A Biomarker for Alzheimer’s Disease Based on Patterns of Regional Brain Atrophy

Introduction: It has been shown that Alzheimer’s disease (AD) is accompanied by marked structural brain changes that can be detected several years before clinical diagnosis via structural magnetic resonance (MR) imaging. In this study, we developed a structural MR-based biomarker for in vivo detection of AD using a supervised machine learning approach. Based on an individual’s pattern of brain atrophy a continuous AD score is assigned which measures the similarity with brain atrophy patterns seen in clinical cases of AD. Methods: The underlying statistical model was trained with MR scans of patients and healthy controls from the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1 screening). Validation was performed within ADNI-1 and in an independent patient sample from the Open Access Series of Imaging Studies (OASIS-1). In addition, our analyses included data from a large general population sample of the Study of Health in Pomerania (SHIP-Trend). Results: Based on the proposed AD score we were able to differentiate patients from healthy controls in ADNI-1 and OASIS-1 with an accuracy of 89% (AUC = 95%) and 87% (AUC = 93%), respectively. Moreover, we found the AD score to be significantly associated with cognitive functioning as assessed by the Mini-Mental State Examination in the OASIS-1 sample after correcting for diagnosis, age, sex, age·sex, and total intracranial volume (Cohen’s f2 = 0.13). Additional analyses showed that the prediction accuracy of AD status based on both the AD score and the MMSE score is significantly higher than when using just one of them. In SHIP-Trend we found the AD score to be weakly but significantly associated with a test of verbal memory consisting of an immediate and a delayed word list recall (again after correcting for age, sex, age·sex, and total intracranial volume, Cohen’s f2 = 0.009). This association was mainly driven by the immediate recall performance. Discussion: In summary, our proposed biomarker well differentiated between patients and healthy controls in an independent test sample. It was associated with measures of cognitive functioning both in a patient sample and a general population sample. Our approach might be useful for defining robust MR-based biomarkers for other neurodegenerative diseases, too

DEEPMIR: A DEEP neural network for differential detection of cerebral Microbleeds and IRon deposits in MRI

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies

Cognitive sequealae of COVID-19 is not predicted by SARS-CoV-2 variants

Background: The long term sequelae of COVID-19 in older adults are only beginning to be clarified, and its predictors and underlying molecular mechanisms may shed light on the relationship between viral infections and Alzheimer\u27s disease and related dementia. Method: A prospective cohort of 874 older adult Amerindians from Argentina with COVID-19 illness confirmed by PCR of nasal swabs as well as controls, was established during the first year of the COVID-19 pandemic. We obtained data on the severity of the acute illness, as well as extensive neuropsychiatric and cognitive assessments, neurological exams (including quantitative hyposmia/anosmia), plasma for biomarkers and preliminary brain MRI images using the ADNI-3 protoco (n=300)l,and whole genome sequencing (n=300). Isolates from SARS-CoV-2 were obtained by the provincial Direction of Epidemiology and sequenced by the national Ministry of Health. Variants of interest/concern were allocated to each case on the basis of the prevalent community isolate at the time of confirmed positive PCR. A deep learning strategy was used to identify predictive factors of cognitive and clinical outcomes. Result: Four distinctive cognitive profiles were identified. Greater cognitive impairment was associated with older age (p = E-9), worse acute COVID-19 illness (p=0.008), unvaccinated status (p = E-7), and severity of anosmia (p = E-5). SARS-CoV-2 variant was associated with severity of acute illness ((p = E-6) but notably not with cognitive impairment. Preliminary analysis of genomic and brain imaging data will be presented. Conclusion: Our data strongly suggest that all SARS-CoV-2 variants of interest up to the omicron wave seem equally likely to result in cognitive impairment in older adults, modulated by the severity of the acute illness

Investigating white matter hyperintensities in a multicenter COVID-19 study using 7T MRI

