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Introduction The molecular underpinnings of the dissociation of cognitive performance and neuropathological burden are poorly understood, and there are currently no known genetic or epigenetic determinants of the dissociation. Methods and findings 'Residual cognition' was quantified by regressing out the effects of cerebral pathologies and demographic characteristics on global cognitive performance proximate to death. To identify genes influencing residual cognition, we leveraged neuropathological, genetic, epigenetic, and transcriptional data available for deceased participants of the Religious Orders Study (n = 492) and the Rush Memory and Aging Project (n = 487). Given that our sample size was underpowered to detect genome-wide significance, we applied a multistep approach to identify genes influencing residual cognition, based on our prior observation that independent genetic and epigenetic risk factors can converge on the same locus. In the first step (n = 979), we performed a genome-wide association study with a predefined suggestive p < 10.sup.-5, and nine independent loci met this threshold in eight distinct chromosomal regions. Three of the six genes within 100 kb of the lead SNP are expressed in the dorsolateral prefrontal cortex (DLPFC): UNC5C, ENC1, and TMEM106B. In the second step, in the subset of participants with DLPFC DNA methylation data (n = 648), we found that residual cognition was related to differential DNA methylation of UNC5C and ENC1 (false discovery rate < 0.05). In the third step, in the subset of participants with DLPFC RNA sequencing data (n = 469), brain transcription levels of UNC5C and ENC1 were evaluated for their association with residual cognition: RNA levels of both UNC5C (estimated effect = -0.40, 95% CI -0.69 to -0.10, p = 0.0089) and ENC1 (estimated effect = 0.0064, 95% CI 0.0033 to 0.0096, p = 5.7 x 10.sup.-5) were associated with residual cognition. In secondary analyses, we explored the mechanism of these associations and found that ENC1 may be related to the previously documented effect of depression on cognitive decline, while UNC5C may alter the composition of presynaptic terminals. Of note, the TMEM106B allele identified in the first step as being associated with better residual cognition is in strong linkage disequilibrium with rs1990622.sup.A (r.sup.2 = 0.66), a previously identified protective allele for TDP-43 proteinopathy. Limitations include the small sample size for the genetic analysis, which was underpowered to detect genome-wide significance, the evaluation being limited to a single cortical region for epigenetic and transcriptomic data, and the use of categorical measures for certain non-amyloid-plaque, non-neurofibrillary-tangle neuropathologies. Conclusions Through a multistep analysis of cognitive, neuropathological, genomic, epigenomic, and transcriptomic data, we identified ENC1 and UNC5C as genes with convergent genetic, epigenetic, and transcriptomic evidence supporting a potential role in the dissociation of cognition and neuropathology in an aging population, and we expanded our understanding of the TMEM106B haplotype that is protective against TDP-43 proteinopathy., Author(s): Charles C. White 1,2, Hyun-Sik Yang 1,2,3,4, Lei Yu 5,6, Lori B. Chibnik 2,7, Robert J. Dawe 5,8, Jingyun Yang 5,6, Hans-Ulrich Klein 1,2,4, Daniel Felsky 1,2,4, Alfredo Ramos-Miguel [...]

It has been previously observed that independent component analysis (ICA), if applied to data pooled in a particular way, may lessen the need for spatial alignment of scans in a functional neuroimaging study. In this paper, we seek to determine analytically the conditions under which this observation is true, not only for spatial ICA, but also for temporal ICA and for principal component analysis (PCA). In each case, we find conditions that the spatial alignment operator must satisfy to ensure invariance of the results. We illustrate our findings using functional magnetic-resonance imaging (fMRI) data. Our analysis is applicable to both intersubject and intrasubject spatial normalization. Index Terms--Functional magnetic-resonance imaging (fMRI), image registration, independent component analysis, neuroimaging.

• Identification of race-relevant biomarkers is critically needed • Examined within-Black variability in associations of DBM to cognitive decline • Less hippocampal tissue related to lower episodic memory but not decline • Less non-hippocampal, temporal & frontal tissue related to cognitive decline • Less parietal WM & splenium tissue related to cognitive level & decline respectively Blacks are at higher risk of developing cognitive impairment with age than non-Hispanic Whites, yet most brain morphometry and cognition research is performed with White samples or with mixed samples that control for race or compare across racial groups. A deeper understanding of the within-group variability in associations between brain structure and cognitive decline in Blacks is critically important for designing appropriate outcomes for clinical trials, predicting adverse outcomes, and developing interventions to preserve cognitive function, but no studies have examined these associations longitudinally within Blacks. We performed deformation-based morphometry in 376 older Black participants without dementia and examined associations of deformation-based morphometry with cognitive level and decline for global cognition and five cognitive domains. After correcting for widespread age-associated effects, there remained regions with less tissue and more cerebrospinal fluid associated with level and rate of decline in global cognition, memory, and perceptual speed. Further study is needed to examine the moderators of these associations, identify adverse outcomes predicted by brain morphometry, and deepen knowledge of underlying biological mechanisms. [ABSTRACT FROM AUTHOR]

