Resting states are resting traits--an FMRI study of sex differences and menstrual cycle effects in resting state cognitive control networks

Creative tempo: Spatiotemporal dynamics of the default mode network in improvisational musicians

The intrinsic dynamics of human brain activity display a recurring pattern of anti-correlated activity between the default mode network (DMN), associated with internal processing and mentation, and task positive regions, associated with externally directed attention. In human functional magnetic resonance imaging (fMRI) data, this anti-correlated pattern is detectable on the infraslow timescale (<0.1 Hz) as a quasi-periodic pattern (QPP). While the DMN is implicated in creativity and musicality in traditional time-averaged functional connectivity studies, no one has yet explored how creative training may alter dynamic spatiotemporal patterns involving the DMN such as QPPs. In the present study, we compare the outputs of two QPP detection approaches, sliding window algorithm and complex principal components analysis (cPCA). We apply both methods to an existing dataset of musicians captured with resting state fMRI, grouped as either classical, improvisational, or minimally trained non-musicians. The original time-averaged functional connectivity (FC) analysis of this dataset used improvisation as a proxy for creative thinking and found that the DMN and visual networks (VIS) display higher connectivity in improvisational musicians. We expand upon this dataset's original study and find that QPP analysis detects convergent results at the group level with both methods. In improvisational musicians, dynamic functional correlation in the group-averaged QPP was found to be increased between the DMN-VIS and DMN-FPN for both the QPP algorithm and complex principal components analysis (cPCA) methods. Additionally, we found an unexpected increase in FC in the group-averaged QPP between the dorsal attention network and amygdala in improvisational musicians; this result was not reported in the original seed-based study of this dataset. The current study represents a novel application of two dynamic FC detection methods with results that replicate and expand upon previous seed-based FC findings. The results show the robustness of both the QPP phenomenon and its detection methods. This study also demonstrates the value of dynamic FC methods in reproducing seed-based findings and their promise in detecting group-wise or individual differences that may be missed by traditional seed-based resting state fMRI studies.

Differential modulation of resting-state functional connectivity between amygdala and precuneus after acute physical exertion of varying intensity: indications for a role in affective regulation

Introduction

Physical activity influences psychological well-being. This study aimed to determine the impact of exercise intensity on psychological well-being and alterations in emotion-related brain functional connectivity (FC).

Methods

Twenty young, healthy, trained athletes performed a low- and high-intensity interval exercise (LIIE and HIIE) as well as a control condition in a within-subject crossover design. Before and after each condition, Positive And Negative Affect Scale (PANAS) was assessed as well as resting-state functional MRI (rs-fMRI). Voxel-wise FC was examined for bilateral amygdala seed region to whole-brain and emotion-related anatomical regions (e.g., insula, temporal pole, precuneus). Data analyses were performed using linear mixed-effect models with fixed factors condition and time.

Results

The PANAS Positive Affect scale showed a significant increase after LIIE and HIIE and a significant reduction in Negative Affect after the control condition. In rs-fMRI, no significant condition-by-time interactions were observed between the amygdala and whole brain. Amygdala-precuneus FC analysis showed an interaction effect, suggesting reduced post-exercise anticorrelation after the control condition, but stable, or even slightly enhanced anticorrelation for the exercise conditions, especially HIIE.

Discussion

In conclusion, both LIIE and HIIE had positive effects on mood and concomitant effects on amygdala-precuneus FC, particularly after HIIE. Although no significant correlations were found between amygdala-precuneus FC and PANAS, results should be discussed in the context of affective disorders in whom abnormal amygdala-precuneus FC has been observed.

Network-based statistics distinguish anomic and Broca's aphasia

INTRODUCTION

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. The neural mechanisms that underpin different types of aphasia and their symptoms are still not fully understood. This study aims to identify differences in resting-state functional connectivity between anomic and Broca's aphasia measured through resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

We used the network-based statistic (NBS) method, as well as voxel- and connectome-based lesion symptom mapping (V-, CLSM), to identify distinct neural correlates of the anomic and Broca's groups. To control for lesion effect, we included lesion volume as a covariate in both the NBS method and LSM.

RESULTS

NBS identified a subnetwork located in the dorsal language stream bilaterally, including supramarginal gyrus, primary sensory, motor, and auditory cortices, and insula. The connections in the subnetwork were weaker in the Broca's group than the anomic group. The properties of the subnetwork were examined through complex network measures, which indicated that regions in right inferior frontal sulcus, right paracentral lobule, and bilateral superior temporal gyrus exhibit intensive interaction. Left superior temporal gyrus, right postcentral gyrus, and left supramarginal gyrus play an important role in information flow and overall communication efficiency. Disruption of this network underlies the constellation of symptoms associated with Broca's aphasia. Whole-brain CLSM did not detect any significant connections, suggesting an advantage of NBS when thousands of connections are considered. However, CLSM identified connections that differentiated Broca's from anomic aphasia when analysis was restricted to a hypothesized network of interest.

DISCUSSION

We identified novel signatures of resting-state brain network differences between groups of individuals with anomic and Broca's aphasia. We identified a subnetwork of connections that statistically differentiated the resting-state brain networks of the two groups, in comparison with standard CLSM results that yielded isolated connections. Network-level analyses are useful tools for the investigation of the neural correlates of language deficits post-stroke.

Uncovering neural pathways underlying bulimia nervosa: resting-state neural connectivity disruptions correlate with maladaptive eating behaviors

PURPOSE

Bulimia nervosa (BN) is characterized by recurrent binge-eating episodes and inappropriate compensatory behaviors. This study investigated alterations in resting-state surface-based neural activity in BN patients and explored correlations between brain activity and eating behavior.

METHODS

A total of 26 BN patients and 28 healthy controls were enrolled. Indirect measurement of cerebral cortical activity and functional connectivity (FC) analyses were performed in Surfstat. A principal component analysis (PCA) model was used to capture the commonalities within the behavioral questionnaires from the BN group.

RESULTS

Compared with the healthy control group, the BN group showed decreased surface-based two-dimensional regional homogeneity in the right superior parietal lobule (SPL). Additionally, the BN group showed decreased FC between the right SPL and the bilateral lingual gyrus and increased FC between the right SPL and the left caudate nucleus and right putamen. In the FC-behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the right SPL and the left caudate nucleus. The third principal component (PC3) was negatively correlated with FC between the right SPL and the left lingual gyrus and positively correlated with FC between the right SPL and the right lingual gyrus.

CONCLUSION

We revealed that the right SPL undergoes reorganization with respect to specific brain regions at the whole-brain level in BN. In addition, our results suggest a correlation between brain reorganization and maladaptive eating behavior. These findings may provide useful information to better understand the neural mechanisms of BN.

LEVEL OF EVIDENCE

V, descriptive study.

