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•Ironsmith offers CSF-only QSM reference masks that minimize partial volume effects.•Ironsmith returns QSM values in 89 ROIs directly from DICOM images.•Ironsmith returns ROI-based SNR values and filters QSM outlier values.•Ironsmith can automatically identify outlier regions/artifacts on phase images.•Ironsmith shows both reliability and external validity. Quantitative susceptibility mapping (QSM) is an MRI-based, computational method for anatomically localizing and measuring concentrations of specific biomarkers in tissue such as iron. Growing research suggests QSM is a viable method for evaluating the impact of iron overload in neurological disorders and on cognitive performance in aging. Several software toolboxes are currently available to reconstruct QSM maps from 3D GRE MR Images. However, few if any software packages currently exist that offer fully automated pipelines for QSM-based data analyses: from DICOM images to region-of-interest (ROI) based QSM values. Even less QSM-based software exist that offer quality control measures for evaluating the QSM output. Here, we address these gaps in the field by introducing and demonstrating the reliability and external validity of Ironsmith; an open-source, fully automated pipeline for creating and processing QSM maps, extracting QSM values from subcortical and cortical brain regions (89 ROIs) and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith also features automatic filtering of QSM outlier values and precise CSF-only QSM reference masks that minimize partial volume effects. Testing of Ironsmith revealed excellent intra- and inter-rater reliability. Finally, external validity of Ironsmith was demonstrated via an anatomically selective relationship between motor performance and Ironsmith-derived QSM values in motor cortex. In sum, Ironsmith provides a freely-available, reliable, turn-key pipeline for QSM-based data analyses to support research on the impact of brain iron in aging and neurodegenerative disease.

Cerebral white matter hyperintensities (WMHs) represent macrostructural brain damage associated with various etiologies. However, the relative contributions of various etiologies to WMH volume, as assessed via different neuroimaging measures, is not well-understood. Here, we explored associations between three potential early markers of white matter hyperintensity volume. Specifically, the unique variance in total and regional WMH volumes accounted for by white matter microstructure, brain iron concentration and cerebral blood flow (CBF) was assessed. Regional volumes explored were periventricular and deep regions. Eighty healthy older adults (ages 60–86) were scanned at 3 Tesla MRI using fluid-attenuated inversion recovery, diffusion tensor imaging (DTI), multi-echo gradient-recalled echo and pseudo-continuous arterial spin labeling sequences. In a stepwise regression model, DTI-based radial diffusivity accounted for significant variance in total WMH volume (adjusted R 2 change = 0.136). In contrast, iron concentration (adjusted R 2 change = 0.043) and CBF (adjusted R 2 change = 0.027) made more modest improvements to the variance accounted for in total WMH volume. However, there was an interaction between iron concentration and location on WMH volume such that iron concentration predicted deep ( p = 0.034) but not periventricular ( p = 0.414) WMH volume. Our results suggest that WM microstructure may be a better predictor of WMH volume than either brain iron or CBF but also draws attention to the possibility that some early WMH markers may be location-specific.

•Cortical iron disrupts cortical networks supporting working memory.•Cortical iron is negatively associated with working memory performance.•Effects were observed after controlling for cerebral volume and blood flow.•Iron-mediated disruptions may be an early marker of age-related cognitive declines. Excessive brain iron negatively affects working memory and related processes but the impact of cortical iron on task-relevant, cortical brain networks is unknown. We hypothesized that high cortical iron concentration may disrupt functional circuitry within cortical networks supporting working memory performance. Fifty-five healthy older adults completed an N-Back working memory paradigm while functional magnetic resonance imaging (fMRI) was performed. Participants also underwent quantitative susceptibility mapping (QSM) imaging for assessment of non-heme brain iron concentration. Additionally, pseudo continuous arterial spin labeling scans were obtained to control for potential contributions of cerebral blood volume and structural brain images were used to control for contributions of brain volume. Task performance was positively correlated with strength of task-based functional connectivity (tFC) between brain regions of the frontoparietal working memory network. However, higher cortical iron concentration was associated with lower tFC within this frontoparietal network and with poorer working memory performance after controlling for both cerebral blood flow and brain volume. Our results suggest that high cortical iron concentration disrupts communication within frontoparietal networks supporting working memory and is associated with reduced working memory performance in older adults.

