Sex differences in default mode network connectivity in healthy aging adults

Female Cerebellum Seems Sociable; An iTBS Investigation

The cerebellum has been shown to be engaged in tasks other than motor control, including cognitive and affective functions. Prior neuroimaging studies have documented the role of this area in social cognition and despite these findings, no studies have yet examined the causal relationship between the cerebellum and social cognition. This study aimed to investigate the role of the cerebellum in empathy and theory of mind (ToM) in a randomized, placebo-controlled, double-blind, parallel study. 32 healthy participants were assigned to either a sham or active group. For the active group, an intermittent theta-burst stimulation (iTBS) protocol at 100% of the motor threshold was applied to the cerebellum, while the control group received sham stimulation. An eyes-closed EEG session, the Empathy Quotient (EQ) test, and the Reading the Mind in the Eyes Test (RMET) were administered before and after the iTBS session. The results demonstrated differences in cognitive empathy, ToM, and a decrease in the activity of the default mode network (DMN) between the active and sham groups in females. Females also showed a decrease in the activity of the affective empathy network and connectivity in the DMN. We conclude that cognitive empathy and ToM are associated with cerebellar activity, and due to sex-related differences in the cortical organization of this area which is modulated by sex hormones, the stimulation of the cerebellum in males and females yields different results.

A preliminary investigation of sex differences in cognitive and fMRI changes following 28 days of cannabis abstinence

BACKGROUND

Previous studies have investigated the influence of cannabis on cognition among people with MS (pwMS), yet the influence of sex in the context of cannabis use remains unexplored. We aim to fill this gap by investigating cannabis-sex related differences in verbal learning, memory and processing speed in association with fMRI (resting state, and task-based) metrics.

METHOD

Our sample consisted of 19 long-term, frequent cannabis users (8 males, 11 females). Assessments were conducted at baseline and after 28 days of cannabis abstinence. The tests included measures of verbal memory (Selective Reminding Test (SRT)), working memory (n-back), information processing speed (Symbol Digit Modalities Test (SDMT)) and the resting state DMN. To evaluate the effects of cannabis abstinence, we performed a group x time interaction analysis using repeated measures ANCOVA. This analysis controlled for several covariates, including the level of disability (EDSS), baseline cannabis THC metabolite levels, and cannabis withdrawal symptoms. By controlling for these variables, we aimed to isolate the impact of cannabis abstinence on cognitive performance over time. Statistical significance was set at p < 0.05.

RESULTS

There were no baseline cognitive differences between the sexes. After 28 days of cannabis abstinence, females performed better on the Selective Reminding Test (SRT) (p = 0.04), with a large effect size (η² = 0.286). The mean correct response improved over time for females, but there was no statistically significant group x time interaction on the Symbol Digit Modalities Test (SDMT) and the n-back task. Resting state default mode network data showed overall increased activation in females relative to males at day 28, which meshed with lower brain activation during task-based fMRI paradigms.

CONCLUSION

Cannabis negated sex-based cognitive differences. Functional MRI task-based paradigms revealed less cerebral activation in females compared to males, which was associated with comparable or better cognitive performance in females, particularly after cannabis abstinence.

Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain

Sex differences in the brain have been widely reported and may hold the key to elucidating sex differences in many medical conditions and drug response. However, the molecular correlates of these sex differences in structural and functional brain measures in the human brain remain unclear. Herein, we used sample entropy (SampEn) to quantify the signal complexity of resting-state functional magnetic resonance imaging (rsfMRI) in a large neuroimaging cohort (N = 1,642). The frontoparietal control network and the cingulo-opercular network had high signal complexity while the cerebellar and sensory motor networks had low signal complexity in both men and women. Compared with those in male brains, we found greater signal complexity in all functional brain networks in female brains with the default mode network exhibiting the largest sex difference. Using the gene expression data in brain tissues, we identified genes that were significantly associated with sex differences in brain signal complexity. The significant genes were enriched in the gene sets that were differentially expressed between the brain cortex and other tissues, the estrogen-signaling pathway, and the biological function of neural plasticity. In particular, the G-protein-coupled estrogen receptor 1 gene in the estrogen-signaling pathway was expressed more in brain regions with greater sex differences in SampEn. In conclusion, greater complexity in female brains may reflect the interactions between sex hormone fluctuations and neuromodulation of estrogen in women.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09954-y.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Objective

fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

Methods

We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).