Diurnal fluctuations in steroid hormones tied to variation in intrinsic functional connectivity in a densely sampled male

Most of mammalian physiology is under the control of biological rhythms, including the endocrine system with time-varying hormone secretion. Precision neuroimaging studies provide unique insights into the means through which our endocrine system regulates dynamic properties of the human brain. Recently, we established estrogen's ability to drive widespread patterns of connectivity and enhance the functional efficiency of large-scale brain networks in a woman sampled every 24h across 30 consecutive days, capturing a complete menstrual cycle. Steroid hormone production also follows a pronounced sinusoidal pattern, with a peak in testosterone between 6-7am and nadir between 7-8pm. To capture the brain's response to diurnal changes in hormone production, we carried out a companion precision imaging study of a healthy adult man who completed MRI and venipuncture every 12-24 hours across 30 consecutive days. Results confirmed robust diurnal fluctuations in testosterone, cortisol, and estradiol. Standardized regression analyses revealed predominantly positive associations between testosterone, cortisol, and estradiol concentrations and whole-brain patterns of coherence. In particular, functional connectivity in Dorsal Attention and Salience/Ventral Attention Networks were coupled with diurnally fluctuating hormones. Further, comparing dense-sampling datasets between a man and naturally-cycling woman revealed that fluctuations in sex hormones are tied to patterns of whole-brain coherence to a comparable degree in both sexes. Together, these findings enhance our understanding of steroid hormones as rapid neuromodulators and provide evidence that diurnal changes in steroid hormones are tied to patterns of whole-brain functional connectivity.

Brain-Based Predictions of Psychiatric Illness-Linked Behaviors Across the Sexes

BACKGROUND

Individual differences in functional brain connectivity can be used to predict both the presence of psychiatric illness and variability in associated behaviors. However, despite evidence for sex differences in functional network connectivity and in the prevalence, presentation, and trajectory of psychiatric illnesses, the extent to which disorder-relevant aspects of network connectivity are shared or unique across the sexes remains to be determined.

METHODS

In this work, we used predictive modeling approaches to evaluate whether shared or unique functional connectivity correlates underlie the expression of psychiatric illness-linked behaviors in males and females in data from the Adolescent Brain Cognitive Development Study (N = 5260; 2571 females).

RESULTS

We demonstrate that functional connectivity profiles predict individual differences in externalizing behaviors in males and females but predict internalizing behaviors only in females. Furthermore, models trained to predict externalizing behaviors in males generalize to predict internalizing behaviors in females, and models trained to predict internalizing behaviors in females generalize to predict externalizing behaviors in males. Finally, the neurobiological correlates of many behaviors are largely shared within and across sexes: functional connections within and between heteromodal association networks, including default, limbic, control, and dorsal attention networks, are associated with internalizing and externalizing behaviors.

CONCLUSIONS

Taken together, these findings suggest that shared neurobiological patterns may manifest as distinct behaviors across the sexes. Based on these results, we recommend that both clinicians and researchers carefully consider how sex may influence the presentation of psychiatric illnesses, especially those along the internalizing-externalizing spectrum.

Sex-Determined Alteration of Frontal Electroencephalographic (EEG) Activity in Social Presence

This study represents a follow-up event-related potential (ERP) analysis of a prior investigation. The previous results showed that participants had most negative-tending ERPs in the mid-frontal brain region during exposure to neutral emotion pictures (compared to negative and positive pictures) while being accompanied by a significant other person (social presence condition). The present analysis aimed at investigating potential sex differences related to this phenomenon. Female and male participants' brain activity data from the previous study were analyzed separately for one representative mid-frontal electrode location selected on the basis of having the highest significance level. As a result, only female participants showed significantly more negative-tending potentials in response to neutral pictures, compared to both other emotion categories (positive and negative) in the social presence condition. This was not found in male participants. The respective ERP effect was most dominant at 838 ms post stimulus onset, which is slightly later than the effect found in the prior study. However, this result is interpreted as evidence that the general effect from the prior study can be understood as a largely female phenomenon. In line with the prior study, the present results are interpreted as a predominantly female activation in the mid-frontal brain region in response to neutral picture stimuli while being accompanied by a significant other person (social presence condition). Although only speculative, this would align with previous studies demonstrating sex-related hormonal and structural differences in the anterior cingulate cortex (ACC). In general, ACC activation has been associated with an integrative weighting function in ambiguous social settings, which makes sense given the ambiguous nature of neutral pictures in combination with a social presence condition.

Network-based statistics distinguish anomic and Broca aphasia

Aphasia is a speech-language impairment commonly caused by damage to the left hemisphere. Due to the complexity of speech-language processing, the neural mechanisms that underpin various symptoms between different types of aphasia are still not fully understood. We used the network-based statistic method to identify distinct subnetwork(s) of connections differentiating the resting-state functional networks of the anomic and Broca groups. We identified one such subnetwork that mainly involved the brain regions in the premotor, primary motor, primary auditory, and primary sensory cortices in both hemispheres. The majority of connections in the subnetwork were weaker in the Broca group than the anomic group. The network properties of the subnetwork were examined through complex network measures, which indicated that the regions in the superior temporal gyrus and auditory cortex bilaterally exhibit intensive interaction, and primary motor, premotor and primary sensory cortices in the left hemisphere play an important role in information flow and overall communication efficiency. These findings underlied articulatory difficulties and reduced repetition performance in Broca aphasia, which are rarely observed in anomic aphasia. This research provides novel findings into the resting-state brain network differences between groups of individuals with anomic and Broca aphasia. We identified a subnetwork of, rather than isolated, connections that statistically differentiate the resting-state brain networks of the two groups, in comparison with standard lesion symptom mapping results that yield isolated connections.

Sex/Gender Differences in Brain Lateralisation and Connectivity

There is now a significant body of literature concerning sex/gender differences in the human brain. This chapter will critically review and synthesise key findings from several studies that have investigated sex/gender differences in structural and functional lateralisation and connectivity. We argue that while small, relative sex/gender differences reliably exist in lateralisation and connectivity, there is considerable overlap between the sexes. Some inconsistencies exist, however, and this is likely due to considerable variability in the methodologies, tasks, measures, and sample compositions between studies. Moreover, research to date is limited in its consideration of sex/gender-related factors, such as sex hormones and gender roles, that can explain inter-and inter-individual differences in brain and behaviour better than sex/gender alone. We conclude that conceptualising the brain as 'sexually dimorphic' is incorrect, and the terms 'male brain' and 'female brain' should be avoided in the neuroscientific literature. However, this does not necessarily mean that sex/gender differences in the brain are trivial. Future research involving sex/gender should adopt a biopsychosocial approach whenever possible, to ensure that non-binary psychological, biological, and environmental/social factors related to sex/gender, and their interactions, are routinely accounted for.