Background: Emerging evidence indicates that COVID-19 can negatively impact patient’s brain health (Douaud et al., 2022) (Cecchetti et al., 2022). Common clinical symptoms include brain fog, headaches, difficulty concentrating, and loss of sense of smell or taste. Some studies suggest that SARS-CoV-2 infection can damage the blood brain barrier either directly or through immune-inflammatory mechanisms (Zhang, et al. 2021). White matter hyperintensities (WMH) are imaging biomarkers of brain vascular or inflammatory injury. We investigated the association between severity of COVID-19 infection and burden of white matter hyperintensity volumes within a diverse multi-nation, multi-racial cohort using 7 Tesla (7T) MRI that can detect more subtle injury than conventional 1.5 or 3T MRI. Method: Participants were recruited at 4 sites: Pittsburgh, San Antonio and Houston, USA, and Nottingham, UK. To date, we have scanned and included the following participants in our analysis (Table 1). Detailed cognitive, neurological, mood and functional assessments and high-resolution MRI scans were collected. Subsequent WMH segmentation was performed using our in-house built deep learning based model (Figure 1). All segmentations were visually inspected and manually corrected before statistical analysis. Normalized WMH is calculated as a ratio of the WMH volume and the intracranial volume (WMH/ICV). Imaging data for an additional 36 age-matched controls were retrieved from the 7 Tesla Bioengineering Research Program (7TBRP) imaging bank at Pittsburgh. Result: Figure 1 shows the WMH segmentation outputs from our deep learning based model on images acquired at the 3 sites. Our Linear regression models along with our non-parametric Kruskal-Wallis test result suggests that compared to mild COVID cases and healthy control, COVID infected individuals that were ICU admitted show elevated WMH burden (Figure 2). Conclusion: Our results demonstrate that white matter hyperintensity volumes were higher among patients who had severe acute COVID infection that required ICU admission, compared to healthy age-matched controls. In contrast, no difference in white matter burden was observed in patients with mild COVID infection compared to healthy controls. Additional data (both cross-sectional and longitudinal), including more sensitive MRI measures is being collected to define the full spectrum of brain injury associated with sequelae of COVID infection

A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium

AbstractIntroductionVirchow-Robin spaces (VRS), or perivascular spaces, are compartments of interstitial fluid enclosing cerebral blood vessels and are potential imaging markers of various underlying brain pathologies. Despite a growing interest in the study of enlarged VRS, the heterogeneity in rating and quantification methods combined with small sample sizes have so far hampered advancement in the field.MethodsThe Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement (UNIVRSE) consortium was established with primary aims to harmonize rating and analysis (www.uconsortium.org). The UNIVRSE consortium brings together 13 (sub)cohorts from five countries, totaling 16,000 subjects and over 25,000 scans. Eight different magnetic resonance imaging protocols were used in the consortium.ResultsVRS rating was harmonized using a validated protocol that was developed by the two founding members, with high reliability independent of scanner type, rater experience, or concomitant brain pathology. Initial analyses revealed risk factors for enlarged VRS including increased age, sex, high blood pressure, brain infarcts, and white matter lesions, but this varied by brain region.DiscussionEarly collaborative efforts between cohort studies with respect to data harmonization and joint analyses can advance the field of population (neuro)imaging. The UNIVRSE consortium will focus efforts on other potential correlates of enlarged VRS, including genetics, cognition, stroke, and dementia

A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium

Introduction: Virchow-Robin spaces (VRS), or perivascular spaces, are compartments of interstitial fluid enclosing cerebral blood vessels and are potential imaging markers of various underlying brain pathologies. Despite a growing interest in the study of enlarged VRS, the heterogeneity in rating and quantification methods combined with small sample sizes have so far hampered advancement in the field. Methods: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement (UNIVRSE) consortium was established with primary aims to harmonize rating and analysis (www.uconsortium.org). The UNIVRSE consortium brings together 13 (sub)cohorts from five countries, totaling 16,000 subjects and over 25,000 scans. Eight different magnetic resonance imaging protocols were used in the consortium. Results: VRS rating was harmonized using a validated protocol that was developed by the two founding members, with high reliability independent of scanner type, rater experience, or concomitant brain pathology. Initial analyses revealed risk factors for enlarged VRS including increased age, sex, high blood pressure, brain infarcts, and white matter lesions, but this varied by brain region. Discussion: Early collaborative efforts between cohort studies with respect to data harmonization and joint analyses can advance the field of population (neuro)imaging. The UNIVRSE consortium will focus efforts on other potential correlates of enlarged VRS, including genetics, cognition, stroke, and dementia

Cross-Vendor Test-Retest Validation of Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) for Evaluating Glymphatic System Function

The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was proposed to evaluate glymphatic system (GS) function. However, few studies have validated its reliability and reproducibility. Fifty participants' DTI data from the MarkVCID consortium were included in this study. Two pipelines by using DSI studio and FSL software were developed for data processing and ALPS index calculation. The ALPS index was obtained by the average of bilateral ALPS index and was used for testing the cross-vendor, inter-rater and test-retest reliability by using R studio software. The ALPS index demonstrated favorable inter-scanner reproducibility (ICC=0.77 to 0.95, P< 0.001), inter-rater reliability (ICC=0.96 to 1, P< 0.001) and test-retest repeatability (ICC=0.89 to 0.95, P< 0.001), offering a potential biomarker for in vivo evaluation of GS function