Introduction: To evaluate the clinical validity of free water (FW), a diffusion tensor imaging–based biomarker kit proposed by the MarkVCID consortium, by investigating the association between mean FW (mFW) and executive function. Methods: Baseline mFW was related to a baseline composite measure of executive function (EFC), adjusting for relevant covariates, in three MarkVCID sub‐cohorts, and replicated in five, large, independent legacy cohorts. In addition, we tested whether baseline mFW predicted accelerated EFC score decline (mean follow‐up time: 1.29 years). Results: Higher mFW was found to be associated with lower EFC scores in MarkVCID legacy and sub‐cohorts (p‐values < 0.05). In addition, higher baseline mFW was associated significantly with accelerated decline in EFC scores (p = 0.0026). Discussion: mFW is a sensitive biomarker of cognitive decline, providing a strong clinical rational for its use as a marker of white matter (WM) injury in multi‐site observational studies and clinical trials of vascular cognitive impairment and dementia (VCID). [ABSTRACT FROM AUTHOR]

Introduction: Development of novel diagnostic tools is a top research priority in vascular dementia. A major obstacle is the lack of a simple, non‐invasive method to visualize cerebral arteriolar walls in vivo. Retinal arterioles offer a window into the cerebral circulation. Methods: Intensity‐based retinal arteriolar visualization in optical coherence tomography (I‐bRAVO) was applied to evaluate mean wall thickness (MWT) and wall‐to‐lumen ratio (WLR) in 250 subjects with sporadic and genetic cerebral small vessel disease (CSVD), non‐vascular neurodegenerative diseases (NVND), and healthy controls (HC) in association with imaging and cognitive markers. Results: MWT and WLR were higher in CSVD, associated with severity of vascular white matter lesions, and correlated with magnetic resonance imaging‐based intracranial arteriolosclerosis score. WLR correlated with gray and white matter volume and differentiated asymptomatic sporadic CSVD from HC (area under the curve = 0.82). Discussion: I‐bRAVO is a rapid, non‐invasive tool. MWT and WLR were associated with imaging markers of CSVD and could contribute to early identification of sporadic CSVD. [ABSTRACT FROM AUTHOR]

Introduction: To describe the protocol and findings of the instrumental validation of three imaging‐based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1‐weighted imaging. Methods: The instrumental validation of imaging‐based biomarker kits included inter‐rater reliability among participating sites, test–retest repeatability, and inter‐scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra‐class correlation coefficients (ICC). Results: The three biomarkers demonstrated excellent inter‐rater reliability (ICC >0.94, P‐values <.001), very high agreement between test and retest sessions (ICC >0.98, P‐values <.001), and were extremely consistent across the three scanners (ICC >0.98, P‐values <.001). Discussion: The three biomarker kits demonstrated very high inter‐rater reliability, test–retest repeatability, and inter‐scanner reproducibility, offering robust biomarkers suitable for future multi‐site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID). [ABSTRACT FROM AUTHOR]

Background: Age‐related neuropathologies have devastating effects on subcortical brain structures. MRI can be used to explore patterns of atrophy associated with various diseases, however, definitive diagnosis of age‐related neuropathologies is only possible at autopsy. The purpose of this work was to combine ex‐vivo MRI and detailed neuropathology in a large community cohort of older adults to conduct the most comprehensive investigation to‐date on the association of age‐related neuropathologies with the shape of subcortical brain structures. Method: Cerebral hemispheres were obtained from 814 participants of the Rush Memory and Aging Project and Religious Orders Study, two longitudinal cohort studies of aging (Fig. 1). A single brain hemisphere from each participant was imaged ex‐vivo on a clinical 3T MRI scanner, while immersed in 4% formaldehyde solution. Following ex‐vivo MRI, all hemispheres underwent detailed neuropathologic examination by a board‐certified neuropathologist blinded to clinical and imaging findings (Fig. 2). T2‐weighted images from all hemispheres were used to segment nucleus accumbens, amygdala, caudate, hippocampus, putamen, and thalamus. Subcortical shape measures were computed using the ENIGMA‐Shape protocol. Vertex‐wise linear regression was performed for each subcortical structure, modeling the logarithm of the Jacobian determinant at each vertex as a function of a comprehensive list of age‐related neuropathologies controlling for demographics and scanners. The analysis first considered data from all hemispheres (left hemispheres were mirrored to appear like right hemispheres and the opposite), and then was repeated considering left and right hemispheres separately. Result: When considering all hemispheres (Fig. 3), Alzheimer's pathology, atherosclerosis, and TDP‐43 pathology were all associated with unique patterns of inward deformation of the hippocampus and amygdala. When considering left and right hemispheres separately (Fig. 4), the results were relatively similar to the combined analysis. Also, when considering the right hemispheres alone, arteriolosclerosis was associated with inward deformation of the putamen and thalamus. Lewy bodies, infarcts and cerebral amyloid angiopathy did not show any links to the shape of subcortical brain structures. Conclusion: Among the different age‐related neuropathologies, Alzheimer's pathology, TDP‐43 pathology, atherosclerosis and arteriolosclerosis are the ones that have independent contributions on shape abnormalities of subcortical brain structures that could potentially contribute towards in‐vivo prediction of these neuropathologies. [ABSTRACT FROM AUTHOR]

