Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI

Image noise and motion artifacts greatly affect the quality of brain MRI and negatively influence downstream medical image analysis. Previous studies often focus on 2D methods that process each volumetric MR image slice-by-slice, thus losing important 3D anatomical information. Additionally, these studies generally treat image denoising and artifact correction as two standalone tasks, without considering their potential relationship, especially on low-quality images where severe noise and motion artifacts occur simultaneously. To address these issues, we propose a Joint image Denoising and motion Artifact Correction (JDAC) framework via iterative learning to handle noisy MRIs with motion artifacts, consisting of an adaptive denoising model and an anti-artifact model. In the adaptive denoising model, we first design a novel noise level estimation strategy, and then adaptively reduce the noise through a U-Net backbone with feature normalization conditioning on the estimated noise variance. The anti-artifact model employs another U-Net for eliminating motion artifacts, incorporating a novel gradient-based loss function designed to maintain the integrity of brain anatomy during the motion correction process. These two models are iteratively employed for joint image denoising and artifact correction through an iterative learning framework. An early stopping strategy depending on noise level estimation is applied to accelerate the iteration process. The denoising model is trained with 9,544 T1-weighted MRIs with manually added Gaussian noise as supervision. The anti-artifact model is trained on 552 T1-weighted MRIs with motion artifacts and paired motion-free images. Experimental results on a public dataset and a clinical study suggest the effectiveness of JDAC in both tasks of denoising and motion artifact correction, compared with several state-of-the-art methods

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks

Calcium and vitamin D intakes may be positively associated with brain lesions in depressed and nondepressed elders

Studies indicate that diet and vascular calcification may be related to the occurrence of brain lesions, although the importance of dietary calcium and vitamin D has not been investigated. The objective of this study was to test the hypothesis that calcium and vitamin D intakes would be positively associated with brain lesion volumes in elderly individuals with and without late-life depression. A cross sectional study was performed as part of a longitudinal clinical study of late-life depression. Calcium and vitamin D intakes were assessed in 232 elderly subjects (95 with current or prior depression, 137 without depression) using a Block 1998 food frequency questionnaire. Calcium, vitamin D, and kilocalorie intake were determined. Brain lesion volumes were calculated from magnetic resonance imaging scan. Subjects were age 60 years or over. Calcium and vitamin D intakes were significantly and positively correlated with brain lesion volume (p < 0.05 and p < 0.001, respectively). In two separate multivariable models, controlling for age, hypertension, diabetes, heart disease, group (depression/comparison), lesion load (high/low), and total kilocalories, these positive associations remained significant (p<0.05 for calcium; p < 0.001 for vitamin D). In conclusion, calcium and vitamin D consumption were associated with brain lesions in elderly subjects, even after controlling for potentially explanatory variables. These associations may be due to vascular calcification or other mechanism. The possibility of adverse effects of high intakes of calcium and vitamin D needs to be further explored in longitudinal studies of elderly subjects

A life in progress: motion and emotion in the autobiography of Robert M. La Follette

This article is a study of a La Follette’s Autobiography, the autobiography of the leading Wisconsin progressive Robert M. La Follette, which was published serially in 1911 and, in book form, in 1913. Rather than focusing, as have other historians, on which parts of La Follette’s account are accurate and can therefore be trusted, it explains instead why and how this major autobiography was conceived and written. The article shows that the autobiography was the product of a sustained, complex, and often fraught series of collaborations among La Follette’s family, friends, and political allies, and in the process illuminates the importance of affective ties as well as political ambition and commitment in bringing the project to fruition. In the world of progressive reform, it argues, personal and political experiences were inseparable

Longitudinal Relationships of Religion with Posttreatment Depression Severity in Older Psychiatric Patients: Evidence of Direct and Indirect Effects

Psychiatric patients (age 59+) were assessed before study treatment for major depressive disorder, and again after 3 months. Measures taken before study treatment included facets of religiousness (subjective religiosity, private prayer, worship attendance, and religious media use), social support, and perceived stress. Clinician-rated depression severity was assessed both before and after treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS). Structural equation modeling was used to test a path model of direct and indirect effects of religious factors via psychosocial pathways. Subjective religiousness was directly related to worse initial MADRS, but indirectly related to better posttreatment MADRS via the pathway of more private prayer. Worship attendance was directly related to better initial MADRS, and indirectly related to better post-treatment MADRS via pathways of lower stress, more social support, and more private prayer. Private prayer was directly related to better post-treatment MADRS. Religious media use was related to more private prayer, but had no direct relationship with MADRS

Mid-life Leukocyte Telomere Length and Dementia Risk: An Observational and Mendelian Randomization Study of 435,046 UK Biobank Participants

