Brain Networks Research Articles

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation

BackgroundVirtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue. ObjectiveIn this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS. MethodsThe model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model. Results: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models. SignificanceIt shows that this model can be used for the recognition of VRMS based on the resting state EEG.

Low-density EEG-based Functional Connectivity Discriminates Minimally Conscious State plus from minus

Objective: Within the continuum of consciousness, patients in a Minimally Conscious State (MCS) may exhibit high-level behavioral responses (MCS+) or may not (MCS−). The evaluation of residual consciousness and related classification is crucial to propose tailored rehabilitation and pharmacological treatments, considering the inherent differences among groups in diagnosis and prognosis. Currently, differential diagnosis relies on behavioral assessments posing a relevant risk of misdiagnosis. In this context, EEG offers a non-invasive approach to model the brain as a complex network. The search for discriminating features could reveal whether behavioral responses in post-comatose patients have a defined physiological background. Additionally, it is essential to determine whether the standard behavioral assessment for quantifying responsiveness holds physiological significance. Methods: In this prospective observational study, we investigated whether low-density EEG-based graph metrics could discriminate MCS+/− patients by enrolling 57 MCS patients (MCS−: 30; males: 28). At admission to intensive rehabilitation, 30 min resting-state closed-eyes EEG recordings were performed together with consciousness diagnosis following international guidelines. After EEG preprocessing, graphs’ metrics were estimated using different connectivity measures, at multiple connection densities and frequency bands (α,θ,δ). Metrics were also provided to cross-validated Machine Learning (ML) models with outcome MCS+/−. Results: A lower level of brain activity integration was found in the MCS− group in the α band. Instead, in the δ band MCS− group presented an higher level of clustering (weighted clustering coefficient) respect to MCS+. The best-performing solution in discriminating MCS+/− through the use of ML was an Elastic-Net regularized logistic regression with a cross-validation accuracy of 79% (sensitivity and specificity of 74% and 85% respectively). Conclusion: Despite tackling the MCS+/− differential diagnosis is highly challenging, a daily-routine low-density EEG might allow to differentiate across these differently responsive brain networks. Significance: Graph-theoretical features are shown to discriminate between these two neurophysiologically similar conditions, and may thus support the clinical diagnosis.

The dynamics of functional brain network segregation in feedback-driven learning

Prior evidence suggests that increasingly efficient task performance in human learning is associated with large scale brain network dynamics. However, the specific nature of this general relationship has remained unclear. Here, we characterize performance improvement during feedback-driven stimulus-response (S-R) learning by learning rate as well as S-R habit strength and test whether and how these two behavioral measures are associated with a functional brain state transition from a more integrated to a more segregated brain state across learning. Capitalizing on two separate fMRI studies using similar but not identical experimental designs, we demonstrate for both studies that a higher learning rate is associated with a more rapid brain network segregation. By contrast, S-R habit strength is not reliably related to changes in brain network segregation. Overall, our current study results highlight the utility of dynamic functional brain state analysis. From a broader perspective taking into account previous study results, our findings align with a framework that conceptualizes brain network segregation as a general feature of processing efficiency not only in feedback-driven learning as in the present study but also in other types of learning and in other task domains.

Illusions of Self-Motion during Magnetic Resonance-Guided Focused Ultrasound Thalamotomy for Tremor.

