Comparison between EEG and MEG of static and dynamic resting-state networks.

The characterisation of resting-state networks (RSNs) using neuroimaging techniques has significantly contributed to our understanding of the organisation of brain activity. Prior work has demonstrated the electrophysiological basis of RSNs and their dynamic nature, revealing transient activations of brain networks with millisecond timescales. While previous research has confirmed the comparability of RSNs identified by electroencephalography (EEG) to those identified by magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), most studies have utilised static analysis techniques, ignoring the dynamic nature of brain activity. Often, these studies use high-density EEG systems, which limit their applicability in clinical settings. Addressing these gaps, our research studies RSNs using medium-density EEG systems (61 sensors), comparing both static and dynamic brain network features to those obtained from a high-density MEG system (306 sensors). We assess the qualitative and quantitative comparability of EEG-derived RSNs to those from MEG, including their ability to capture age-related effects, and explore the reproducibility of dynamic RSNs within and across the modalities. Our findings suggest that both MEG and EEG offer comparable static and dynamic network descriptions, albeit with MEG offering some increased sensitivity and reproducibility. Such RSNs and their comparability across the two modalities remained consistent qualitatively but not quantitatively when the data were reconstructed without subject-specific structural MRI images.

Body mass index associated with respiration predicts motion in resting-state functional magnetic resonance imaging scans.

Decreasing body mass index (BMI) reduces head motion in resting-state fMRI (rs-fMRI) data. Yet, the mechanism by which BMI affects head motion remains poorly understood. Understanding how BMI interacts with respiration to affect head motion can improve head motion reduction strategies. A total of 254 patients with back pain were included in this study, each of whom had two visits (interval time = 13.85 ± 7.81 weeks) during which two consecutive re-fMRI scans were obtained. We investigated the relationships between head motion and demographic and pain-related characteristics-head motion was reliable across scans and correlated with age, pain intensity, and BMI. Multiple linear regression models determined that BMI was the main determinant in predicting head motion. BMI was also associated with two features derived from respiration signal. Anterior-posterior and superior-inferior motion dominated both overall motion magnitude and the coupling between motion and respiration. BMI interacted with respiration to influence motion only in the pitch dimension. These findings indicate that BMI should be a critical parameter in both study designs and analyses of fMRI data.

Associations of Rest-Activity Rhythm Disturbances With Stroke Risk and Poststroke Adverse Outcomes.

BACKGROUND: Many disease processes are influenced by circadian clocks and display ~24-hour rhythms. Whether disruptions to these rhythms increase stroke risk is unclear. We evaluated the association between 24-hour rest-activity rhythms, stroke risk, and major poststroke adverse outcomes. METHODS AND RESULTS: We examined ~100 000 participants from the UK Biobank (aged 44-79 years; ~57% women) assessed with actigraphy (6-7 days) and 5-year median follow-up. We derived (1) most active 10-hour activity counts across the 24-hour cycle and the timing of its midpoint timing; (2) the least active 5-hour count and its midpoint; (3) relative amplitude; (4) interdaily stability; and (5) intradaily variability, for stability and fragmentation of the rhythm. Cox proportional hazard models were constructed for time to (1) incident stroke (n=1652) and (2) poststroke adverse outcomes (dementia, depression, disability, or death). Suppressed relative amplitude (lowest quartile [quartile 1] versus the top quartile [quartile 4]) was associated with stroke risk (hazard ratio [HR], 1.61 [95% CI, 1.35-1.92]; P<0.001) after adjusting for demographics. Later most active 10-hour activity count midpoint timing (14:00-15:26; HR, 1.26 [95% CI, 1.07-1.49]; P=0.007) also had higher stroke risk than earlier (12:17-13:10) participants. A fragmented rhythm (intradaily variability) was also associated with higher stroke risk (quartile 4 versus quartile 1; HR, 1.26 [95% CI, 1.06-1.49]; P=0.008). Suppressed relative amplitude was associated with risk for poststroke adverse outcomes (quartile 1 versus quartile 4; HR, 2.02 [95% CI, 1.46-2.48]; P<0.001). All associations were independent of age, sex, race, obesity, sleep disorders, cardiovascular diseases or risks, and other comorbidity burdens. CONCLUSIONS: Suppressed 24-hour rest-activity rhythm may be a risk factor for stroke and an early indicator of major poststroke adverse outcomes.

Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up.

This dataset consists of 64-channels resting-state EEG recordings of 608 participants aged between 20 and 70 years, 61.8% female, as well as follow-up measurements after approximately 5 years of 208 participants, starting 2021. The EEG was measured for three minutes with eyes open and eyes closed before and after a 2-hour block of cognitive experimental tasks. The data set is part of the Dortmund Vital Study, a prospective study on the determinants of healthy cognitive aging. The dataset can be used for (1) analyzing cross-sectional resting-state EEG of healthy individuals across the adult life span; (2) generating normalization data sets for comparison of resting-state EEG data of patients with clinically relevant disorders; (3) studying effects of performing cognitive tasks on resting-state EEG and age; (4) exploring intra-individual changes in resting-state EEG and effects of task performance over a time period of about 5 years. The data are provided in Brain Imaging Data Structure (BIDS) format and are available on OpenNeuro.

Structure-function coupling in highly sampled individual brains.

Structural connectivity (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to SC may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 min of diffusion-weighted MRI for SC and 360 min of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas.

A novel method for sparse dynamic functional connectivity analysis from resting-state fMRI.

BACKGROUND: There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods. NEW METHODS: We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation. RESULTS: The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data. COMPARISON WITH EXISTING METHODS: Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC. CONCLUSION: We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.

Altered functional connectivity of resting-state networks and the correlation with clinical characteristics in intermittent exotropia adult patients: a resting-state magnetic resonance imaging study.

BACKGROUND: The pathogenesis of intermittent exotropia (IXT) remains unclear. The study aims to investigate alterations of resting-state networks (RSNs) in IXT adult patients using resting-state functional magnetic resonance imaging (rs-fMRI) data to explore the potential neural mechanisms. METHODS: Twenty-six IXT adult patients and 22 age-, sex-, handedness-, and education-matched healthy controls (HCs) underwent fMRI scanning and ophthalmological examinations. Brain areas with significant functional connectivity (FC) differences between the IXT and HC groups were selected as regions of interest (ROI) and mean z-scores were calculated to control for individual differences. RESULTS: Compared with HCs, IXT patients exhibited altered FC in various brain regions within RSNs involved in binocular fusion, stereopsis, ocular movement, emotional processes and social cognition, including the default mode network (DMN), the dorsal attention network (DAN), the visual network (VN), the sensorimotor network (SMN), the executive control network (ECN), the frontoparietal network (FPN) and the auditory network (AN). The degree of exodeviation was positively correlated with FC value of left middle occipital gyrus (MOG) within the VN. Correspondingly, we found a negative correlation between the degree of exodeviation and the FC value of left angular gyrus (AG) within FPN (P < 0.05). The FNC analysis between different RSNs also provides evidence on visual-motor cortical plasticity. CONCLUSIONS: IXT patients showed widespread changes of brain activity within RSNs related to binocular fusion, stereopsis, oculomotor control, emotional processes, and social cognition. These findings extend our current understanding of the neuropathological mechanisms of IXT. TRIAL REGISTRATION: Beginning date of the trial: 2021-09-01. Date of registration:2021-07-18. Trial registration number: ChiCTR 2,100,048,852. Trial registration site: http://www.chictr.org.cn/index.aspx .

Fibre-specific mitochondrial protein abundance is linked to resting and post-training mitochondrial content in the muscle of men.

