The global recognition of the importance of physical exercise (PE) for human health has resulted in increased research on its effects on cortical activity. Neural oscillations, which are prominent features of brain activity, serve as crucial indicators for studying the effects of PE on brain function. Existing studies support the idea that PE modifies various types of neural oscillations. While EEG-related literature in exercise science exists, a comprehensive review of the effects of exercise specifically in healthy populations has not yet been conducted. Given the demonstrated influence of exercise on neural plasticity, particularly cortical oscillatory activity, it is imperative to consolidate research on this phenomenon. Therefore, this review aims to summarize numerous PE studies on neuromodulatory mechanisms in the brain over the past decade, covering (1) effects of resistance and aerobic training on brain health via neural oscillations; (2) how mind-body exercise affects human neural activity and cognitive functioning; (3) age-Related effects of PE on brain health and neurodegenerative disease rehabilitation via neural oscillation mechanisms; and (4) conclusion and future direction. In conclusion, the effect of PE on cortical activity is a multifaceted process, and this review seeks to comprehensively examine and summarize existing studies\' understanding of how PE regulates neural activity in the brain, providing a more scientific theoretical foundation for the development of personalized PE programs and further research.

OBJECTIVE: Schizophrenia (SCZ) is a globally prevalent, severe chronic mental disorder, with cognitive dysfunction being one of its core symptoms. Notably, overweight is exceedingly common among individuals with SCZ, and overweight can also impact cognitive function. Therefore, the relationship between overweight and cognition in SCZ is a clinical issue that is in need of research attention.
METHODS: This study enrolled 77 patients with SCZ, including 36 overweight and 41 non-overweight patients. The Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity, while cognitive functions were evaluated using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Electroencephalography (EEG) testing was performed, with power spectral analysis conducted across various frequency bands (δ, θ, α, β, low γ, and high γ).
RESULTS: Compared to non-overweight SCZ patients, those overweight exhibited significantly lower RBANS total and index scores in immediate memory, visuospatial/constructional abilities, and delayed memory. EEG spectral analysis revealed that overweight SCZ patients demonstrated significantly lower oscillation power ratios in the β, low γ, and high γ frequency bands compared to their non-overweight counterparts. Correlation analyses indicated a significant positive relationship between β wave activity and RBANS total scores among overweight SCZ patients, suggesting that reduced β power correlates with more severe cognitive dysfunction.
CONCLUSIONS: Our findings indicate that overweight SCZ patients experience more severe cognitive impairments in a resting state than those who are not overweight, with significant differences in EEG spectrum observed in the β and γ frequency bands. Additionally, our study establishes a correlation between various EEG spectrum dimensions and cognition. This research highlights the effects of overweight on cognition in individuals with SCZ. Additionally, employing EEG technology to study cognitive function in overweight SCZ patients can offer valuable electrophysiological insights.

Electroencephalographic Neurofeedback Training (EEG NFT) aims to improve sport performance by teaching athletes to control their mental states, leading to better cognitive, emotional, and physical outcomes. The psychomotor efficiency hypothesis suggests that optimizing brain function could enhance athletic ability, indicating the potential of EEG NFT. However, evidence for EEG-NFT\'s ability to alter critical brain activity patterns, such as sensorimotor rhythm and frontal midline theta-key for concentration and relaxation-is not fully established. Current research lacks standardized methods and comprehensive studies. This shortfall is due to inconsistent EEG target selection and insufficient focus on coherence in training. This review aims to provide empirical support for EEG target selection, conduct detailed control analyses, and examine the specificity of electrodes and frequencies to relation to the psychomotor efficiency hypothesis. Following the PRISMA method, 2,869 empirical studies were identified from PubMed, Science Direct, Web of Science, Embase, CNKI, and PsycINFO. Thirteen studies met the inclusion criteria: (i) proficient skill levels; (ii) use of EEG; (iii) neurofeedback training (NFT); (iv) motor performance metrics (reaction time, precision, dexterity, balance); (v) control group for NFT comparison; (vi) peer-reviewed English-language publication; and (vii) randomized controlled trial (RCT) design. Studies indicate that NFT can enhance sports performance, including improvements in shooting accuracy, golf putting, and overall motor skills, as supported by the psychomotor efficiency hypothesis. EEG NFT demonstrates potential in enhancing sports performance by optimizing performers\' mental states and psychomotor efficiency. However, the current body of research is hampered by inconsistent methodologies and a lack of standardized EEG target selection. To strengthen the empirical evidence supporting EEG NFT, future studies need to focus on standardizing target selection, employing rigorous control analyses, and investigating underexplored EEG markers. These steps are vital to bolster the evidence for EEG NFT and enhance its effectiveness in boosting sport performance.

