Gender Differences in Global Functional Connectivity During Facial Emotion Processing: A Visual MMN Study

Gender and cytoarchitecture differences: Functional connectivity of the hippocampal sub-regions

Introduction

The hippocampus plays a significant role in learning, memory encoding, and spatial navigation. Typically, the hippocampus is investigated as a whole region of interest. However, recent work has developed fully detailed atlases based on cytoarchitecture properties of brain regions, and the hippocampus has been sub-divided into seven sub-areas that have structural differences in terms of distinct numbers of cells, neurons, and other structural and chemical properties. Moreover, gender differences are of increasing concern in neuroscience research. Several neuroscience studies have found structural and functional variations between the brain regions of females and males, and the hippocampus is one of these regions.

Aim

The aim of this study to explore whether the cytoarchitecturally distinct sub-regions of the hippocampus have varying patterns of functional connectivity with different networks of the brain and how these functional connections differ in terms of gender differences.

Method

This study investigated 200 healthy participants using seed-based resting-state functional magnetic resonance imaging (rsfMRI). The primary aim of this study was to explore the resting connectivity and gender distinctions associated with specific sub-regions of the hippocampus and their relationship with major functional brain networks.

Results

The findings revealed that the majority of the seven hippocampal sub-regions displayed functional connections with key brain networks, and distinct patterns of functional connectivity were observed between the hippocampal sub-regions and various functional networks within the brain. Notably, the default and visual networks exhibited the most consistent functional connections. Additionally, gender-based analysis highlighted evident functional resemblances and disparities, particularly concerning the anterior section of the hippocampus.

Conclusion

This study highlighted the functional connectivity patterns and involvement of the hippocampal sub-regions in major brain functional networks, indicating that the hippocampus should be investigated as a region of multiple distinct functions and should always be examined as sub-regions of interest. The results also revealed clear gender differences in functional connectivity.

Phase Lag Analysis Scalp Electroencephalography May Predict Seizure Frequencies in Patients with Childhood Epilepsy with Centrotemporal Spikes

Background

Childhood epilepsy with centrotemporal spikes (CECTS) is the most common epilepsy syndrome in school-aged children. However, predictors for seizure frequency are yet to be clarified using the phase lag index (PLI) analyses. We investigated PLI of scalp electroencephalography data at onset to identify potential predictive markers for seizure times.

Methods

We compared the PLIs of 13 patients with CECTS and 13 age- and sex-matched healthy controls. For the PLI analysis, we used resting-state electroencephalography data (excluding paroxysmal discharges), and analyzed the mean PLIs among all electrodes and between interest electrodes (C3, C4, P3, P4, T3, and T4) and other electrodes. Furthermore, we compared PLIs between CECTS and control data and analyzed the associations between PLIs and total seizure times in CECTS patients.

Results

No differences were detected in clinical profiles or visual electroencephalography examinations between patients with CECTS and control participants. In patients with CECTS, the mean PLIs among all electrodes and toward interest electrodes were higher at the theta and alpha bands and lower at the delta and gamma bands than those in control participants. Additionally, the mean PLIs toward interest electrodes in the beta frequency band were negatively associated with seizure times (P = 0.02).

Conclusion

The resting-state delta, theta, alpha, and gamma band PLIs might reflect an aberrant brain network in patients with CECTS. The resting-state PLI among the selected electrodes of interest in the beta frequency band may be a predictive marker of seizure times in patients with CECTS.