Background: Limbic predominant age‐related TDP‐43 encephalopathy neuropathological change (LATE‐NC) refers to the stereotypical accumulation of TDP‐43 proteinopathy in older adults, and has been associated with lower memory, cognitive decline and increased likelihood of dementia. However, association of R2 with LATE‐NC is still unclear. In this work, we examined the association and spatial profile of R2 alterations with LATE‐NC in a large community cohort of older adults. Method: This study included 797 individuals participating in three longitudinal studies: Rush Memory and Aging Project, Religious Orders Study, and Minority Aging Research Study (Fig. 1). Ex‐vivo images were collected using a 2D multi‐echo spin‐echo (ME‐SE) sequence. Afterwards, detailed neuropathologic examination was performed by a board‐certified neuropathologist blinded to all data (Fig. 2). The R2 relaxation rate was quantified voxel‐wise by fitting a monoexponential decay function S = S0∙exp(‐R2∙t) to the ME‐SE data. Images from one of the echoes were non‐linearly registered to a brain template, and the resulting transformations were applied to the R2‐maps to enable voxel‐wise analysis. Voxel‐wise linear regression was used to investigate the association of R2 relaxation rate with LATE‐NC controlling for Alzheimer's pathology, Lewy bodies, gross and microscopic infarcts, atherosclerosis, cerebral amyloid angiopathy, arteriolosclerosis, as well as age at death, sex, years of education, postmortem interval to fixation and postmortem interval to imaging, and scanner. Statistical significance was set at p<0.05. Result: Voxel‐wise analysis demonstrated lower R2 for greater LATE‐NC burden in a number of brain regions in the temporal, frontal, occipital lobes and basal ganglia (Fig. 3). The strongest effects were observed in the amygdala, hippocampus, and neighboring temporal lobe regions. In addition, lower R2 with greater LATE‐NC burden was demonstrated in the caudate, insular, frontal and occipital white matter regions. No brain regions showed positive associations of R2 with LATE‐NC. Conclusion: The present study demonstrated a spatial pattern of lower R2 relaxation rate for greater LATE‐NC burden in a large community cohort of older adults. This pattern involved mainly the temporal, frontal, occipital lobes and basal ganglia. The link between R2 and LATE‐NC may be exploited towards the development of algorithms for the prediction of this devastating, recently recognized disease entity. [ABSTRACT FROM AUTHOR]

Background: TAR DNA‐binding protein 43 (TDP‐43), has been shown to be involved in various neurodegenerative disorders involving axonal damage including ALS, FTLD, and LATE. Studying the relationships between postmortem TDP‐43 and antemortem white matter (WM) structural integrity can allow for a better understanding of the disease, rather than exploring clinical presentations alone. Methods: In‐vivo diffusion‐weighted images were gathered on a 1.5T scanner from subjects from the Religious Orders Study and the Rush Memory and Aging Project. Images were processed using TORTOISE to calculate fractional anisotropy (FA). We utilized the IIT probabilistic WM atlas to identify voxels containing fibers that originate or terminate in the hippocampus. A semi‐quantitative rating of TDP‐43 severity was assessed in 5 brain regions. We utilized regression models to relate postmortem disease and antemortem FA within each voxel. Coexisting disease including β‐amyloid plaques, tau tangles, and cerebrovascular disease were used as covariates, along with demographic variables. Results: The 58 subjects were 91.10 (SD = 6.37) years old at death with a median interval from MRI to death of 2.8 years (SD = 1.19). Results revealed a significant negative relationship (p<0.05) between postmortem TDP‐43 and FA within fibers connecting the hippocampus to the parahippocampal and entorhinal cortices, fornix, and lingual gyrus. Preliminary results did not survive multiple comparisons correction. Conclusions: Our findings indicate that greater TDP‐43 disease burden is associated with lower white matter integrity within regions of the limbic system. These findings suggest the TDP‐43 disease process may contribute to reduced structural connectivity between the hippocampus and related regions, which may impact larger brain networks. [ABSTRACT FROM AUTHOR]

