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Longitudinal clinical–pathological studies have increasingly recognized the importance of mixed pathologies (the coexistence of one or more neurodegenerative and cerebrovascular disease pathologies) as important factors in the development of Alzheimer’s disease (AD) and other forms of dementia. Older persons with AD pathology, often have concomitant cerebrovascular disease pathologies (macroinfarcts, microinfarcts, atherosclerosis, arteriolosclerosis, cerebral amyloid angiopathy) as well as other concomitant neurodegenerative disease pathologies (Lewy bodies, TDP-43, hippocampal sclerosis). These additional pathologies lower the threshold for clinical diagnosis of AD. Many of these findings from pathologic studies, especially for CVD, have been confirmed using sophisticated neuroimaging technologies. In vivo biomarker studies are necessary to provide an understanding of specific pathologic contributions and time course relationships along the spectrum of accumulating pathologies. In this review, we provide a clinical–pathological perspective on the role of multiple brain pathologies in dementia followed by a review of the available clinical and biomarker data on some of the mixed pathologies.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies—amyloid plaques and cerebral amyloid angiopathy—in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist’s ability suggests a route to neuropathologic deep phenotyping. Convolutional neural networks have been applied to various areas of medical imaging and histology. Here the authors develop an automated approach using interpretable neural networks to determine Alzheimer’s disease plaque and cerebral amyloid angiopathy burden in post-mortem human brain tissue.

Previous studies have identified effects of age and vascular risk factors on brain injury in elderly individuals. We aimed to establish whether the effects of high blood pressure in the brain are evident as early as the fifth decade of life. In an investigation of the third generation of the Framingham Heart Study, we approached all participants in 2009 to ask whether they would be willing to undergo MRI. Consenting patients underwent clinical assessment and cerebral MRI that included T1-weighted and diffusion tensor imaging to obtain estimates of fractional anisotropy, mean diffusivity, and grey-matter volumes. All images were coregistered to a common minimum deformation template for voxel-based linear regressions relating fractional anisotropy, mean diffusivity, and grey-matter volumes to age and systolic blood pressure, with adjustment for potential confounders. 579 (14·1%) of 4095 participants in the third-generation cohort (mean age 39·2 years, SD 8·4) underwent brain MRI between June, 2009 and June, 2010. Age was associated with decreased fractional anisotropy and increased mean diffusivity in almost all cerebral white-matter voxels. Age was also independently associated with reduced grey-matter volumes. Increased systolic blood pressure was linearly associated with decreased regional fractional anisotropy and increased mean diffusivity, especially in the anterior corpus callosum, the inferior fronto-occipital fasciculi, and the fibres that project from the thalamus to the superior frontal gyrus. It was also strongly associated with reduced grey-matter volumes, particularly in Brodmann's area 48 on the medial surface of the temporal lobe and Brodmann's area 21 of the middle temporal gyrus. Our results suggest that subtle vascular brain injury develops insidiously during life, with discernible effects even in young adults. These findings emphasise the need for early and optimum control of blood pressure. National Institutes of Health and National Heart, Lung, and Blood Institute; National Institute on Aging; and National Institute of Neurological Disorders and Stroke.

As the practice of aggregating multi-site neuroimaging data has become more common, the field of neuroscience has increasingly recognized the importance of harmonization , or the removal of scanner effects from brain imaging data. While many harmonization methods exist, like ComBat and CovBat, few explicitly incorporate the network structure of the brain. Researchers studying structural connectivity are therefore not guaranteed to model the true underlying brain network. This study offers a new harmonization method, called NetBat, which was designed to incorporate network parameters from the weighted stochastic block model (WSBM) as covariates in the popular ComBat harmonization method. NetBat is demonstrated through analysis of eighteen neurotypical individuals each scanned on four MRI scanners. Results suggest that under tested circumstances NetBat provides more accurate overall harmonization and better retention of network structure than competing methods. •We proposed the NetBat harmonization method for network retention.•We evaluated NetBat by analyzing a multi-scanner matched MRI dataset.•We compared NetBat to the existing harmonization methods ComBat and CovBat.•We implemented harmonization-based and network-based criteria for investigation.•NetBat outperformed ComBat and CovBat on evaluation of repeated scanner data.