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI

Brain network interactions are commonly assessed via functional (network) connectivity, captured as an undirected matrix of Pearson correlation coefficients. Functional connectivity can represent static and dynamic relations, but often these are modeled using a fixed choice for the data window Alternatively, deep learning models may flexibly learn various representations from the same data based on the model architecture and the training task. However, the representations produced by deep learning models are often difficult to interpret and require additional posthoc methods, e.g., saliency maps. In this work, we integrate the strengths of deep learning and functional connectivity methods while also mitigating their weaknesses. With interpretability in mind, we present a deep learning architecture that exposes a directed graph layer that represents what the model has learned about relevant brain connectivity. A surprising benefit of this architectural interpretability is significantly improved accuracy in discriminating controls and patients with schizophrenia, autism, and dementia, as well as age and gender prediction from functional MRI data. We also resolve the window size selection problem for dynamic directed connectivity estimation as we estimate windowing functions from the data, capturing what is needed to estimate the graph at each time-point. We demonstrate efficacy of our method in comparison with multiple existing models that focus on classification accuracy, unlike our interpretability-focused architecture. Using the same data but training different models on their own discriminative tasks we are able to estimate task-specific directed connectivity matrices for each subject. Results show that the proposed approach is also more robust to confounding factors compared to standard dynamic functional connectivity models. The dynamic patterns captured by our model are naturally interpretable since they highlight the intervals in the signal that are most important for the prediction. The proposed approach reveals that differences in connectivity among sensorimotor networks relative to default-mode networks are an important indicator of dementia and gender. Dysconnectivity between networks, specially sensorimotor and visual, is linked with schizophrenic patients, however schizophrenic patients show increased intra-network default-mode connectivity compared to healthy controls. Sensorimotor connectivity was important for both dementia and schizophrenia prediction, but schizophrenia is more related to dysconnectivity between networks whereas, dementia bio-markers were mostly intra-network connectivity.

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.

Visual Mismatch Negativity: A Mini-Review of Non-pathological Studies With Special Populations and Stimuli

In this mini-review, we summarized the results of 12 visual mismatch negativity (vMMN) studies that attempted to use this component as a tool for investigating differences between non-clinical samples of participants as well as the possibility of automatic discrimination in the case of specific categories of visual stimuli. These studies investigated the effects of gender, the effects of long-term differences between the groups of participants (fitness, experience in different sports, and Internet addiction), and the effects of short-term states (mental fatigue and hypoxia), as well as the vMMN effect elicited by artworks as a special stimulus category.

Impaired Self-Referential Cognitive Processing in Bipolar Disorder: A Functional Connectivity Analysis

Patients with bipolar disorder have deficits in self-referenced information. The brain functional connectivity during social cognitive processing in bipolar disorder is unclear. Electroencephalogram (EEG) was recorded in 23 patients with bipolar disorder and 19 healthy comparison subjects. We analyzed the time-frequency distribution of EEG power for each electrode associated with self, other, and font reflection conditions and used the phase lag index to characterize the functional connectivity between electrode pairs for 4 frequency bands. Then, the network properties were assessed by graph theoretic analysis. The results showed that bipolar disorder induced a weaker response power and phase lag index values over the whole brain in both self and other reflection conditions. Moreover, the characteristic path length was increased in patients during self-reflection processing, whereas the global efficiency and the node degree were decreased. In addition, when discriminating patients from normal controls, we found that the classification accuracy was high. These results suggest that patients have impeded integration of attention, memory, and other resources of the whole brain, resulting in a deficit of efficiency and ability in self-referential processing.

Does mismatch negativity have utility for NMDA receptor drug development in depression?

CLINICAL TRIAL REGISTRATION

Rapid antidepressant effects associated with ketamine have shifted the landscape for the development of therapeutics to treat major depressive disorder (MDD) from a monoaminergic to glutamatergic model. Treatment with ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, may be effective, but has many non-glutamatergic targets, and clinical and logistical problems are potential challenges. These factors underscore the importance of manipulations of binding mechanics to produce antidepressant effects without concomitant clinical side effects. This will require identification of efficient biomarkers to monitor target engagement. The mismatch negativity (MMN) is a widely used electrophysiological signature linked to the activity of NMDA receptors (NMDAR) in humans and animals and validated in pre-clinical and clinical studies of ketamine. In this review, we explore the flexibility of the MMN and its capabilities for reliable use in drug development for NMDAR antagonists in MDD. We supplement this with findings from our own research with three distinct NMDAR antagonists. The research described illustrates that there are important distinctions between the mechanisms of NMDAR antagonism, which are further crystallized when considering the paradigm used to study the MMN. We conclude that the lack of standardized methodology currently prevents MMN from being ready for common use in drug discovery. This manuscript describes data collected from the following National Institutes of Health (NIH) and Veterans Affairs (VA) studies: AV-101, NCT03583554; lanicemine, NCT03166501; ketamine, NCT02556606.

