Imaging Markers of Vascular Brain Health: Quantification, Clinical Implications, and Future Directions

Cerebrovascular disease (CVD) manifests through a broad spectrum of mechanisms that negatively impact brain and cognitive health. Oftentimes, CVD changes (excluding acute stroke) are insufficiently considered in aging and dementia studies which can lead to an incomplete picture of the etiologies contributing to the burden of cognitive impairment. Our goal with this focused review is 3-fold. First, we provide a research update on the current magnetic resonance imaging methods that can measure CVD lesions as well as early CVD-related brain injury specifically related to small vessel disease. Second, we discuss the clinical implications and relevance of these CVD imaging markers for cognitive decline, incident dementia, and disease progression in Alzheimer disease, and Alzheimer-related dementias. Finally, we present our perspective on the outlook and challenges that remain in the field. With the increased research interest in this area, we believe that reliable CVD imaging biomarkers for aging and dementia studies are on the horizon.

MRI and CT imaging biomarkers of cerebral amyloid angiopathy in lobar intracerebral hemorrhage

Background: Cerebral amyloid angiopathy (CAA), a common cause of intracerebral hemorrhage (ICH), is diagnosed using the Boston criteria including magnetic resonance imaging (MRI) biomarkers (cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS). The simplified Edinburgh criteria include computed tomography (CT) biomarkers (subarachnoid extension (SAE) and finger-like projections (FLPs)). The underlying mechanisms and diagnostic accuracy of CT compared to MRI biomarkers of CAA are unknown. Methods: We included 140 survivors of spontaneous lobar supratentorial ICH with both acute CT and MRI. We assessed associations between MRI and CT biomarkers and the diagnostic accuracy of CT- compared to MRI-based criteria. Results: FLPs were more common in patients with strictly lobar CMB (44.7% vs 23.5%; p = 0.014) and SAE was more common in patients with cSS (61.3% vs 31.2%; p = 0.002). The high probability of the CAA category of the simplified Edinburgh criteria showed 87.2% (95% confidence interval (CI): 78.3–93.4) specificity, 29.6% (95% CI: 18.0–43.6) sensitivity, 59.3% (95% CI: 38.8–77.6) positive predictive value, and 66.4% (95%: CI 56.9–75.0) negative predictive value, 2.3 (95% CI: 1.2–4.6) positive likelihood ratio and 0.8 (95% CI 0.7–1.0) negative likelihood ratio for probable CAA (vs non-probable CAA), defined by the modified Boston criteria; the area under the receiver operating characteristic curve (AUROC) was 0.62 (95% CI: 0.54–0.71). Conclusion: In lobar ICH survivors, we found associations between putative biomarkers of parenchymal CAA (FLP and strictly lobar CMBs) and putative biomarkers of leptomeningeal CAA (SAE and cSS). In a hospital population, CT biomarkers might help rule-in probable CAA (diagnosed using the Boston criteria), but their absence is probably not as useful to rule it out, suggesting an important continued role for MRI in ICH survivors with suspected CAA.

Association Between DCE-MRI Perfusion Histogram Parameters and EGFR and VEGF Expressions in Different Lauren Classifications of Advanced Gastric Cancer

Objective: To investigate the correlations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) perfusion histogram parameters and vascular endothelial growth factor (VEGF) and epidermal growth factor receptor (EGFR) expressions in advanced gastric cancer (AGC).Methods: This retrospective study included 80 pathologically confirmed patients with AGC who underwent DCE-MRI before surgery from February 2017 to May 2021. The DCE-MRI perfusion histogram parameters were calculated by Omni Kinetics software in four quantitative parameter maps. Immunohistochemical methods were used to detect VEGF and EGFR expressions and calculate the immunohistochemical score.Results: VEGF expression was relatively lower in patients with intestinal-type AGC than those with diffuse-type AGC (p < 0.05). For VEGF, Receiver operating characteristics (ROC) curve analysis revealed that Quantile 90 of Ktrans, Meanvalue of Kep and Quantile 50 of Ve provided the perfect combination of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for distinguishing high and low VEGF expression, For EGFR, Skewness of Ktrans, Energy of Kep and Entropy of Vp provided the perfect combination of sensitivity, specificity, PPV and NPV for distinguishing high and low EGFR expression. Ktrans (Quantile 90, Entropy) showed the strongest correlation with VEGF and EGFR in patients with intestinal-type AGC (r = 0.854 and r = 0.627, respectively); Ktrans (Mean value, Entropy) had the strongest correlation with VEGF and EGFR in patients with diffuse-type AGC (r = 0.635 and 0.656, respectively).Conclusion: DCE-MRI perfusion histogram parameters can serve as imaging biomarkers to reflect VEGF and EGFR expressions and estimate their difference in different Lauren classifications of AGC.