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

Sex differences in brain homotopic co-activations: a meta-analytic study

An element of great interest in functional connectivity is 'homotopic connectivity' (HC), namely the connectivity between two mirrored areas of the two hemispheres, mainly mediated by the fibers of the corpus callosum. Despite a long tradition of studying sexual dimorphism in the human brain, to our knowledge only one study has addressed the influence of sex on HC.We investigated the issue of homotopic co-activations in women and men using a coordinate-based meta-analytic method and data from the BrainMap database. A first unexpected observation was that the database was affected by a sex bias: women-only groups are investigated less often than men-only ones, and they are more often studied in certain domains such as emotion compared to men, and less in cognition. Implementing a series of sampling procedures to equalize the size and proportion of the datasets, our results indicated that females exhibit stronger interhemispheric co-activation than males, suggesting that the female brain is less lateralized and more integrated than that of males. In addition, males appear to show less intense but more extensive co-activation than females. Some local differences also appeared. In particular, it appears that primary motor and perceptual areas are more co-activated in males, in contrast to the opposite trend in the rest of the brain. This argues for a multidimensional view of sex brain differences and suggests that the issue should be approached with more complex models than previously thought.

Hormone-based models for comparing menstrual cycle and hormonal contraceptive effects on human resting-state functional connectivity

Oral contraceptives (OCs) are widely used yet understudied given their potential for public health consequences. Emerging investigations scaling from single-subject, dense-sampling neuroimaging studies to population-level metrics have linked OCs to altered brain structure and function. Modeling the hypogonadal, hypergonadal, or mixed state effects of OCs in terms of their impact on hormone action in the brain is a valuable approach to synthesizing results across neuroimaging studies and comparing OC effects to companion findings from research on menstrual cycle phase effects on brain anatomy and function. Resting-state functional connectivity studies provide a powerful tool to evaluate the role of OCs on the intrinsic network connectivity that underlies multiple behavioral domains. The preponderance (but not consensus) of the current literature indicates that (1) as the menstrual cycle proceeds from a low to high progesterone state, prefrontal connectivity increases and parietal connectivity decreases; (2) OCs tend to mimic this connectivity pattern; therefore (3) OCs may produce a hyperprogestogenic state in the brain, in spite of overall reductions in endogenous steroid hormone levels. Alternative models are also considered.

Sex differences in the functional topography of association networks in youth

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Menstrual Cycle-Related Changes in Women with Schizophrenia: A Resting-State fMRI Study

INTRODUCTION

Different influences of ovarian hormones in schizophrenia (SCZ) have been reported, but no study to date has assessed their effects on the brain dynamics at rest. The present study aimed to examine the hormonal and clinical changes related to the menstrual cycle and alterations in the resting-state functional connectivity (RS-FC) depending on cycle phase and/or hormonal fluctuations in SCZ.

METHOD

This study was conducted based on both between- and within-subject experimental designs, including 13 clinically stable female patients with SCZ (32 ± 7.7 years) and 13 healthy women (30 ± 7.3 years). RS-functional magnetic resonance imaging (fMRI) scanning, as well as hormonal and clinical assessments, was applied to each participant twice during two cycle phases: early follicular and mid-luteal.

RESULTS

A difference in mid-luteal progesterone levels was found between groups, with a large effect size (Cohen's d) of 0.8 (p < 0.05). Also, the estradiol levels negatively correlated with the negative symptom severity of the patients during their mid-luteal phase. In the patients, estrogen positively correlated with the auditory network connectivity in the left amygdala during the early follicular phase. In the controls, progesterone had positive correlations with the connectivity of the posterior default mode and the left frontoparietal networks in the bilateral precuneus during the early follicular phase and had a negative correlation with the executive control network connectivity in the mid-luteal phase.

CONCLUSION

The present study showed hormonal differences between groups and suggested that the levels of cycle-dependent hormones might be associated with the changes in clinical symptom severity and the RS-FC in the groups. Our RS-fMRI findings warrant further investigation.

Ovarian steroid hormones: A long overlooked but critical contributor to brain aging and Alzheimer's disease

Ovarian hormones, particularly 17β-estradiol, are involved in numerous neurophysiological and neurochemical processes, including those subserving cognitive function. Estradiol plays a key role in the neurobiology of aging, in part due to extensive interconnectivity of the neural and endocrine system. This aspect of aging is fundamental for women's brains as all women experience a drop in circulating estradiol levels in midlife, after menopause. Given the importance of estradiol for brain function, it is not surprising that up to 80% of peri-menopausal and post-menopausal women report neurological symptoms including changes in thermoregulation (vasomotor symptoms), mood, sleep, and cognitive performance. Preclinical evidence for neuroprotective effects of 17β-estradiol also indicate associations between menopause, cognitive aging, and Alzheimer's disease (AD), the most common cause of dementia affecting nearly twice more women than men. Brain imaging studies demonstrated that middle-aged women exhibit increased indicators of AD endophenotype as compared to men of the same age, with onset in perimenopause. Herein, we take a translational approach to illustrate the contribution of ovarian hormones in maintaining cognition in women, with evidence implicating menopause-related declines in 17β-estradiol in cognitive aging and AD risk. We will review research focused on the role of endogenous and exogenous estrogen exposure as a key underlying mechanism to neuropathological aging in women, with a focus on whether brain structure, function and neurochemistry respond to hormone treatment. While still in development, this research area offers a new sex-based perspective on brain aging and risk of AD, while also highlighting an urgent need for better integration between neurology, psychiatry, and women's health practices.

Amygdalar Functional Connectivity Differences Associated With Reduced Pain Intensity in Pediatric Peripheral Neuropathic Pain

Background

There is evidence of altered corticolimbic circuitry in adults with chronic pain, but relatively little is known of functional brain mechanisms in adolescents with neuropathic pain (NeuP). Pediatric NeuP is etiologically and phenotypically different from NeuP in adults, highlighting the need for pediatric-focused research. The amygdala is a key limbic region with important roles in the emotional-affective dimension of pain and in pain modulation.

Objective

To investigate amygdalar resting state functional connectivity (rsFC) in adolescents with NeuP.

Methods

This cross-sectional observational cohort study compared resting state functional MRI scans in adolescents aged 11–18 years with clinical features of chronic peripheral NeuP (n = 17), recruited from a tertiary clinic, relative to healthy adolescents (n = 17). We performed seed-to-voxel whole-brain rsFC analysis of the bilateral amygdalae. Next, we performed post hoc exploratory correlations with clinical variables to further explain rsFC differences.

Results

Adolescents with NeuP had stronger negative rsFC between right amygdala and right dorsolateral prefrontal cortex (dlPFC) and stronger positive rsFC between right amygdala and left angular gyrus (AG), compared to controls (P_FDR<0.025). Furthermore, lower pain intensity correlated with stronger negative amygdala-dlPFC rsFC in males (r = 0.67, P = 0.034, n = 10), and with stronger positive amygdala-AG rsFC in females (r = −0.90, P = 0.006, n = 7). These amygdalar rsFC differences may thus be pain inhibitory.

Conclusions

Consistent with the considerable affective and cognitive factors reported in a larger cohort, there are rsFC differences in limbic pain modulatory circuits in adolescents with NeuP. Findings also highlight the need for assessing sex-dependent brain mechanisms in future studies, where possible.

