How sex and gender shape functional brain networks.

Sex and gender differences exist in the prevalence and clinical manifestation of common brain disorders. Identifying their neural correlates may help improve clinical care.

It is the habit not the handle that affects tooth brushing - a randomised counterbalanced cross over study with young and healthy adults.

OBJECTIVE: To assess the effect of the toothbrush handle on video-observed toothbrushing behaviour and toothbrushing effectiveness. METHODS: This is a randomized counterbalanced cross-over study. N = 50 university students and employees brushed their teeth at two occasions, one week apart, using either a commercial ergonomically designed manual toothbrush (MT) or Brushalyze V1 (BV1), a manual toothbrush with a thick cylindrical handle without any specific ergonomic features. Brushing behaviour was video-analysed. Plaque was assessed at the second occasion immediately after brushing. Participants also rated their self-perceived oral cleanliness and directly compared the two brushes regarding their handling and compared them to the brushed they used at home. RESULTS: The study participants found the BV1 significantly more cumbersome than the M1 or their brush at home. (p < 0.05). However, correlation analyses revealed a strong consistency of brushing behavior with the two brushes (0.71 < r < 0.91). Means differed only slightly (all d < 0.36). These differences became statistically significant only for the brushing time at inner surfaces (d = 0.31 p = 0.03) and horizontal movements at inner surfaces (d = 0.35, p = 0.02). Plaque levels at the gingival margins did not differ while slightly more plaque persisted at the more coronal aspects of the crown after brushing with BV1 (d = 0.592; p 0.042). DISCUSSION: The results of the study indicate that the brushing handle does not play a major role in brushing behavior or brushing effectiveness.

Modeling brain sex in the limbic system as phenotype for female-prevalent mental disorders.

BACKGROUND: Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics. METHODS: We here investigated if a regionally constrained model of brain volumetric imaging data may provide estimates that are more sensitive to mental health than whole brain-based estimates. Given its known role in emotional processing and mood disorders, we focused on the limbic system. Using two different cohorts of healthy subjects, the Human Connectome Project and the Queensland Twin IMaging, we investigated sex differences and heritability of brain volumes of limbic structures compared to non-limbic structures, and subsequently applied regionally constrained machine learning models trained solely on limbic or non-limbic features. To investigate the biological underpinnings of such models, we assessed the heritability of the obtained sex class probability estimates, and we investigated the association with major depression diagnosis in an independent clinical sample. All analyses were performed both with and without controlling for estimated total intracranial volume (eTIV). RESULTS: Limbic structures show greater sex differences and are more heritable compared to non-limbic structures in both analyses, with and without eTIV control. Consequently, machine learning models performed well at classifying sex based solely on limbic structures and achieved performance as high as those on non-limbic or whole brain data, despite the much smaller number of features in the limbic system. The resulting class probabilities were heritable, suggesting potentially meaningful underlying biological information. Applied to an independent population with major depressive disorder, we found that depression is associated with male-female class probabilities, with largest effects obtained using the limbic model. This association was significant for models not controlling for eTIV whereas in those controlling for eTIV the associations did not pass significance correction. CONCLUSIONS: Overall, our results highlight the potential utility of regionally constrained models of brain sex to better understand the link between sex differences in the brain and mental disorders.

Psychiatric disorders have different prevalence between sexes, with women being twice as likely to develop depression and anxiety across the lifespan. Previous studies have investigated sex differences in brain structure that might contribute to this prevalence but have mostly focused on a single-structure level, potentially overlooking the interplay between brain regions. Sex differences in structures responsible for emotional regulation (limbic system), affected in many psychiatric disorders, have been previously reported. Here, we apply a machine learning model to obtain an estimate of brain sex for each participant based on the volumes of multiple brain regions. Particularly, we compared the estimates obtained with a model based solely on limbic structures with those obtained with a non-limbic model (entire brain except limbic structures) and a whole brain model. To investigate the genetic determinants of the models, we assessed the heritability of the estimates between identical twins and fraternal twins. The estimates of all our models were heritable, suggesting a genetic component contributing to brain sex. Finally, to investigate the association with mental health, we compared brain sex estimates in healthy subjects and in a depressed population. We found an association between depression and brain sex in females for the limbic model, but not for the non-limbic model. No effect was found in males. Overall, our results highlight the potential utility of machine learning models of brain sex based on relevant structures to better understand the link between sex differences in the brain and mental disorders.

