Functional Connectivity Metrics Research Articles

Pseudo-resting-state functional MRI derived from dynamic susceptibility contrast perfusion MRI can predict cognitive impairment in glioma.

Resting-state functional MRI (rs-fMRI) can be used to estimate functional connectivity (FC) between different brain regions, which may be of value for identifying cognitive impairment in patients with brain tumors. Unfortunately, neither rs-fMRI nor neurocognitive assessments are routinely assessed clinically, mostly due to limitations in exam time and cost. Since DSC perfusion MRI is often used clinically to assess tumor vascularity and similarly uses a gradient echo-EPI sequence for T2*sensitivity, we theorized a "pseudo-rs-fMRI" signal could be derived from DSC perfusion to simultaneously quantify FC and perfusion metrics, and these metrics can be used to estimate cognitive impairment in patients with brain tumors. N=24 consecutive patients with gliomas were enrolled in a prospective study that included DSC perfusion MRI, rs-fMRI, and neurocognitive assessment. Voxel-wise modeling of contrast bolus dynamics during DSC acquisition was performed and then subtracted from the original signal to generate a residual "pseudo-rs-fMRI" signal. Following the pre-processing of pseudo-rs-fMRI, full rs-fMRI, and a truncated version of the full rs-fMRI (first 100 timepoints) data, the default mode, motor, and language network maps were generated with atlas-based ROIs. Dice scores were calculated for the resting-state network maps from pseudo-rs-fMRI and truncated rs-fMRI using the full rs-fMRI maps as reference. Seed-to-voxel and ROI-to-ROI analyses were performed to assess FC differences between cognitively impaired and non-impaired patients. Dice scores for the group-level and patient-level (mean±SD) default mode, motor, and language network maps using pseudo-rs-fMRI were 0.905/0.689±0.118 (group/patient), 0.973/0.730±0.124, and 0.935/0.665±0.142, respectively. There was no significant difference in Dice scores between pseudo-rs-fMRI and the truncated rs-fMRI default mode (P=0.97) or language networks (P=0.30), but there was a difference in motor networks (P=0.02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (Sensitivity=84.6%, Specificity=63.6%, ROC AUC=0.7762±0.0954 (SE), P=0.0221) and performance was not significantly different than full rs-fMRI predictions (AUC=0.8881±0.0733 (SE), P=0.0013, P=0.29 compared to pseudo-rs-fMRI). DSC perfusion MRI-derived pseudo-rs-fMRI data can be used to perform typical rs-fMRI FC analyses that may identify cognitive decline in patients with brain tumors while still simultaneously performing perfusion analyses.ABBREVIATIONS: AUC = Area under curve; BOLD = Blood oxygenation level dependent; FC = Functional connectivity; MNI = Montreal Neurological Institute; ROC = Receiver operating characteristic; Rs-fMRI = Resting-state functional MRI.

Functional and structural connectivity success predictors of real-time fMRI neurofeedback targeting DLPFC: Contributions from central executive, salience, and default mode networks.

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF), a training method for the self-regulation of brain activity, has shown promising results as a neurorehabilitation tool, depending on the ability of the patient to succeed in neuromodulation. This study explores connectivity-based structural and functional success predictors in an NF n-back working memory paradigm targeting the dorsolateral prefrontal cortex (DLPFC). We established as the NF success metric the linear trend on the ability to modulate the target region during NF runs and performed a linear regression model considering structural and functional connectivity (intrinsic and seed-based) metrics. We found a positive correlation between NF success and the default mode network (DMN) intrinsic functional connectivity and a negative correlation with the DLPFC-precuneus connectivity during the 2-back condition, indicating that success is associated with larger uncoupling between DMN and the executive network. Regarding structural connectivity, the salience network emerges as the main contributor to success. Both functional and structural classification models showed good performance with 77% and 86% accuracy, respectively. Dynamic switching between DMN, salience network and central executive network seems to be the key for neurofeedback success, independently indicated by functional connectivity on the localizer run and structural connectivity data.