Estrogen, Cognitive Performance, and Functional Imaging Studies: What Are We Missing About Neuroprotection?

Menopause transition can be interpreted as a vulnerable state characterized by estrogen deficiency with detrimental systemic effects as the low-grade chronic inflammation that appears with aging and partly explains age-related disorders as cancer, diabetes mellitus and increased risk of cognitive impairment. Over the course of a lifetime, estrogen produces several beneficial effects in healthy neurological tissues as well as cardioprotective effects, and anti-inflammatory effects. However, clinical evidence on the efficacy of hormone treatment in menopausal women has failed to confirm the benefit reported in observational studies. Unambiguously, enhanced verbal memory is the most robust finding from longitudinal and cross-sectional studies, what merits consideration for future studies aiming to determine estrogen neuroprotective efficacy. Estrogen related brain activity and functional connectivity remain, however, unexplored. In this context, the resting state paradigm may provide valuable information about reproductive aging and hormonal treatment effects, and their relationship with brain imaging of functional connectivity may be key to understand and anticipate estrogen cognitive protective effects. To go in-depth into the molecular and cellular mechanisms underlying rapid-to-long lasting protective effects of estrogen, we will provide a comprehensive review of cognitive tasks used in animal studies to evaluate the effect of hormone treatment on cognitive performance and discuss about the tasks best suited to the demonstration of clinically significant differences in cognitive performance to be applied in human studies. Eventually, we will focus on studies evaluating the DMN activity and responsiveness to pharmacological stimulation in humans.

Cognitive function evaluation in premenstrual syndrome during the follicular and luteal phases using near-infrared spectroscopy

Background

Many adult females experience periodic mental and physical symptoms associated with premenstrual syndrome (PMS). Diagnosis of PMS is generally based on self-reported symptoms over several menstrual cycles, but there are concerns about its accuracy and duration. It is well known that decreased cognitive function is one of the symptoms of PMS. Near-infrared spectroscopy (NIRS) is one of the methods for imaging brain activity, similar to magnetic resonance imaging (MRI), electroencephalography (EEG), and positron emission tomography (PET). NIRS has been used to assess cognitive function decline demonstrated by decline in brain activity. However, to the best of our knowledge, there have been no report characterizing brain activity pattern in females with PMS during the luteal and follicular phases separately.

Objective

We aimed to characterize the cognitive function of females with PMS during the follicular and luteal phases using NIRS.

Methods

The level of brain activity in the prefrontal cortex was detected with NIRS while PMS women were performing cognitive tasks. NIRS detected brain activity by measuring the oxy-hemoglobin and deoxy-hemoglobin levels. Participants were females between the ages of 20 and 25 with PMS (n = 11) and without PMS (n = 11). During the participants’ follicular and luteal phases, the participants were asked to perform the cognitive task; an N-back task (0-, 1-, and 2-back tasks), which is widely used for assessing cognitive function. We also calculated the oxyhemoglobin integral value during the N-back task using the NIRS signal; this value represented the total amount of change in cerebral oxyhemoglobin and the brain activation level.

Results

The correct response rate on the 2-back task was significantly lower during both the follicular and luteal phases in females with PMS compared to that in females without PMS (P = 0.01; P = 0.02, during the follicular and luteal phases, respectively). During the luteal phase, brain activation was significantly lower in participants with PMS than in that in females without PMS (P = 0.04). In addition, during the luteal phase, the participants with PMS also had higher negative mood than those without PMS.

Conclusion

The cognitive decline during the luteal phase in participants with PMS was detected by NIRS with significant differences from participants without PMS. The difference was observed only during the luteal phase, not in the follicular phase and were related to the increase in negative mood. These results may provide an objective method for diagnosing PMS based on brain activity. We believe that the use of instruments (e.g., NIRS, MRI, EEG … etc.) to detect cognitive function decline will lead to rapid and reliable diagnosis of PMS and premenstrual dysphoric disorder.

'That Time of the Month' - Investigating the Influence of the Menstrual Cycle and Oral Contraceptives on the Brain Using Magnetic Resonance Imaging

The stereotypic and oversimplified relationship between female sex hormones and undesirable behavior dates to the earliest days of human society, as already the ancient Greek word for the uterus, "hystera" indicated an aversive connection. Remaining and evolving throughout the centuries, transcending across cultures and various aspects of everyday life, its perception was only recently reframed. Contemporarily, the complex interaction of hormonal phases (i. e., the menstrual cycle), hormonal medication (i. e., oral contraceptives), women's psychological well-being, and behavior is the subject of multifaceted and more reflected discussions. A driving force of this ongoing paradigm shift was the introduction of this highly interesting and important topic into the realm of scientific research. This refers to neuroscientific research as it enables a multimodal approach combining aspects of physiology, medicine, and psychology. Here a growing body of literature points towards significant alterations of both brain function, such as lateralization of cognitive functions, and structure, such as gray matter concentrations, due to fluctuations and changes in hormonal levels. This especially concerns female sex hormones. However, the more research is conducted within this field, the less reliable these observations and derived insights appear. This may be due to two particular factors: measurement inconsistencies and diverse hormonal phases accompanied by interindividual differences. The first factor refers to the prominent unreliability of one of the primarily utilized neuroscientific research instruments: functional magnetic resonance imaging (fMRI). This unreliability is seemingly present in paradigms and analyses, and their interplay, and is additionally affected by the second factor. In more detail, hormonal phases and levels further influence neuroscientific results obtained through fMRI as outcomes vary drastically across different cycle phases and medication. This resulting vast uncertainty thus tremendously hinders the further advancement of our understanding of how female sex hormones might alter brain structure and function and, ultimately, behavior.This review summarizes parts of the current state of research and outlines the essential requirements to further investigate and understand the female brain's underlying physiological and anatomical features.

Sex differences in brain regional homogeneity during acute abstinence in cocaine use disorder

There are significant sex differences in the clinical characteristics of cocaine use disorder (CUD). As this is a brain disorder that involves changes in functional connectivity, we investigated the existence of sex differences among people with CUD and controls. We used a data‐driven method comparing males (n = 20, CK‐M) and females with CUD (n = 20, CK‐F) and healthy controls (20 males, HC‐M and 20 females, HC‐F). The participants undertook a resting‐state functional magnetic resonance imaging exam. Regional homogeneity (ReHo) was performed to identify group and sex differences. Persons with CUD of both sexes presented lower ReHo parameters than controls, especially within the parietal lobule. Males with CUD showed higher ReHo than females in three right‐side brain areas: postcentral gyrus, putamen and fusiform gyrus. It was found that abstinence symptoms severity was associated with lower ReHo values in the right postcentral gyrus and the right fusiform gyrus. Participants with CUD exhibited altered ReHo parameters compared to controls, similar to what is found in ageing‐related disorders. Our data also indicate that cocaine has sex‐specific effects on brain functioning when analysing ReHo.