Multi-compartment diffusion MRI metrics [such as metrics from free water elimination diffusion tensor imaging (FWE-DTI) and neurite orientation dispersion and density imaging (NODDI)] may reflect more specific underlying white-matter tract characteristics than traditional, single-compartment metrics [i.e. metrics from Diffusion Tensor Imaging (DTI)]. However, it remains unclear if multi-compartment metrics are more closely associated with age and/or cognitive performance than single-compartment metrics. Here we compared the associations of single-compartment [Fractional Anisotropy (FA)] and multi-compartment diffusion MRI metrics (FWE-DTI metrics: Free Water Eliminated Fractional Anisotropy (FWE-FA) and Free Water (FW); NODDI metrics: Intracellular Volume Fraction (ICVF), Orientation Dispersion Index (ODI), and CSF-Fraction) with both age and working memory performance. A functional magnetic resonance imaging (fMRI) guided, white matter tractography approach was employed to compute diffusion metrics within a network of tracts connecting functional regions involved in working memory. Ninety-nine healthy older adults (aged 60-85) performed an in-scanner working memory task while fMRI was performed and also underwent multi-shell diffusion acquisition. The network of white matter tracts connecting functionally-activated regions was identified using probabilistic tractography. Diffusion metrics were extracted from skeletonized white matter tracts connecting fMRI activation peaks. Diffusion metrics derived from both single and multi-compartment models were associated with age (ps<=0.011 for FA, FWE-FA, ICVF and ODI). However, only multi-compartment metrics, specifically FWE-FA (p=0.045) and ICVF (p=0.020), were associated with working memory performance. Our results suggest that while most current diffusion metrics are sensitive to age, several multi-compartment metrics (i.e. FWE-FA and ICVF) appear more sensitive to cognitive performance in healthy older adults.

Cerebral small vessel disease (SVD) has been suggested to contribute to the pathogenesis of Alzheimer's disease (AD). Yet, the role of SVD in potentially contributing to AD pathology is unclear. The main objective of this study was to test the hypothesis that WMHs influence amyloid β (Aβ) levels within connected default mode network (DMN) tracts and cortical regions in cognitively unimpaired older adults. Regional standard uptake value ratios (SUVr) from Aβ-PET and white matter hyperintensity (WMH) volumes from three-dimensional magnetic resonance imaging FLAIR images were analyzed across a sample of 72 clinically unimpaired (mini-mental state examination ≥26), older adults (mean age 74.96 and standard deviation 8.13) from the Alzheimer's Disease Neuroimaging Initiative (ADNI3). The association of WMH volumes in major fiber tracts projecting from cortical DMN regions and Aβ-PET SUVr in the connected cortical DMN regions was analyzed using linear regression models adjusted for age, sex, ApoE, and total brain volumes. The regression analyses demonstrate that increased WMH volumes in the superior longitudinal fasciculus were associated with increased regional SUVr in the inferior parietal lobule (p = .011). The findings suggest that the relation between Aβ in parietal cortex is associated with SVD in downstream white matter (WM) pathways in preclinical AD. The biological relationships and interplay between Aβ and WM microstructure alterations that precede overt WMH development across the continuum of AD progression warrant further study.

Introduction We evaluated the relationship between plasma levels of transactive response DNA binding protein of 43 kDa (TDP‐43) and neuroimaging (magnetic resonance imaging [MRI]) measures of brain structure in aging. Methods Plasma samples were collected from 72 non‐demented older adults (age range 60–94 years) in the University of Kentucky Alzheimer's Disease Research Center cohort. Multivariate linear regression models were run with plasma TDP‐43 level as the predictor variable and brain structure (volumetric or cortical thickness) measurements as the dependent variable. Covariates included age, sex, intracranial volume, and plasma markers of Alzheimer's disease neuropathological change (ADNC). Results Negative associations were observed between plasma TDP‐43 level and both the volume of the entorhinal cortex, and cortical thickness in the cingulate/parahippocampal gyrus, after controlling for ADNC plasma markers. Discussion Plasma TDP‐43 levels may be directly associated with structural MRI measures. Plasma TDP‐43 assays may prove useful in clinical trial stratification. HIGHLIGHTS Plasma transactive response DNA binding protein of 43 kDa (TDP‐43) levels were associated with entorhinal cortex volume. Biomarkers of TDP‐43 and Alzheimer's disease neuropathologic change (ADNC) may help distinguish limbic‐predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) from ADNC. A comprehensive biomarker kit could aid enrollment in LATE‐NC clinical trials.