Machine-learning and Automatically Segmented Retinal Biomarkers Generate Spatial Heatmaps Predictive for Standard and Low Luminance Visual Acuity in Geographic Atrophy

Objective: Predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in geographic atrophyDesign: Post-hoc analysis of data from a clinical trial and routine clinical care.Methods: Automated segmentation of OCT scans from 476 eyes (325 patients) with geographic atrophy. Machine learning modelling of resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under both standard luminance (VA) and low luminance (LLVA) conditions.Main Outcome Measure: Correlation coefficient (R2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters.Results: Best-corrected VA under both standard luminance (R2 0.46 MAE 10.2 ETDRS letters) and low-luminance conditions (R2 0.25 MAE 12.1) could be predicted. The foveal region contributed the most (46.5%) toward model performance, with retinal pigment epithelium loss and outer retinal atrophy contributing the most (31.1%). For LLVA, however, features in the non-foveal regions were most important (74.5%), led by photoreceptor degeneration (38.9%).Conclusions: Our method of automatic qOCT segmentation demonstrates functional significance for vision in geographic atrophy, including LLVA. LLVA is itself predictive of geographic atrophy progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.

Brain Imaging Biomarkers for Chronic Pain

The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.

Recent Developments in Positron Emission Tomography Tracers for Proteinopathies Imaging in Dementia

An early detection and intervention for dementia represent tremendous unmet clinical needs and priorities in society. A shared feature of neurodegenerative diseases causing dementia is the abnormal accumulation and spreading of pathological protein aggregates, which affect the selective vulnerable circuit in a disease-specific pattern. The advancement in positron emission tomography (PET) biomarkers has accelerated the understanding of the disease mechanism and development of therapeutics for Alzheimer’s disease and Parkinson’s disease. The clinical utility of amyloid-β PET and the clinical validity of tau PET as diagnostic biomarker for Alzheimer’s disease continuum have been demonstrated. The inclusion of biomarkers in the diagnostic criteria has introduced a paradigm shift that facilitated the early and differential disease diagnosis and impacted on the clinical management. Application of disease-modifying therapy likely requires screening of patients with molecular evidence of pathological accumulation and monitoring of treatment effect assisted with biomarkers. There is currently still a gap in specific 4-repeat tau imaging probes for 4-repeat tauopathies and α-synuclein imaging probes for Parkinson’s disease and dementia with Lewy body. In this review, we focused on recent development in molecular imaging biomarkers for assisting the early diagnosis of proteinopathies (i.e., amyloid-β, tau, and α-synuclein) in dementia and discussed future perspectives.

At a Glance: An Update on Neuroimaging and Retinal Imaging in Alzheimer’s Disease and Related Research

Neuroimaging serves a variety of purposes in Alzheimer’s disease (AD) and related dementias (ADRD) research - from measuring microscale neural activity at the subcellular level, to broad topological patterns seen across macroscale-brain networks, and everything in between. In vivo imaging provides insight into the brain’s structure, function, and molecular architecture across numerous scales of resolution; allowing examination of the morphological, functional, and pathological changes that occurs in patients across different AD stages (1). AD is a complex and potentially heterogenous disease, with no proven cure and no single risk factor to isolate and measure, whilst known risk factors do not fully account for the risk of developing this disease (2). Since the 1990’s, technological advancements in neuroimaging have allowed us to visualise the wide organisational structure of the brain (3) and later developments led to capturing information of brain ‘functionality’, as well as the visualisation and measurement of the aggregation and accumulation of AD-related pathology. Thus, in vivo brain imaging has and will continue to be an instrumental tool in clinical research, mainly in the pre-clinical disease stages, aimed at elucidating the biological complex processes and interactions underpinning the onset and progression of cognitive decline and dementia. The growing societal burden of AD/ADRD means that there has never been a greater need, nor a better time, to use such powerful and sensitive tools to aid our understanding of this undoubtedly complex disease. It is by consolidating and reflecting on these imaging advancements and developing long-term strategies across different disciplines, that we can move closer to our goal of dementia prevention. This short commentary will outline recent developments in neuroimaging in the field of AD and dementia by first describing the historical context of AD classification and the introduction of AD imaging biomarkers, followed by some examples of significant recent developments in neuroimaging methods and technologies.