Analyses of mitochondrial adaptations in human skeletal muscle have mostly used whole-muscle samples, where results may be confounded by the presence of a mixture of type I and II muscle fibres. Using our adapted mass spectrometry-based proteomics workflow, we provide insights into fibre-specific mitochondrial differences in the human skeletal muscle of men before and after training. Our findings challenge previous conclusions regarding the extent of fibre-type-specific remodelling of the mitochondrial proteome and suggest that most baseline differences in mitochondrial protein abundances between fibre types reported by us, and others, might be due to differences in total mitochondrial content or a consequence of adaptations to habitual physical activity (or inactivity). Most training-induced changes in different mitochondrial functional groups, in both fibre types, were no longer significant in our study when normalised to changes in markers of mitochondrial content.

PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

The impact of insomnia on brain networks topology in depressed patients: A resting-state fMRI study.

OBJECTIVE: Depression and insomnia frequently co-occur, but the neural mechanisms between patients with varying degrees of these conditions are not fully understood. The specific topological features and connectivity patterns of this co-morbidity have not been extensively studied. This study aimed to investigate the topological characteristics of topological characteristics and functional connectivity of brain networks in depressed patients with insomnia. METHODS: Resting-state functional magnetic resonance imaging data from 32 depressed patients with a high level of insomnia (D-HI), 35 depressed patients with a low level of insomnia (D-LI), and 81 healthy controls (HC) were used to investigate alterations in brain topological organization functional networks. Nodal and global properties were analyzed using graph-theoretic techniques, and network-based statistical analysis was employed to identify changes in brain network functional connectivity. RESULTS: Compared to the HC group, both the D-HI and D-LI groups showed an increase in the global efficiency (Eglob) values, local efficiency (Eloc) was decreased in the D-HI group, and Lambda and shortest path length (Lp) values were decreased in the D-LI group. At the nodal level, the right parietal nodal clustering coefficient (NCp) values were reduced in D-HI and D-LI groups compared to those in HC. The functional connectivity of brain networks in patients with D-HI mainly involves default mode network (DMN)-cingulo-opercular network (CON), DMN-visual network (VN), DMN-sensorimotor network (SMN), and DMN-cerebellar network (CN), while that in patients with D-LI mainly involves SMN-CON, SMN-SMN, SMN-VN, and SMN-CN. The values of the connection between the midinsula and postoccipital gyrus was negatively correlated with scores for early awakening in D-HI. CONCLUSION: These findings may contribute to our understanding of the underlying neuropsychological mechanisms in depressed patients with insomnia.

Decoding acceptance and reappraisal strategies from resting state macro networks.

Acceptance and reappraisal are considered adaptive emotion regulation strategies. While previous studies have explored the neural underpinnings of these strategies using task-based fMRI and sMRI, a gap exists in the literature concerning resting-state functional brain networks' contributions to these abilities, especially regarding acceptance. Another intriguing question is whether these strategies rely on similar or different neural mechanisms. Building on the well-known improved emotion regulation and increased cognitive flexibility of individuals who rely on acceptance, we expected to find decreased activity inside the affective network and increased activity inside the executive and sensorimotor networks to be predictive of acceptance. We also expect that these networks may be associated at least in part with reappraisal, indicating a common mechanism behind different strategies. To test these hypotheses, we conducted a functional connectivity analysis of resting-state data from 134 individuals (95 females; mean age: 30.09 ± 12.87 years, mean education: 12.62 ± 1.41 years). To assess acceptance and reappraisal abilities, we used the Cognitive Emotion Regulation Questionnaire (CERQ) and a group-ICA unsupervised machine learning approach to identify resting-state networks. Subsequently, we conducted backward regression to predict acceptance and reappraisal abilities. As expected, results indicated that acceptance was predicted by decreased affective, and executive, and increased sensorimotor networks, while reappraisal was predicted by an increase in the sensorimotor network only. Notably, these findings suggest both distinct and overlapping brain contributions to acceptance and reappraisal strategies, with the sensorimotor network potentially serving as a core common mechanism. These results not only align with previous findings but also expand upon them, illustrating the complex interplay of cognitive, affective, and sensory abilities in emotion regulation.

