Abnormal Static and Dynamic Local Functional Connectivity in First-Episode Schizophrenia: A Resting-State fMRI Study.

Brain connectivity is highly dynamic, but functional connectivity (FC) studies using resting-state functional magnetic resonance imaging (rs-fMRI) assume it to be static. This study assessed differences in dynamic FC between young healthy adults (YH) and elderly healthy adults (EH) compared to static FC. Using rs-fMRI data from 12 YH and 31 EH, FC was assessed in six functional regions (subcortical, auditory [AUD], sensorimotor [SM], visuospatial [VS], cognitive control [CC], and default mode network [DMN]). Static FC was calculated as Fisher's z-transformed correlation coefficient. The sliding time window correlation (window size 30s, step size 3s) was applied for dynamic FC, and the standard deviation across sliding windows was calculated. Differences in static and dynamic FC between EH and YH were calculated and compared by region. EH showed decreased static FC in the subcortical, CC, and DMN regions (FDR corrected p=0.0013; 74 regions), with no regions showing static FC higher than that in YH. EH showed increased dynamic FC in the subcortical, CC, and DMN regions, whereas decreased dynamic FC in CC and DMN regions (p<0.01). However, the regions showing differences between EH and YH did not overlap between static and dynamic FC. Dynamic FC exhibited differences from static FC in EH and YH, mainly in regions involved in cognitive control and the DMN. Altered dynamic FC demonstrated both qualitatively and quantitatively distinct patterns of transient brain activity and needs to be studied as an imaging biomarker in the aging process.

Reconfigurations of dynamic functional network connectivity after 1HZ repetitive transcranial magnetic stimulation in insomnia disorder.

Graphical network characteristics and nonstationary functional connectivity features, both derived from resting-state functional magnetic resonance imaging (rsfMRI) data, have been associated with cognitive performance in healthy subjects. How these features jointly relate to cognition in diseased states has not been investigated. In this study, 46 relapsing-remitting multiple sclerosis subjects underwent rsfMRI scans and a focused cognitive battery. With a sliding window approach, we examined six dynamic network features that indicated how connectivity changed over time as well as six measures derived from graph theory to reflect static network characteristics. Multiset canonical correlation analysis (MCCA) was then carried out to investigate the relations between dynamic network features, stationary network characteristics, cognitive testing, demographic, disease severity, and mood. Multiple sclerosis (MS) subjects demonstrated weaker connectivity strength, decreased network density, reduced global changes, but increased changes in interhemispheric connectivity compared to controls. The MCCA model determined that executive functions and processing speed ability measured by Wechsler Adult Intelligence Scale IV (WAIS-IV) Working Memory Index, WAIS-IV Processing Speed Index, and the Verbal Fluency Test were positively correlated with education, dynamic connectivity, and static connectivity strength; while poor task switching was correlated with disease severity, psychiatric comorbidities such as depression, anxiety, and fatigue, and static network density. Taken together, our results suggest that better executive functioning in MS requires maintenance of a continued coordination between stationary and dynamic functional connectivity as well as the support of education, and dynamic functional connectivity may provide an additional cognitive biomarker of disease severity in the MS population.

Longitudinal alterations of modular functional-metabolic coupling in first-episode schizophrenia

Association between function and structure of the triple network and catechol-O-methyltransferase val158met polymorphism in the first episode schizophrenia

Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

Background:Previous studies reported higher incidences of venous thromboembolism and cardiovascular disease in schizophrenia patients and higher indicators of thrombosis, thrombocyte activation, and platelet dysfunction.Objectives:To check if first-episode schizophrenia (FES) patients have a hypercoagulable state and determine whether acute and chronic antipsychotics have the same effect on blood coagulation or fibrinolysis-related biomarkers.Design:Case-control study.Methods:A total of 81 participants were grouped in FES, chronic schizophrenia (CS), and healthy controls (HCs). In addition to demographic data and clinical characteristics, immunological analyses were performed to measure plasma levels of D-dimer, plasminogen activator inhibitor-1 (PAI-1), soluble P selectin (sP-sel), tissue plasminogen activator (tPA), thrombotic precursor protein (TpP), and von Willebrand’s disease factor (vWF).Results:Compared to HC group, FES patients showed higher PAI-1 (28.61 ng/ml versus 15.69 ng/ml), sP-sel (2.78 ng/ml versus 1.18 ng/ml), and TpP (15.61 µg/ml versus 5.59 µg/ml) along with a higher PAI-1/tPA (3.12 versus 2.00). Acute antipsychotic medication reduced higher PAI-1 (28.61 → 21.99), sP-sel (2.78 → 1.87), tPA (9.59 → 5.83), TpP (15.61 → 10.54), and vWF (383.18 → 291.08) in FES patients. However, plasma sP-sel and vWF in CS patients returned to the pre-treatment levels in FES patients, and PAI-1/tPA significantly decreased compared to FES patients.Conclusion:These results suggest a hypercoagulable state in FES patients and demonstrate contrast effects of acute and chronic antipsychotics on coagulation or fibrinolysis in schizophrenia patients.