Menarche, pubertal timing and the brain: female-specific patterns of brain maturation beyond age-related development.

BACKGROUND: Puberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce. METHODS: We investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status. RESULTS: The machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities-not the brain age gaps-were associated with age at menarche. CONCLUSIONS: This study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.

Puberty is a period of substantial changes in the life of youths, and these include profound brain changes. Most studies have investigated age related changes in brain development, recent work however suggests that looking at brain development through the lens of pubertal development can provide additional insights beyond age effects. We here analyzed brain imaging data from a group of same-aged adolescent girls from the Adolescent Brain Cognitive Development study. Our goal was to investigate if we could determine from brain images whether a girl had started her menstrual period (menarche) or not, and we used machine learning to classify between them. This machine learning model does not just return a "yes/no" decision, but also returns a number between 0 and 1 indicating a probability to be pre- (0) or post- (1) menarche. To rule out that our approach only maps age-related development, we selected a strictly age-matched sample of girls and compared our classification model to a brain age model trained on independent individuals. Our model classified between pre- and post-menarche with moderate accuracy. The obtained class probability was partly related to age-related brain development, but only the probability was significantly associated with pubertal timing (age at menarche). In summary, our study uses a machine learning model to estimate whether a girl has reached menarche based on her brain structure. This approach offers new insights into the connection between puberty and brain development and might serve as an objective way to assess pubertal timing from imaging data.

Domain adapted brain network fusion captures variance related to pubertal brain development and mental health.

Puberty demarks a period of profound brain dynamics that orchestrates changes to a multitude of neuroimaging-derived phenotypes. This complexity poses a dimensionality problem when attempting to chart an individual's brain development over time. Here, we illustrate that shifts in subject similarity of brain imaging data relate to pubertal maturation in the longitudinal ABCD study. Given that puberty depicts a critical window for emerging mental health issues, we additionally show that our model is capable of capturing variance in the adolescent brain related to psychopathology in a population-based and a clinical cohort. These results suggest that low-dimensional reference spaces based on subject similarities render useful to chart variance in brain development in youths.

Individual differences in resilience to stress are associated with affective flexibility.

Cognitive flexibility is frequently linked to resilience because of its important contribution to stress regulation. In this context, particularly affective flexibility, defined as the ability to flexibly attend and disengage from affective information, may play a significant role. In the present study, the relationship of cognitive and affective flexibility and resilience was examined in 100 healthy participants. Resilience was measured with three self-report questionnaires, two defining resilience as a personality trait and one focusing on resilience as an outcome in the sense of stress coping abilities. Cognitive and affective flexibility were assessed in two experimental task switching paradigms with non-affective and affective materials and tasks, respectively. The cognitive flexibility paradigm additionally included measures of cognitive stability and spontaneous switching in ambiguous situations. In the affective flexibility paradigm, we explicitly considered the affective valence of the stimuli. Response time switch costs in the affective flexibility paradigm were significantly correlated to all three measures of resilience. The correlation was not specific for particular valences of the stimuli before or during switching. For cognitive (non-affective) flexibility, a significant correlation of response time switch costs was found with only one resilience measure. A regression analysis including both affective and cognitive switch costs as predictors of resilience indicated that only affective, but not cognitive switch costs, explained unique variance components. Furthermore, the experimental measures of cognitive stability and the rate of spontaneous switching in ambiguous situations did not correlate with resilience scores. These findings suggest that specifically the efficiency of flexibly switching between affective and non-affective information is related to resilience.

Neural correlates of affective task switching and asymmetric affective task switching costs.

The control of emotions is of potentially great clinical relevance. Accordingly, there has been increasing interest in understanding the cognitive mechanisms underlying the ability to switch efficiently between the processing of affective and non-affective information. Reports of asymmetrically increased switch costs when switching toward the more salient emotion task indicate specific demands in the flexible control of emotion. The neural mechanisms underlying affective task switching, however, are so far not fully understood. Using functional Magnetic Resonance Imaging (MRI) (N = 57), we observed that affective task switching was accompanied by increased activity in domain-general fronto-parietal control systems. Blood-oxygen-level-dependent (BOLD) activity in the posterior medial frontal and anterolateral prefrontal cortex was directly related to affective switch costs, indicating that these regions play a particular role in individual differences in (affective) task-switching ability. Asymmetric switch costs were associated with increased activity in the right inferior frontal and dorsal anterior medial prefrontal cortex, two brain regions critical for response inhibition. This suggests that asymmetric switch costs might-to a great extent-reflect higher demands on inhibitory control of the dominant emotion task. These results contribute to a refined understanding of brain systems for the flexible control of emotions and thereby identify valuable target systems for future clinical research.

