Introduction: Gender and sex hormones influence brain function, but their effects on functional network organization within the brain are not yet understood.
Methods: We investigated the influence of gender, prenatal sex hormones (estimated by the 2D:4D digit ratio), and the menstrual cycle on the intrinsic functional network organization of the brain (as measured by 3T resting-state functional MRI (rs-fMRI)) using right-handed, age-matched university students (100 males and 100 females). The mean (±) age was 20.9 ± 1.5 (range: 18-24) years and 20.8 ± 1.3 (range: 18-24) years for males and females, respectively. Using two parameters derived from the normalized alpha centrality analysis (one for local and another for global connectivity strength), we created mean functional connectivity strength maps.
Results: There was a significant difference between the male mean map and female mean map in the distributions of network properties in almost all cortical regions and the basal ganglia but not in the medial parietal, limbic, and temporal regions and the thalamus. A comparison between the mean map for the low 2D:4D digit ratio group (indicative of high exposure to testosterone during the prenatal period) and that for the high 2D:4D digit ratio group revealed a significant difference in the network properties of the medial parietal region for males and in the temporal region for females. The menstrual cycle affected network organization in the brain, which varied with the 2D:4D digit ratio. Most of these findings were reproduced with our other datasets created with different preprocessing steps.
Conclusions: The results suggest that differences in gender, prenatal sex hormone exposure, and the menstrual cycle are useful for understanding the normal brain and investigating the mechanisms underlying the variable prevalence and symptoms of neurological and psychiatric diseases.
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http://dx.doi.org/10.1002/brb3.890 | DOI Listing |
Lancet Digit Health
January 2025
Biomedical Engineering Department, Duke University, Durham, NC, USA; Biostatistics and Bioinformatics Department, Duke University, Durham, NC, USA. Electronic address:
Background: Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement.
View Article and Find Full Text PDFHear Res
December 2024
Clinics of Otolaryngology, Hannover Medical School, Hearing Center Hannover (DHZ), Karl-Wiechert-Allee 3, 30625 Hannover, Germany; Institute of AudioNeuroTechnology (VIANNA) & Dept. of Experimental Otology, Hannover Medical School, Stadtfelddamm 34, 30625 Hannover, Germany. Electronic address:
Objective: We investigated auditory working-memory using behavioural measures and electroencephalography (EEG) in adult Cochlear Implant (CI) users with varying degrees of CI performance.
Methods: 24 adult CI listeners (age: M = 61.38, SD = 12.
Small Methods
December 2024
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083, China.
Memristors and magnetic tunnel junctions are showing great potential in data storage and computing applications. A magnetoelectrically coupled memristor utilizing electron spin and electric field-induced ion migration can facilitate their operation, uncover new phenomena, and expand applications. In this study, devices consisting of Pt/(LaCoO/SrTiO)/LaCoO/Nb:SrTiO (Pt/(LCO/STO)/LCO/NSTO) are engineered using pulsed laser deposition to form the LCO/STO superlattice layer, with Pt and NSTO serving as the top and bottom electrodes, respectively.
View Article and Find Full Text PDFPLOS Digit Health
December 2024
Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
Digital clinical decision support tools have contributed to improved quality of care at primary care level health facilities. However, data from real-world randomized trials are lacking. We conducted a cluster randomized, open-label trial in Tanzania evaluating the use of a digital clinical decision support algorithm (CDSA), enhanced by point-of-care tests, training and mentorship, compared with usual care, among sick children 2 to 59 months old presenting to primary care facilities for an acute illness in Tanzania (ClinicalTrials.
View Article and Find Full Text PDFPLOS Digit Health
December 2024
Heart and Vascular Institute, Stamford Hospital, Stamford, Connecticut, United States of America.
Breast artery calcification (BAC) obtained from standard mammographic images is currently under evaluation to stratify risk of major adverse cardiovascular events in women. Measuring BAC using artificial intelligence (AI) technology, we aimed to determine the relationship between BAC and coronary artery calcification (CAC) severity with Major Adverse Cardiac Events (MACE). This retrospective study included women who underwent chest computed tomography (CT) within one year of mammography.
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