Brain networks mediating vestibular perception of self-motion overlap with those mediating balance. A systematic mapping of vestibular perceptual pathways in the thalamus may reveal new brain modulation targets for improving balance in neurological conditions. Here, we systematically report how magnetic resonance-guided focused ultrasound surgery of the nucleus ventralis intermedius of the thalamus commonly evokes transient patient-reported illusions of self-motion. In 46 consecutive patients, we linked the descriptions of self-motion to sonication power and 3-dimensional (3D) coordinates of sonication targets. Target coordinates were normalized using a standard atlas, and a 3D model of the nucleus ventralis intermedius and adjacent structures was created to link sonication target to the illusion. A total of 63% of patients reported illusions of self-motion, which were more likely with increased sonication power and with targets located more inferiorly along the rostrocaudal axis. Higher power and more inferiorly targeted sonications increased the likelihood of experiencing illusions of self-motion by 4 and 2 times, respectively (odds ratios = 4.03 for power, 2.098 for location). The phenomenon of magnetic vestibular stimulation is the most plausible explanation for these illusions of self-motion. Temporary unilateral modulation of vestibular pathways (via magnetic resonance-guided focused ultrasound) unveils the central adaptation to the magnetic field-induced peripheral vestibular bias, leading to an explicable illusion of motion. Consequently, systematic mapping of vestibular perceptual pathways via magnetic resonance-guided focused ultrasound may reveal new intracerebral targets for improving balance in neurological conditions. ANN NEUROL 2024.

Functional brain network analysis using electroencephalography in late-onset Lennox–Gastaut syndrome

ObjectiveWe aimed to explore the clinical characteristics and functional network properties of patients with late-onset Lennox–Gastaut syndrome (LGS). MethodsLate-onset LGS was defined by the appearance of LGS features after 8 years of age. We reviewed the medical charts of 9 patients with late-onset LGS, and performed electroencephalography connectivity analysis using graph theory. We assessed the clustering coefficient (CC) and characteristic path length (CPL), which are common basic measures of functional networks that represent local segregation and global integration. The characteristics and brain parameters of late-onset LGS were compared with a typical age-onset LGS group. ResultsLate onset LGS subjects were older than typical age onset LGS at the time of testing, but otherwise there were no significant differences in clinical characteristics. The late-onset group showed higher median CC values in the alpha (p = 0.045) and beta (p < 0.001) bands over brain regions implicated in cognitive processing. There were no significant differences in CPL between the LGS groups. ConclusionsHigher clustering coefficient values, in alpha/beta bands over brain regions implicated in cognitive processing, are consistent with increased cognitive network segregation in late onset LGS compared to typical age-onset LGS. Given network segregation is a normal aspect of brain maturation, these results imply that this process is less disturbed when the LGS process begins later in childhood.

Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

Predicting Alzheimer’s progression in MCI: a DTI-based white matter network model

ObjectiveThis study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer’s disease (AD) in MCI patients.MethodsThis study enrolled 121 MCI patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation.ResultsAPOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers.ConclusionsA comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Hypoactivation of the language network during auditory imagery contributes to hallucinations in Schizophrenia

Auditory verbal hallucinations (AVHs) involve perceptions, often voices, in the absence of external stimuli, and rank among the most common symptoms of schizophrenia. Metrical stress evaluation requires determination of the stronger syllable in words, and therefore requires auditory imagery, of interest for investigation of hallucinations in schizophrenia. The current functional magnetic resonance imaging study provides an updated whole-brain network analysis of a previously published study on metrical stress, which showed reduced directed connections between Broca's and Wernicke's regions of interest (ROIs) for hallucinations. Three functional brain networks were extracted, with the language network (LN) showing an earlier and shallower blood-oxygen-level dependent (BOLD) response for hallucinating patients, in the auditory imagery condition only (the reduced activation for hallucinations observed in the original ROI-based results were not specific to the imagery condition). This suggests that hypoactivation of the LN during internal auditory imagery may contribute to the propensity to hallucinate. This accords with cognitive accounts holding that an impaired balance between internal and external linguistic processes (underactivity in networks involved in internal auditory imagery and overactivity in networks involved in speech perception) contributes to our understanding of the biological underpinnings of hallucinations.

Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task.

Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.

Neuroplasticity of visual brain network induced by hypoxia.