Neural Mechanisms of Hearing Recovery for Cochlear-Implanted Patients: An Electroencephalogram Follow-Up Study

Background

Patients with severe profound hearing loss could benefit from cochlear implantation (CI). However, the neural mechanism of such benefit is still unclear. Therefore, we analyzed the electroencephalogram (EEG) and behavioral indicators of auditory function remodeling in patients with CI. Both indicators were sampled at multiple time points after implantation (1, 90, and 180 days).

Methods

First, the speech perception ability was evaluated with the recording of a list of Chinese words and sentences in 15 healthy controls (HC group) and 10 patients with CI (CI group). EEG data were collected using an oddball paradigm. Then, the characteristics of event-related potentials (ERPs) and mismatch negative (MMN) were compared between the CI group and the HC group. In addition, we analyzed the phase lag indices (PLI) in the CI group and the HC group and calculated the difference in functional connectivity between the two groups at different stages after implantation.

Results

The behavioral indicator, speech recognition ability, in CI patients improved as the implantation time increased. The MMN analysis showed that CI patients could recognize the difference between standard and deviation stimuli just like the HCs 90 days after cochlear implantation. Comparing the latencies of N1/P2/MMN between the CI group and the HC group, we found that the latency of N1/P2 in CI patients was longer, while the latency of MMN in CI users was shorter. In addition, PLI-based whole-brain functional connectivity (PLI-FC) showed that the difference between the CI group and the HC group mainly exists in electrode pairs between the bilateral auditory area and the frontal area. Furthermore, all those differences gradually decreased with the increase in implantation time.

Conclusion

The N1 amplitude, N1/P2/MMN latency, and PLI-FC in the alpha band may reflect the process of auditory function remodeling and could be an objective index for the assessment of speech perception ability and the effect of cochlear implantation.

Enhancing Working Memory Based on Mismatch Negativity Neurofeedback in Subjective Cognitive Decline Patients: A Preliminary Study

Mismatch negativity (MMN) is suitable for studies of preattentive auditory discriminability and the auditory memory trace. Subjective cognitive decline (SCD) is an ideal target for early therapeutic intervention because SCD occurs at preclinical stages many years before the onset of Alzheimer’s disease (AD). According to a novel lifespan-based model of dementia risk, hearing loss is considered the greatest potentially modifiable risk factor of dementia among nine health and lifestyle factors, and hearing impairment is associated with cognitive decline. Therefore, we propose a neurofeedback training based on MMN, which is an objective index of auditory discriminability, to regulate sensory ability and memory as a non-pharmacological intervention (NPI) in SCD patients. Seventeen subjects meeting the standardized clinical evaluations for SCD received neurofeedback training. The auditory frequency discrimination test, the visual digital N-back (1-, 2-, and 3-back), auditory digital N-back (1-, 2-, and 3-back), and auditory tone N-back (1-, 2-, and 3-back) tasks were used pre- and post-training in all SCD patients. The intervention schedule comprised five 60-min training sessions over 2 weeks. The results indicate that the subjects who received neurofeedback training had successfully improved the amplitude of MMN at the parietal electrode (Pz). A slight decrease in the threshold of auditory frequency discrimination was observed after neurofeedback training. Notably, after neurofeedback training, the working memory (WM) performance was significantly enhanced in the auditory tone 3-back test. Moreover, improvements in the accuracy of all WM tests relative to the baseline were observed, although the changes were not significant. To the best of our knowledge, our preliminary study is the first to investigate the effects of MMN neurofeedback training on WM in SCD patients, and our results suggest that MMN neurofeedback may represent an effective treatment for intervention in SCD patients and the elderly with aging memory decline.

Training away face-type bias: perception and decisions about emotional expression in stereotypically Black faces

Prior research indicates that stereotypical Black faces (e.g., wide nose, full lips) are perceived negatively relative to non-stereotypical faces (face-type bias). The current study investigated whether stereotypical faces may bias the interpretation of a neutral facial expression to seem threatening. Moreover, could biased responses be trained away with feedback? In two experiments, stimuli (face images) were presented in a speeded identification task that included corrective feedback, and participants indicated whether the face stimuli were stereotypical or not and threatening or not. Stimuli were pre-rated by face-type (stereotypical, non-stereotypical) and expression (neutral, threatening). Computational modeling based on General Recognition Theory indicated that training increased perceptual discriminability between all the faces. By the end of training (in both experiments), discriminability for emotional expression was slightly higher for stereotypical faces. Model parameters (for both experiments) also showed that, early in training, decision boundaries were more biased toward the threatening response for stereotypical faces relative to non-stereotypical faces. The results suggest that decision bias may be malleable with training.