Results

We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity.

Conclusion

Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.

Significance

Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Objective

fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

Methods

We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).

Results

We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity.

Conclusion

Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.

Significance

Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Whole-brain functional connectivity predicts groupwise and sex-specific tau PET in preclincal Alzheimer's disease

Preclinical Alzheimer's disease, characterized by the initial accumulation of amyloid and tau pathologies without symptoms, presents a critical opportunity for early intervention. Yet, the interplay between these pathological markers and the functional connectome during this window remains understudied. We therefore set out to elucidate the relationship between the functional connectome and amyloid and tau, as assessed by PET imaging, in individuals with preclinical AD using connectome-based predictive modeling (CPM). We found that functional connectivity predicts tau PET, outperforming amyloid PET models. These models were predominantly governed by linear relationships between functional connectivity and tau. Tau models demonstrated a stronger correlation to global connectivity than underlying tau PET. Furthermore, we identify sex-based differences in the ability to predict regional tau, without any underlying differences in tau PET or global connectivity. Taken together, these results suggest tau is more closely coupled to functional connectivity than amyloid in preclinical disease, and that multimodal predictive modeling approaches stand to identify unique relationships that any one modality may be insufficient to discern.

Somatomotor-visual resting state functional connectivity increases after 2 years in the UK Biobank longitudinal cohort

Purpose

Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort.

Approach

We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t -test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets.

Results

We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p < 10 - 15 ) that occurs in male, female, older subject (> 65 years old), and younger subject (< 55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC.

Conclusions

We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.

Longitudinal monitoring of the mouse brain reveals heterogenous network trajectories during aging

The human aging brain is characterized by changes in network efficiency that are currently best captured through longitudinal resting-state functional MRI (rs-fMRI). These studies however are challenging due to the long human lifespan. Here we show that the mouse animal model with a much shorter lifespan allows us to follow the functional network organization over most of the animal's adult lifetime. We used a longitudinal study of the functional connectivity of different brain regions with rs-fMRI under anesthesia. Our analysis uncovers network modules similar to those reported in younger mice and in humans (i.e., prefrontal/default mode network (DMN), somatomotor and somatosensory networks). Statistical analysis reveals different patterns of network reorganization during aging. Female mice showed a pattern akin to human aging, with de-differentiation of the connectome, mainly due to increases in connectivity of the prefrontal/DMN cortical networks to other modules. Our male cohorts revealed heterogenous aging patterns with only one group confirming the de- differentiation, while the majority showed an increase in connectivity of the somatomotor cortex to the Nucleus accumbens. In summary, in line with human work, our analysis in mice supports the concept of de-differentiation in the aging mammalian brain and reveals additional trajectories in aging mice networks.

The virtual aging brain: Causal inference supports interhemispheric dedifferentiation in healthy aging

The mechanisms of cognitive decline and its variability during healthy aging are not fully understood, but have been associated with reorganization of white matter tracts and functional brain networks. Here, we built a brain network modeling framework to infer the causal link between structural connectivity and functional architecture and the consequent cognitive decline in aging. By applying in-silico interhemispheric degradation of structural connectivity, we reproduced the process of functional dedifferentiation during aging. Thereby, we found the global modulation of brain dynamics by structural connectivity to increase with age, which was steeper in older adults with poor cognitive performance. We validated our causal hypothesis via a deep-learning Bayesian approach. Our results might be the first mechanistic demonstration of dedifferentiation during aging leading to cognitive decline.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Neural response to stress differs by sex in young adulthood