Effect of blood oxygen-level-dependent signal data trimming on functional connectivity metrics

In the big data era, with a lack of comparable functional neuroimaging data, researchers try to combine heterogeneous data of different lengths, trimming those to the same number of timepoints (NTPs). However, the effects of trimming blood-oxygen-level dependent (BOLD) signal data on functional connectivity (FC) are still poorly understood.Resting-state functional magnetic resonance imaging data from thirty healthy subjects were pre-processed for five different NTPs, from which FC matrices were computed. These BOLD signal correlation matrices were binarized for several thresholds, excluding weak correlations. Graph metrics were computed to study FC differences between different NTPs. The study included node degree analysis for each brain region and assessment of small-worldness coefficients (σ and ω), whereas in small-world networks, characteristic values are σ > 1 and ω≈ 0, indicating a balance between high clustering coefficients and short characteristic path lengths.A tendency of decreasing global network degrees for higher NTPs was observed, translating the loss of stronger correlations with longer BOLD signals. Trimming such data affects brain regions differently, probably due to brain network dynamics. Regarding small-worldness, we observed that σ was greater than 1 for all the different NTPs, showing an increasing trend for higher NTPs (median value: σBRAIN= 3.05). In addition, ω consistently remained greater than 0 for all NTPs, gradually approaching 0 as the NTPs increased (median value ωBRAIN= 0.20). As such, the results suggest a tendency for an increase of small-world properties for increasing NTPs. Nonetheless, the overall properties of brain networks almost remain constant. In conclusion, trimming BOLD signal data leads to small differences in FC.

Modeling the association between functional connectivity and lateralization with the activity flow framework

The human brain is localized and distributed. On the one hand, each cognitive function tends to involve one hemisphere more than the other, also known as the principle of lateralization. On the other hand, interactions among brain regions in the form of functional connectivity (FC) are indispensable for intact function. Recent years have seen growing interest in the association between lateralization and FC. However, FC metrics vary from spurious correlation to causal associations. If lateralization manifests local processing and causal network interactions, more causally valid FC metrics should predict lateralization index (LI) better than FC based on simple correlations. The present study directly investigates this hypothesis within the activity flow framework to compare the association between lateralization and four brain connectivity metrics: correlation-based FC, multiple-regression FC, partial-correlation FC, and combinedFC. We propose two modeling approaches: the one-step approach, which models the relationship between LI and FC directly, and the two-step approach, which predicts the brain activation and calculates the LI. Our results indicated that multiple-regression FC, partial-correlation FC, and combinedFC could significantly improve the model prediction compared to correlation-based FC, which was consistent in a spatial working memory task (typically right-lateralized) and a language task (typically left-lateralized). The one-step and two-step approach yielded similar conclusions. In addition, the finding was replicated in a clinical sample of schizophrenia (SZ), bipolar disorder (BP), and attention deficit hyperactivity disorder (ADHD). The present study suggests that the causal interactions among brain regions help shape the lateralization pattern.

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

Dynamic functional connectivity (FC) analyses have provided ample information on the disturbances of global functional brain organization in patients with schizophrenia. However, our understanding about the dynamics of local FC in never-treated first episode schizophrenia (FES) patients is still rudimentary. Dynamic Regional Phase Synchrony (DRePS), a newly developed dynamic local FC analysis method that could quantify the instantaneous phase synchronization in local spatial scale, overcomes the limitations of commonly used sliding-window methods. The current study performed a comprehensive examination on both the static and dynamic local FC alterations in FES patients (N = 74) from healthy controls (HCs, N = 41) with resting-state functional magnetic resonance imaging using DRePS, and compared the static local FC metrics derived from DRePS with those calculated from two commonly used regional homogeneity (ReHo) analysis methods that are defined based on Kendall's coefficient of concordance (KCC-ReHo) and frequency coherence (Cohe-ReHo). Symptom severities of FES patients were assessed with a set of clinical scales. Cognitive functions of FES patients and HCs were assessed with the MATRICS consensus cognitive battery. Group-level analysis revealed that compared with HCs, FES patients exhibited increased static local FC in right superior, middle temporal gyri, hippocampus, parahippocampal gyrus, putamen, and bilateral caudate nucleus. Nonetheless, the dynamic local FC metrics did not show any significant differences between the two groups. The associations between all local FC metrics and clinical characteristics manifested scores were explored using a relevance vector machine. Results showed that the Global Assessment of Functioning score highest in past year and the Brief Visuospatial Memory Test-Revised task score were statistically significantly predicted by a combination of all static and dynamic features. The diagnostic abilities of different local FC metrics and their combinations were compared by the classification performance of linear support vector machine classifiers. Results showed that the inclusion of zero crossing ratio of DRePS, one of the dynamic local FC metrics, alongside static local FC metrics improved the classification accuracy compared to using static metrics alone. These results enrich our understanding of the neurocognitive mechanisms underlying schizophrenia, and demonstrate the potential of developing diagnostic biomarker for schizophrenia based on DRePS.

