Brain Functional Connectivity Research Articles

Age-associated alteration of innate defensive response to a looming stimulus and brain functional connectivity pattern in mice

Innate defensive behaviors are essential for species survival. While these behaviors start to develop early in an individual’s life, there is still much to be understood about how they evolve with advancing age. Considering that aging is often accompanied by various cognitive and physical declines, we tested the hypothesis that innate fear behaviors and underlying cerebral mechanisms are modified by aging. In our study we investigated this hypothesis by examining how aged mice respond to a looming visual threat compared to their younger counterparts. Our findings indicate that aged mice exhibit a different fear response than young mice when facing this imminent threat. Specifically, unlike young mice, aged mice tend to predominantly display freezing behavior without seeking shelter. Interestingly, this altered behavioral response in aged mice is linked to a distinct pattern of functional brain connectivity compared to young mice. Notably, our data highlights a lack of a consistent brain activation following the fear response in aged mice, suggesting that innate defensive behaviors undergo changes with aging.

Effects of Manual Acupuncture Versus Sham Acupuncture in patients with Post-Stroke Depression: A Randomized Clinical Trial.

Post-stroke depression (PSD) is a prevalent psychiatric complication in stroke patients, severely reducing quality of life and delaying social recovery in stroke survivors. Clinical studies have shown that acupuncture can be used as an alternative approach for PSD. The aim of this study was to examine the safety, efficacy, and electroencephalogram (EEG) mechanism of acupuncture in treating PSD patients. From October 28, 2022 to May 16, 2023, this single-center, single-blind, randomized clinical trial was conducted at the Second Affiliated Hospital of Nanchang University. A total of 56 eligible subjects were assigned in a random manner, with an equal distribution between two groups: the manual acupuncture (MA) group and the sham acupuncture (SA) group. The primary outcome was the Hamilton Depression Scale-24 (HAMD-24); the secondary outcomes included the Pittsburgh Sleep Quality Index (PSQI), the National Institutes of Health Stroke Scale (NIHSS), the Barthel index, EEG power spectrum, and EEG imaginary coherent (iCOH). Compared to the SA group, the MA group exhibited significant improvements in HAMD-24, NIHSS, PSIQ, and Barthel index at week 6. The total improvement rate was 85.71% in the MA group and 28.57% in the SA group. After 6weeks of treatment, the alpha and beta bands power spectrum increased significantly, while the delta and theta bands power spectrum decreased significantly in the MA group compared to the SA group. The iCOH analysis showed that the MA group had significantly higher functional connectivity in the four bands than the SA group. Acupuncture might be regarded as an adjunctive treatment for PSD patients with improvements in their neurological deficits, sleep quality, and depression. Meanwhile, the mechanism of acupuncture in treating PSD patients may be through decreasing the slow wave power spectrum and increasing the fast wave power spectrum, and enhancing brain functional connectivity. Chinese Clinical Trial Registry (ChiCTR2200065112/2022-10-28).

Functional connectivity density of brain in children with primary nocturnal enuresis: results from a resting-state fMRI study.

Primary nocturnal enuresis (PNE) is a disease characterized by involuntary urination during sleep after the age of five, causing inconvenience and psychological burden to children and their families. The onset of PNE is related to many factors, and in recent years, delayed central nervous system maturation has been considered one of the important causes of PNE. Previous studies have demonstrated functional changes in multiple brain regions in children with PNE. However, these studies either focused on changes in local brain regions or the functional connection (FC) between specific brain regions, and there is currently a lack of research on the whole brain FC in children with PNE. This study analyzed functional connectivity density (FCD) across the entire brain based on voxels and comprehensively evaluated the global FCD (gFCD), local FCD (lFCD), and long-range FCD (lrFCD). Decreased gFCD and lFCD were found in the left temporal lobe and the right posterior cerebellum in the children with PNE compared with the HC. The FCD values in these regions were negatively correlated with the scores of hyperactivity/impulsivity in the children with PNE. This study may help to reveal the neural mechanisms underlying the onset of PNE in children from a new perspective.

