FC Patterns Research Articles

1248 Optimal Connectivity Map Predicts Seizure Freedom Rates After Laser Interstitial Thermal Therapy in Mesial Temporal Lobe Epilepsy

INTRODUCTION: Laser Interstitial Thermal Therapy (LiTT) is increasingly becoming a first-line surgical therapy for mesial temporal lobe epilepsy (mTLE). While promising, its seizure outcomes have also been highly variable, presumably due to an incomplete understanding of mechanisms behind LiTT. Epilepsy is a network disorder, and therefore it is crucial to account for both the effect on local destruction of epileptogenic foci, as well as global intervention of limbic circuitry, to elucidate the circuit-level mechanisms of LiTT and devise improved ablation strategies. In this study, we explore LiTT's network-level effect by investigating functional connectivity patterns and identifying optimal connectivity patterns predictive of seizure freedom. METHODS: 27 mTLE patients who underwent unilateral LiTT were classified as seizure-free (ILAE Class I) or seizure-present (ILAE Class II–VI) cohort based on one-year postoperative outcomes. Individual ablation volumes were segmented from intraoperative MRI sequences. Ablation volumes were seeded into a normative functional connectome (Brain Genomics Superstruct Project) to extract patient-specific connectivity profiles associated with ablation volumes. FC patterns were compared between the two cohorts to produce voxel-wise “t-maps” to identify local and global differences in connectivity strengths predictive of seizure freedom. RESULTS: Ten mTLE patients achieved complete seizure freedom. In seizure-free patients, right ablated volumes shared higher FC with regions such as calcarine, cuneus, and thalamic anterior pulvinar nucleus than patients with seizure presence. CONCLUSIONS: For the right-sided ablations, seizure freedom was associated with a distinctive whole-brain connectivity pattern beyond the local surgical target. Further investigation is required to explore if this pattern is specific between the two surgical sides in mTLE population.

P-204 The pattern of the zygote cleavage to three cells must be considered before embryo transfer

Abstract Study question What is the clinical outcome of instant irregular cleavage (IDC) from zygote to three cells? Summary answer IDC embryos, did not develop to blastocysts and did not acieve pregnancy; FC embryos that reached blastocyst stage exerted similar pregnancy rates as control embryos What is known already Evaluation of the zygote morphology is one of the earliest embryonic stages investigated. Several zygote grading systems were proposed based on pronuclei size and NPB (nucleolus precursor bodies) distribution, thus is an effective indicator of the embryo’s potential to result in a life birth. The first cleavage of a zygote is a highly coordinated event. Disruptive timing of the first cleavage, named direct cleavage (DC, less than 5 hours), is associated with formation of poor embryo quality. Early identification of pathological embryos is challenging and impacts the timing of embryo fate decision in ART. Study design, size, duration A total of 1978 fresh IVF cycles from single unit were analyzed and a total of 4012 embryos were studied. Only 2pn embryos, cultured exclusively in time-lapse system for 5 days were included. The morphokinetics of embryos and their outcome were recorded, from z-score analysis through day 5. We discriminated between IDC, FC and normal cleavage pattern of embryos and compared clinical outcome of various morphokinetic parameters. We also evaluated possible early predictors for IDC. Participants/materials, setting, methods We generated three study groups according to embryo cleavage pattern: (I) Control, normal cleavage (n = 551); (II) FC, zygote to three cells within 5 hours (n = 1587); (III) IDC, instant cleavage from zygote to three cells (n = 922). The association between z-score of 138 IDC embryos and their sibling random controls was thoroughly investigated. All time lapse annotations were performed by senior embryologists. Statistical analysis was performed by SPSS software. Main results and the role of chance IDC embryos were mainly arrested at day 3 and the number of usable embryos (reached blastocyst stage and were suitable for embryo transfer or cryopreservation) was negligible; 4/922 (0.4%). In comparison, the amount of usable FC embryos was 108/1587 (6.6%) and control embryos usage reached 180/551 (32.7%). While the pregnancy rate of control and FC embryos, which reached embryo transfer were similar (40.35% and 42.55%, respectively); transfer of IDC embryos did not result in pregnancy. Additionally, the timetable as measured by time of PN fading and time from fading to first cleavage, differed significantly between the three groups. Therefore, we performed a thorough analysis of zygote morphology data of IDC embryos compared to control sibling embryos in search of early possible markers for these findings. We have not detected significant differences in z-score analysis, reflected by number and size of nucleoli, as well as pronuclear symmetry. Limitations, reasons for caution IDC embryos number used for z-score analysis was limited since many didn’t meet the inclusion criteria (lack of overlapping of the pronuclei, presence of random of control embryo and good quality image). Wider implications of the findings The decision regarding the fate of IDC and FC embryos should include the pattern of first cycle cleavage. Culture IDC and FC embryos for 5 days up to the blastocyst will spare transfer of embryos that are fated to arrest even when their morphological grade on day 3 is acceptable. Trial registration number 0043-22-MMC

Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study.

Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.

Interrelating differences in structural and functional connectivity in the older adult's brain.

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.

Resting-state functional connectivity of salience network in schizophrenia and depression

To explore the salience network (SN) functional alterations in schizophrenia and depression, resting-state functional magnetic resonance imaging (rs-fMRI) data from 29 patients with schizophrenia (SCH), 28 patients with depression (DEP) and 30 healthy controls (HC) were obtained. The SN was derived from data-driven group independent component analysis (gICA). ANCOVA and post hoc tests were performed to discover the FC differences of SN between groups. The ANCOVA demonstrated a significant group effect in FC with right inferior and middle temporal gyrus (ITG and MTG), left caudate, and right precentral gyrus. Post-hoc analyses revealed an opposite altered FC pattern between SN and right ITG and MTG for both patient groups. The DEP group showed a reduced FC between SN and right ITG and MTG compared with HC whereas the SCH group showed an increased FC. In addition, the SCH group showed decreased FC between SN and left caudate, and enhanced FC between SN and right precentral gyrus compared to the other two groups. Our findings suggest distinct FC of SN in schizophrenia and depression, supporting that the resting-state FC pattern of SN may be a transdiagnostic difference between depression and schizophrenia and may play a critical role in the pathogenesis of these two disorders.

Linking Plasma Amyloid Beta and Neurofilament Light Chain to Intracortical Myelin Content in Cognitively Normal Older Adults

Evidence suggests that lightly myelinated cortical regions are vulnerable to aging and Alzheimer’s disease (AD). However, it remains unknown whether plasma markers of amyloid and neurodegeneration are related to deficits in intracortical myelin content, and whether this relationship, in turn, is associated with altered patterns of resting-state functional connectivity (rs-FC). To shed light into these questions, plasma levels of amyloid-β fragment 1–42 (Aβ1–42) and neurofilament light chain (NfL) were measured using ultra-sensitive single-molecule array (Simoa) assays, and the intracortical myelin content was estimated with the ratio T1-weigthed/T2-weighted (T1w/T2w) in 133 cognitively normal older adults. We assessed: (i) whether plasma Aβ1–42 and/or NfL levels were associated with intracortical myelin content at different cortical depths and (ii) whether cortical regions showing myelin reductions also exhibited altered rs-FC patterns. Surface-based multiple regression analyses revealed that lower plasma Aβ1–42 and higher plasma NfL were associated with lower myelin content in temporo-parietal-occipital regions and the insular cortex, respectively. Whereas the association with Aβ1–42 decreased with depth, the NfL-myelin relationship was most evident in the innermost layer. Older individuals with higher plasma NfL levels also exhibited altered rs-FC between the insula and medial orbitofrontal cortex. Together, these findings establish a link between plasma markers of amyloid/neurodegeneration and intracortical myelin content in cognitively normal older adults, and support the role of plasma NfL in boosting aberrant FC patterns of the insular cortex, a central brain hub highly vulnerable to aging and neurodegeneration.

Disruption in Surface-Based Functional Connectivity in the Right Posterior Hippocampal CA3 Subfield: A Probable Neural Basis of Visuospatial Working Memory Impairment in Patients With Right Temporal Lobe Epilepsy.