Recent studies using resting-state functional magnetic resonance imaging have shown that loneliness is associated with altered blood oxygenation in several brain regions. However, the relationship between loneliness and changes in neuronal rhythm activity in the brain remains unclear. To evaluate brain rhythm, we conducted an exploratory resting-state electroencephalogram (EEG) study of loneliness. We recorded resting-state EEG signals from 139 participants (94 women; mean age = 19.96 years) and analyzed power spectrum density (PSD) and functional connectivity (FC) in both the electrode and source spaces. The PSD analysis revealed significant correlations between loneliness scores and decreased beta-band powers, which may indicate negative emotion, attention, reward, and/or sensorimotor processing. The FC analysis revealed a trend of alpha-band FC associated with individuals\' loneliness scores. These findings provide new insights into the neural basis of loneliness, which will facilitate the development of neurobiologically informed interventions for loneliness.

BACKGROUND: \"Metacontrol\" describes the ability to maintain an optimal balance between cognitive control styles that are either more persistent or more flexible. Recent studies have shown a link between metacontrol and aperiodic EEG patterns. The present study aimed to gain more insight into the neurobiological underpinnings of metacontrol by using methylphenidate (MPH), a compound known to increase postsynaptic catecholamine levels and modulate cortical noise.
METHODS: In a double-blind, randomized, placebo-controlled study design, we investigated the effect of MPH (0.5 mg/kg) on aperiodic EEG activity during a flanker task in a sample of n = 25 neurotypical adults. To quantify cortical noise, we employed the fitting oscillations and one over f algorithm.
RESULTS: Compared with placebo, MPH increased the aperiodic exponent, suggesting that it reduces cortical noise in 2 ways. First, it did so in a state-like fashion, as the main effect of the drug was visible and significant in both pre-trial and within-trial periods. Second, the electrode-specific analyses showed that the drug also affects specific processes by dampening the downregulation of noise in conditions requiring more control.
CONCLUSIONS: Our findings suggest that the aperiodic exponent provides a neural marker of metacontrol states and changes therein. Further, we propose that the effectiveness of medications targeting catecholaminergic signaling can be evaluated by studying changes of cortical noise, fostering the idea of using the quantification of cortical noise as an indicator in pharmacological treatment.

BACKGROUND: The limited understanding of the physiology and psychology of polar expedition explorers has prompted concern over the potential cognitive impairments caused by exposure to extreme environmental conditions. Prior research has demonstrated that such stressors can negatively impact cognitive function, sleep quality, and behavioral outcomes. Nevertheless, the impact of the polar environment on neuronal activity remains largely unknown.
METHODS: In this study, we aimed to investigate spatiotemporal alterations in brain oscillations of 13 individuals (age range: 22-48 years) who participated in an Arctic expedition. We utilized electroencephalography (EEG) to record cortical activity before and during the Arctic journey, and employed standardized low resolution brain electromagnetic tomography to localize changes in alpha, beta, theta, and gamma activity.
RESULTS: Our results reveal a significant increase in the power of theta oscillations in specific regions of the Arctic, which differed significantly from pre-expedition measurements. Furthermore, microstate analysis demonstrated a significant reduction in the duration of microstates (MS) D and alterations in the local synchrony of the frontoparietal network.
CONCLUSIONS: Overall, these findings provide novel insights into the neural mechanisms underlying adaptation to extreme environments. These findings have implications for understanding the cognitive consequences of polar exploration and may inform strategies to mitigate potential neurological risks associated with such endeavors. Further research is warranted to elucidate the long-term effects of Arctic exposure on brain function.