Reproducibility of autonomic cardiovascular function and hemodynamics at rest and during recovery from exercise.

Smartwatches and home-based blood pressure (BP) devices have permitted easy use of heart rate variability (HRV) and BP to identify the recovery status of users after acute exercise training. The reproducibility of HRV and BP after exercise in healthy young participants is not well known. Eighteen participants (age 27 ± 6 years, female n = 8) performed test and retest aerobic exercises (cycling, 30 min, 60% of peak workload, W) and a control session in randomized order. RMSSD, high and low-frequency power of RR intervals, and BP were measured at rest and 30-60 min after interventions. The relative reproducibility was assessed by the intraclass correlation coefficient (ICC) and 95% confidence interval (95% CI). The absolute reproducibility was evaluated using the coefficient of variation (CV%). HRV indices revealed moderate-to-excellent reproducibility at rest (ICC 0.81-0.86; 95% CI 0.53-0.95) but not after exercise (ICC -0.06 to 0.60; 95% CI -1.85 to 0.85). Systolic BP had a good-to-excellent reproducibility before (ICC 0.93; 95% CI 0.81-0.98, CV% 4.2) and after exercise (ICC 0.93; 95% CI 0.81-0.97, CV% 4.2). The reproducibility of HRV indices is poor after exercise in young participants. However, the reproducibility of BP is excellent at rest and after aerobic exercise.

On the Search for Data-Driven and Reproducible Schizophrenia Subtypes Using Resting State fMRI Data From Multiple Sites.

For decades, fMRI data have been used to search for biomarkers for patients with schizophrenia. Still, firm conclusions are yet to be made, which is often attributed to the high internal heterogeneity of the disorder. A promising way to disentangle the heterogeneity is to search for subgroups of patients with more homogeneous biological profiles. We applied an unsupervised multiple co-clustering (MCC) method to identify subtypes using functional connectivity data from a multisite resting-state data set. We merged data from two publicly available databases and split the data into a discovery data set (143 patients and 143 healthy controls (HC)) and an external test data set (63 patients and 63 HC) from independent sites. On the discovery data, we investigated the stability of the clustering toward data splits and initializations. Subsequently we searched for cluster solutions, also called "views," with a significant diagnosis association and evaluated these based on their subject and feature cluster separability, and correlation to clinical manifestations as measured with the positive and negative syndrome scale (PANSS). Finally, we validated our findings by testing the diagnosis association on the external test data. A major finding of our study was that the stability of the clustering was highly dependent on variations in the data set, and even across initializations, we found only a moderate subject clustering stability. Nevertheless, we still discovered one view with a significant diagnosis association. This view reproducibly showed an overrepresentation of schizophrenia patients in three subject clusters, and one feature cluster showed a continuous trend, ranging from positive to negative connectivity values, when sorted according to the proportions of patients with schizophrenia. When investigating all patients, none of the feature clusters in the view were associated with severity of positive, negative, and generalized symptoms, indicating that the cluster solutions reflect other disease related mechanisms.

Resting energy expenditure in patients with liver cirrhosis: Indirect calorimetry vs. predictive equations.

BACKGROUND AND OBJECTIVES: The objective of our study was to explore the accuracy of previously published prediction equations in predicting resting energy expenditure (REE) in patients with liver cirrhosis (LC). We also aimed to develop a novel equation to estimate REE for Chinese patients with LC. METHODS AND STUDY DESIGN: In 90 patients with LC, the agreement between REE measured by Indirect calorimetry (IC) and predictive equations was quantified using paired T-test and visualized using a Bland-Altman Plot. Pearson correlation coefficient (R) was used to measure a linear correlation between REE measured by IC and different predictive equations. Stepwise multiple regression analysis was used to create a new REE equation. RESULTS: The estimated REEs of previous equations were underestimated against REE measured by IC (1610 ± 334 kcal). Lean body mass (LBM) was positively correlated with REE measured by IC (r = 0.723, p < 0.01). The newly derived estimation equation for REE (kcal) was 1274.3 - 209.0 * sex - 5.73 * age + 3.69 * waist circumference + 22.89 * LBM. The newly derived estimation equation was found to have a Pearson-r value of 0.765 compared with REE measured by IC. CONCLUSIONS: REE in liver cirrhosis was underestimated by using predictive equations. The new predictive equation developed by using age, sex, waist circumference, and LBM may help estimate REE in Chinese patients with LC accurately and easily.

