We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49-89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06-1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76-3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33-2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44-3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43-4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

Older adults with anxiety have lower gray matter brain volume-a component of accelerated aging. We have previously validated a machine learning model to predict brain age, an estimate of an individual's age based on voxel-wise gray matter images. We investigated associations between brain age and anxiety, depression, stress, and emotion regulation. We recruited 78 participants (≥50 years) along a wide range of worry severity. We collected imaging data and computed voxel-wise gray matter images, which were input into an existing machine learning model to estimate brain age. We conducted a multivariable linear regression between brain age and age, sex, race, education, worry, anxiety, depression, rumination, neuroticism, stress, reappraisal, and suppression. We found that greater brain age was significantly associated with greater age, male sex, greater worry, greater rumination, and lower suppression. Male sex, worry, and rumination are associated with accelerated aging in late life and expressive suppression may have a protective effect. These results provide evidence for the transdiagnostic model of negative repetitive thoughts, which are associated with cognitive decline, amyloid, and tau.

Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD). We showed that amyloid status is a critical feature for brain age prediction models. We trained a model on T1-weighted MRI images participants without amyloid pathology. MRI data were processed to estimate gray matter density voxel-wise, which were then used to predict chronological age. Our model performed accurately comparable to previous models. Notably, we demonstrated more significant differences between AD diagnostic groups than other models. In addition, our model was able to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid. Incorporation of amyloid status in brain age prediction models ultimately improves the utility of brain age as a biomarker for AD.

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of "brain age" analysis on disability in MS using a large, real-world dataset.

It is well established that aging is associated with declines in episodic memory. In recent years, an emphasis has emerged on the development of behavioral tasks and the identification of biomarkers that are predictive of cognitive decline in healthy as well as pathological aging. Here, we describe a memory task designed to assess the accuracy of discrimination ability for the locations of objects. Object locations were initially encoded incidentally, and appeared in a single space against a 5 × 7 grid. During retrieval, subjects viewed repeated object-location pairings, displacements of 1, 2, 3, or 4 grid spaces, and maximal corner-to-opposite-corner displacements. Subjects were tasked with judging objects in this second viewing as having retained their original location, or having moved. Performance on a task such as this is thought to rely on the capacity of the individual to perform hippocampus-mediated pattern separation. We report a performance deficit associated with a physically healthy aged group compared to young adults specific to trials with low mnemonic interference. Additionally, for aged adults, performance on the task was correlated with performance on the delayed recall portion of the Rey Auditory Verbal Learning Test (RAVLT), a neuropsychological test sensitive to hippocampal dysfunction. In line with prior work, dividing the aged group into unimpaired and impaired subgroups based on RAVLT Delayed Recall scores yielded clearly distinguishable patterns of performance, with the former subgroup performing comparably to young adults, and the latter subgroup showing generally impaired memory performance even with minimal interference. This study builds on existing tasks used in the field, and contributes a novel paradigm for differentiation of healthy from possible pathological aging, and may thus provide an avenue for early detection of age-related cognitive decline.

Chronic low back pain (CLBP) is a leading cause of disability and is associated with neurodegenerative changes in brain structure. These changes lead to impairments in cognitive function and are consistent with those seen in aging, suggesting an accelerated aging pattern. In this study we assessed this using machine-learning estimated brain age (BA) as a holistic metric of morphometric changes associated with aging. Structural imaging data from 31 non-depressed CLBP patients and 32 healthy controls from the Pain and Interoception Imaging Network were included. Using our previously developed algorithm, we estimated BA per individual based on grey matter density. We then conducted multivariable linear modeling for effects of group, chronological age, and their interaction on BA. We also performed two voxel-wise analyses comparing grey matter density between CLBP and control individuals and the association between gray matter density and BA. There was an interaction between CLBP and greater chronological age on BA such that the discrepancy in BA between healthy and CLBP individuals was greater for older individuals. In CLBP individuals, BA was not associated with sex, current level of pain, duration of CLBP, or mild to moderate depressive symptoms. CLBP individuals had lower cerebellar grey matter density compared to healthy individuals. Brain age was associated with lower gray matter density in numerous brain regions. CLBP was associated with greater BA, which was more profound in later life. BA as a holistic metric was sensitive to differences in gray matter density in numerous regions which eluded direct comparison between groups.

Exposure to tobacco smoke (TS) has been considered a risk factor for osteonecrosis of the femoral head (ONFH). Soluble epoxide hydrolase inhibitors (sEHIs) have been found to reduce inflammation and oxidative stress in a variety of pathologies. This study was designed to assess the effect of sEHI on the development of ONFH phenotypes induced by TS exposure in spontaneously hypertensive (SH) rats. SH and normotensive Wistar Kyoto (WKY) rats were exposed to filtered air (FA) or TS (80 mg/m3 particulate concentration) 6 h/day, 3 days/week for 8 weeks. During this period, sEHI was delivered through drinking water at a concentration of 6 mg/L. Histology, immunohistochemistry, and micro-CT morphometry were performed for phenotypic evaluation. As results, TS exposure induced significant increases in adipocyte area, bone specific surface (BS/BV), and trabecular separation (Tb.SP), as well as significant decreases in bone mineral density (BMD), percent trabecular area (Tb.Ar), HIF-1a expression, bone volume fraction (BV/TV), trabecular numbers (Tb.N), and trabecular thickness (Tb.Th) in both SH and WKY rats. However, the protective effects of sEHI were mainly observed in TS-exposed SH rats, specifically in the density of osteocytes, BMD, Tb.Ar, HIF-1a expression, BV/TV, BS/BV, Tb.N, and Tb.SP. Our study confirms that TS exposure can induce ONFH especially in SH rats, and suggests that sEHI therapy may protect against TS exposure-induced osteonecrotic changes in the femoral head.