Purpose: To investigate the dynamic functional connectivity (DFC) and static parameters of graph theory in individuals with subjective cognitive decline (SCD) and the associations of DFC and topological properties with cognitive performance.Methods: Thirty-three control subjects and 32 SCD individuals were enrolled in this study, and neuropsychological evaluations and resting-state functional magnetic resonance imaging scanning were performed. Thirty-three components were selected by group independent component analysis to construct 7 functional networks. Based on the sliding window approach and k-means clustering, distinct DFC states were identified. We calculated the temporal properties of fractional windows in each state, the mean dwell time in each state, and the number of transitions between each pair of DFC states. The global and local static parameters were assessed by graph theory analysis. The differences in DFC and topological metrics, and the associations of the altered neuroimaging measures with cognitive performance were assessed.Results: The whole cohort demonstrated 4 distinct connectivity states. Compared to the control group, the SCD group showed increased fractional windows and an increased mean dwell time in state 4, characterized by hypoconnectivity both within and between networks. The SCD group also showed decreased fractional windows and a decreased mean dwell time in state 2, dominated by hyperconnectivity within and between the auditory, visual and somatomotor networks. The number of transitions between state 1 and state 2, between state 2 and state 3, and between state 2 and state 4 was significantly reduced in the SCD group compared to the control group. No significant differences in global or local topological metrics were observed. The altered DFC properties showed significant correlations with cognitive performance.Conclusion: Our findings indicated DFC network reconfiguration in the SCD stage, which may underlie the early cognitive decline in SCD subjects and serve as sensitive neuroimaging biomarkers for the preclinical detection of individuals with incipient Alzheimer's disease.

The history of childhood trauma is associated with lipid disturbances and blood pressure in adult first-episode schizophrenia patients

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural and functional abnormalities in patients with LBP; however, how the static and dynamic large-scale functional network connectivity (FNC) of the brain is affected in these patients remains unclear. Forty-one patients with chronic low back pain (cLBP) and 42 healthy controls underwent resting-state functional MRI scanning. The independent component analysis method was employed to extract the resting-state networks. Subsequently, we calculate and compare between groups for static intra- and inter-network functional connectivity. In addition, we investigated the differences between dynamic functional network connectivity and dynamic temporal metrics between cLBP patients and healthy controls. Finally, we tried to distinguish cLBP patients from healthy controls by support vector machine method. The results showed that significant reductions in functional connectivity within the network were found within the DMN,DAN, and ECN in cLBP patients. Significant between-group differences were also found in static FNC and in each state of dynamic FNC. In addition, in terms of dynamic temporal metrics, fraction time and mean dwell time were significantly altered in cLBP patients. In conclusion, our study suggests the existence of static and dynamic large-scale brain network alterations in patients with cLBP. The findings provide insights into the neural mechanisms underlying various brain function abnormalities and altered pain experiences in patients with cLBP.

Schizophrenia (SZ) is a debilitating and heterogeneous disease. We hypothesized that the oxytocin (OXT) system, inflammation and one-carbon metabolism would have a link with SZ. In this study, serum OXT, OXT receptor (OXTR), interleukin-6 (IL-6), high sensitivity CRP (hsCRP) and homocysteine (Hcy) levels were measured in 52 first-episode schizophrenia (FES) patients and 41 healthy controls (HC) from the Second Xiangya Hospital of Central South University. Meanwhile, the mRNA expressions of OXT and OXTR genes were determined by real-time quantitative PCR. Serum OXT and OXTR levels were significantly lower in FES patients (518.96 ± 22.22 and 174.60 ± 17.11 pg/ml) than the HC group (711.58 ± 40.57 and 252.15 ± 20.62 pg/ml). Serum IL-6 and hsCRP levels showed no difference between the two groups (1.82 ± 0.30 vs. 1.69 ± 0.36 pg/ml, 0.66 (0.22, 1.07) vs. 0.31 (0.13, 0.91) mg/L), but serum Hcy levels were significantly higher in FES patients (20.18 ± 1.83 vs. 15.24 ± 0.82 μmol/ml). The FES patients (0.27 ± 0.02 and 0.20 ± 0.02) have relatively higher mRNA expressions of OXT and OXTR genes than the HC group (0.16 ± 0.01 and 0.14 ± 0.01). In summary, our results suggested the possible function of the OXT system and Hcy in the pathogenesis of SZ.

Impact of duration of untreated psychosis and premorbid intelligence on cognitive functioning in patients with first-episode schizophrenia

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA). Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups. In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra- VN and inter- cerebellum network (CN) and high-level cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292. In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroid-associated eye diseases.