Background: A recent instrumental validation analysis positioned peak‐width of skeletonized mean diffusivity (PSMD) as a biomarker for vascular contributions to cognitive impairment and dementia (VCID) with excellent reliability, repeatability, and reproducibility. As the next step of biomarker development, the current study aimed to (1) perform a clinical validation of PSMD and cognitive function in MarkVCID and three independent replication samples, and (2) assess whether PSMD explains cognitive function above and beyond white matter hyperintensities (WMH). Method: The clinical validation of PSMD included n = 395 participants from the multi‐site MarkVCID consortium, n = 6172 from population‐based CHARGE cohorts, n = 287 from RUSH, and n = 435 from the UC Davis ADRC cohort spanning diverse ages and racial/ethnic backgrounds. PSMD was derived from DTI using an automated algorithm and further log‐transformed to normalize its distribution. A composite measure of general cognitive function was calculated from neuropsychological tests assessing at least three different cognitive domains. Linear regression models were run to determine the association between PSMD and cognitive function, adjusting for age, sex, and education. A secondary model was adjusted for vascular risk factors: hypertension, diabetes, and smoking. Lastly, both models included WMH volume to evaluate PSMD beyond WMH. Result: Higher PSMD values were associated with lower general cognitive function in the MarkVCID (Beta (95% CI), ‐0.82 (‐1.03, ‐0.61), p<0.001) in primary models, and remained unchanged after additional adjustment for vascular risk factors in secondary model (‐0.87 (‐1.09, ‐0.65), p<0.001). These findings were replicated across the CHARGE, RUSH, and UC Davis ADRC cohorts (Table 1). We further observed that PSMD explained an additional 0.2% of the variance in cognitive function beyond WMH in the youngest cohort (48.1 ±8.9 years), whereas the variance explained rose to 2.51% for the oldest cohort (76.4 ±5.2 years). Conclusion: This comprehensive clinical validation study suggests that PSMD is related to general cognition across diverse samples, potentially explaining more variation in cognitive function than a classic cerebrovascular marker such as WMH. Together, our instrumental and clinical validation studies support using PSMD as a robust biomarker with potential for risk stratification and disease monitoring in multi‐site clinical trials of VCID. Additional longitudinal validation studies are underway. [ABSTRACT FROM AUTHOR]

Background: Cerebral microbleed (CMB) detection on MRI scans of autopsied brains of community‐based older adults is crucial for MR‐pathology studies of cerebral small vessel disease (SVD). However, training an ex‐vivo CMB detection model is difficult due to the low prevalence of CMBs in the brains of community‐based older adults, and the fact that CMB mimics greatly outnumber CMBs on ex‐vivo MRI. We demonstrate that confidence‐aware deep learning, a technique for enforcing correct ranking of examples during neural network training, significantly improves ex‐vivo CMB detection performance and enhances the interpretability of model predictions. Method: CMBs from 286 participants from two longitudinal cohort studies of aging were included in this work. T2*‐weighted gradient echo scans of autopsied brains with a voxel size of 1×1×1 mm were used. Confidence‐aware learning was used to regularize deep learning model predictions such that they reflect the degree of confidence the detection model has in a prediction. Model confidence was estimated during training by correctness, the number of correct prediction events across epochs divided by the total number of epochs. The objective loss function was modified to include a correctness ranking loss (CRL) term that encourages correspondence between correctness and predicted probabilities. Models are trained with and without the CRL loss term to evaluate the impact of confidence‐aware learning on model performance. An end‐to‐end CMB detection framework that combines data synthesis, candidate selection, false positive reduction, and full scan evaluation was used as the backbone for this work. Result: CMB detection models trained using correctness ranking loss achieved a significantly higher ensemble average precision (0.2915) compared with models trained without correctness ranking loss (0.2379). The CRL trained model achieved a 25% higher sensitivity at 0.5 false positives per subject and 12% higher sensitivity at 3 false positives per subject when compared to models trained without CRL. Conclusion: This work demonstrates that training with confidence‐aware learning can improve the performance and interpretability of ex‐vivo CMB detection algorithms in community‐based cohorts. The use of a confidence‐aware CMB detection algorithm for assisted CMB annotation in ex‐vivo MRI will aid in future MR‐pathology studies into the relationships between CMBs and neuropathology. [ABSTRACT FROM AUTHOR]