Background Face perception is fundamental for human social interactions, with declines in this ability linked to increased social anxiety, impaired social interactions, and reduced quality of life. For older adults, who are particularly vulnerable to social isolation—a known risk factor for dementia—face perception is especially critical. However, face perception deteriorates earlier and independently of general cognitive decline, increasing the risk of adverse functional outcomes. Our goal is to elucidate mechanisms that give rise to this deterioration. Our recent longitudinal findings reveal localized age‐related iron accumulation in specific cortical regions, including key face‐processing areas in inferior temporal cortex. Given iron's role as both essential for brain health and a potent oxidizer capable of damaging neurons and myelin, we hypothesize that age‐related iron accumulation in face‐processing regions contributes to face perception decline. Methods Eighteen healthy older adults (65‐78 years old) with corrected vision underwent structural MRI, quantitative susceptibility mapping to assess brain iron, and functional MRI during a faces vs houses task to identify face‐processing regions (e.g. the fusiform face area; FFA) and control regions (e.g. the house‐selective parahippocampal place area; PPA). Behavioral performance was assessed using the Cambridge Face and Car Memory Tests (CFMT/CCMT) with the car category as a control. Linear regression analyses assessed relationships between iron concentration in localized regions and CFMT/CCMT performance, controlling for age, gender, and NIH Toolbox measures of processing speed, working memory, and episodic memory. Results Higher iron concentration in the right FFA significantly correlated with lower CFMT performance (inverse efficiency scores: higher scores = lower performance; p = 0.026; r2 = 0.377; β = 1.06; t = 2.58; Figure 1). No significant associations were observed between FFA iron and CCMT performance (p = 0.104) or between PPA iron and CFMT performance (p = 0.960). Processing speed, working memory, and episodic memory were not significantly associated with CFMT scores (p > 0.19). Conclusion These preliminary findings suggest that higher iron levels in face‐processing regions are specifically associated with declines in face perception, independent of general cognitive performance. Further research is needed to assess how brain iron impacts the functional and white‐matter connectivity of brain networks supporting face perception.

Background/Objectives: Non-heme iron is essential for critical neuronal functions such as ATP generation, synaptogenesis, neurotransmitter synthesis, and myelin formation. However, as non-heme iron accumulates with age, excessive levels can contribute to oxidative stress, potentially disrupting neuronal integrity and contributing to cognitive decline. Despite growing evidence linking high brain iron with poorer cognitive performance, there are currently no proven methods to reduce brain iron accumulation in aging or to protect cognitive function from iron’s negative effects. Recent studies suggest that nutrition may influence brain iron levels, though the evidence remains limited and mixed. Methods: In this review, we explore recent findings, including our own cross-sectional and longitudinal studies, to evaluate the potential effectiveness of healthy diets and specific nutrients in mitigating brain iron accumulation during aging. We also briefly assess the roles of age and gender as factors in the relationship between dietary factors and brain iron load. Results: The limited findings in the literature indicate that dietary choices may impact brain iron levels. In particular, nutrients such as vitamins, antioxidants, iron-chelators, and polyunsaturated fatty acids may slow brain iron accumulation in older adults. Conclusions: Our review highlights the multiple gaps in current knowledge and underscores a critical need for additional research on this important topic.