Increase in stress-related disorders in women begins post-puberty and persists throughout the lifespan. To characterize sex differences in stress response in early adulthood, we used functional magnetic resonance imaging while participants underwent a stress task in conjunction with serum cortisol levels and questionnaires assessing anxiety and mood. Forty-two healthy subjects aged 18-25 years participated (21M, 21F). Interaction of stress and sex in brain activation and connectivity were examined. Results demonstrated significant sex differences in brain activity with women exhibiting increased activation in regions that inhibit arousal compared to men during the stress paradigm. Women had increased connectivity among stress circuitry regions and default mode network, whereas men had increased connectivity between stress and cognitive control regions. In a subset of subjects (13F, 17M), we obtained gamma-aminobutyric acid (GABA) magnetic resonance spectroscopy in rostral anterior cingulate cortex (rostral ACC) and dorsolateral prefrotal cortex (dlPFC) and conducted exploratory analyses to relate GABA measurements with sex differences in brain activation and connectivity. Prefrontal GABA levels were negatively associated with inferior temporal gyrus activation in men and women and with ventromedial prefrontal cortex activation in men. Despite sex differences in neural response, we found similar subjective ratings of anxiety and mood, cortisol levels, and GABA levels between sexes, suggesting sex differences in brain activity result in similar behavioral responses among the sexes. These results help establish sex differences in healthy brain activity from which we can better understand sex differences underlying stress-associated illnesses.

Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study (N > 6,000)

Introduction

Numerous studies have shown that aging has important effects on specific functional networks of the brain and leads to brain functional connectivity decline. However, no studies have addressed the effect of aging at the whole-brain level by studying both brain functional networks (i.e., within-network connectivity) and their interaction (i.e., between-network connectivity) as well as their joint changes.

Methods

In this work, based on a large sample size of neuroimaging data including 6300 healthy adults aged between 49 and 73 years from the UK Biobank project, we first use our previously proposed priori-driven independent component analysis (ICA) method, called NeuroMark, to extract the whole-brain functional networks (FNs) and the functional network connectivity (FNC) matrix. Next, we perform a two-level statistical analysis method to identify robust aging-related changes in FNs and FNCs, respectively. Finally, we propose a combined approach to explore the synergistic and paradoxical changes between FNs and FNCs.

Results

Results showed that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network, and cerebellum. Aging also greatly affects the connectivity within FNs, and the increased within-network connectivity along with aging are mainly within the sensorimotor network, while the decreased within-network connectivity significantly involves the default mode network. More importantly, many significant joint changes between FNs and FNCs involve default mode and sub-cortical networks. Furthermore, most synergistic changes are present between the FNCs with reduced amplitude and their linked FNs, and most paradoxical changes are present in the FNCs with enhanced amplitude and their linked FNs.

Discussion

In summary, our study emphasizes the diversity of brain aging and provides new evidence via novel exploratory perspectives for non-pathological aging of the whole brain.

Connectome-based predictive modeling shows sex differences in brain-based predictors of memory performance

Alzheimer's disease (AD) takes a more aggressive course in women than men, with higher prevalence and faster progression. Amnestic AD specifically targets the default mode network (DMN), which subserves short-term memory; past research shows relative hyperconnectivity in the posterior DMN in aging women. Higher reliance on this network during memory tasks may contribute to women's elevated AD risk. Here, we applied connectome-based predictive modeling (CPM), a robust linear machine-learning approach, to the Lifespan Human Connectome Project-Aging (HCP-A) dataset (n = 579). We sought to characterize sex-based predictors of memory performance in aging, with particular attention to the DMN. Models were evaluated using cross-validation both across the whole group and for each sex separately. Whole-group models predicted short-term memory performance with accuracies ranging from ρ = 0.21-0.45. The best-performing models were derived from an associative memory task-based scan. Sex-specific models revealed significant differences in connectome-based predictors for men and women. DMN activity contributed more to predicted memory scores in women, while within- and between- visual network activity contributed more to predicted memory scores in men. While men showed more segregation of visual networks, women showed more segregation of the DMN. We demonstrate that women and men recruit different circuitry when performing memory tasks, with women relying more on intra-DMN activity and men relying more on visual circuitry. These findings are consistent with the hypothesis that women draw more heavily upon the DMN for recollective memory, potentially contributing to women's elevated risk of AD.