Probing the association between resting-state brain network dynamics and psychological resilience.

This study aimed at replicating a previously reported negative correlation between node flexibility and psychological resilience, that is, the ability to retain mental health in the face of stress and adversity. To this end, we used multiband resting-state BOLD fMRI (TR = .675 sec) from 52 participants who had filled out three psychological questionnaires assessing resilience. Time-resolved functional connectivity was calculated by performing a sliding window approach on averaged time series parcellated according to different established atlases. Multilayer modularity detection was performed to track network reconfigurations over time, and node flexibility was calculated as the number of times a node changes community assignment. In addition, node promiscuity (the fraction of communities a node participates in) and node degree (as proxy for time-varying connectivity) were calculated to extend previous work. We found no substantial correlations between resilience and node flexibility. We observed a small number of correlations between the two other brain measures and resilience scores that were, however, very inconsistently distributed across brain measures, differences in temporal sampling, and parcellation schemes. This heterogeneity calls into question the existence of previously postulated associations between resilience and brain network flexibility and highlights how results may be influenced by specific analysis choices.

Internal consistency and test-retest reliability of an affective task-switching paradigm.

Affective flexibility refers to the flexible adaptation of behavior or thought given emotionally relevant stimuli, tasks, or contexts, and has been associated with the efficiency of emotion regulation and dealing with stress and adversity. Experimentally, individual differences in affective flexibility have been measured as behavioral costs (response times, errors rates) of switching between affective and neutral tasks. However, behavioral task measures can only be treated as trait-like characteristics if they have sufficient psychometric quality. We report an analysis of the test-retest reliability (interval 2 weeks) as well as internal consistencies of behavioral switch costs measured in an affective task-switching paradigm. This paradigm elicits strong response time switch costs for both tasks, but higher when switching to the emotion than to the gender task. These "asymmetric switch costs" suggest dominance of the emotional task rule. Reliability analyses indicated excellent internal consistency estimates (Spearman-Brown corrected r = .92 for both switch directions) and good test-retest reliabilities (ICC(2,1) of .78 and .82, respectively) for response time-based switch costs. Effect sizes and reliability estimates were substantially lower for switch costs calculated from error rates, which is consistent with previous literature discussing the psychometric properties of task-based cognitive measures. Reliability measures were lower but still acceptable for valence-specific response time-based switch costs, potentially due to lower trial numbers per cell when increasing granularity of the analysis. In conclusion, our results indicate that response time-based affective switch costs are well-suited as individual differences measure, and thus may be a valuable proxy for assessing affective flexibility. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Cognitive, Affective, and Feedback-Based Flexibility - Disentangling Shared and Different Aspects of Three Facets of Psychological Flexibility.

Cognitive flexibility - the ability to adjust one ´s behavior to changing environmental demands - is crucial for controlled behavior. However, the term 'cognitive flexibility' is used heterogeneously, and associations between cognitive flexibility and other facets of flexible behavior have only rarely been studied systematically. To resolve some of these conceptual uncertainties, we directly compared cognitive flexibility (cue-instructed switching between two affectively neutral tasks), affective flexibility (switching between a neutral and an affective task using emotional stimuli), and feedback-based flexibility (non-cued, feedback-dependent switching between two neutral tasks). Three experimental paradigms were established that share as many procedural features (in terms of stimuli and/or task rules) as possible and administered in a pre-registered study plan (N = 100). Correlation analyses revealed significant associations between the efficiency of cognitive and affective task switching (response time switch costs). Feedback-based flexibility (measured as mean number of errors after rule reversals) did not correlate with task switching efficiency in the other paradigms, but selectively with the effectiveness of affective switching (error rate costs when switching from neutral to emotion task). While preregistered confirmatory factor analysis (CFA) provided no clear evidence for a shared factor underlying the efficiency of switching in all three domains of flexibility, an exploratory CFA suggested commonalities regarding switching effectiveness (accuracy-based switch costs). We propose shared mechanisms controlling the efficiency of cue-dependent task switching across domains, while the relationship to feedback-based flexibility may depend on mechanisms controlling switching effectiveness. Our results call for a more stringent conceptual differentiation between different variants of psychological flexibility.