Machine Learning Evidence for Sex Differences Consistently Influences Resting-State Functional Magnetic Resonance Imaging Fluctuations Across Multiple Independently Acquired Data Sets

Background/Introduction: Sex classification using functional connectivity from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results. This suggested that sex difference might also be embedded in the blood-oxygen-level-dependent properties such as the amplitude of low-frequency fluctuation (ALFF) and the fraction of ALFF (fALFF). This study comprehensively investigates sex differences using a reliable and explainable machine learning (ML) pipeline. Five independent cohorts of rs-fMRI with over than 5500 samples were used to assess sex classification performance and map the spatial distribution of the important brain regions. Methods: Five rs-fMRI samples were used to extract ALFF and fALFF features from predefined brain parcellations and then were fed into an unbiased and explainable ML pipeline with a wide range of methods. The pipeline comprehensively assessed unbiased performance for within-sample and across-sample validation. In addition, the parcellation effect, classifier selection, scanning length, spatial distribution, reproducibility, and feature importance were analyzed and evaluated thoroughly in the study. Results: The results demonstrated high sex classification accuracies from healthy adults (area under the curve >0.89), while degrading for nonhealthy subjects. Sex classification showed moderate to good intraclass correlation coefficient based on parcellation. Linear classifiers outperform nonlinear classifiers. Sex differences could be detected even with a short rs-fMRI scan (e.g., 2 min). The spatial distribution of important features overlaps with previous results from studies. Discussion: Sex differences are consistent in rs-fMRI and should be considered seriously in any study design, analysis, or interpretation. Features that discriminate males and females were found to be distributed across several different brain regions, suggesting a complex mosaic for sex differences in rs-fMRI. Impact statement The presented study unraveled that sex differences are embedded in the blood-oxygen-level dependent (BOLD) and can be predicted using unbiased and explainable machine learning pipeline. The study revealed that psychiatric disorders and demographics might influence the BOLD signal and interact with the classification of sex. The spatial distribution of the important features presented here supports the notion that the brain is a mosaic of male and female features. The findings emphasize the importance of controlling for sex when conducting brain imaging analysis. In addition, the presented framework can be adapted to classify other variables from resting-state BOLD signals.

Proximal improvement and higher-order resting state network change after multidomain cognitive training intervention in healthy older adults

Prior randomized control trials have shown that cognitive training interventions resulted in improved proximal task performance, improved functioning of activities of daily living, and reduced dementia risk in healthy older adults. Neural correlates implicated in cognitive training include hub brain regions of higher-order resting state networks including the default mode network, dorsal attention network, frontoparietal control network, and cingulo-opercular network. However, little is known about resting state network change after cognitive training, or the relation between functional brain changes and improvement in proximal task performance. We assessed the 1) change in proximal task performance, 2) change in higher-order resting state network connectivity via functional magnetic resonance imaging, and 3) association between these variables after a multidomain attention/speed-of-processing and working memory randomized control trial in a sample of 58 healthy older adults. Participants in the cognitive training group improved significantly on seven out of eight training tasks immediately after the training intervention with the largest magnitude of improvement in a divided attention/speed-of-processing task, the Double Decision task. Only the frontoparietal control network had significantly strengthened connectivity in the cognitive training group at the post-intervention timepoint. Lastly, higher frontoparietal control network connectivity was associated with improved Double Decision task performance after training in the cognitive training group. These findings show that the frontoparietal control network may strengthen after multidomain cognitive training interventions, and this network may underlie improvements in divided attention/speed-of-processing proximal improvement.

Functional connectivity patterns of trait empathy are associated with age

Empathy is the capacity to feel and understand others' mental states. In some individuals, there is an imbalance between the affective and cognitive components of empathy, which can lead to deficits. This study investigated the functional connectivity of the anterior insula (AI) and dorsomedial prefrontal cortex (dmPFC), which play key roles in empathy, in covariation with the affective and cognitive subscales of the Interpersonal Reactivity Index (IRI), as a function of age and sex, as an exploratory analysis. Seed-based functional connectivity analyses were performed on 33 healthy participants that were subdivided according to their age (16 adults and 17 adolescents) and sex (16 women and 17 men). Adolescents reported lower cognitive empathy than adults and men less affective empathy than women. The connectivity of the dmPFC and AI, in covariation with the cognitive and affective subscales of empathy, respectively, differed between adolescents and adults, but was similar in men and women. Adolescents had patterns of negative covariations between the regions of interest and many brain regions associated with the default-mode and salience networks. These findings support that lower self-report levels of empathy in certain individuals could be reflected in the functional connectivity patterns of the dmPFC and AI.

Resting-state functional magnetic resonance imaging signal variations in aging: The role of neural activity

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link.

Shared functional connections within and between cortical networks predict cognitive abilities in adult males and females

A thorough understanding of sex-independent and sex-specific neurobiological features that underlie cognitive abilities in healthy individuals is essential for the study of neurological illnesses in which males and females differentially experience and exhibit cognitive impairment. Here, we evaluate sex-independent and sex-specific relationships between functional connectivity and individual cognitive abilities in 392 healthy young adults (196 males) from the Human Connectome Project. First, we establish that sex-independent models comparably predict crystallised abilities in males and females, but only successfully predict fluid abilities in males. Second, we demonstrate sex-specific models comparably predict crystallised abilities within and between sexes, and generally fail to predict fluid abilities in either sex. Third, we reveal that largely overlapping connections between visual, dorsal attention, ventral attention, and temporal parietal networks are associated with better performance on crystallised and fluid cognitive tests in males and females, while connections within visual, somatomotor, and temporal parietal networks are associated with poorer performance. Together, our findings suggest that shared neurobiological features of the functional connectome underlie crystallised and fluid abilities across the sexes.

EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks

The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.

Investigating the sex-dependent effects of prefrontal cortex stimulation on response execution and inhibition

Context-dependent execution or inhibition of a response is an important aspect of executive control, which is impaired in neuropsychological and addiction disorders. Transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC) has been considered a remedial approach to address deficits in response control; however, considerable variability has been observed in tDCS effects. These variabilities might be related to contextual differences such as background visual-auditory stimuli or subjects' sex. In this study, we examined the interaction of two contextual factors, participants' sex and background acoustic stimuli, in modulating the effects of tDCS on response inhibition and execution. In a sham-controlled and cross-over (repeated-measure) design, 73 participants (37 females) performed a Stop-Signal Task in different background acoustic conditions before and after tDCS (anodal or sham) was applied over the DLPFC. Participants had to execute a speeded response in Go trials but inhibit their response in Stop trials. Participants' sex was fully counterbalanced across all experimental conditions (acoustic and tDCS). We found significant practice-related learning that appeared as changes in indices of response inhibition (stop-signal reaction time and percentage of successful inhibition) and action execution (response time and percentage correct). The tDCS and acoustic stimuli interactively influenced practice-related changes in response inhibition and these effects were uniformly seen in both males and females. However, the effects of tDCS on response execution (percentage of correct responses) were sex-dependent in that practice-related changes diminished in females but heightened in males. Our findings indicate that participants' sex influenced the effects of tDCS on the execution, but not inhibition, of responses.