Exploring Electrophysiological Responses to Hypnosis in Patients with Fibromyalgia

Background/Objectives: Hypnosis shows great potential for managing patients suffering from fibromyalgia and chronic pain. Several studies have highlighted its efficacy in improving pain, quality of life, and reducing psychological distress. Despite its known feasibility and efficacy, the mechanisms of action remain poorly understood. Building on these insights, this innovative study aims to assess neural activity during hypnosis in fibromyalgia patients using high-density electroencephalography (EEG) and self-reported measures. Methods: Thirteen participants with fibromyalgia were included in this study. EEG recordings were done during resting state and hypnosis conditions. After both conditions, levels of pain, comfort, absorption, and dissociation were assessed using a numerical rating scale. Time perception was collected via an open-ended question. The study was prospectively registered in the ClinicalTrials.gov public registry (NCT04263324). Results: Neural oscillations showed increased theta power during hypnosis in the left parietal and occipital electrodes, increased beta power in the frontal and left temporal electrodes, and increased slow-gamma power in the frontal and left parietal electrodes. Functional connectivity using pairwise-phase consistency measures showed decreased connectivity in the frontal electrodes during hypnosis. Graph-based measures, the node strength, and the cluster coefficient were lower in frontal electrodes in the slow-gamma bands during hypnosis compared to resting state. Key findings indicate significant changes in neural oscillations and brain functional connectivity, suggesting potential electrophysiological markers of hypnosis in this patient population.

Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results. We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses. We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms. Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern. The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

Resting state network changes induced by experimental inaudible infrasound exposure and associations with self-reported noise sensitivity and annoyance.

The effects of prolonged infrasound (IS) exposure on brain function and behavior are largely unknown, with only one prior study investigating functional connectivity (FC) changes. In a long-term randomized-controlled trial, 38 participants were exposed to inaudible airborne IS (6Hz, 80-90 dB) or sham devices for four weeks (8h/night). We assessed FC changes in resting-state networks (auditory, default mode (DMN), sensorimotor (SMN), and executive control (ECN)), and explored IS 'sensitivity' as a predictor of identified significant FC changes. We also examined correlations between somatic symptoms and FC. IS exposure led to decreased FC in the right precuneus (DMN) and increased FC in the Vermis IV and V (SMN). In the ECN, we observed increased FC in the right frontal middle gyrus (BA8) and the right inferior parietal lobe, and decreased FC in another region of the right frontal middle gyrus. Changes in the ECN (right inferior parietal lobe) were negatively associated with self-reported annoyance from IS/low-frequency noise. A significant negative association was found between FC changes in the DMN (right precuneus) and somatic symptoms. Our study is the first to investigate prolonged IS exposure effects on brain FC, revealing changes in the vDMN, SMN, and ECN, but not in the auditory network. Future studies should assess annoyance and sensitivity markers, fine-grained measures of somatic symptoms, and stratify samples by sensitivity to uncover individual differences in response to IS.

Dynamic patterns of functional connectivity in the human brain underlie individual memory formation

Remembering our everyday experiences involves dynamically coordinating information distributed across different brain regions. Investigating how momentary fluctuations in connectivity in the brain are relevant for episodic memory formation, however, has been challenging. Here we leverage the high temporal precision of intracranial EEG to examine sub-second changes in functional connectivity in the human brain as 20 participants perform a paired associates verbal memory task. We first identify potential functional connections by selecting electrode pairs across the neocortex that exhibit strong correlations with a consistent time delay across random recording segments. We then find that successful memory formation during the task involves dynamic sub-second changes in functional connectivity that are specific to each word pair. These patterns of dynamic changes are reinstated when participants successfully retrieve the word pairs from memory. Therefore, our data provide direct evidence that specific patterns of dynamic changes in human brain connectivity are associated with successful memory formation.

Contrasting topologies of synchronous and asynchronous functional brain networks

ABSTRACT We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity, which the other type of network does not.

Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery

BackgroundAlthough numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).MethodsDemographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.ResultsLSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).ConclusionThese findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.Trial registrationChinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/.

Patients with temporomandibular disorders and chronic pain of myofascial origin display reduced alpha power density and altered small-world properties of brain networks

BACKGROUND: Chronic pain is one of the most common symptoms of temporomandibular disorders (TMD). Although its pathophysiology is still a challenge, TMD has been associated with changes in central nervous system activity related to pain modulatory capacity. OBJECTIVE: To assess the cortical activity of patients with temporomandibular disorders and chronic pain of myofascial origin using quantitative electroencephalography (qEEG) in different mental states. METHOD: This study consists of a cross-sectional study. Individuals with TMD and chronic pain and healthy controls were evaluated using qEEG in four consecutive conditions, all with closed eyes: 1) initial resting condition; 2) non-painful motor imagery task of hand movement; 3) painful motor imagery task of clenching the teeth; 4) final resting condition. RESULTS: Participants with TMD and chronic pain overall presented decreased alpha power density during baseline at rest, non-painful and painful motor imagery tasks when compared to healthy controls. Furthermore, functional brain connectivity was distinct between groups, with TMD and chronic pain showing lower small-world values for the delta (all conditions), theta (painful and non-painful motor imagery task), and alpha bands (painful motor imagery task), and an increase in the beta band (all conditions). CONCLUSION: These results suggest that TMD and chronic pain could be associated with maladaptive plasticity in the brain, which may correspond to a reduced ability to modify brain activity during different mental tasks, including painful and non-painful motor imagery.

Correlations between alterations in global brain functional connectivity in patients with major depressive disorder and their genetic characteristics

This study aims to elucidate the neuroimaging changes associated with major depressive disorder (MDD) and their relationship with genetic characteristics. We conducted a global-brain functional connectivity (GFC) and genetic-neuroimaging correlation analysis on 42 MDD patients and 42 healthy controls (HCs), exploring the correlation between GFC abnormalities and clinical variables. Results showed that compared to HCs, MDD patients had significantly decreased GFC values in the bilateral posterior cingulate cortex/precuneus and increased GFC values in the left and right cerebellum Crus I/II. Additionally, a negative correlation was observed between the GFC values of the left cerebellum Crus I/II and subjective support scores, as well as social support revalued scale total scores. We identified genes associated with GFC changes in MDD, which are enriched in biological processes such as synaptic transmission and ion transport. Our findings indicate the presence of abnormal GFC values in severe depression, complementing the pathological research on the condition. Furthermore, this study provides preliminary evidence for the correlation between social support levels and brain functional connectivity, offering insights into the potential association between GFC changes and gene expression in MDD patients.

Privacy-Preserving Visualization of Brain Functional Connectivity.

Privacy protection is important in visualization due to the risk of leaking personal sensitive information. In this paper, we study the problem of privacy-preserving visualizations using differential privacy, employing biomedical data from neuroimaging as a use case. We investigate several approaches based on perturbing correlation values and characterize their privacy cost and the impact of pre- and post-processing. To obtain a better privacy/visual utility tradeoff, we propose workflows for connectogram and seed-based connectivity visualizations, respectively. These workflows successfully generate visualizations similar to their non-private counterparts. Experiments show that qualitative assessments can be preserved while guaranteeing privacy. These results show that differential privacy is a promising method for protecting sensitive information in data visualization.