Graph Autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification.

Brain functional connectivity (FC) networks inferred from functional magnetic resonance imaging (fMRI) have shown altered or aberrant brain functional connectome in various neuropsychiatric disorders. Recent application of deep neural networks to connectome-based classification mostly relies on traditional convolutional neural networks (CNNs) using input FCs on a regular Euclidean grid to learn spatial maps of brain networks neglecting the topological information of the brain networks, leading to potentially sub-optimal performance in brain disorder identification. We propose a novel graph deep learning framework that leverages non-Euclidean information inherent in the graph structure for classifying brain networks in major depressive disorder (MDD). We introduce a novel graph autoencoder (GAE) architecture, built upon graph convolutional networks (GCNs), to embed the topological structure and node content of large fMRI networks into low-dimensional representations. For constructing the brain networks, we employ the Ledoit-Wolf (LDW) shrinkage method to efficiently estimate high-dimensional FC metrics from fMRI data. We explore both supervised and unsupervised techniques for graph embedding learning. The resulting embeddings serve as feature inputs for a deep fully-connected neural network (FCNN) to distinguish MDD from healthy controls (HCs). Evaluating our model on resting-state fMRI MDD dataset, we observe that the GAE-FCNN outperforms several state-of-the-art methods for brain connectome classification, achieving the highest accuracy when using LDW-FC edges as node features. The graph embeddings of fMRI FC networks also reveal significant group differences between MDD and HCs. Our framework demonstrates the feasibility of learning graph embeddings from brain networks, providing valuable discriminative information for diagnosing brain disorders.

Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images.

This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies.

Cerebellar‐cortical functional connectivity is associated with Alzheimer’s disease pathology and cerebellar atrophy

AbstractBackgroundBrain atrophy has been associated with Alzheimer’s disease (AD) pathology. Previous research also suggests that functional connectivity (FC) among distributed neural networks is disrupted in preclinical AD. The cerebellum has been thought to play a crucial role in motor control, yet recent evidence points to its additional role in non‐motor domains as well as cognitive decline in AD. Cerebellar volume has been shown to decline along the course of AD progression. Moreover, neuroimaging studies have delineated intrinsic cerebellar‐cortical functional networks that presumably support cognitive functions. Despite the accumulating findings on neurodegeneration or FC, how cerebellar FC and atrophy relate to AD pathology in the early stage has not been extensively investigated.MethodData from 83 cognitively normal older adults (mean 71.0 yrs, 66% females) in the Alzheimer’s Disease Neuroimaging Initiative were analyzed. Volumetric data were obtained from FreeSurfer 6.0 segmentation. Resting state fMRI scans were preprocessed using the CONN Toolbox. Aß and tau standardized uptake value ratio (SUVR) data were obtained from [18F]‐Florbetapir or [18F]‐Florbetaben (Aß) and [18F]‐Flortaucipir (tau) PET scans. For Aß, SUVR values were transformed to the Centiloid scale, and a global summary measure was used. For tau, SUVR values in the temporal meta regions of interest were used. Seed‐based functional connectivity metrics were computed using the 7 cerebellar parcellations (Buckner et al., 2011) that are functionally coupled to different cortical networks (Yeo et al., 2011).ResultCerebellar volume showed no significant relationship with amyloid or tau SUVR. By contrast, hippocampal atrophy was positively associated with amyloid or tau SUVR and showed trending significance (ps < 0.1). Increased Aß burden was associated with increased cerebellar‐cortical FC in the attention, frontoparietal, and default mode networks. Increased tau load was associated with increased cerebellar‐cortical FC in the visual, somatomotor, and attention networks. Cerebellar volume was associated with cerebellar‐cortical FC in the visual, somatomotor, attention networks, but not with the limbic, frontoparietal, or default mode networks.ConclusionOur findings suggest that functional cerebellar‐cortical connectivity varies with amyloid and tau pathology in different networks, and such functional changes may precede cerebellar atrophy in preclinical AD.