Time to reach equilibrium deep body temperatures in young and older adults resting in the heat: a descriptive secondary analysis.

It is commonly thought that steady-state thermoregulatory responses are achieved within 30-90 min of compensable heat stress. However, this assumption is based on measurements of whole body heat exchange during exercise, which stabilize (equilibrate) more rapidly than deep body temperatures, especially under resting conditions. To support the design of ecologically relevant heat exposure studies, we quantified equilibrium times for deep body temperature, as indexed by rectal temperature, in young and older adults resting in the heat. We also evaluated the lag in rectal temperature equilibrium relative to whole body heat storage (direct calorimetry). Equilibrium times were estimated with data from two laboratory-based trials (NCT04353076 and NCT04348630) in which 83 adults aged 19-80 yr (34 female) were exposed to simulated heat-wave conditions for 8-9 h. When assessed at the group level, it took rectal temperature 3.3 [bootstrap 95% confidence interval: 2.9-3.9] h to reach thermal equilibrium (<0.05°C/h rate of change) in young adults exposed to 40°C, 9% relative humidity (RH). In older adults, who were exposed to a greater range of conditions (31°C-40°C, 9-45% RH), equilibrium times were longer, ranging from 4.4 [3.8-5.3] to 5.2 [4.9-5.4] h. Furthermore, rectal temperature equilibrium was delayed 0.9 [0.5-1.4] and 1.8 [0.9-2.7] h compared with whole body heat storage in young and older adults, respectively (only assessed in 40°C, 9% RH). Individual-level equilibrium times ranged from 1 to 8 h. These findings highlight the importance of ecologically relevant exposure durations in translational research assessing the physiological impacts of hot weather.NEW & NOTEWORTHY Deep body (rectal) temperature took 3-5 h on average and up to 6-8 h at the individual level to reach thermal equilibrium in young and older adults resting in the heat. Furthermore, stable rectal temperatures were delayed by up to 2 h relative to the achievement of heat balance (0 kJ/min rate of heat storage). We provide the first quantification of the temporal profiles of thermal strain during extended rest in conditions simulating hot weather.

Effects of sodium bicarbonate ingestion on ventilatory and cerebrovascular responses in resting heated humans.

Hyperthermia stimulates ventilation in humans. This hyperthermia-induced hyperventilation may be mediated by the activation of peripheral chemoreceptors implicated in the regulation of respiration in reaction to various chemical stimuli, including reductions in arterial pH. Here, we investigated the hypothesis that during passive heating at rest, the increases in arterial pH achieved with sodium bicarbonate ingestion, which could attenuate peripheral chemoreceptor activity, mitigate hyperthermia-induced hyperventilation. We also assessed the effect of sodium bicarbonate ingestion on cerebral blood flow responses, which are associated with hyperthermia-induced hyperventilation. Twelve healthy men ingested sodium bicarbonate (0.3 g/kg body weight) or sodium chloride (0.208 g/kg). One hundred minutes after the ingestion, the participants were passively heated using hot-water immersion (42°C) combined with a water-perfused suit. Increases in esophageal temperature (an index of core temperature) and minute ventilation (V̇E) during the heating were similar in the two trials. Moreover, when V̇E is expressed as a function of esophageal temperature, there were no between-trial differences in the core temperature threshold for hyperventilation (38.0 ± 0.3 vs. 38.0 ± 0.4°C, P = 0.469) and sensitivity of hyperthermia-induced hyperventilation as assessed by the slope of the core temperature-V̇E relation (13.5 ± 14.2 vs. 15.8 ± 15.5 L/min/°C, P = 0.831). Furthermore, middle cerebral artery mean blood velocity (an index of cerebral blood flow) decreased similarly with heating duration in both trials. These results suggest that sodium bicarbonate ingestion does not mitigate hyperthermia-induced hyperventilation and the reductions in cerebral blood flow index in resting heated humans.NEW & NOTEWORTHY Hyperthermia leads to hyperventilation and associated cerebral hypoperfusion, both of which may impair heat tolerance. This hyperthermia-induced hyperventilation may be mediated by peripheral chemoreceptors, which can be activated by reductions in arterial pH. However, our results suggest that sodium bicarbonate ingestion, which can increase arterial pH, is not an effective intervention in alleviating hyperthermia-induced hyperventilation and cerebral hypoperfusion in resting heated humans.