Background Understanding biological mechanisms related to cerebrovascular disease (CVD) can provide insight into vascular contributions to cognitive impairment and dementia (VCID). There has been increased interest in exploring blood brain barrier (BBB) function as it relates to other well‐established CVD markers, such as magnetic resonance imaging (MRI) based white matter hyperintensities (WMHs) and free water (FW). Here, we examine how an MRI‐metric of BBB function [water exchange rate (kw)] relates to change in WMHs and FW after approximately 2.5 years of follow‐up. Method A total of 68 older adults recruited from the University of Kentucky had neuroimaging data. Water exchange rate (kw) across the BBB was measured by diffusion prepared pseudo‐continuous arterial spin labeling (DP‐pCASL) at baseline for the whole brain. Cerebral blood flow (CBF) and arterial transit time (ATT) were also measured from the DP‐pCASL sequence. Whole brain WMH volume was quantified using T2‐weighted fluid‐attenuated inversion recovery (FLAIR) and whole brain white matter FW was quantified using diffusion weighted imaging at baseline and follow‐up. Mixed‐effects models controlling for age, gender, and intracranial volume were used to test associations between baseline DP‐pCASL measures (i.e. kw, CBF, ATT) and change in WMHs or FW between two visits. Result Whole brain kw was not related to WMH volume at baseline, however, higher kw was associated with an increase in WMHs over time. Conversely, higher whole brain CBF was associated with fewer WMHs at baseline, but not with change over time. ATT was not related to WMHs at baseline or over time. When considering the FW measure, higher whole brain kw was related to lower FW cross‐sectionally but results did not persist longitudinally. Both whole brain CBF and ATT were not associated with FW at baseline or over time. Conclusion Our preliminary results suggest that BBB kw is related to change in WMHs over time and FW at baseline. Greater water exchange across the BBB may contribute to a greater susceptibility to white matter damage. The FW results are consistent with our prior studies correlating kw and FW. More work is needed to determine the effects of BBB efficiency on downstream cerebrovascular health.

Background Cognitive reserve (CR) in the context of Alzheimer’s’ disease has been widely studied, yet less is known about how CR protects against vascular brain pathologies. Here, we explored whether dietary factors might attenuate the association between magnetic resonance imaging (MRI)‐derived vascular biomarkers and cognition. Method Seventy‐one older adults (ages 60‐85) were scanned using a 3‐Tesla MRI Siemens Magnetom Prisma at the University of Kentucky. Data from a 3D T1‐weighted sequence, a 3D fluid‐attenuated inversion recovery sequence, and a 126‐direction diffusion MRI sequence were acquired. The vascular biomarkers used [White Matter Hyperintensity Volume (WMHV), Free Water (FW), and Peak Width of Skeletonized Mean Diffusivity (PSMD)] were developed and validated through the MarkVCID consortium (https://markvcid.partners.org). WMHV was computed using a 4‐tissue segmentation model, mean FW in all white matter was calculated using a two‐compartment model, and PSMD was calculated as the difference between the 95th and 5th percentiles in white matter MD values. Grey matter volume (GMV; non‐vascular biomarker) and intracranial volume (ICV) were estimated using FreeSurfer. The “Newly Developed Antioxidant Nutrient Questionnaire” was used to quantify dietary‐intake for the preceding month. Nutrients were grouped into nutrition factors based on previous literature and factor analysis (Factor 1= representing fruits and vegetables; Factor 2= representing nuts, healthy oils, and fish; Factor 3= representing green tea). All participants completed the Montreal Cognitive Assessment (MoCA). Multivariate linear regression models tested whether dietary factors, vascular biomarkers, and/or their interaction (i.e. moderation) were associated with MoCA scores. All models controlled for age, sex, ICV, and education. Result There were no significant main effects of WMHV, FW, PSMD, or GMV on MoCA scores. However, Factor 2 (but not other factors) positively moderated all 3 vascular biomarkers [WMHV (β=0.309, p=0.009); FW (β=0.324, p=0.007); PSMD (β=0.354, p=0.008)] such that a negative association between vascular markers and MoCA scores was only present in those with low but not high Factor 2 intake (Figure 1). Factor 2 did not moderate the association between non‐vascular biomarkers (i.e. GMV) and MoCA scores. Conclusion Our results suggest that consuming more nuts, healthy oils, and fish may help protect against vascular contributions to cognitive impairment.