ABSTRACT The influence of sex and age on cerebral perfusion is recognized, but the specific impacts on regional cerebral blood flow (CBF) and vascular risk remain to be fully characterized. In this study, 3D pseudo-continuous arterial spin labeling (pCASL) MRI was used to identify sex and age related CBF patterns, and a vascular risk score (VRS) was developed based on normative perfusion profiles. Perfusion data from 186 cognitively healthy participants (89 males, 97 females; aged 8 to 92 years), obtained from a publicly available dataset, were analyzed. An extension of the 3D Simple Linear Iterative Clustering (SLIC) supervoxel algorithm was applied to CBF maps to group neighboring voxels with similar intensities into anatomically meaningful regions. Regional CBF features were extracted and used to train a convolutional neural network (CNN) for sex classification and perfusion pattern analysis. Global, age related CBF changes were also assessed. Participant specific VRS was computed by comparing individual CBF profiles to age and sex specific normative data to quantify perfusion deficits. A 95 percent accuracy in sex classification was achieved using the proposed supervoxel based method, and distinct perfusion signatures were identified. Higher CBF was observed in females in medial Brod-mann areas 6 and 10, area V5, occipital polar cortex, and insular regions. A global decline in CBF with age was observed in both sexes. Individual perfusion deficits were quantified using VRS, providing a personalized biomarker for early hy-poperfusion. Sex and age specific CBF patterns were identified, and a personalized vascular risk biomarker was proposed, contributing to advancements in precision neurology. Keywords-- 3D pCASL MRI, CBF, age-and sex-specific perfusion patterns, vascular risk score, cognitively healthy 1. INTRODUCTION Arterial Spin Labeling (ASL) is a non-invasive Magnetic Resonance Imaging (MRI) technique designed to quantitatively assess cerebral blood flow (CBF) by magnetically labeling endogenous arterial blood water protons without the need for exogenous contrast agents or ionizing radiation [1]. The ASL technique involves three key steps: (i) magnetic labeling of arterial blood proximal to the imaging region, (ii) delivery of magnetically tagged blood to brain tissue altering the local MR signal, and (iii) acquisition of paired labeled and control images whose subtraction yields perfusion-weighted maps [1].

Pain is an inherent part of human existence, manifesting as both physical and emotional experiences, and can be categorized as either acute or chronic. Over the years, extensive research has been conducted to understand the causes of pain and explore potential treatments, with contributions from various scientific disciplines. However, earlier studies often overlooked the role of gender in pain experiences. In this study, we utilized Natural Language Processing (NLP) to analyze and gain deeper insights into individuals' pain experiences, with a particular focus on gender differences. We successfully classified posts into male and female corpora using the Hidden Attribute Model-Convolutional Neural Network (HAM-CNN), achieving an F1 score of 0.86 by aggregating posts based on usernames. Our analysis revealed linguistic differences between genders, with female posts tending to be more emotionally focused. Additionally, the study highlighted that conditions such as migraine and sinusitis are more prevalent among females and explored how pain medication affects individuals differently based on gender.

If you've ever had an argument with the opposite sex, it may be tempting to conclude that men and women just aren't on the same wavelength. Now, a study not only suggests this is indeed the case, but that males and females really are wired differently from birth. In what's described as one of the biggest studies of newborn brain anatomy, scientists performed head scans of more than 500 babies. Overall, the female babies had more grey matter in their brains, while the males had more white matter. Grey matter is mostly found on the outer-most layer of the brain, or cortex, and plays a big role in mental functions, such as memory, emotions and processing information.

Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models. To capture sex-specific patterns, we develop the Group-Specific Discriminant Analysis (GSDA) for first-order classification. The evaluation on two public neuroimaging datasets demonstrates the efficacy of GSDA in learning sex-specific models from functional networks, achieving a significant improvement in group specificity over baseline methods. The major sex differences are in the strength of lateralization and the interactions within and between lobes. The GSDA-based method is generic in nature and can be adapted to other group-specific analyses such as handedness-specific or disease-specific analyses.

