Working Memory Impairment in Fibromyalgia Patients Associated with Altered Frontoparietal Memory Network

BACKGROUND: Fibromyalgia (FM) is a disorder characterized by chronic widespread pain and frequently associated with other symptoms. Patients with FM commonly report cognitive complaints, including memory problem. The objective of this study was to investigate the differences in neural correlates of working memory between FM patients and healthy subjects, using functional magnetic resonance imaging (MRI). METHODOLOGY/PRINCIPAL FINDINGS: Nineteen FM patients and 22 healthy subjects performed an n-back memory task during MRI scan. Functional MRI data were analyzed using within- and between-group analysis. Both activated and deactivated brain regions during n-back task were evaluated. In addition, to investigate the possible effect of depression and anxiety, group analysis was also performed with depression and anxiety level in terms of Beck depression inventory (BDI) and Beck anxiety inventory (BAI) as a covariate. Between-group analyses, after controlling for depression and anxiety level, revealed that within the working memory network, inferior parietal cortex was strongly associated with the mild (r = 0.309, P = 0.049) and moderate (r = 0.331, P = 0.034) pain ratings. In addition, between-group comparison revealed that within the working memory network, the left DLPFC, right VLPFC, and right inferior parietal cortex were associated with the rating of depression and anxiety? CONCLUSIONS/SIGNIFICANCE: Our results suggest that the working memory deficit found in FM patients may be attributable to differences in neural activation of the frontoparietal memory network and may result from both pain itself and depression and anxiety associated with pain

Estrogen replacement, muscle composition, and physical function: The health ABC study

Purpose: Although the beneficial effects of estrogen use on cardiovascular and cognitive function in postmenopausal women have been recently discredited, controversy remains regarding its usefulness for maintaining skeletal muscle mass or strength. Therefore, the purpose of this study was to determine whether estrogen use is associated with enhanced muscle composition and, if so, whether this translates into improved strength and physical function. Methods: Cross-sectional analysis of 840 well-functioning community-dwelling white women (current estrogen replacement therapy (ERT) users = 259, nonusers = 581) aged 70-79 yr participating in the Health, Aging and Body Composition Study. Muscle composition of the midthigh by computed tomography included cross-sectional area (CSA) of the quadriceps, hamstrings, intermuscular fat and subcutaneous fat, and muscle attenuation in Hounsfield units (HU) as a measure of muscle density. Isometric hand grip and isokinetic knee extensor strength were assessed by dynamometry. Physical function was assessed using a summary scale that included usual 6-m walk and narrow walk speed, repeated chair stands, and standing balance. Results: In analyses of covariance adjusted for relevant confounders. quadriceps muscle CSA and HU were greater in Current ERT than non-ERT women (P < 0.05). Grip strength was also greater (P < 0.05) in women taking ERT while knee extensor strength approached significance (P < 0.10). However, differences in muscle composition and strength were modest at <= 3.3%. There was no difference by ERT status for the hamstring, muscles. fat CSA. or for physical function. Conclusion: The associations between ERT and muscle composition and strength were minor and did not translate into improved physical function. Initiation of ERT for preservation of muscle composition and function may not be indicated

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics