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.

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.

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.

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.

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.

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.

Progressive alterations of resting-state hypothalamic dysconnectivity in schizophrenia.

BACKGROUND: The hypothalamus may be involved in the pathogenesis of schizophrenia. Investigating hypothalamus dysfunction in schizophrenia and probing how it is related to symptoms and responds to antipsychotic medication is crucial for understanding the potential mechanism of hypothalamus dysfunction under the long-term illness. METHODS: We recruited 216 patients with schizophrenia, including 140 antipsychotic-naïve first-episode patients (FES, including 44 patients with 1-year follow-up data), 76 chronically treated schizophrenia (CTS), and 210 healthy controls (HC). Hypothalamic seed-based functional connectivity (FC) was calculated and compared among the FES, CTS, and HC groups using analysis of covariance. Exploratory analysis was conducted between the FES patients at baseline and after 1-year follow-up. Significantly altered hypothalamic FCs were then related to clinical symptomology, while age- and illness-related regression analyses were also conducted and compared between diagnostic groups. RESULTS: The FES patients showed decreased hypothalamic FCs with the midbrain and right thalamus, whereas the CTS patients showed more severe decreased hypothalamic FCs with the midbrain, right thalamus, left putamen, right caudate, and bilateral anterior cingulate cortex compared to HCs. These abnormalities were not correlated to the symptomology or illness duration, or not reversed by the antipsychotic treatment. Age-related hypothalamic FC decrease was also identified in the abovementioned regions, and a faster age-related decline of the hypothalamic FC was observed with the left putamen and bilateral anterior cingulate cortex. CONCLUSION: Age-related hypothalamic FC decrease extends the functional alterations that characterize the neurodegenerative nature of schizophrenia. Future studies are required to further probe the hormonal or endocrinal underpinnings of such alterations and trace the precise progressive trajectories.

Increased resting lactate levels and reduced carbohydrate intake cause νLa.max underestimation by reducing net lactate accumulation-A pilot study in young adults.

Modulation of testing conditions such as resting lactate (Larest) levels or carbohydrate intake may affect the calculation of the maximal glycolytic rate (νLa.max). To evaluate the impact of elevated Larest as well as reduced and increased carbohydrate availability on νLa.max in running sprints (RST), twenty-one participants completed five 15-s RST tests on a running track under five different conditions: (I). baseline: Larest ≤1.5 mmol·L-1; (II). Lactate+: Larest ≥2.5 mmol·L-1; (III). CHO-: carbohydrate intake: ≤ 1 g·kg-1 BW d-1 for 3 days; (IV). CHO+: carbohydrate intake: ≥ 9 g·kg-1 BW d-1 for one day; and (V). acuteCHO: 500 mL glucose containing beverage consumed before RST. νLa.max was significantly reduced in lactate+ and CHO- conditions compared to the baseline RST, due to a reduction in the arithmetic mean delta (∆) between Lapeak and Larest lactate concentration (Lapeak, mmol · L-1). AcuteCHO led to an increase in Larest compared to baseline, CHO- and CHO+ with a high interindividual variability but did not significantly reduce νLa.max. Therefore, avoiding low carbohydrate nutrition before νLa.max testing, along with carefully adjusting Larest to below ≤1.5 mmol·L-1, is crucial to prevent the unintentional underestimation of νLa.max.

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model.

BACKGROUND: The widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD. METHODS: This study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability. RESULTS: Model constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = - 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN. CONCLUSIONS: This study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.

Directed brain interactions over time: A resting-state EEG comparison between schizophrenia and healthy individuals.

Understanding the neurophysiological mechanisms of schizophrenia (SZ) is one of the challenges of neuroscience. Many anatomical and functional studies have pointed to problems in brain connectivity in SZ individuals. However, little is known about the relationships between specific brain regions and impairments in brain connectivity in SZ individuals. Herein we propose a new approach using time-varying graphs and the motif synchronization method to build dynamic brain functional networks (BFNs). Dynamic BFNs were constructed from resting-state electroencephalography (rs-EEG) of 14 schizophrenia (SZ) individuals and 14 healthy controls (HCs). BFNs were evaluated based on the percentage of synchronization importance between a pair of regions (considering external and internal interactions) over time. We found differences in the directed interaction between brain regions in SZ individuals compared to the control group. Our method revealed low bilaterally directed interactions between the temporal lobes in SZ individuals compared to HCs, indicating a potential link between altered brain connectivity and the characteristic symptoms of schizophrenia. From a clinical perspective, these results shed light on developing new therapeutic approaches targeting these specific neural interactions that are altered in individuals with SZ. This knowledge allows the application of better interventions focused on restoring or compensating for interrupted connectivity patterns.