Questions and controversies in the study of time-varying functional connectivity in resting fMRI.

The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.

Aberrant brain network topology in fronto-limbic circuitry differentiates euthymic bipolar disorder from recurrent major depressive disorder.

INTRODUCTION: Previous studies have established graph theoretical analysis of functional network connectivity (FNC) as a potential tool to detect neurobiological underpinnings of psychiatric disorders. Despite the promising outcomes in studies that examined FNC aberrancies in bipolar disorder (BD) and major depressive disorder (MDD), there is still a lack of research comparing both mood disorders, especially in a nondepressed state. In this study, we used graph theoretical network analysis to compare brain network properties of euthymic BD, euthymic MDD and healthy controls (HC) to evaluate whether these groups showed distinct features in FNC. METHODS: We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 BD patients, 15 patients with recurrent MDD as well as 30 age- and gender-matched HC. Graph theoretical analyses were then applied to investigate functional brain networks on a global and regional network level. RESULTS: Global network analysis revealed a significantly higher mean global clustering coefficient in BD compared to HC. We further detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality. CONCLUSION: In conclusion, our findings indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.

Associative Memory Impairments Are Associated With Functional Alterations Within the Memory Network in Schizophrenia Patients and Their Unaffected First-Degree Relatives: An fMRI Study.

Memory impairments are a major characteristic of schizophrenia (SZ). In the current study, we used an associative memory task to test the hypothesis that SZ patients and first-degree relatives have altered functional patterns in comparison to healthy controls. We analyzed the fMRI activation pattern during the presentation of a face-name task in 27 SZ patients, 23 first-degree relatives, and 27 healthy controls. In addition, we performed correlation analyses between individual psychopathology, accuracy and reaction time of the task and the beta scores of the functional brain activations. We observed a lower response accuracy and increased reaction time during the retrieval of face-name pairs in SZ patients compared with controls. Deficient performance was accompanied by abnormal functional activation patterns predominantly in DMN regions during encoding and retrieval. No significant correlation between individual psychopathology and neuronal activation during encoding or retrieval of face-name pairs was observed. Findings of first-degree relatives indicated slightly different functional pattern within brain networks in contrast to controls without significant differences in the behavioral task. Both the accuracy of memory performance as well as the functional activation pattern during retrieval revealed alterations in SZ patients, and, to a lesser degree, in relatives. The results are of potential relevance for integration within a comprehensive model of memory function in SZ. The development of a neurophysiological model of cognition in psychosis may help to clarify and improve therapeutic options to improve memory and functioning in the illness.

Visual working memory encoding in schizophrenia and first-degree relatives: neurofunctional abnormalities and impaired consolidation.

BACKGROUND: Working memory (WM) deficits in schizophrenia (SCZ) have been linked to impairments in the encoding phase that are associated with aberrant neuronal functioning. Similar abnormalities have been observed in unaffected first-degree relatives (REL) and are thus discussed as candidate endophenotypes. The process of WM consolidation - i.e. the formation of durable WM representations - is assumed to be impaired in SCZ, but no study has investigated WM consolidation and neuronal correlates of visual WM encoding in REL before. METHOD: We examined whole-brain activation during the encoding phase with an event-related functional magnetic resonance imaging study design in 25 SCZ subjects, 22 REL subjects, and 25 healthy controls. Subjects performed a visual masked change detection task that assessed WM performance and consolidation. RESULTS: SCZ showed deficient WM performance indicating an impairment consolidation process, accompanied by broad neuronal hypoactivation, most prominently in frontal brain regions, as well as increased activity of the anterior cingulate during the encoding phase. REL showed decreased neuronal activity in the middle and medial frontal gyrus and increased activity in the precentral gyrus and insula during encoding, but no significant behavioral deficits were observed. In respect of given consolidation times, REL showed a shift from decreased frontal activity at short time intervals to increased frontal activity at longer time intervals. CONCLUSIONS: Findings suggest WM consolidation may be slowed in REL so that the deployment of compensatory neuronal resources during encoding is needed to assure proper WM performance. This supports the view of WM-related neuronal dysfunctions as a potential endophenotypic marker.