BACKGROUND: The neuropathophysiological mechanisms of brain damage underlying hypothyroidism remain unclear. Fractional amplitude of low-frequency fluctuations (fALFF) has been established as a reliable indicator for investigation of abnormal spontaneous brain activity that occurs at specific frequencies in different types of mental disorder. However, the changes of fALFF in specific frequency bands in hypothyroidism have not yet been investigated.
METHODS: Fifty-three hypothyroid patients and 39 healthy controls (HCs) underwent thyroid-related hormone levels tests, neuropsychological assessment, and magnetic resonance imaging (MRI) scans. The fALFF in the standard band (0.01-0.1 Hz), slow-4 (0.027-0.073 Hz), and slow-5 bands (0.01-0.027 Hz) were analyzed. An analysis of Pearson correlation was conducted between fALFF, thyroid-related hormone levels, and neuropsychological scores in hypothyroid patients.
RESULTS: Compared to HCs, within the routine band, hypothyroidism group showed significantly decreased fALFF in left lingual gyrus, middle temporal gyrus (MTG), precentral gyrus, calcarine cortex, and right inferior occipital gyrus; within the slow-5 band, the hypothyroidism group exhibited decreased fALFF in left lingual gyrus, MTG, superior temporal gyrus, postcentral gyrus, and paracentral lobule, and increased fALFF in supplementary motor area (SMA) and right middle frontal gyrus; additionally, fALFF in the left lingual gyrus within the routine and slow-5 bands were negatively correlated with the level of thyroid stimulating hormone.
CONCLUSIONS: In this study, the slow-5 frequency band exhibits better sensitivity than the standard band in detecting fALFF values. A decrease of fALFF values in the lingual gyrus and MTG was observed in both the standard and slow-5 bands and might present potential neuroimaging biomarkers for hypothyroidism.
BACKGROUND: No: ChiCTR2000028966. Registered 9 January, 2020, https://www.chictr.org.cn.

OBJECTIVE: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy.
METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results.
RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ.
CONCLUSIONS: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.

BACKGROUND: Repetitive mild traumatic brain injury (rmTBI) often occurs in individuals engaged in contact sports, particularly boxing. This study aimed to elucidate the effects of rmTBI on phase-locking value (PLV)-based graph theory and functional network architecture in individuals with boxing-related injuries in five frequency bands by employing resting-state electroencephalography (EEG).
METHODS: Twenty-fore professional boxers and 25 matched healthy controls were recruited to perform a resting-state task, and their noninvasive scalp EEG data were collected simultaneously. Based on the construction of PLV matrices for boxers and controls, phase synchronization and graph-theoretic characteristics were identified in each frequency band. The significance of the calculated functional brain networks between the two populations was analyzed using a network-based statistical (NBS) approach.
RESULTS: Compared to controls, boxers exhibited an increasing trend in PLV synchronization and notable differences in the distribution of functional centers, especially in the gamma frequency band. Additionally, attenuated nodal network parameters and decreased small-world measures were observed in the theta, beta, and gamma bands, suggesting that the functional network efficiency and small-world characteristics were significantly weakened in boxers. NBS analysis revealed that boxers exhibited a significant increase in network connectivity strength compared to controls in the theta, beta, and gamma frequency bands. The functional connectivity of the significance subnetworks exhibited an asymmetric distribution between the bilateral hemispheres, indicating that the optimized organization of information integration and segregation for the resting-state networks was imbalanced and disarranged for boxers.
CONCLUSIONS: This is the first study to investigate the underlying deficits in PLV-based graph-theoretic characteristics and NBS-based functional networks in patients with rmTBI from the perspective of whole-brain resting-state EEG. Joint analyses of distinctive graph-theoretic representations and asymmetrically hyperconnected subnetworks in specific frequency bands may serve as an effective method to assess the underlying deficiencies in resting-state network processing in patients with sports-related rmTBI.

BACKGROUND: To explore the time-frequency structure and cross-scale coupling of electroencephalography (EEG) signals during seizure in juvenile myoclonic epilepsy (JME), correlations between different leads, as well as dynamic evolution in epileptic discharge, progression and end of seizure were examined.
METHODS: EEG data were obtained for 10 subjects with JME and 10 normal controls and were decomposed using gauss continuous wavelet transform (CWT). The phase amplitude coupling (PAC) relationship between the 11th (4.57 Hz) and 17th (0.4 Hz) scale was investigated. Correlations were examined between the 11th and 17th scale EEG signals in different leads during seizure, using multi-scale cross correlation analysis.
RESULTS: The time-frequency structure of JME subjects showed strong rhythmic activity in the 11th and 17th scales and a close PAC was identified. Correlation analysis revealed that the ictal JME correlation first increased in the anterior head early in seizure and gradually expanded to the posterior head.
CONCLUSIONS: PAC was exhibited between the 11th and 17th scales during JME seizure. The results revealed that the correlation in the anterior leads was higher than the posterior leads. In the perictal period, the 17th scale EEG signal preceded the 11th scale signal and remained for some time after a seizure. This suggests that the 17th scale signal may play an important role in JME seizure.