Revealing the Influences of Sex Hormones and Sex Differences in Atrial Fibrillation and Vascular Cognitive Impairment

The impacts of sex differences on the biology of various organ systems and the influences of sex hormones on modulating health and disease have become increasingly relevant in clinical and biomedical research. A growing body of evidence has recently suggested fundamental sex differences in cardiovascular and cognitive function, including anatomy, pathophysiology, incidence and age of disease onset, symptoms affecting disease diagnosis, disease severity, progression, and treatment responses and outcomes. Atrial fibrillation (AF) is currently recognized as the most prevalent sustained arrhythmia and might contribute to the pathogenesis and progression of vascular cognitive impairment (VCI), including a range of cognitive deficits, from mild cognitive impairment to dementia. In this review, we describe sex-based differences and sex hormone functions in the physiology of the brain and vasculature and the pathophysiology of disorders therein, with special emphasis on AF and VCI. Deciphering how sex hormones and their receptor signaling (estrogen and androgen receptors) potentially impact on sex differences could help to reveal disease links between AF and VCI and identify therapeutic targets that may lead to potentially novel therapeutic interventions early in the disease course of AF and VCI.

Spectral dynamic causal modelling in healthy women reveals brain connectivity changes along the menstrual cycle

Longitudinal menstrual cycle studies allow to investigate the effects of ovarian hormones on brain organization. Here, we use spectral dynamic causal modelling (spDCM) in a triple network model to assess effective connectivity changes along the menstrual cycle within and between the default mode, salience and executive control networks (DMN, SN, and ECN). Sixty healthy young women were scanned three times along their menstrual cycle, during early follicular, pre-ovulatory and mid-luteal phase. Related to estradiol, right before ovulation the left insula recruits the ECN, while the right middle frontal gyrus decreases its connectivity to the precuneus and the DMN decouples into anterior/posterior parts. Related to progesterone during the mid-luteal phase, the insulae (SN) engage to each other, while decreasing their connectivity to parietal ECN, which in turn engages the posterior DMN. When including the most confident connections in a leave-one out cross-validation, we find an above-chance prediction of the left-out subjects' cycle phase. These findings corroborate the plasticity of the female brain in response to acute hormone fluctuations and may help to further understand the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.

Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well-being

The menstrual cycle (MC) is a sex hormone‐related phenomenon that repeats itself cyclically during the woman's reproductive life. In this explorative study, we hypothesized that coordinated variations of multiple sex hormones may affect the large‐scale organization of the brain functional network and that, in turn, such changes might have psychological correlates, even in the absence of overt clinical signs of anxiety and/or depression. To test our hypothesis, we investigated longitudinally, across the MC, the relationship between the sex hormones and both brain network and psychological changes. We enrolled 24 naturally cycling women and, at the early‐follicular, peri‐ovulatory, and mid‐luteal phases of the MC, we performed: (a) sex hormone dosage, (b) magnetoencephalography recording to study the brain network topology, and (c) psychological questionnaires to quantify anxiety, depression, self‐esteem, and well‐being. We showed that during the peri‐ovulatory phase, in the alpha band, the leaf fraction and the tree hierarchy of the brain network were reduced, while the betweenness centrality (BC) of the right posterior cingulate gyrus (rPCG) was increased. Furthermore, the increase in BC was predicted by estradiol levels. Moreover, during the luteal phase, the variation of estradiol correlated positively with the variations of both the topological change and environmental mastery dimension of the well‐being test, which, in turn, was related to the increase in the BC of rPCG. Our results highlight the effects of sex hormones on the large‐scale brain network organization as well as on their possible relationship with the psychological state across the MC. Moreover, the fact that physiological changes in the brain topology occur throughout the MC has widespread implications for neuroimaging studies.

Sex/gender differences in brain activity - it's time for a biopsychosocial approach to cognitive neuroscience

There is compelling evidence that men and women differ in brain activity in long-term memory and other cognitive functions. However, until the origins of sex/gender differences in brain activity, and consequently behavior, are not fully understood, the factor sex/gender should be considered as imperfect proxy of a combination of yet unknown biological and psychosocial factors underlying these sex/gender differences. The key avenue to a full understanding of sex/gender differences in brain and behavior depends largely on cognitive neuroscience investigating sex/gender differences in brain activity within a biopsychosocial approach.

Resting state functional connectivity in the default mode network: Relationships between cannabis use, gender, and cognition in adolescents and young adults

INTRODUCTION

Cannabis is the most commonly used illicit substance in the United States, and nearly 1 in 4 young adults are current cannabis users. Chronic cannabis use is associated with changes in resting state functional connectivity (RSFC) in the default mode network (DMN) in adolescents and young adults; results are somewhat inconsistent across studies, potentially due to methodological differences. The aims of the present study were to examine potential differences in DMN RSFC between cannabis users and controls, and to examine, as an exploratory analysis, if gender moderated any findings. We further examined whether differences in RSFC related to differences in performance on selected neuropsychological measures.

MATERIALS AND METHODS

Seventy-seven 16-26-year-old participants underwent an MRI scan (including resting state scan), neuropsychological battery, toxicology screening, and drug use interview. Differences in DMN connectivity were examined between groups (cannabis vs. control) and with an exploratory group by gender interaction, using a left posterior cingulate cortex (PCC) seed-based analysis conducted in AFNI.

RESULTS

Cannabis users demonstrated weaker connectivity than controls between the left PCC and various DMN nodes, and the right Rolandic operculum/Heschl's gyrus. Cannabis users demonstrated stronger connectivity between the left PCC and the cerebellum and left supramarginal gyrus. The group by gender interaction was not significantly associated with connectivity differences. Stronger left PCC-cerebellum connectivity was associated with poorer performance on cognitive measures in cannabis users. In controls, intra-DMN connectivity was positively correlated with performance on a speeded selective/sustained attention measure.

DISCUSSION

Consistent with our hypotheses and other studies, cannabis users demonstrated weaker connectivity between the left PCC and DMN nodes. Chronic THC exposure may alter GABA and glutamate concentrations, which may alter brain communication. Future studies should be conducted with a larger sample size and examine gender differences and the mechanism by which these differences may arise.

Sex difference in cerebral blood flow and cerebral glucose metabolism: an activation-likelihood estimation meta-analysis

OBJECTIVES

Sex differences exist in a variety of aspects including neurochemicals as well as behavioral traits of cognition, language, and aggression. We performed a meta-analysis of studies using a coordinate-based technique of activation-likelihood estimation (ALE) to identify the pooled estimated effect of sex difference.

METHODS

We performed a systematic search of MEDLINE and EMBASE for English-language publications using the keywords of 'positron emission tomography (PET)', 'single-photon emission computed tomography (SPECT)', and 'sex'. A threshold of uncorrected P < 0.001 (minimum volume of 200 mm3) was applied to the resulting ALE map.

