derived Bayesian networks to distinguish among 50 diagnostic entities on multimodal brain MRIs. We tested whether these systems could augment radiologist performance through an interactive clinical decision support tool known as Adaptive Radiology Interpretation and Education System (ARIES) in 194 test cases. Four radiology residents and three academic neuroradiologists viewed half of the cases unassisted and half with the results of the AI system displayed on ARIES. Diagnostic accuracy of radiologists for top diagnosis (TDx) and top three differential diagnosis (T3DDx) was compared with and without ARIES. Radiology resident performance was significantly better with ARIES for both TDx (55% vs 30%; P < .001) and T3DDx (79% vs 52%; P = 0.002), with the largest improvement for rare diseases (39% increase for T3DDx; P < 0.001). There was no significant difference between attending performance with and without ARIES for TDx (72% vs 69%; P = 0.48) or T3DDx (86% vs 89%; P = 0.39). These findings suggest that a hybrid deep learning and Bayesian inference clinical decision support system has the potential to augment diagnostic accuracy of non-specialists to approach the level of subspecialists for a large array of diseases on brain MRI.

Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI.

Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

Mycobacterial spindle cell pseudotumor of the spinal cord: Case report and literature review.

We report the first description of spinal cord mycobacterial spindle cell pseudotumor. A patient with newly diagnosed advanced HIV presented with recent-onset bilateral leg weakness and was found to have a hypermetabolic spinal cord mass on structural and molecular imaging. Biopsy and cultures from blood and cerebrospinal fluid confirmed spindle cell pseudotumor due to Mycobacterium avium-intracellulare. Despite control of HIV and initial reduction in pseudotumor volume on antiretrovirals and antimycobacterials (azithromycin, ethambutol, rifampin/rifabutin), he ultimately experienced progressive leg weakness due to pseudotumor re-expansion. Here, we review literature and discuss multidisciplinary diagnosis, monitoring and management challenges, including immune reconstitution inflammatory syndrome.

Neuroimaging Patterns of Intracranial Infections: Meningitis, Cerebritis, and Their Complications.

Neuroimaging provides rapid, noninvasive visualization of central nervous system infections for optimal diagnosis and management. Generalizable and characteristic imaging patterns help radiologists distinguish different types of intracranial infections including meningitis and cerebritis from a variety of bacterial, viral, fungal, and/or parasitic causes. Here, we describe key radiologic patterns of meningeal enhancement and diffusion restriction through profiles of meningitis, cerebritis, abscess, and ventriculitis. We discuss various imaging modalities and recent diagnostic advances such as deep learning through a survey of intracranial pathogens and their radiographic findings. Moreover, we explore critical complications and differential diagnoses of intracranial infections.

Tau-Neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum.

Variability in the relationship of tau-based neurofibrillary tangles (T) and degree of neurodegeneration (N) in Alzheimer's Disease (AD) is likely attributable to the non-specific nature of N, which is also modulated by such factors as other co-pathologies, age-related changes, and developmental differences. We studied this variability by partitioning patients within the Alzheimer's continuum into data-driven groups based on their regional T-N dissociation, which reflects the residuals after the effect of tau pathology is "removed". We found six groups displaying distinct spatial T-N mismatch and thickness patterns despite similar tau burden. Their T-N patterns resembled the neurodegeneration patterns of non-AD groups partitioned on the basis of z-scores of cortical thickness alone and were similarly associated with surrogates of non-AD factors. In an additional sample of individuals with antemortem imaging and autopsy, T-N mismatch was associated with TDP-43 co-pathology. Finally, T-N mismatch training was then applied to a separate cohort to determine the ability to classify individual patients within these groups. These findings suggest that T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability to Alzheimer's disease.

Interinstitutional Portability of a Deep Learning Brain MRI Lesion Segmentation Algorithm.

PURPOSE: To assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss. MATERIALS AND METHODS: In this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients. RESULTS: The U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98). CONCLUSION: For brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

Dissociation of tau pathology and neuronal hypometabolism within the ATN framework of Alzheimer's disease.