Telomere attrition is one of biological aging hallmarks and may be intervened to target multiple aging-related diseases, including Alzheimer\u27s disease and Alzheimer\u27s disease related dementias (AD/ADRD). The objective of this study was to assess associations of leukocyte telomere length (TL) with AD/ADRD and early markers of AD/ADRD, including cognitive performance and brain magnetic resonance imaging (MRI) phenotypes. Data from European-ancestry participants in the UK Biobank (n = 435,046) were used to evaluate whether mid-life leukocyte TL is associated with incident AD/ADRD over a mean follow-up of 12.2 years. In a subsample without AD/ADRD and with brain imaging data (n = 43,390), we associated TL with brain MRI phenotypes related to AD or vascular dementia pathology. Longer TL was associated with a lower risk of incident AD/ADRD (adjusted Hazard Ratio [aHR] per SD = 0.93, 95% CI 0.90–0.96, p = 3.37 × 10−7). Longer TL also was associated with better cognitive performance in specific cognitive domains, larger hippocampus volume, lower total volume of white matter hyperintensities, and higher fractional anisotropy and lower mean diffusivity in the fornix. In conclusion, longer TL is inversely associated with AD/ADRD, cognitive impairment, and brain structural lesions toward the development of AD/ADRD. However, the relationships between genetically determined TL and the outcomes above were not statistically significant based on the results from Mendelian randomization analysis results. Our findings add to the literature of prioritizing risk for AD/ADRD. The causality needs to be ascertained in mechanistic studies

Elevated brain lesion volumes in older adults who use calcium supplements: a cross-sectional clinical observational study

Recent studies have implicated calcium supplements with elevated vascular risk, and therefore these supplements may also relate to the occurrence of brain lesions (or hyperintensities) in older adults. These lesions represent damage to brain tissue that is caused by ischaemia. This cross sectional clinical observational study examined the association between use of calcium-containing dietary supplements and lesion volumes in a sample of 227 older adults (60 years and older). Food and supplemental calcium intakes were assessed with a Block 1998 FFQ; participants with supplemental calcium intakes above zero were categorized as supplement users. Lesion volumes were determined from cranial MRI (1.5 Tesla) using a semi-automated technique; volumes were log-transformed because they were non-normal. An ANCOVA model showed that supplement users had greater lesion volumes than non-users, even after controlling for dietary food calcium, age, sex, race, education, energy intake, depression and hypertension (Calcium supplement use: β = 0.34, SE = 0.10, F1,217 = 10.98, p = 0.0011). The influence of supplemental calcium use on lesion volume was of similar magnitude to that of hypertension, a well-established risk factor for lesions. Among supplement users, the amount of supplemental calcium was not related to lesion volume (β = −0.000035, SE = 0.00015, F1,139 = 0.06, p = 0.81). This study indicates that the use of calcium-containing dietary supplements, even low dose supplements, by older adults may be associated with greater lesion volumes. Evaluation of randomised, controlled trials is warranted to determine if this relationship is a causal one

Variability in Frontotemporal Brain Structure: The Importance of Recruitment of African Americans in Neuroscience Research

BACKGROUND: Variation in brain structure is both genetically and environmentally influenced. The question about potential differences in brain anatomy across populations of differing race and ethnicity remains a controversial issue. There are few studies specifically examining racial or ethnic differences and also few studies that test for race-related differences in context of other neuropsychiatric research, possibly due to the underrepresentation of ethnic minorities in clinical research. It is within this context that we conducted a secondary data analysis examining volumetric MRI data from healthy participants and compared the volumes of the amygdala, hippocampus, lateral ventricles, caudate nucleus, orbitofrontal cortex (OFC) and total cerebral volume between Caucasian and African-American participants. We discuss the importance of this finding in context of neuroimaging methodology, but also the need for improved recruitment of African Americans in clinical research and its broader implications for a better understanding of the neural basis of neuropsychiatric disorders. METHODOLOGY/PRINCIPAL FINDINGS: This was a case control study in the setting of an academic medical center outpatient service. Participants consisted of 44 Caucasians and 33 ethnic minorities. The following volumetric data were obtained: amygdala, hippocampus, lateral ventricles, caudate nucleus, orbitofrontal cortex (OFC) and total cerebrum. Each participant completed a 1.5 T magnetic resonance imaging (MRI). Our primary finding in analyses of brain subregions was that when compared to Caucasians, African Americans exhibited larger left OFC volumes (F (1,68) = 7.50, p = 0.008). CONCLUSIONS: The biological implications of our findings are unclear as we do not know what factors may be contributing to these observed differences. However, this study raises several questions that have important implications for the future of neuropsychiatric research

Brain morphometric features predict depression symptom phenotypes in late-life depression using a deep learning model

Objectives Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study.DesignCross-sectional.MeasurementsStructural magnetic resonance imaging (sMRI) was used to predict five depression symptom phenotypes from the Hamilton and MADRS depression scales previously derived from factor analysis: (1) Anhedonia, (2) Suicidality, (3) Appetite, (4) Sleep Disturbance, and (5) Anxiety. Our deep learning model was deployed to predict each factor score via learning deep feature representations from 3D sMRI patches in 34 a priori regions-of-interests (ROIs). ROI-level prediction accuracy was used to identify the most discriminative brain regions associated with prediction of factor scores representing each of the five symptom phenotypes. Results Factor-level results found significant predictive models for Anxiety and Suicidality factors. ROI-level results suggest the most LLD-associated discriminative regions in predicting all five symptom factors were located in the anterior cingulate and orbital frontal cortex. Conclusions We validated the effectiveness of using deep learning approaches on sMRI for predicting depression symptom phenotypes in LLD. We were able to identify deep embedded local morphological differences in symptom phenotypes in the brains of those with LLD, which is promising for symptom-targeted treatment of LLD. Future research with machine learning models integrating multimodal imaging and clinical data can provide additional discriminative information