Visuospatial working memory (VSWM) impairment is common in patients with right temporal lobe epilepsy (rTLE). The posterior hippocampus is critical for spatial memory, but the contributions of the different subfields to VSWM deficits remain unclear. Forty-six rTLE patients and 42 healthy controls (HCs) were recruited. Resting-state fMRI (rsfMRI) and structural MRI scans were administered, followed by a VSWM_Nback test. The right posterior hippocampus was automatically segmented, and the surface-based functional connectivity (SBFC) of the subiculum (Sub), CA1, CA3, dentate gyrus (DG), hippocampal tail, and right entorhinal cortex (EC) were compared between groups. Correlation analysis was performed between the altered SBFC and VSWM_Nback scores for rTLE patients. The results showed that rTLE patients underperformed in the VSWM_Nback test, with longer mean reaction time of accurate response (ACCmeanRT) in 0back and 2back condition, lower hit rate (HR) and higher false alarm rate (FAR) in 2back condition. Compared with HCs, the rCA3 in the rTLE group exhibited decreased SBFC with inferior parietal cortex (IPC), temporal lateral cortex (TLC), and posterior visual cortex (PVC) in the right hemisphere as well as the bilateral dorsolateral prefrontal cortex (DLPFC). The SBFC of the rEC and right anterior cingulate cortex (rACC) increased in the rTLE group. Within the rTLE group, the decreased SBFC of the rCA3-rIPC and rCA3-rLTC were correlated with worse VSWM performance. Therefore, the decreased SBFC of the rCA3-rIPC and rCA3-rLTC might be the critical aberrant FC pattern reflecting VSWM impairment in rTLE patients. The mechanism might involve functional disruption between the core subsystem and the medial temporal subsystem of the default mode network (DMN).

Hippocampal Resting-State Functional Connectivity Forecasts Individual Posttraumatic Stress Disorder Symptoms: A Data-Driven Approach

BackgroundPosttraumatic stress disorder (PTSD) is a debilitating disorder, and there is no current accurate prediction of who develops it after trauma. Neurobiologically, individuals with chronic PTSD exhibit aberrant resting-state functional connectivity (rsFC) between the hippocampus and other brain regions (e.g., amygdala, prefrontal cortex, posterior cingulate), and these aberrations correlate with severity of illness. Previous small-scale research (n < 25) has also shown that hippocampal rsFC measured acutely after trauma is predictive of future severity using a region-of-interest–based approach. While this is a promising biomarker, to date, no study has used a data-driven approach to test whole-brain hippocampal FC patterns in forecasting the development of PTSD symptoms. MethodsA total of 98 adults at risk of PTSD were recruited from the emergency department after traumatic injury and completed resting-state functional magnetic resonance imaging (8 min) within 1 month; 6 months later, they completed the Clinician-Administered PTSD Scale for DSM-5 for assessment of PTSD symptom severity. Whole-brain rsFC values with bilateral hippocampi were extracted (using CONN) and used in a machine learning kernel ridge regression analysis (PRoNTo); a k-folds (k = 10) and 70/30 testing versus training split approach were used for cross-validation (1000 iterations to bootstrap confidence intervals for significance values). ResultsAcute hippocampal rsFC significantly predicted Clinician-Administered PTSD Scale for DSM-5 scores at 6 months (r = 0.30, p = .006; mean squared error = 120.58, p = .006; R2 = 0.09, p = .025). In post hoc analyses, hippocampal rsFC remained significant after controlling for demographics, PTSD symptoms at baseline, and depression, anxiety, and stress severity at 6 months (B = 0.59, SE = 0.20, p = .003). ConclusionsFindings suggest that functional connectivity of the hippocampus across the brain acutely after traumatic injury is associated with prospective PTSD symptom severity.

A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation.

An important question in neuroscience is whether or not we can interpret spontaneous variations in the pattern of correlation between brain areas, which we refer to as functional connectivity or FC, as an index of dynamic neuronal communication in fMRI. That is, can we measure time-varying FC reliably? And, if so, can FC reflect information transfer between brain regions at relatively fast-time scales? Answering these questions in practice requires dealing with the statistical challenge of having high-dimensional data and a comparatively lower number of time points or volumes. A common strategy is to use PCA to reduce the dimensionality of the data, and then apply some model, such as the hidden Markov model (HMM) or a mixture model of Gaussian distributions, to find a set of distinct FC patterns or states. The distinct spatial properties of these FC states together with the time-resolved switching between them offer a flexible description of time-varying FC. In this work, I show that in this context PCA can suffer from systematic biases and loss of sensitivity for the purposes of finding time-varying FC. To get around these issues, I propose a novel variety of the HMM, named HMM-PCA, where the states are themselves PCA decompositions. Since PCA is based on the data covariance, the state-specific PCA decompositions reflect distinct patterns of FC. I show, theoretically and empirically, that fusing dimensionality reduction and time-varying FC estimation in one single step can avoid these problems and outperform alternative approaches, facilitating the quantification of transient communication in the brain.

Rapidly Decoding Image Categories From MEG Data Using a Multivariate Short-Time FC Pattern Analysis Approach.

Recent advances in the development of multivariate analysis methods have led to the application of multivariate pattern analysis (MVPA) to investigate the interactions between brain regions using graph theory (functional connectivity, FC) and decode visual categories from functional magnetic resonance imaging (fMRI) data from a continuous multicategory paradigm. To estimate stable FC patterns from fMRI data, previous studies required long periods in the order of several minutes, in comparison to the human brain that categories visual stimuli within hundreds of milliseconds. Constructing short-time dynamic FC patterns in the order of milliseconds and decoding visual categories is a relatively novel concept. In this study, we developed a multivariate decoding algorithm based on FC patterns and applied it to magnetoencephalography (MEG) data. MEG data were recorded from participants presented with image stimuli in four categories (faces, scenes, animals and tools). MEG data from 17 participants demonstrate that short-time dynamic FC patterns yield brain activity patterns that can be used to decode visual categories with high accuracy. Our results show that FC patterns change over the time window, and FC patterns extracted in the time window of 0∼200 ms after the stimulus onset were most stable. Further, the categorizing accuracy peaked (the mean binary accuracy is above 78.6% at individual level) in the FC patterns estimated within the 0∼200 ms interval. These findings elucidate the underlying connectivity information during visual category processing on a relatively smaller time scale and demonstrate that the contribution of FC patterns to categorization fluctuates over time.

Abstract 15029: Atrial Arrhythmias and Pulmonary Hypertension Across Wsph Groups

Introduction: Previous reports have shown that atrial and ventricular arrhythmias (VA) are common in all forms of pulmonary hypertension (PH) with prevalence of supraventricular arrhythmias ranging from 13-25% and VA 27%. Arrhythmias are also associated with clinical worsening events. Reports have limited numbers of patients with a variety of forms of PH and limited data comparing the prevalence of atrial arrhythmias across WSPH groups and whether advancing age, WHO FC, echocardiographic, exercise capacity and hemodynamic patterns are associated with a higher prevalence of atrial arrhythmias. Methods: As part of the PVDOMICS program, we enrolled 1195 PH participants, controls and PH comparators and prospectively collected electrocardiograms (ECGs) at enrollment for 1147 of them. Comparisons across participants with and without atrial fibrillation/atrial flutter (afib/flutter) used analysis of variance (ANOVA) or Kruskal-Wallis test in continuous variables based on distribution with statistics presented as mean + SD or median [P25, P75] respectively. Pearson’s chi-square test was used for categorical variables. Results: Of 732 PH participants (467F) who had ECGs available at baseline, 527 (72.0%) were in normal sinus rhythm and 62 had afib/flutter (8.5%). Afib/flutter participants were older (75 + 8 vs 57 + 14 years; p&lt;0.001), most commonly in WSPH group 2 (28.1%) vs. WSPH 1 (3.3%), WSPH 3(6.5%) and WSPH group 4 (3.7%), had predominantly WHO FC II/III symptoms (89.5%), higher RAP (12.6 + 5 vs.7.8 + 5mmHg; p&lt;0.001) and PCWPm (17.8 + 5.8 vs. 12.3 + 6.2mmHg; p&lt;0.001) and lower CO (4.3+ 1.5 vs. 5.2 +1.8L/min;p&lt;0.001). Participants with afib/flutter had lower 6MWD 277 + 117 vs.356 + 134meters (p&lt;0.001) than PH participants without. Echocardiography demonstrated increase in LA volume index (mL/m2), RA volume (mL) and tricuspid regurgitation severity with reduced TAPSE and LVEF (54 + 10 vs. 61 + 10%;p&lt;0.001). ProBNP was higher in afib/flutter participants 2029 [1073, 2964] vs.268 [100, 1074]pg/ml; P&lt;0.001) Conclusions: In this analysis of atrial arrhythmias in a large cohort of PH participants across all WSPH groups, afib/flutter was present most often in older WSPH group 2 patients who demonstrated clinical and hemodynamic features of worse heart failure.

Dynamic neural circuit disruptions associated with antisocial behaviors.

Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting‐state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. This is the first study using low‐frequency fluctuations of the dynamic FC to unravel potential system‐level neural correlates with ASB. Specifically, we individually associated the dynamic FC patterns with the ASB scores (measured by Antisocial Process Screening Device) of the male offenders (age: 23.29 ± 3.36 years) based on machine learning. Results showed that the dynamic FCs were associated with individual ASB scores. Moreover, we found that it was mainly the inter‐network dynamic FCs that were negatively associated with the ASB severity. Three major high‐order cognitive functional networks and the sensorimotor network were found to be more associated with ASB. We further found that impaired behavior in the ASB subjects was mainly associated with decreased FC dynamics in these networks, which may explain why ASB subjects usually have impaired executive control and emotional processing functions. Our study shows that temporal variation of the FC could be a promising tool for ASB assessment, treatment, and prevention.

Aberrant static and dynamic functional connectivity of the executive control network in lung cancer patients after chemotherapy: a longitudinal fMRI study

ObjectiveThe purpose of the current study was to investigate chemotherapy-related variations in the intrinsic static and dynamic functional connectivity (sFC and dFC, respectively) of the executive control network (ECN) in lung cancer patients.Materials and methodsIn this study, we evaluated 18 lung cancer patients scanned before and after adjuvant chemotherapy treatment and compared the patients with 21 healthy controls (HCs). All subjects underwent resting-state functional MRI (rs-fMRI). We constructed the sFC and dFC of the bilateral dorsolateral prefrontal cortex (DLPFC) using a sliding-window approach, and the correlations between the changed sFC or dFC and cognitive performance were analyzed.ResultsWhole-brain sFC analysis showed that the lung cancer patients showed significant FC pattern changes in the bilateral DLPFC, mainly in the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus, left superior temporal gyrus, left inferior parietal lobe and the right insula. Furthermore, after chemotherapy, the lung cancer patients showed significantly reduced dFC variability between the right DLPFC and right precuneus compared with HCs. In addition, the decreased dFC between the right DLPFC and left SFG in the lung cancer patients after chemotherapy in state 1 and between the right DLPFC and left insula in the lung cancer patients before chemotherapy in state 2 were negatively correlated with MoCA scores ((r = -0.520, p = 0.039; r = -0.548, p = 0.028, respectively).ConclusionsOur results reveal that dynamic connectivity analysis is more effective and sensitive than methods that assume static brain states for linking brain FC patterns and chemotherapy.

The Insular Subregions Topological Characteristics of Patients With Bipolar Depressive Disorder.

The insular cortex appears to have a crucial role in emotional processing and cognitive control in bipolar disorder (BD). However, most previous studies focused on the entire insular region of BD, neglecting the topological profile of its subregions. Our study aimed to investigate its subregion topological characteristics using the resting-state functional connectivity (rsFC) in patients with BD on depression episode. The magnetic resonance imaging (MRI) data of 28 depressed BD patients and 28 age- and gender-matched healthy controls (HCs) were acquired. We observed that compared to HCs, depressed patients with BD exhibited significantly decreased rsFC between the right ventral anterior insula (vAI) and the left middle temporal gyrus/the right angular, the right dorsal anterior insula (dAI) and the left precuneus, as well as the right posterior insula and the right lingual gyrus. Furthermore, hyperconnectivity was observed between the left dAI and the left medial frontal gyrus, as well as right dAI and left superior temporal gyrus in BD depression. However, no significant group effect was observed between aberrant FC patterns and clinical variables. These findings revealed the functional connectivity patterns of insular subregions for the depressed BD patients, suggesting the potential neural substrate of insular subregions involved in depressive episode of BD. Hence, these results may provide a neural substrate for the potential treatment target of BD on depression episode.

Aberrant functional connectivity of the cingulate subregions in right-sided temporal lobe epilepsy.

Patients with temporal lobe epilepsy (TLE) have been indicated to exhibit abnormal resting-state functional connectivity (rsFC) of the cingulate cortex. However, it has remained elusive whether cingulate subregions show different connectivity patterns in TLE. The present study aimed to investigate the differences in rsFC of each cingulate subregion between patients with right-sided TLE (rTLE) and healthy controls (HCs), as well as their association with executive control performance in rTLE. A total of 27 patients with rTLE and 20 age-, sex- and education-matched healthy controls were recruited and all participants underwent resting-state functional MRI and an attention network test for the assessment executive control function. In each hemisphere, the cingulate gyrus (CG) was divided into CG-1 (dorsal area 23; A23d), CG-2 (rostroventral area 24; A24rv), CG-3 (pregenual area 32; A32p), CG-4 (ventral area 23; A23v), CG-5 (caudodorsal area 24; A24cd), CG-6 (caudal area 24; A23c) and CG-7 (subgenual area 32; A32sg). Pearson's correlation analysis was performed to assess the correlation between the altered FCs of the cingulate subregions and clinical variables. In patients with rTLE, the majority of the cingulate subregions exhibited decreased rsFC; this was primarily restricted to the right CG-2, the bilateral CG-6 and the bilateral CG-7. Increased rsFC was only detected in rTLE restricted to the left CG-1. Impairments in executive control efficiency were identified in patients with rTLE in comparison with the HCs. Significant alterations in rsFC between the cingulate subregion and the brain regions were mostly decreased (and some slightly increased), suggesting that FC may potentially have a left-side advantage in patients with rTLE. FC variations of the cingulate subregions were indicated to be specific to rTLE. In addition, increased connectivity in the left CG-1 and left superior frontal gyrus were negatively correlated with executive control performance, suggesting a compensatory mechanism on executive control deficits in pathological conditions. This information on differentially altered FC patterns of the cingulate subregions may provide a deeper understanding of the complex neurological mechanisms and executive control dysfunctions underlying rTLE.

Detecting the Information of Functional Connectivity Networks in Normal Aging Using Deep Learning From a Big Data Perspective.

A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.

Role of Mac-1 integrin in generation of extracellular vesicles with antibacterial capacity from neutrophilic granulocytes

ABSTRACT Production of extracellular vesicles (EVs) involved in intercellular communication is a common capacity of most cell types. Upon encountering opsonized microorganisms, neutrophilic granulocytes release EVs that compromise bacterial growth. We carried out a systematic investigation of the involvement of potential opsonin receptors in EV-generation from human and murine neutrophils. Applying flow cytometric, proteomic and functional analysis as well as using genetically modified mice, we demonstrate that formation of antibacterial EVs depends upon stimulation of the multifunctional Mac-1 integrin complex, also called as complement receptor 3 (CR3), whereas activation of immunoglobulin binding Fc receptors or pattern recognition receptors alone or in combination is ineffective. Mac-1/CR3 stimulation and downstream tyrosine kinase signalling affect both the numbers, the cargo content and the antibacterial capacity of the produced vesicles. In contrast, Mac-1/CR3 signalling is not required for spontaneous EV formation, clearly indicating the existence of separate molecular pathways in EV biogenesis. We propose that EVs are “tailor-made” with different composition and functional properties depending on the environmental circumstances.

Modeling the Switching Behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment.

The need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI) is evident. MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings on the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys) function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings. The activity of different brain rhythms {δ, 𝜃, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine 90 anatomical regions of interest (ROIs). A DFCG was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 MCI patients and 22 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments, which further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. An estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). The NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (n μstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation. We succeeded in gaining a high classification accuracy on the blind dataset (85%), which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could properly manipulate the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

Assessing spatiotemporal variability of brain spontaneous activity by multiscale entropy and functional connectivity

Brain signaling occurs across a wide range of spatial and temporal scales, and analysis of brain signal variability and synchrony has attracted recent attention as markers of intelligence, cognitive states, and brain disorders. However, current technologies to measure brain signals in humans have limited resolutions either in space or in time and cannot fully capture spatiotemporal variability, leaving it untested whether temporal variability and spatiotemporal synchrony are valid and reliable proxy of spatiotemporal variability in vivo. Here we used optical voltage imaging in mice under anesthesia and wakefulness to monitor cortical voltage activity at both high spatial and temporal resolutions to investigate functional connectivity (FC, a measure of spatiotemporal synchronization), Multi-Scale Entropy (MSE, a measure of temporal variability), and their relationships to Regional Entropy (RE, a measure of spatiotemporal variability). We observed that across cortical space, MSE pattern can largely explain RE pattern at small and large temporal scales with high positive and negative correlation respectively, while FC pattern strongly negatively associated with RE pattern. The time course of FC and small scale MSE tightly followed that of RE, while large scale MSE was more loosely coupled to RE. fMRI and EEG data simulated by reducing spatiotemporal resolution of the voltage imaging data or considering hemodynamics yielded MSE and FC measures that still contained information about RE based on the high resolution voltage imaging data. This suggested that MSE and FC could still be effective measures to capture spatiotemporal variability under limitation of imaging modalities applicable to human subjects. Our results support the notion that FC and MSE are effective biomarkers for brain states, and provide a promising viewpoint to unify these two principal domains in human brain data analysis.