RESULTS

Cerebral blood flow (CBF) in right precuneus, left superior temporal gyrus, left inferior temporal, left inferior frontal gyrus, right cerebellar tonsil, and right middle temporal gyrus was higher in females than males. CBF in left anterior cingulate was higher in males than females. Whereas, the cerebral metabolic rate for glucose (CMRglu) in left thalamus, left cingulate gyrus, right inferior parietal lobule, left medial frontal gyrus, right middle frontal gyrus, right midbrain, and left inferior parietal lobule was higher in females than males. However, there was no brain region that showed higher CMRglu in males than females.

CONCLUSION

Regional CBF and CMRglu from PET and SPECT showed the difference between males and females.

Sex differences in functional network dynamics observed using coactivation pattern analysis

Sex differences in the organization of large-scale resting-state brain networks have been identified using traditional static measures, which average functional connectivity over extended time periods. In contrast, emerging dynamic measures have the potential to define sex differences in network changes over time, providing additional understanding of neurobiological sex differences. To meet this goal, we used a Coactivation Pattern Analysis (CAP) using resting-state functional magnetic resonance imaging data from 181 males and 181 females from the Human Connectome Project. Significant main effects of sex were observed across two independent imaging sessions. Relative to males, females spent more total time in two transient network states (TNSs) spatially overlapping with the dorsal attention network and occipital/sensory-motor network. Greater time spent in these TNSs was related to females making more frequent transitions into these TNSs compared to males. In contrast, males spent more total time in TNSs spatially overlapping with the salience network, which was related to males staying for longer periods once entering these TNSs compared to females. State-to-state transitions also significantly differed between sexes: females transitioned more frequently from default mode network (DMN) states to the dorsal attention network state, whereas males transitioned more frequently from DMN states to salience network states. Results show that males and females spend differing amounts of time at rest in two distinct attention-related networks and show sex-specific transition patterns from DMN states into these attention-related networks. This work lays the groundwork for future investigations into the cognitive and behavioral implications of these sex-specific network dynamics.

Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.

Sex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood whether day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network connectivity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the course of a menstrual cycle, temporary reorganization of several large-scale functional brain networks occurs during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with nodes belonging to the temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.

Development of the brain functional connectome follows puberty-dependent nonlinear trajectories

Adolescence is a developmental period that dramatically impacts body and behavior, with pubertal hormones playing an important role not only in the morphological changes in the body but also in brain structure and function. Understanding brain development during adolescence has become a priority in neuroscience because it coincides with the onset of many psychiatric and behavioral disorders. However, little is known about how puberty influences the brain functional connectome. In this study, taking a longitudinal human sample of typically developing children and adolescents (of both sexes), we demonstrate that the development of the brain functional connectome better fits pubertal status than chronological age. In particular, centrality, segregation, efficiency, and integration of the brain functional connectome increase after the onset of the pubertal markers. We found that these effects are stronger in attention and task control networks. Lastly, after controlling for this effect, we showed that functional connectivity between these networks is related to better performance in cognitive flexibility. This study points out the importance of considering longitudinal nonlinear trends when exploring developmental trajectories, and emphasizes the impact of puberty on the functional organization of the brain in adolescence.

Sex differences in executive control: A systematic review of functional neuroimaging studies

The number of studies investigating sex differences in executive functions, particularly those using human functional neuroimaging techniques, has risen dramatically in the past decade. However, the influences of sex on executive function are still underexplored and poorly characterized. To address this, we conducted a systematic literature review of functional neuroimaging studies investigating sex differences in three prominent executive control domains of cognitive set-shifting, performance monitoring, and response inhibition. PubMed, Web of Science, and Scopus were systematically searched. Following the application of exclusion criteria, 21 studies were included, with a total of 677 females and 686 males. Ten of these studies were fMRI and PET, eight were EEG, and three were NIRS. At present, there is evidence for sex differences in the neural networks underlying all tasks of executive control included in this review suggesting males and females engage different strategies depending on task demands. There was one task exception, the 2-Back task, which showed no sex differences. Due to methodological variability and the involvement of multiple neural networks, a simple overarching statement with regard to gender differences during executive control cannot be provided. As such, we discuss limitations within the current literature and methodological considerations that should be employed in future research. Importantly, sex differences in neural mechanisms are present in the majority of tasks assessed, and thus should not be ignored in future research. PROSPERO registration information: CRD42019124772.

Neuroimaging the menstrual cycle: A multimodal systematic review

Increasing evidence indicates that ovarian hormones affect brain structure, chemistry and function of women in their reproductive age, potentially shaping their behavior and mental health. Throughout the reproductive years, estrogens and progesterone levels fluctuate across the menstrual cycle and can modulate neural circuits involved in affective and cognitive processes. Here, we review seventy-seven neuroimaging studies and provide a comprehensive and data-driven evaluation of the accumulating evidence on brain plasticity associated with endogenous ovarian hormone fluctuations in naturally cycling women (n = 1304). The results particularly suggest modulatory effects of ovarian hormones fluctuations on the reactivity and structure of cortico-limbic brain regions. These findings highlight the importance of performing multimodal neuroimaging studies on neural correlates of systematic ovarian hormone fluctuations in naturally cycling women based on careful menstrual cycle staging.

A whole-brain modeling approach to identify individual and group variations in functional connectivity

Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.

Menstrual Cycle Variations in Gray Matter Volume, White Matter Volume and Functional Connectivity: Critical Impact on Parietal Lobe

The role of gonadal hormones in neural plasticity remains unclear. This study aimed to examine the effects of naturally fluctuating hormone levels over the menstrual cycle in healthy females. Gray matter, functional connectivity (FC) and white matter changes over the cycle were assessed by using functional magnetic resonance imaging (fMRI), resting state fMRI, and structural MRIs, respectively, and associated with serum gonadal hormone levels. Moreover, electrocutaneous sensitivity was evaluated in 14 women in four phases of their menstrual cycle (menstrual, follicular, ovulatory, and luteal). Electrocutaneous sensitivity was greater during follicular compared to menstrual phase. Additionally, pain unpleasantness was lower in follicular phase than other phases while pain intensity ratings did not change over the cycle. Significant variations in cycle phase effects on gray matter volume were found in the left inferior parietal lobule (IPL) using voxel-based morphometry. Subsequent Freesurfer analysis revealed greater thickness of left IPL during the menstrual phase when compared to other phases. Also, white matter volume fluctuated across phases in left IPL. Blood estradiol was positively correlated with white matter volume both in left parietal cortex and whole cortex. Seed-driven FC between left IPL and right secondary visual cortex was enhanced during ovulatory phase. A seed placed in right IPL revealed enhanced FC between left and right IPL during the ovulatory phase. Additionally, we found that somatosensory cortical gray matter was thinner during follicular compared to menstrual phase. We discuss these results in the context of likely evolutionary pressures selecting for enhanced perceptual sensitivity across modalities specifically during ovulation.