We postulate that myelin injury contributes to cholesterol release from myelin and cholesterol dysmetabolism which contributes to Abeta dysmetabolism, and combined with genetic and AD risk factors, leads to increased Abeta and amyloid plaques. Increased Abeta damages myelin to form a vicious injury cycle. Thus, white matter injury, cholesterol dysmetabolism and Abeta dysmetabolism interact to produce or worsen AD neuropathology. The amyloid cascade is the leading hypothesis for the cause of Alzheimer’s disease (AD). The failure of clinical trials based on this hypothesis has raised other possibilities. Even with a possible new success (Lecanemab), it is not clear whether this is a cause or a result of the disease. With the discovery in 1993 that the apolipoprotein E type 4 allele (APOE4) was the major risk factor for sporadic, late-onset AD (LOAD), there has been increasing interest in cholesterol in AD since APOE is a major cholesterol transporter. Recent studies show that cholesterol metabolism is intricately involved with Abeta (Aβ)/amyloid transport and metabolism, with cholesterol down-regulating the Aβ LRP1 transporter and upregulating the Aβ RAGE receptor, both of which would increase brain Aβ. Moreover, manipulating cholesterol transport and metabolism in rodent AD models can ameliorate pathology and cognitive deficits, or worsen them depending upon the manipulation. Though white matter (WM) injury has been noted in AD brain since Alzheimer’s initial observations, recent studies have shown abnormal white matter in every AD brain. Moreover, there is age-related WM injury in normal individuals that occurs earlier and is worse with the APOE4 genotype. Moreover, WM injury precedes formation of plaques and tangles in human Familial Alzheimer’s disease (FAD) and precedes plaque formation in rodent AD models. Restoring WM in rodent AD models improves cognition without affecting AD pathology. Thus, we postulate that the amyloid cascade, cholesterol dysmetabolism and white matter injury interact to produce and/or worsen AD pathology. We further postulate that the primary initiating event could be related to any of the three, with age a major factor for WM injury, diet and APOE4 and other genes a factor for cholesterol dysmetabolism, and FAD and other genes for Abeta dysmetabolism.

Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).

Background The DVCID project, funded by the National Institute of Neurological Disorders and the National Institute on Aging, aims to enroll 2250 diverse at‐risk Americans from Black/African, Latino/Hispanic, and non‐Hispanic White backgrounds. The study integrates cognitive assessments, blood‐based analyses, and neuroimaging. As the Repository Core (RC) of the coordinating center at the University of California, Davis (UCD), our responsibilities include blood processing, banking, biomarker measurements, and analysis. Standard operating procedures (SOPs) are essential to ensure consistency, reliability, and comparability of the analytical results across the 20 participating sites. Method We detailed the comprehensive strategies implemented by the RC to harmonize biofluid sample collection, processing, storage, and data management across sites. Pre‐analytical, analytical, and post‐analytical factors influencing sample integrity and data reliability were evaluated. Specifically, post‐analytical quality control included assessments of within‐run, between‐run, and between‐lot variabilities. Measurements were implemented to maintain consistent and reliable quantitative biomarker data, analyzed using Quanterix Simoa HD‐X™ Analyzer. Result Harmonization strategies included standardized blood processing protocols, such as the use of approved vacutainer tubes, uniform blood handling steps, and consistent cryovial formats for storage. To minimize variability in biomarker quantification, we employed data harmonization approaches, including re‐analysis of a subset of samples for batch‐bridging purposes. Post‐analytical quality control measures like this ensured that within‐run, between‐run, and between‐lot variability remained within acceptable limits (CV ≤10‐15%). As a result, no significant variance in Quanterix biomarker data was observed across study sites, confirming that the implemented measures improved sample integrity and analytical consistency across sites. Conclusions This work highlights the importance of harmonized strategies and SOPs for biofluid collection, processing, storage, and biomarker quantification in large, multi‐centered clinical research. The outlined practices ensure sample integrity and data consistency, enabling more reliable biomarker research. These methods also make cross‐cohort studies possible by improving data comparability. Such harmonization could facilitate larger‐scale investigations which advance the field of clinical and biomarker research.