Alzheimer's disease (AD) is defined by amyloid (A) and tau (T) pathologies, with T better correlated to neurodegeneration (N). However, T and N have complex regional relationships in part related to non-AD factors that influence N. With machine learning, we assessed heterogeneity in 18F-flortaucipir vs. 18F-fluorodeoxyglucose positron emission tomography as markers of T and neuronal hypometabolism (NM) in 289 symptomatic patients from the Alzheimer's Disease Neuroimaging Initiative. We identified six T/NM clusters with differing limbic and cortical patterns. The canonical group was defined as the T/NM pattern with lowest regression residuals. Groups resilient to T had less hypometabolism than expected relative to T and displayed better cognition than the canonical group. Groups susceptible to T had more hypometabolism than expected given T and exhibited worse cognitive decline, with imaging and clinical measures concordant with non-AD copathologies. Together, T/NM mismatch reveals distinct imaging signatures with pathobiological and prognostic implications for AD.

Cholesterol, Atherosclerosis, and APOE in Vascular Contributions to Cognitive Impairment and Dementia (VCID): Potential Mechanisms and Therapy.

Vascular contributions to cognitive impairment and dementia (VCID) are a common cause of cognitive decline, yet limited therapies exist. This cerebrovascular disease results in neurodegeneration via acute, chronic, local, and systemic mechanisms. The etiology of VCID is complex, with a significant impact from atherosclerosis. Risk factors including hypercholesterolemia and hypertension promote intracranial atherosclerotic disease and carotid artery stenosis (CAS), which disrupt cerebral blood flow and trigger ischemic strokes and VCID. Apolipoprotein E (APOE) is a cholesterol and phospholipid carrier present in plasma and various tissues. APOE is implicated in dyslipidemia and Alzheimer disease (AD); however, its connection with VCID is less understood. Few experimental models for VCID exist, so much of the present information has been drawn from clinical studies. Here, we review the literature with a focus on the clinical aspects of atherosclerotic cerebrovascular disease and build a working model for the pathogenesis of VCID. We describe potential intermediate steps in this model, linking cholesterol, atherosclerosis, and APOE with VCID. APOE4 is a minor isoform of APOE that promotes lipid dyshomeostasis in astrocytes and microglia, leading to chronic neuroinflammation. APOE4 disturbs lipid homeostasis in macrophages and smooth muscle cells, thus exacerbating systemic inflammation and promoting atherosclerotic plaque formation. Additionally, APOE4 may contribute to stromal activation of endothelial cells and pericytes that disturb the blood-brain barrier (BBB). These and other risk factors together lead to chronic inflammation, atherosclerosis, VCID, and neurodegeneration. Finally, we discuss potential cholesterol metabolism based approaches for future VCID treatment.

Astrocyte activation imaging with 11C-acetate and amyloid PET in mild cognitive impairment due to Alzheimer pathology.

BACKGROUND: Neuroinflammation is a well-known feature of early Alzheimer disease (AD) yet astrocyte activation has not been extensively evaluated with in vivo imaging in mild cognitive impairment (MCI) due to amyloid plaque pathology. Unlike neurons, astrocytes metabolize acetate, which has potential as a glial biomarker in neurodegeneration in response to AD pathologic features. Since the medial temporal lobe (MTL) is a hotspot for AD neurodegeneration and inflammation, we assessed astrocyte activity in the MTL and compared it to amyloid and cognition. METHODS: We evaluate spatial patterns of in vivo astrocyte activation and their relationships to amyloid deposition and cognition in a cross-sectional pilot study of six participants with MCI and five cognitively normal participants. We measure 11C-acetate and 18F-florbetaben amyloid standardized uptake values ratios (SUVRs) and kinetic flux compared to the cerebellum on PET, with MRI and neurocognitive testing. RESULTS: MTL 11C-acetate SUVR was significantly elevated in MCI compared to cognitively normal participants (P = 0.03; Cohen d = 1.76). Moreover, MTL 11C-acetate SUVR displayed significant associations with global and regional amyloid burden in MCI. Greater MTL 11C-acetate retention was significantly related with worse neurocognitive measures including the Montreal Cognitive Assessment (P = 0.001), word list recall memory (P = 0.03), Boston naming test (P = 0.04) and trails B test (P = 0.04). CONCLUSIONS: While further validation is required, this exploratory pilot study suggests a potential role for 11C-acetate PET as a neuroinflammatory biomarker in MCI and early AD to provide clinical and translational insights into astrocyte activation as a pathological response to amyloid.