A Brain Network Analysis Model for Motion Sickness in Electric Vehicles Based on EEG and fNIRS Signal Fusion

Motion sickness is a common issue in electric vehicles, significantly impacting passenger comfort. This study aims to develop a functional brain network analysis model by integrating electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals to evaluate motion sickness symptoms. During real-world testing with the Feifan F7 series of new energy-electric vehicles from SAIC Motor Corp, data were collected from 32 participants. The EEG signals were divided into four frequency bands: delta-range, theta-range, alpha-range, and beta-range, and brain oxygenation variation was calculated from the fNIRS signals. Functional connectivity between brain regions was measured to construct functional brain network models for motion sickness analysis. A motion sickness detection model was developed using a graph convolutional network (GCN) to integrate EEG and fNIRS data. Our results show significant differences in brain functional connectivity between participants in motion and non-motion sickness states. The model that combined fNIRS data with high-frequency EEG signals achieved the best performance, improving the F1 score by 11.4% compared to using EEG data alone and by 8.2% compared to using fNIRS data alone. These results highlight the effectiveness of integrating EEG and fNIRS signals using GCN for motion sickness detection. They demonstrate the model’s superiority over single-modality approaches, showcasing its potential for real-world applications in electric vehicles.

Diffusion and functional MRI reveal microstructural and network connectivity impairment in adult-onset neuronal intranuclear inclusion disease.

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder lacking reliable neuroimaging biomarkers. This study aimed to evaluate microstructural and functional connectivity alterations using diffusion kurtosis imaging (DKI) and resting-state fMRI (rs-fMRI), and to investigate their diagnostic potential as biomarkers. Twenty-three patients with NIID and 40 matched healthy controls (HCs) were recruited. Firstly, gray matter (GM) and white matter (WM) changes were assessed by voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Then we explored modifications in brain functional networks connectivity by independent component analysis. And the relationship between the altered DKI parameters and neuropsychological evaluation was analyzed. Finally, a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different gray matter and white matter parameters. Compared with the HCs, NIID patients showed reduced mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), and kurtosis fractional anisotropy (KFA) values in deep gray matter regions. Significantly decreased MK, RK, AK, KFA and fractional anisotropy (FA), and increased mean diffusivity (MD) values were observed in extensive white matter fiber tracts. Notable alterations in functional connectivity were also detected. Among all DKI parameters, the diagnostic efficiency of AK in GM and FA in WM regions was the highest. Adult-onset NIID patients exhibited altered microstructure and functional network connectivity. Our findings suggest that DKI parameters may serve as potential imaging biomarkers for diagnosing adult-onset NIID.

Impaired brain glucose metabolism in glucagon-like peptide-1 receptor knockout mice

BackgroundQuantitative mapping of the brain’s metabolism is a critical tool in studying and diagnosing many conditions, from obesity to neurodegenerative diseases. In particular, noninvasive approaches are urgently required. Recently, there have been promising drug development approaches for the treatment of disorders related to glucose metabolism in the brain and, therefore, against obesity-associated diseases. One of the most important drug targets to emerge has been the Glucagon-like peptide-1 (GLP-1) and its receptor (GLP-1R). GLP and GLP-1R play an important role in regulating blood sugar and maintaining energy homeostasis. However, the macroscopic effects on brain metabolism and function due to the presence of GLP-1R are unclear.MethodsTo explore the physiological role of GLP-1R in mouse brain glucose metabolism, and its relationship to brain function, we used three methods. We used deuterium magnetic resonance spectroscopy (DMRS) to provide quantitative information about metabolic flux, fluorodeoxyglucose positron emission tomography (FDG-PET) to measure brain glucose metabolism, and resting state-functional MRI (rs-fMRI) to measure brain functional connectivity. We used these methods in both mice with complete GLP-1R knockout (GLP-1R KO) and wild-type C57BL/6N (WT) mice.ResultsThe metabolic rate of GLP-1R KO mice was significantly slower than that of WT mice (p = 0.0345, WT mice 0.02335 ± 0.057 mM/min, GLP-1R KO mice 0.01998 ± 0.07 mM/min). Quantification of the mean [18F]FDG signal in the whole brain also showed significantly reduced glucose uptake in GLP-1R KO mice versus control mice (p = 0.0314). Observing rs-fMRI, the functional brain connectivity in GLP-1R KO mice was significantly lower than that in the WT group (p = 0.0032 for gFCD, p = 0.0002 for whole-brain correlation, p < 0.0001 for ALFF).ConclusionsGLP-1R KO mice exhibit impaired brain glucose metabolism to high doses of exogenous glucose, and they also have reduced functional connectivity. This suggests that the GLP-1R KO mouse model may serve as a model for correlated metabolic and functional connectivity loss.

Bayesian inference of frequency-specific functional connectivity in MEG imaging using a spectral graph model

Abstract Understanding the relationship between structural connectivity (SC) and functional connectivity (FC) of the human brain is an important goal of neuroscience. Highly detailed mathematical models of neural masses exist that can simulate the interactions between functional activity and structural wiring. These models are often complex and require intensive computation. Most importantly, they do not provide a direct or intuitive interpretation of this structure–function relationship. In this study, we employ the emerging concepts of spectral graph theory to obtain this mapping in terms of graph harmonics, which are eigenvectors of the structural graph’s Laplacian matrix. In order to imbue these harmonics with biophysical underpinnings, we leverage recent advances in parsimonious spectral graph modeling (SGM) of brain activity. Here, we show that such a model can indeed be cast in terms of graph harmonics, and can provide a closed-form prediction of FC in an arbitrary frequency band. The model requires only three global, spatially invariant parameters, yet is capable of generating rich FC patterns in different frequency bands. Only a few harmonics are sufficient to reproduce realistic FC patterns. We applied the method to predict FC obtained from pairwise magnitude coherence of source-reconstructed resting-state magnetoencephalography (MEG) recordings of 36 healthy subjects. To enable efficient model inference, we adopted a deep neural network-based Bayesian procedure called simulation-based inference. Using this tool, we were able to speedily infer not only the single most likely model parameters, but also their full posterior distributions. We also implemented several other benchmark methods relating SC to FC, including graph diffusion and coupled neural mass models. The present method was shown to give the best performance overall. Notably, we discovered that a single biophysical parameterization is capable of fitting FCs from all relevant frequency bands simultaneously, an aspect that did not receive adequate attention in prior computational studies.

Longitudinal single-subject neuroimaging study reveals effects of daily environmental, physiological, and lifestyle factors on functional brain connectivity.

Our behavior and mental states are constantly shaped by our environment and experiences. However, little is known about the response of brain functional connectivity to environmental, physiological, and behavioral changes on different timescales, from days to months. This gives rise to an urgent need for longitudinal studies that collect high-frequency data. To this end, for a single subject, we collected 133 days of behavioral data with smartphones and wearables and performed 30 functional magnetic resonance imaging (fMRI) scans measuring attention, memory, resting state, and the effects of naturalistic stimuli. We find traces of past behavior and physiology in brain connectivity that extend up as far as 15 days. While sleep and physical activity relate to brain connectivity during cognitively demanding tasks, heart rate variability and respiration rate are more relevant for resting-state connectivity and movie-watching. This unique data set is openly accessible, offering an exceptional opportunity for further discoveries. Our results demonstrate that we should not study brain connectivity in isolation, but rather acknowledge its interdependence with the dynamics of the environment, changes in lifestyle, and short-term fluctuations such as transient illnesses or restless sleep. These results reflect a prolonged and sustained relationship between external factors and neural processes. Overall, precision mapping designs such as the one employed here can help to better understand intraindividual variability, which may explain some of the observed heterogeneity in fMRI findings. The integration of brain connectivity, physiology data and environmental cues will propel future environmental neuroscience research and support precision healthcare.