Background White matter hyperintensities (WMH) on brain MRIs reflect tissue damage and are linked to cognitive decline and neurodegeneration. While studies have identified “normal” and “high” WMH accumulation in aging cohorts, literature on favorable aging subgroups and their health characteristics is limited. For this study, we hypothesize that individuals with below age, sex and head size‐expected WMH will have fewer vascular risk factors and less evidence of neurodegeneration suggesting better brain health. Methods Data are from (n = 3859) Framingham Heart Study individuals with MRI, log normal transformed WMH volumes as dependent variables in a linear regression model of age, sex, and total cranial volume to create adjusted residuals. These residuals were then categorized as “low”, “high”, and “remaining” WMH groups based on the lowest 5% (n = 195), highest 5% (n = 194), and middle 90% (n = 3470) of volume, respectively. Separate regression models examined associations between age, sex, total cranial volume, group, and the interaction between age and group with MRI measures of brain atrophy. Additionally, between‐groups comparisons of vascular factors were performed. Results As expected, the low group had smaller WMH volumes and limited age‐related increases, whereas the other groups had higher mean and significant age‐related increases in WMH volumes (Figure 1). The low group had lower mean ventricular volumes than the remaining and high groups. Separate regression modeling found significantly less age‐related gray matter volume loss compared to the high group (Table 2). The low group also had significantly lower systolic blood pressure, rates of hypertension, and Framingham Stroke Risk Profile Scores compared to the remaining and high groups (Table 1). These vascular factors were also associated with higher WMH and ventricle volumes, lower cerebral gray matter, and had significant interactions with subgroup designation. Conclusions These findings indicate that a group with favorable brain outcomes may exist with low WMH, smaller average ventricle size, and less average neurodegeneration. One explanation for this group's favorable outcomes is their elevated vascular health, which has been closely tied to healthy brain aging.

The DVCID project, funded by the National Institute of Neurological Disorders and the National Institute on Aging, aims to enroll 2250 diverse at-risk Americans from Black/African, Latino/Hispanic, and non-Hispanic White backgrounds. The study integrates cognitive assessments, blood-based analyses, and neuroimaging. As the Repository Core (RC) of the coordinating center at the University of California, Davis (UCD), our responsibilities include blood processing, banking, biomarker measurements, and analysis. Standard operating procedures (SOPs) are essential to ensure consistency, reliability, and comparability of the analytical results across the 20 participating sites. We detailed the comprehensive strategies implemented by the RC to harmonize biofluid sample collection, processing, storage, and data management across sites. Pre-analytical, analytical, and post-analytical factors influencing sample integrity and data reliability were evaluated. Specifically, post-analytical quality control included assessments of within-run, between-run, and between-lot variabilities. Measurements were implemented to maintain consistent and reliable quantitative biomarker data, analyzed using Quanterix Simoa HD-X™ Analyzer. Harmonization strategies included standardized blood processing protocols, such as the use of approved vacutainer tubes, uniform blood handling steps, and consistent cryovial formats for storage. To minimize variability in biomarker quantification, we employed data harmonization approaches, including re-analysis of a subset of samples for batch-bridging purposes. Post-analytical quality control measures like this ensured that within-run, between-run, and between-lot variability remained within acceptable limits (CV ≤10-15%). As a result, no significant variance in Quanterix biomarker data was observed across study sites, confirming that the implemented measures improved sample integrity and analytical consistency across sites. This work highlights the importance of harmonized strategies and SOPs for biofluid collection, processing, storage, and biomarker quantification in large, multi-centered clinical research. The outlined practices ensure sample integrity and data consistency, enabling more reliable biomarker research. These methods also make cross-cohort studies possible by improving data comparability. Such harmonization could facilitate larger-scale investigations which advance the field of clinical and biomarker research.

Deep learning implementations using convolutional neural nets have recently demonstrated promise in many areas of medical imaging. In this article we lay out the methods by which we have achieved consistently high quality, high throughput computation of intra-cranial segmentation from whole head magnetic resonance images, an essential but typically time-consuming bottleneck for brain image analysis. We refer to this output as “production-level” because it is suitable for routine use in processing pipelines. Training and testing with an extremely large archive of structural images, our segmentation algorithm performs uniformly well over a wide variety of separate national imaging cohorts, giving Dice metric scores exceeding those of other recent deep learning brain extractions. We describe the components involved to achieve this performance, including size, variety and quality of ground truth, and appropriate neural net architecture. We demonstrate the crucial role of appropriately large and varied datasets, suggesting a less prominent role for algorithm development beyond a threshold of capability.