Diverse Applications of Artificial Intelligence in Neuroradiology.

Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke. Here, the authors review the diverse landscape of emerging neuroimaging applications of AI, including workflow optimization, lesion segmentation, and precision education. Given the many modalities used in diagnosing neurologic diseases, AI may be deployed to integrate across modalities (MR imaging, computed tomography, PET, electroencephalography, clinical and laboratory findings), facilitate crosstalk among specialists, and potentially improve diagnosis in patients with trauma, multiple sclerosis, epilepsy, and neurodegeneration. Together, there are myriad applications of AI for neuroradiology."

Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study.

PURPOSE: To develop and validate a system that could perform automated diagnosis of common and rare neurologic diseases involving deep gray matter on clinical brain MRI studies. MATERIALS AND METHODS: In this retrospective study, multimodal brain MRI scans from 212 patients (mean age, 55 years ± 17 [standard deviation]; 113 women) with 35 neurologic diseases and normal brain MRI scans obtained between January 2008 and January 2018 were included (110 patients in the training set, 102 patients in the test set). MRI scans from 178 patients (mean age, 48 years ± 17; 106 women) were used to supplement training of the neural networks. Three-dimensional convolutional neural networks and atlas-based image processing were used for extraction of 11 imaging features. Expert-derived Bayesian networks incorporating domain knowledge were used for differential diagnosis generation. The performance of the artificial intelligence (AI) system was assessed by comparing diagnostic accuracy with that of radiologists of varying levels of specialization by using the generalized estimating equation with robust variance estimator for the top three differential diagnoses (T3DDx) and the correct top diagnosis (TDx), as well as with receiver operating characteristic analyses. RESULTS: In the held-out test set, the imaging pipeline detected 11 key features on brain MRI scans with 89% accuracy (sensitivity, 81%; specificity, 95%) relative to academic neuroradiologists. The Bayesian network, integrating imaging features with clinical information, had an accuracy of 85% for T3DDx and 64% for TDx, which was better than that of radiology residents (n = 4; 56% for T3DDx, 36% for TDx; P < .001 for both) and general radiologists (n = 2; 53% for T3DDx, 31% for TDx; P < .001 for both). The accuracy of the Bayesian network was better than that of neuroradiology fellows (n = 2) for T3DDx (72%; P = .003) but not for TDx (59%; P = .19) and was not different from that of academic neuroradiologists (n = 2; 84% T3DDx, 65% TDx; P > .09 for both). CONCLUSION: A hybrid AI system was developed that simultaneously provides a quantitative assessment of disease burden, explainable intermediate imaging features, and a probabilistic differential diagnosis that performed at the level of academic neuroradiologists. This type of approach has the potential to improve clinical decision making for common and rare diseases.Supplemental material is available for this article.© RSNA, 2020.

Artificial intelligence for precision education in radiology.

In the era of personalized medicine, the emphasis of health care is shifting from populations to individuals. Artificial intelligence (AI) is capable of learning without explicit instruction and has emerging applications in medicine, particularly radiology. Whereas much attention has focused on teaching radiology trainees about AI, here our goal is to instead focus on how AI might be developed to better teach radiology trainees. While the idea of using AI to improve education is not new, the application of AI to medical and radiological education remains very limited. Based on the current educational foundation, we highlight an AI-integrated framework to augment radiology education and provide use case examples informed by our own institution's practice. The coming age of "AI-augmented radiology" may enable not only "precision medicine" but also what we describe as "precision medical education," where instruction is tailored to individual trainees based on their learning styles and needs.