Differential impacts of high and low-intensity physical exercise on brain wave activity and functional connectivity in professional athletes: a systematic review

Introduction: This systematic review explores the differential impacts of high and low-intensity physical exercise on brain wave activity and functional connectivity in professional athletes. The study aims to elucidate how varying exercise intensities influence cognitive and emotional responses, brain connectivity, and overall mental health. Additionally, it examines the potential synergistic effects of integrating neurofeedback training with physical exercise. Method: A comprehensive literature search was conducted using databases such as PubMed, Scopus, and Google Scholar. Keywords included "high-intensity exercise," "low-intensity exercise," "brain wave activity," "functional connectivity," "professional athletes," and "cognitive performance." Inclusion criteria were studies published in peer-reviewed journals involving professional athletes and examining the impact of exercise intensity on brain wave activity and functional connectivity. Data extraction focused on study design, sample size, exercise intensity, neuroimaging techniques, brain wave activity, and functional connectivity outcomes. Results: High-intensity physical exercise was found to induce significant changes in functional connectivity within affect and reward networks, enhance mood, and improve cognitive performance through increased brain wave coherence and synchronization. Low-intensity exercise primarily enhanced cognitive and attentional processing by increasing resting-state functional connectivity in the fronto-parietal network. Neurofeedback training was shown to enhance brain wave activity, reduce stress levels, and increase self-control over physiological factors. The combined approach of neurofeedback and physical exercise demonstrated potential for optimizing both mental and physical performance in athletes. Conclusion: The findings indicate that high-intensity exercise leads to significant and prolonged changes in brain connectivity and cognitive performance, while low-intensity exercise benefits cognitive and attentional processing. The discussion introduces the Neurofeedback and Physical Fitness Synergy Theory, which posits that integrating neurofeedback with physical exercise can lead to optimal improvements in both physical fitness and mental health. This combined approach suggests a promising strategy for enhancing overall athletic performance and mental well-being. Future research should focus on standardized measures and long-term studies to further validate these findings and explore the underlying neurophysiological mechanisms. Keywords: Brain, Emotions, Physical Fitness, Cognition, Athletes, Self-Control

Discovering prominent differences in structural and functional connectomes using a multinomial stochastic block model

ABSTRACT Understanding the differences between functional and structural human brain connectivity has been a focus of an extensive amount of neuroscience research. We employ a novel approach using the multinomial stochastic block model (MSBM) to explicitly extract components that characterize prominent differences across graphs. We analyze structural and functional connectomes derived from high-resolution diffusion-weighted MRI and fMRI scans of 250 Human Connectome Project subjects, analyzed at group connectivity level across 50 subjects. The inferred brain partitions revealed consistent, spatially homogeneous clustering patterns across inferred resolutions demonstrating the MSBM’s reliability in identifying brain areas with prominent structure-function differences. Prominent differences in low-resolution brain maps (K = {3, 4} clusters) were attributed to weak functional connectivity in the bilateral anterior temporal lobes, while higher resolution results (K ≥ 25) revealed stronger interhemispheric functional than structural connectivity. Our findings emphasize significant differences in high-resolution functional and structural connectomes, revealing challenges in extracting meaningful connectivity measurements from both modalities, including tracking fibers through the corpus callosum and attenuated functional connectivity in anterior temporal lobe fMRI data, which we attribute to increased noise levels. The MSBM emerges as a valuable tool for understanding differences across graphs, with potential future applications and avenues beyond the current focus on characterizing modality-specific distinctions in connectomics data.

Neuroimaging studies of resting-state functional magnetic resonance imaging in eating disorders

Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), binge-eating disorder (BED), and pica, are psychobehavioral conditions characterized by abnormal eating behaviors and an excessive preoccupation with weight and body shape. This review examines changes in brain regions and functional connectivity in ED patients over the past decade (2013–2023) using resting-state functional magnetic resonance imaging (rs-fMRI). Key findings highlight alterations in brain networks such as the default mode network (DMN), central executive network (CEN), and emotion regulation network (ERN). In individuals with AN, there is reduced functional connectivity in areas associated with facial information processing and social cognition, alongside increased connectivity in regions linked to sensory stimulation, aesthetic judgment, and social anxiety. Conversely, BED patients show diminished connectivity in the dorsal anterior cingulate cortex within the salience network and increased connectivity in the posterior cingulate cortex and medial prefrontal cortex within the DMN. These findings suggest that rs-fMRI could serve as a valuable biomarker for assessing brain function and predicting treatment outcomes in EDs, paving the way for personalized therapeutic strategies.