Men and women have "distinct brain organization patterns" according to a new Stanford Medicine study. The findings were published in the "Proceedings of the National Academy of Sciences" journal on Tuesday. According to Stanford Medicine's statement on the study, it was conducted utilizing a new artificial intelligence model to scan around 1,500 brains. The AI was then instructed to determine whether the brain scan came from a man or a woman, predicting correctly with a 90% accuracy rate. "A key motivation for this study is that sex plays a crucial role in human brain development, in aging, and in the manifestation of psychiatric and neurological disorders," Vinod Menon, PhD, professor of psychiatry and behavioral sciences and director of the Stanford Cognitive and Systems Neuroscience Laboratory, said.

Relationship columnists and pop psychologists have long claimed that men and women are wired differently, and a new study has proven them correct. Scientists developed an artificial intelligence model that was able to tell the difference between scans of men's and women's brain activity with more than 90-percent accuracy. Most of these differences are in the default mode network, striatum, and limbic network - areas involved in a wide range of processes including daydreaming, remembering the past, planning for the future, making decisions, and smelling. With these results, scientists at Stanford Medicine add a new piece to the puzzle, supporting the idea that biological sex shapes the brain. The researchers said they are optimistic that this work will help shed light on brain conditions that affect men and women differently.

The electrocardiogram (ECG) is routinely used in cardiology, though its interpretation is confounded by anatomical variability. A novel, automated computational pipeline enables quantification of torso-ventricular anatomy metrics from magnetic resonance imaging, and comparison to ECG characteristics. Sex and myocardial infarction differences are investigated based on 1051 healthy and 425 post-MI subjects from UK Biobank. Smaller ventricles in females explain ~50% of shorter QRS durations than in males, and contribute to lower STJ amplitudes in females (also due to more superior and posterior position). In females, torso-ventricular anatomy, particularly from larger BMI, is a stronger modulator of T wave amplitude reductions and left-deviated R axis angles in post-MI than in males. Thus, female MI phenotype is less reflective of pathology, and baseline STJ amplitudes and QRS durations are further from clinical thresholds. Therefore, quantification of anatomical sex-differences and impact on ECG in health and disease is critical to avoid clinical sex-bias.

PD is linked to faster brain aging. Sex is recognized as an important factor in PD, such that males are twice as likely as females to have the disease and have more severe symptoms and a faster progression rate. Despite previous research, there remains a significant gap in understanding the function of sex in the process of brain aging in PD patients. The T1-weighted MRI-driven brain-predicted age difference was computed in a group of 373 PD patients from the PPMI database using a robust brain-age estimation framework that was trained on 949 healthy subjects. Linear regression models were used to investigate the association between brain-PAD and clinical variables in PD, stratified by sex. All female PD patients were used in the correlational analysis while the same number of males were selected based on propensity score matching method considering age, education level, age of symptom onset, and clinical symptom severity. Despite both patient groups being matched for demographics, motor and non-motor symptoms, it was observed that males with Parkinson's disease exhibited a significantly higher mean brain age-delta than their female counterparts . In the propensity score-matched PD male group, brain-PAD was found to be associated with a decline in general cognition, a worse degree of sleep behavior disorder, reduced visuospatial acuity, and caudate atrophy. Conversely, no significant links were observed between these factors and brain-PAD in the PD female group.

Differences between males and females have been extensively documented in biological, psychological, and behavioral domains. Among these, sex differences in the rate and typology of antisocial behavior remains one of the most conspicuous and enduring patterns among humans. However, the nature and extent of sexual dimorphism in the brain among antisocial populations remains mostly unexplored. Here, we seek to understand sex differences in brain structure between incarcerated males and females in a large sample (n 1,300) using machine learning. We apply source‐based morphometry, a contemporary multivariate approach for quantifying gray matter measured with magnetic resonance imaging, and carry these parcellations forward using machine learning to classify sex.

Cardiologist and Us2.ai co-founder Dr Carolyn Lam talks to AIMed about the potential of AI to democratize heart ultrasound, her experience as an accidental entrepreneur, and the importance of championing women in cardiovascular science. You serve as a senior consultant cardiologist at the National Heart Centre Singapore, a full professor at Duke-National University of Singapore, and co-founder of Us2.ai. How do you split your time between these demanding positions? Time-wise I fortunately don't have to struggle since my time commitments are spelled out very clearly for me (days in clinics, days in clinical research, etc.); the challenge is really in staying ultra-focused on delivering my very best in the time that I have. To do that, I have had to learn the hard lesson of saying "no" – in fact my mission this year is to focus on my "not to do" list rather than on my "to do" list.