Cerebellar network organization across the human menstrual cycle

The cerebellum contains the vast majority of neurons in the brain and houses distinct functional networks that constitute at least two homotopic maps of cerebral networks. It is also a major site of sex steroid hormone action. While the functional organization of the human cerebellum has been characterized, the influence of sex steroid hormones on intrinsic cerebellar network dynamics has yet to be established. Here we investigated the extent to which endogenous fluctuations in estradiol and progesterone alter functional cerebellar networks at rest in a woman densely sampled over a complete menstrual cycle (30 consecutive days). Edgewise regression analysis revealed robust negative associations between progesterone and cerebellar coherence. Graph theory metrics probed sex hormones' influence on topological brain states, revealing relationships between sex hormones and within-network integration in Ventral Attention, Dorsal Attention, and SomatoMotor Networks. Together these results suggest that the intrinsic dynamics of the cerebellum are intimately tied to day-by-day changes in sex hormones.

Neurovisceral integration in the executive control network: A resting state analysis

Neurovisceral integration models emphasize the role of frontal lobes in cognitive, behavioral, and emotional regulation. Two candidate hubs for the regulation of cardio-autonomic control, anxiety, and executive attention are the dorsolateral prefrontal cortex (DLPFC) and middle frontal gyrus (MFG). Two-hundred and seventy-one adults (62.9 % female) aged 18-85 years were selected from the NKI-Rockland Sample. Resting state functional imaging data was preprocessed, and seeds extracted from bilateral DLPFC and MFG to test 4 regression models predicting connectivity with high frequency HRV (HF-HRV), trait anxiety (TA), and reaction time on an executive attention task. After controlling for age, sex, body mass index and head motion, the right DLPFC-MFG seed pair provided strongest support for neurovisceral integration indexed by HF-HRV, low TA and shorter reaction time on the attention network task. This hemispheric effect may underlie the inhibitory role of right PFC in the regulation of cardio-autonomic function, emotion, and executive attention.

Resting-State Functional Magnetic Resonance Imaging Connectivity Between Semantic and Phonological Regions of Interest May Inform Language Targets in Aphasia

Purpose Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the "functional" dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic-phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia. Method Clinical assessment of language, using the Western Aphasia Battery-Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery-Revised scores. Results Network coherence was observed in healthy controls and participants with stroke. The left-right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical-semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic-phonological representations and processing for verbal production. Conclusions Network connectivity of brain regions associated with semantic-phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs-particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia. Supplemental Material https://doi.org/10.23641/asha.12735785.

A systematic review and meta-analysis of behavioural sex differences in executive control

Literature investigating whether an individuals' sex affects their executive control abilities and performance on cognitive tasks in a normative population has been contradictory and inconclusive. Using meta-analytic procedures (abiding by PRISMA guidelines), this study attempts to identify the magnitude of behavioural sex differences in three prominent executive control domains of cognitive set-shifting, performance monitoring, and response inhibition. PubMed, Web of Science, and Scopus were systematically searched. Across 46 included studies, a total of 1988 females and 1884 males were included in the analysis. Overall, males and females did not differ on performance in any of the three domains of performance monitoring, response inhibition, or cognitive set-shifting. Task-specific sex differences were observed in the domains of performance monitoring, in the CANTAB Spatial Working Memory task-males scored statistically higher than females (Hedges' g = -0.60), and response inhibition, in the Delay Discounting task-females scored statistically higher than males (Hedges' g = 0.64). While the meta-analysis did not detect overall behavioural sex differences in executive control, significant heterogeneity and task-specific sex differences were found. To further understand sex differences within these specific tasks and domains, future research must better control for age and sex hormone levels.

Sex classification using long-range temporal dependence of resting-state functional MRI time series

A thorough understanding of sex differences that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit phenotypic differences between males and females. Here we evaluate sex differences in regional temporal dependence of resting-state brain activity in 195 adult male-female pairs strictly matched for total grey matter volume from the Human Connectome Project. We find that males have more persistent temporal dependence in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies up to 81%. Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, and frontal and occipital cortices. Secondarily, we show that even after strict matching of total gray matter volume, significant volumetric sex differences persist; males have larger absolute cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger absolute cingulates, precunei, and frontal and parietal cortices. Sex classification based on regional volume achieves accuracies up to 85%, highlighting the importance of strict volume-matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between the sexes identifies a potential neurobiological substrate or environmental effect underlying sex differences in functional brain activation patterns.

Functional reorganization of brain networks across the human menstrual cycle

The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone's impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone's widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded-and perhaps driven-by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain's functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.

Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis

Graph neural networks (GNN) rely on graph operations that include neural network training for various graph related tasks. Recently, several attempts have been made to apply the GNNs to functional magnetic resonance image (fMRI) data. Despite recent progresses, a common limitation is its difficulty to explain the classification results in a neuroscientifically explainable way. Here, we develop a framework for analyzing the fMRI data using the Graph Isomorphism Network (GIN), which was recently proposed as a powerful GNN for graph classification. One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix. This understanding enables us to exploit CNN-based saliency map techniques for the GNN, which we tailor to the proposed GIN with one-hot encoding, to visualize the important regions of the brain. We validate our proposed framework using large-scale resting-state fMRI (rs-fMRI) data for classifying the sex of the subject based on the graph structure of the brain. The experiment was consistent with our expectation such that the obtained saliency map show high correspondence with previous neuroimaging evidences related to sex differences.

The medial temporal lobe in nociception: a meta-analytic and functional connectivity study

Recent neuroimaging studies implicate the medial temporal lobe (MTL) in nociception and pain modulation. Here, we aim to identify which subregions of the MTL are involved in human pain and to test its connectivity in a cohort of chronic low-back pain patients (CBP). We conducted 2 coordinate-based meta-analyses to determine which regions within the MTL showed consistent spatial patterns of functional activation (1) in response to experimental pain in healthy participants and (2) in chronic pain compared with healthy participants. We followed PRISMA guidelines and performed activation likelihood estimate (ALE) meta-analyses. The first meta-analysis revealed consistent activation in the right anterior hippocampus (right antHC), parahippocampal gyrus, and amygdala. The second meta-analysis revealed consistently less activation in patients' right antHC, compared with healthy participants. We then conducted a seed-to-voxel resting state functional connectivity of the right antHC seed with the rest of the brain in 77 CBP and 79 age-matched healthy participants. We found that CBP had significantly weaker antHC functional connectivity to the medial prefrontal cortex compared with healthy participants. Taken together, these data indicate that the antHC has abnormally lower activity in chronic pain and reduced connectivity to the medial prefrontal cortex in CBP. Future studies should investigate the specific role of the antHC in the development and management of chronic pain.

Current Challenges in Translational and Clinical fMRI and Future Directions

Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as resting-state fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within- and between-subject variability, which affects the reliability of both task-related, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.