Cerebellar‐cortical functional connectivity is associated with Alzheimer’s disease pathology and cerebellar atrophy

AbstractBackgroundBrain atrophy has been associated with Alzheimer’s disease (AD) pathology. Previous research also suggests that functional connectivity (FC) among distributed neural networks is disrupted in preclinical AD. The cerebellum has been thought to play a crucial role in motor control, yet recent evidence points to its additional role in non‐motor domains as well as cognitive decline in AD. Cerebellar volume has been shown to decline along the course of AD progression. Moreover, neuroimaging studies have delineated intrinsic cerebellar‐cortical functional networks that presumably support cognitive functions. Despite the accumulating findings on neurodegeneration or FC, how cerebellar FC and atrophy relate to AD pathology in the early stage has not been extensively investigated.MethodData from 83 cognitively normal older adults (mean 71.0 yrs, 66% females) in the Alzheimer’s Disease Neuroimaging Initiative were analyzed. Volumetric data were obtained from FreeSurfer 6.0 segmentation. Resting state fMRI scans were preprocessed using the CONN Toolbox. Aβ and tau standardized uptake value ratio (SUVR) data were obtained from [18F]‐Florbetapir or [18F]‐Florbetaben (Aβ) and [18F]‐Flortaucipir (tau) PET scans. For Aβ, SUVR values were transformed to the Centiloid scale, and a global summary measure was used. For tau, SUVR values in the temporal meta regions of interest were used. Seed‐based functional connectivity metrics were computed using the 7 cerebellar parcellations (Buckner et al., 2011) that are functionally coupled to different cortical networks (Yeo et al., 2011).ResultCerebellar volume showed no significant relationship with amyloid or tau SUVR. By contrast, hippocampal atrophy was positively associated with amyloid or tau SUVR and showed trending significance (ps < 0.1). Increased Aβ burden was associated with increased cerebellar‐cortical FC in the attention, frontoparietal, and default mode networks. Increased tau load was associated with increased cerebellar‐cortical FC in the visual, somatomotor, and attention networks. Cerebellar volume was associated with cerebellar‐cortical FC in the visual, somatomotor, attention networks, but not with the limbic, frontoparietal, or default mode networks.ConclusionOur findings suggest that functional cerebellar‐cortical connectivity varies with amyloid and tau pathology in different networks, and such functional changes may precede cerebellar atrophy in preclinical AD.

Localization of Silent Cerebral Infarcts in Children with Sickle Cell Disease Impacts Structural Disconnection