Functional Connectivity Anomalies in Adolescents with Psychotic Symptoms.

BACKGROUND: Previous magnetic resonance imaging (MRI) research suggests that, prior to the onset of psychosis, high risk youths already exhibit brain abnormalities similar to those present in patients with schizophrenia. OBJECTIVES: The goal of the present study was to describe the functional organization of endogenous activation in young adolescents who report auditory verbal hallucinations (AVH) in view of the "distributed network" hypothesis of psychosis. We recruited 20 young people aged 13-16 years who reported AVHs and 20 healthy controls matched for age, gender and handedness from local schools. METHODS: Each participant underwent a semi-structured clinical interview and a resting state (RS) neuroimaging protocol. We explored functional connectivity (FC) involving three different networks: 1) default mode network (DMN) 2) salience network (SN) and 3) central executive network (CEN). In line with previous findings on the role of the auditory cortex in AVHs as reported by young adolescents, we also investigated FC anomalies involving both the primary and secondary auditory cortices (A1 and A2, respectively). Further, we explored between-group inter-hemispheric FC differences (laterality) for both A1 and A2. Compared to the healthy control group, the AVH group exhibited FC differences in all three networks investigated. Moreover, FC anomalies were found in a neural network including both A1 and A2. The laterality analysis revealed no between-group, inter-hemispheric differences. CONCLUSIONS: The present study suggests that young adolescents with subclinical psychotic symptoms exhibit functional connectivity anomalies directly and indirectly involving the DMN, SN, CEN and also a neural network including both primary and secondary auditory cortical regions.

Omega-3 Fatty Acids: Repurposing Opportunities for Cognition and Biobehavioral Disturbances in MCI and Dementia.

Neurodegenerative diseases may directly affect memory performance, thus leading to functional impairments. An increasing body of evidence suggests an association between dietary intake of omega-3 fatty acids and memory functioning in animal models as well as in human studies. Recent evidence supports a potential beneficial role of omega-3 fatty acid supplementation on psychopathological and cognitive symptoms, beside their established positive effects on cardiovascular health. OBJECTIVE: We summarize relevant and recent evidence from epidemiological, interventional and experimental studies investigating dietary consumption of omega-3 fatty acids and emphazing mechanisms of memory disorders, with a focus on mild cognitive impairment (MCI) and dementia. Omega-3 fatty acid could represent an affordable and accessible adjunctive treatment option to improve cognitive and non-cognitive function with a focus on MCI or dementia. However, apart from its translational promise, which is based on mechanistic models and evidence from animal studies, evidence for clinical benefits in humans is lacking. METHOD: To follow this research question, a search through electronic databases for the following search terms to identify relevant studies was conducted: 'omega 3 fatty acids', 'cognition', 'memory', ´Alzheimer´s Disease ´, ´dementia´, ´MCI`. Studies were included if they presented original data and were published in English between 1990 and 2015. RESULTS: To our the best of our knowledge, there are only 8 interventional studies that investigated the effects of n3-PUFAs in dementia patients, while 6 studies were conducted in healthy individuals, which in combination show equivocal results. CONCLUSION: This verifies the need for larger and (more) well designed clinical trials.

Integrated Biomarkers for Depression in Alzheimer's Disease: A Critical Review.

Depression is a common neuropsychiatric manifestation among Alzheimer's disease (AD) patients. It may compromise everyday activities and lead to a faster cognitive decline as well as worse quality of life. The identification of promising biomarkers may therefore help to timely initiate and improve the treatment of preclinical and clinical states of AD, and to improve the long-term functional outcome. In this narrative review, we report studies that investigated biomarkers for AD-related depression. Genetic findings state AD-related depression as a rather complex, multifactorial trait with relevant environmental and inherited contributors. However, one specific set of genes, the brain derived neurotrophic factor (BDNF), specifically the Val66Met polymorphism, may play a crucial role in AD-related depression. Regarding neuroimaging markers, the most promising findings reveal structural impairments in the cortico-subcortical networks that are related to affect regulation and reward / aversion control. Functional imaging studies reveal abnormalities in predominantly frontal and temporal regions. Furthermore, CSF based biomarkers are seen as potentially promising for the diagnostic process showing abnormalities in metabolic pathways that contribute to AD-related depression. However, there is a need for standardization of methodological issues and for replication of current evidence with larger cohorts and prospective studies.