The effects of hypoxia on brain function remain largely unknown. This study aimed to clarify this issue by visual-stimulated functional magnetic resonance imaging design. Twenty-three college students with a 30-d high-altitude exposure were tested before, 1week and 3months after returning to sea level. Brain functional magnetic resonance imaging and retinal electroretinogram were acquired. One week after returning to sea level, decreased blood oxygenation level dependent in the right lingual gyrus accompanied with increased blood oxygenation level dependent in the frontal cortex and insular cortex, and decreased amplitude of electroretinogram a-wave in right eye; moreover, the bilateral lingual gyri showed increased functional connectivity within the dorsal visual stream pathway, and the blood oxygenation level dependent signals in the right lingual gyrus showed positive correlation with right retinal electroretinogram a-wave. Three months after returning to sea level, the blood oxygenation level dependent signals recovered to normal level, while intensively increased blood oxygenation level dependent signals in a broad of brain regions and decreased retinal electroretinogram were also existed. In conclusion, hypoxic exposure has long-term effects on visual cortex, and the impaired retinal electroretinogram may contribute to it. The increased functional connectivity of dorsal stream may compensate for the decreased function of retinal photoreceptor cells to maintain normal visual function.

Aberrant auditory metabolite levels and topological properties are associated with cognitive decline in presbycusis patients.

Presbycusis has been reported as related to cognitive decline, but its underlying neurophysiological mechanism is still unclear. This study aimed to investigate the relationship between metabolite levels, cognitive function, and node characteristics in presbycusis based on graph theory methods. Eighty-four elderly individuals with presbycusis and 63 age-matched normal hearing controls underwent magnetic resonance spectroscopy, functional magnetic resonance imaging scans, audiological assessment, and cognitive assessment. Compared with the normal hearing group, presbycusis patients exhibited reduced gamma-aminobutyric acid and glutamate levels in the auditory region, increased nodal characteristics in the temporal lobe and precuneus, as well as decreased nodal characteristics in the superior occipital gyrus and medial orbital. The right gamma-aminobutyric acid levels were negatively correlated with the degree centrality in the right precuneus and the executive function. Degree centrality in the right precuneus exhibited significant correlations with information processing speed and executive function, while degree centrality in the left medial orbital demonstrated a negative association with speech recognition ability. The degree centrality and node efficiency in the superior occipital gyrus exhibited a negative association with hearing loss and speech recognition ability, respectively. These observed changes indicate alterations in metabolite levels and reorganization patterns at the brain network level after auditory deprivation.

Mechanisms and benefits of cardiac rehabilitation in individuals with stroke: emerging role of its impact on improving cardiovascular and neurovascular health.

Human and animal studies have demonstrated the mechanisms and benefits of aerobic exercise for both cardiovascular and neurovascular health. Aerobic exercise induces neuroplasticity and neurophysiologic reorganization of brain networks, improves cerebral blood flow, and increases whole-body VO2peak (peak oxygen consumption). The effectiveness of a structured cardiac rehabilitation (CR) program is well established and a vital part of the continuum of care for people with cardiovascular disease. Individuals post stroke exhibit decreased cardiovascular capacity which impacts their neurologic recovery and extends disability. Stroke survivors share the same risk factors as patients with cardiac disease and can therefore benefit significantly from a comprehensive CR program in addition to neurorehabilitation to address their cardiovascular health. The inclusion of individuals with stroke into a CR program, with appropriate adaptations, can significantly improve their cardiovascular health, promote functional recovery, and reduce future cardiovascular and cerebrovascular events thereby reducing the economic burden of stroke.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients.

Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.

Tumors Affect the Metabolic Connectivity of the Human Brain Measured by 18F-FDG PET.