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.

Similarity of brain activity patterns during learning and subsequent resting state predicts memory consolidation.

Spontaneous reactivation of brain activity from learning to a subsequent off-line period has been implicated as a neural mechanism underlying memory consolidation. However, similarities in brain activity may also emerge as a result of individual, trait-like characteristics. Here, we introduced a novel approach for analyzing continuous electroencephalography (EEG) data to investigate learning-induced changes as well as trait-like characteristics in brain activity underlying memory consolidation. Thirty-one healthy young adults performed a learning task, and their performance was retested after a short (∼1 h) delay. Consolidation of two distinct types of information (serial-order and probability) embedded in the task were tested to reveal similarities in functional networks that uniquely predict the changes in the respective memory performance. EEG was recorded during learning and pre- and post-learning rest periods. To investigate brain activity associated with consolidation, we quantified similarities in EEG functional connectivity between learning and pre-learning rest (baseline similarity) and learning and post-learning rest (post-learning similarity). While comparable patterns of these two could indicate trait-like similarities, changes from baseline to post-learning similarity could indicate learning-induced changes, possibly spontaneous reactivation. Higher learning-induced changes in alpha frequency connectivity (8.5-9.5 Hz) were associated with better consolidation of serial-order information, particularly for long-range connections across central and parietal sites. The consolidation of probability information was associated with learning-induced changes in delta frequency connectivity (2.5-3 Hz) specifically for more local, short-range connections. Furthermore, there was a substantial overlap between the baseline and post-learning similarities and their associations with consolidation performance, suggesting robust (trait-like) differences in functional connectivity networks underlying memory processes.

Dynamic cerebral autoregulation during repeated handgrip exercise: comparisons with spontaneous rest and sit-stand maneuvers.

Induced arterial pressure oscillation may improve the assessment of dynamic cerebral autoregulation (dCA) with transfer function analysis (TFA). This study investigated dCA during repeated handgrip exercise (RHE) compared with spontaneous rest and sit-stand maneuvers (SSM), often used in cerebrovascular research. After a 5-min rest, 20 healthy young adults (10 women and 10 men) underwent 5 min of RHE (30% maximal voluntary contraction) and SSM at 0.05 Hz and 0.10 Hz each in random order. Power spectral density (PSD) and TFA gain, phase, coherence of mean arterial pressure (MAP), and blood velocity in the middle cerebral artery (MCAvmean) were measured in very low (VLF: 0.02-0.07 Hz) and low (LF: 0.07-0.20 Hz) frequencies. End-tidal CO2 (EtCO2) was continuously recorded throughout data collection. Compared with rest, RHE increased the PSD of MAP and MCAvmean in VLF (444% and 273%, respectively) and LF (1,571% and 1,765%, respectively) (all P < 0.001). Coherence increased during RHE (VLF: 131%, LF: 128%) and SSM (VLF: 166%, LF: 136%) compared with rest (all P < 0.05). TFA gain and phase were similar between RHE and rest, but VLF gain was higher, whereas VLF and LF phases were lower during SSM than RHE (all P < 0.05). EtCO2 was higher during SSM than rest and RHE (both P < 0.05), with the individual EtCO2 changes positively correlated with VLF gain (r = 0.538, P < 0.001). These results indicate that RHE significantly increases arterial pressure oscillation and TFA coherence and may improve dCA assessment in individuals unable to perform repeated postural changes.NEW & NOTEWORTHY This is the first study investigating dynamic cerebral autoregulation (dCA) during light-intensity repeated handgrip exercise (RHE) compared with rest and sit-stand maneuvers (SSM) using transfer function analysis (TFA). Compared with rest, RHE significantly increased oscillations of arterial blood pressure and cerebral blood velocity and coherence, whereas SSM exhibited the highest oscillations and coherence. These findings suggest that RHE may serve as an alternative method for assessing dCA in individuals unable to perform repeated postural changes.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by 'electrophysiology-invisible' signals. These findings offer a novel perspective on our understanding of RSN interpretation.