A latent profile analysis of rest-activity behavior patterns among community-dwelling older adults and its relationship with intrinsic capacity.

Rest-activity behavior clusters within individuals to form patterns are of significant importance to their intrinsic capacity (IC), yet they have rarely been studied. A total of 1253 community-dwelling older adults were recruited between July and December 2021 based on the baseline survey database of the Fujian Prospective Cohort Study on Aging. Latent profile analysis was used to identify profiles of participants based on rest-activity behaviors, whereas logistic regression analysis was carried out to investigate the relationship between profiles and IC. We identified three latent profiles including: (1) Profile 1-labeled "Gorillas": High physical activity (PA), moderate sedentary behaviors (SB), screen time (ST) and sleep (n = 154, 12%), (2) Profile 2-labeled as "Zebras": Moderate PA, low SB, ST and high sleep (n = 779, 62%), and (3) Profile 3-labeled as"Koalas": High SB, ST, low PA and sleep (n = 320, 26%). Logistic regression revealed a negative correlation between low IC and the "Gorillas" profile (ß = - 0.945, P < 0.001) as well as the "Zebras" profile (ß = - 0.693, P < 0.001) among community-dwelling older adults, with the "Koalas" profile showing the weakest IC compared to the other profiles. The demographic traits i.e., female, older age, living alone, and low educational level also correlated with low IC. Identifying trends of rest-activity behaviors may help in drawing focus on older adults at risk of decreasing IC, and develop personalized improvement plans for IC.

Brain mechanisms underlying the modulation of heart rate variability when accepting and reappraising emotions.

Heart rate variability (HRV) has been linked to resilience and emotion regulation (ER). How HRV and brain processing interact during ER, however, has remained elusive. Sixty-two subjects completed the acquisition of resting HRV and task HRV while performing an ER functional Magnetic Resonance Imaging (fMRI) paradigm, which included the differential strategies of ER reappraisal and acceptance in the context of viewing aversive pictures. We found high correlations of resting and task HRV across all emotion regulation strategies. Furthermore, individuals with high levels of resting, but not task, HRV showed numerically lower distress during ER with acceptance. Whole-brain fMRI parametrical modulation analyses revealed that higher task HRV covaried with dorso-medial prefrontal activation for reappraisal, and dorso-medial prefrontal, anterior cingulate and temporo-parietal junction activation for acceptance. Subjects with high resting HRV, compared to subjects with low resting HRV, showed higher activation in the pre-supplementary motor area during ER using a region of interest approach. This study demonstrates that while resting and task HRV exhibit a positive correlation, resting HRV seems to be a better predictor of ER capacity. Resting and task HRV were associated with ER brain activation in mid-line frontal cortex (i.e. DMPFC).

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models.

BACKGROUD: Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE. METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database. RESULTS: Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function. CONCLUSIONS: This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

Loneliness and brain rhythmic activity in resting state: an exploratory report.

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.