Robustness of sex-differences in functional connectivity over time in middle-aged marmosets

Nonhuman primates (NHPs) are an essential research model for gaining a comprehensive understanding of the neural mechanisms of neurocognitive aging in our own species. In the present study, we used resting state functional connectivity (rsFC) to investigate the relationship between prefrontal cortical and striatal neural interactions, and cognitive flexibility, in unanaesthetized common marmosets (Callithrix jacchus) at two time points during late middle age (8 months apart, similar to a span of 5-6 years in humans). Based on our previous findings, we also determine the reproducibility of connectivity measures over the course of 8 months, particularly previously observed sex differences in rsFC. Male marmosets exhibited remarkably similar patterns of stronger functional connectivity relative to females and greater cognitive flexibility between the two imaging time points. Network analysis revealed that the consistent sex differences in connectivity and related cognitive associations were characterized by greater node strength and/or degree values in several prefrontal, premotor and temporal regions, as well as stronger intra PFC connectivity, in males compared to females. The current study supports the existence of robust sex differences in prefrontal and striatal resting state networks that may contribute to differences in cognitive function and offers insight on the neural systems that may be compromised in cognitive aging and age-related conditions such as mild cognitive impairment and Alzheimer's disease.

Predicting Biological Gender and Intelligence From fMRI via Dynamic Functional Connectivity

OBJECTIVE

This paper explores the predictive capability of dynamic functional connectivity extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast to static connectivity used in past research.

METHODS

Several state-of-the-art features extracted from static functional connectivity of the brain are employed to predict biological gender and intelligence using publicly available Human Connectome Project (HCP) database. Next, a novel tensor parallel factor (PARAFAC) decomposition model is proposed to decompose sequence of dynamic connectivity matrices into common connectivity components that are orthonormal to each other, common time-courses, and corresponding distinct subject-wise weights. The subject-wise loading of the components are employed to predict biological gender and intelligence using a random forest classifier (respectively, regressor) using 5-fold cross-validation.

RESULTS

The results demonstrate that dynamic functional connectivity can indeed classify biological gender with a high accuracy (0.94, where male identification accuracy was 0.87 and female identification accuracy was 0.97). It can also predict intelligence with less normalized mean square error (0.139 for fluid intelligence and 0.031 for fluid ability metrics) compared with other functional connectivity measures (the nearest mean square error were 0.147 and 0.037 for fluid intelligence and fluid ability metrics, respectively, using static connectivity approaches).

CONCLUSION

Our work is an important milestone for the understanding of non-stationary behavior of hemodynamic blood-oxygen level dependent (BOLD) signal in brain and how they are associated with biological gender and intelligence.

SIGNIFICANCE

The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence.

Predicting Male vs. Female from Task-fMRI Brain Connectivity

A number of behavioral and cognitive functions of brain differ between male and female. Occurrences of psychiatric disorders, e.g., attention deficit hyperactivity disorder, autism, depression and schizophrenia also vary from male to female. Understanding the unique cognitive expressions in gender-specific brain functions may lead to insights into the risks and associated responses for a certain external simulation or medications. Previously resting-state functional magnetic resonance imaging (r-fMRI) has been used extensively to understand gender differences using functional network connectivity analysis. However, how the brain functional network changes during a cognitive task for different genders is relatively unknown. This paper makes use of a large data set to test whether task-fMRI functional connectivity can be utilized to predict male vs. female. In addition, it also identifies functional connectivity features that are most predictive of gender. The cognitive task-fMRI data consisting 475 healthy controls is taken from the Human Connectome Project (HCP) database. Pearson correlation coefficients are extracted using mean time-series from anatomical brain regions. Partial least squares (PLS) regression with feature selection on the correlation coefficients achieves a classification accuracy of 0.88 for classifying male vs. female using emotion task data. In addition it is found that inter hemispheric connectivity is most important for predicting gender from task-fMRI.