The brain contains many cells known as neurons that send and receive messages in the form of electrical signals. The neurons in different regions of the brain must coordinate their activities to enable the brain to operate properly. Researchers often use a method called resting-state functional magnetic resonance imaging (rsfMRI) to study how different areas of the brain work together. This method indirectly measures brain activity by detecting the changes in blood flow to different areas of the brain. Regions that are working together will become active (that is, have higher blood flow and corresponding rsfMRI signal) and inactive (have lower blood flow and a lower rsfMRI signal) at the same time. These coordinated patterns of brain activity are known as "resting-state brain networks" (RSNs). Previous studies have identified RSNs in many different situations, but we still do not fully understand how these changes in blood flow are related to what is happening in the neurons themselves. To address this question, Tu et al. performed rsfMRI while also measuring the electrical activity (referred to as electrophysiology signals) in two distinct regions of the brains of rats. The team then used the data to generate maps of RSNs in those brain regions. This revealed that rsfMRI signals and electrophysiology signals produced almost identical maps in terms of the locations of the RSNs. However, the electrophysiology signals only contributed a small amount to the changes in the local rsfMRI signals over time at the same recording site. This suggests that RSNs may arise from cell activities that are not detectable by electrophysiology but do regulate blood flow to neurons. The findings of Tu et al. offer a new perspective for interpreting how rsfMRI signals relate to the activities of neurons. Further work is needed to explore all the features of the electrophysiology signals and test other methods to compare these features with rsfMRI signals in the same locations.

A visual representation of the hand in the resting somatomotor regions of the human brain.

Hand visibility affects motor control, perception, and attention, as visual information is integrated into an internal model of somatomotor control. Spontaneous brain activity, i.e., at rest, in the absence of an active task, is correlated among somatomotor regions that are jointly activated during motor tasks. Recent studies suggest that spontaneous activity patterns not only replay task activation patterns but also maintain a model of the body's and environment's statistical regularities (priors), which may be used to predict upcoming behavior. Here, we test whether spontaneous activity in the human somatomotor cortex as measured using fMRI is modulated by visual stimuli that display hands vs. non-hand stimuli and by the use/action they represent. A multivariate pattern analysis was performed to examine the similarity between spontaneous activity patterns and task-evoked patterns to the presentation of natural hands, robot hands, gloves, or control stimuli (food). In the left somatomotor cortex, we observed a stronger (multivoxel) spatial correlation between resting state activity and natural hand picture patterns compared to other stimuli. No task-rest similarity was found in the visual cortex. Spontaneous activity patterns in somatomotor brain regions code for the visual representation of human hands and their use.

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.

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.

Alteration in early resting­state functional MRI activity in comatose survivors of cardiac arrest: a prospective cohort study.

BACKGROUND: This study aimed to explore the characteristics of abnormal regional resting-state functional magnetic resonance imaging (rs-fMRI) activity in comatose patients in the early period after cardiac arrest (CA), and to investigate their relationships with neurological outcomes. We also explored the correlations between jugular venous oxygen saturation (SjvO2) and rs-fMRI activity in resuscitated comatose patients. We also examined the relationship between the amplitude of the N20-baseline and the rs-fMRI activity within the intracranial conduction pathway of somatosensory evoked potentials (SSEPs). METHODS: Between January 2021 and January 2024, eligible post-resuscitated patients were screened to undergo fMRI examination. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of rs-fMRI blood oxygenation level-dependent (BOLD) signals were used to characterize regional neural activity. Neurological outcomes were evaluated using the Glasgow-Pittsburgh cerebral performance category (CPC) scale at 3 months after CA. RESULTS: In total, 20 healthy controls and 31 post-resuscitated patients were enrolled in this study. The rs-fMRI activity of resuscitated patients revealed complex changes, characterized by increased activity in some local brain regions and reduced activity in others compared to healthy controls (P < 0.05). However, the mean ALFF values of the whole brain were significantly greater in CA patients (P = 0.011). Among the clusters of abnormal rs-fMRI activity, the cluster values of ALFF in the left middle temporal gyrus and inferior temporal gyrus and the cluster values of ReHo in the right precentral gyrus, superior frontal gyrus and middle frontal gyrus were strongly correlated with the CPC score (P < 0.001). There was a strong correlation between the mean ALFF and SjvO2 in CA patients (r = 0.910, P < 0.001). The SSEP N20-baseline amplitudes in CA patients were negatively correlated with thalamic rs-fMRI activity (all P < 0.001). CONCLUSIONS: This study revealed that abnormal rs-fMRI BOLD signals in resuscitated patients showed complex changes, characterized by increased activity in some local brain regions and reduced activity in others. Abnormal BOLD signals were associated with neurological outcomes in resuscitated patients. The mean ALFF values of the whole brain were closely related to SjvO2 levels, and changes in the thalamic BOLD signals correlated with the N20-baseline amplitudes of SSEP responses. TRIAL REGISTRATION: NCT05966389 (Registered July 27, 2023).

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.