18F-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity. Forty-three patients with left hemispheric tumors and 18F-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test (P < 0.05). Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC (P < 0.001), except assortativity and betweenness centrality (P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right (P < 0.01), with the exception of betweenness centrality (P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics (P < 0.001) than temporal tumors, but lower betweenness centrality (P = 4.5-7). Patients with high KPS showed higher distance local efficiency (P = 0.01), rich club coefficient (P = 0.0048), clustering coefficient (P = 0.00032), betweenness centrality (P = 0.008), and similarity (P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses. Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

Effective modulation from the ventral medial to the dorsal medial portion of the prefrontal cortex in memory confidence-based behavioral control

Metacognition includes the ability to refer to one’s own cognitive states, such as confidence, and adaptively control behavior based on this information. This ability is thought to allow us to predictably control our behavior without external feedback, for example, even before we take action. Many studies have suggested that metacognition requires a brain-wide network of multiple brain regions. However, the modulation of effective connectivity within this network during metacognitive tasks remains unclear. This study focused on medial prefrontal regions, which have recently been suggested to be particularly involved in metacognition. We examined whether modulation of effective connectivity specific to metacognitive behavioral control is observed using model-based network analysis and dynamic causal modeling (DCM). The results showed that negative modulation from the ventral medial prefrontal cortex to the dorsal medial prefrontal cortex was observed in situations that required metacognitive behavioral control but not in situations that did not require such metacognitive control. Furthermore, this modulation was particularly pronounced in the group of participants who could better use metacognition for behavioral control. These results imply hierarchical properties of metacognition-related brain networks.

Short communication: Lifetime musical activity and resting-state functional connectivity in cognitive networks.

Participation in multimodal leisure activities, such as playing a musical instrument, may be protective against brain aging and dementia in older adults (OA). Potential neuroprotective correlates underlying musical activity remain unclear. This cross-sectional study investigated the association between lifetime musical activity and resting-state functional connectivity (RSFC) in three higher-order brain networks: the Default Mode, Fronto-Parietal, and Salience networks. We assessed 130 cognitively unimpaired participants (≥ 60 years) from the baseline cohort of the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) study. Lifetime musical activity was operationalized by the self-reported participation in musical instrument playing across early, middle, and late life stages using the Lifetime of Experiences Questionnaire (LEQ). Participants who reported musical activity during all life stages (n = 65) were compared to controls who were matched on demographic and reserve characteristics (including education, intelligence, socioeconomic status, self-reported physical activity, age, and sex) and never played a musical instrument (n = 65) in local (seed-to-voxel) and global (within-network and between-network) RSFC patterns using pre-specified network seeds. Older participants with lifetime musical activity showed significantly higher local RSFC between the medial prefrontal cortex (Default Mode Network seed) and temporal as well as frontal regions, namely the right temporal pole and the right precentral gyrus extending into the superior frontal gyrus, compared to matched controls. There were no significant group differences in global RSFC within or between the three networks. We show that playing a musical instrument during life relates to higher RSFC of the medial prefrontal cortex with distant brain regions involved in higher-order cognitive and motor processes. Preserved or enhanced functional connectivity could potentially contribute to better brain health and resilience in OA with a history in musical activity. German Clinical Trials Register (DRKS00007966, 04/05/2015).

Effect of re-warm-up practices on brain networks and physical performance in soccer players

Objectives: The warm-up (WU) and re-warm-up (R-WU) regimes in many team sports, especially football and competition, plays an essential role in improving physical function and increasing communication between brain networks. The present study aimed to evaluate the effects of the R-WU on brain networks and physical performance in young soccer players.Methods: After familiarizing the laboratory conditions, twelve young male soccer players (age 18.2± 0.5 years) performed only one WU protocol, which included 25 minutes and two parts of general and specialized warming up in one session. In addition to warming up in the next session, the participant performed an R-WU that included four sets of plyometric movements, five jumps from obstacles to a height of 40 cm, followed by crossing the agile ladder. Functional magnetic resonance imaging (fMRI), 20m speed, and Illinois test were used to evaluate the function of brain networks and physical function.Results: The subjects showed a significant increase in the performance of both speed and agility in the R-WU session compared to the warm-up session (P≤0.01). Also, a positive relationship was observed between the performance of default, executive, and attention networks in the R-WU session compared to the WU session (P≤0.01, P≤0.03). Conclusions. The present study demonstrated that the R-WU program improved the relationship between brain networks and physical function in soccer players.