Silent cerebral infarcts (SCIs) occur in 40% of children with sickle cell disease (SCD) by 18 years of age and are associated with cognitive dysfunction. SCIs localize to the border zone of the brain, but the significance of this localization is unclear. In adults with stroke, an infarct's disruption of white matter (WM) tracts, or structural disconnection, is more strongly associated with the functional impact of the infarct than the size of the lesion. We aim to define the WM tracts disconnected by SCIs and the relationship between structural disconnection and functional connectivity (FC) in SCD. Simulated and real-world data were used to test the hypothesis that specific WM tracts are vulnerable to disconnection if an infarct occurs in the region at highest risk for SCIs, and that FC will decrease with increased structural disconnection in SCD. Utilizing data from the Silent Infarct Transfusion (SITT) trial, the regions at greatest risk for SCIs were defined (risk region, PMID 30061156). A simulated dataset comprised of 1000 infarcts within the risk region and 1000 infarcts within the white matter outside of the risk region was created. The real-world dataset was comprised of 44 participants with SCD and SCIs (median age 13.0 [11.0, 15.8] years, 29 female/15 male). The Human Connectome Project (HCP) 842 atlas contains thousands of streamlines comprising 28 WM tracts (excluding brainstem and cerebellar tracts). The Lesion Connectivity Toolbox defined streamlines within the HCP 842 atlas that intersected with each individual lesion, permitting quantification of two measures of structural disconnection: tract-wise disconnection (the percentage of streamlines within each tract that intersect a lesion) and global disconnection (the average percentage of streamlines connecting each pair of gray matter (GM) parcels in the FC parcellation (PMID 25316338)). Tract density images (TDi) illustrate the number of streamlines impacted by a lesion within each voxel of the brain permitting computation of total lesion impact as the volume of the TDi image associated with each lesion. Resting state functional connectivity MRI was obtained in 27 of the real-world participants for computation of whole brain modularity as a metric of global FC. The WM tracts with the highest mean tract-wise disconnection due to lesions within the risk region were the posterior and mid-anterior corpus callosum, superior longitudinal fasciculus, frontal aslant tract, and middle longitudinal fasciculus while the inferior frontal occipital fasciclus, anterior commissure, inferior longitudinal fasciculus, posterior corpus callosum, and arculate fasciculus were most impacted by lesions outside of the risk region. Numerous tracts showed a significant difference in tract-wise disconnection resulting from lesions in the risk region vs. lesions outside the risk region. The tracts impacted due to real-world lesions were similar to tracts impacted from lesions within the risk region (Table 1). Despite lesioned volume being equivalent (p = 0.31) for simulated data within and outside the risk region, the average volume of lesion impact as measured by volume of the TDi image was significantly larger for lesions occurring inside the risk region (p < 0.001; Fig 1). In the real-world dataset, whole brain modularity, a metric of global FC, decreased as global structural disconnection increased (r = -0.49, p =0.009) Using simulated data that was validated with a real-world dataset, specific WM tracts vulnerable to SCIs were identified. Previous work on cerebral hemodynamics in SCD elucidated the mechanism of injury in the border zone, and these results demonstrate the significance of the localization of this injury to the border zone. SCIs have a far-reaching effect as lesions in the risk region within the border zone were associated with a greater volume of structural disconnection compared to lesions of the same size distributed outside the risk region. These results suggest that the regions in which the SCIs localize represent “high impact” areas of the brain, where a lesion of a given volume may intersect a large number of streamlines. Further, given the correlation between global structural disconnection and whole brain modularity, these data link structural disconnection with function of the brain, suggesting these “high impact” regions may also play a key role in mediating communication between functionally connected GM regions.

A new strategy for optimizing marine protection networks by considering functional connectivity: An example of Tachypleus tridentatus

The concept of creating a conservation network through the establishment of multiple protected areas to protect marine fauna is gaining momentum. To effectively establish a network that yields favorable outcomes, it is imperative to identify suitable areas that foster optimal connectivity. Given the swift development of coastal areas, it has become necessary to develop cost-effective measures that yield maximum conservation benefits. However, exploration of these ideas for application to marine conservation networks remains scarce. In this study, we used the horseshoe crab (Tachypleus tridentatus) in the Beibu Gulf as an example. The species and environmental data were collected through field sampling and remote sensing. The species distribution model, the least-cost path model, and circuit theory were employed to establish a marine conservation network. The functional connectivity metrics were employed to optimize both the interconnection and economic efficiency of the network. The results indicated a low presence of adult horseshoe crabs in the Beibu Gulf, with average densities of 1.47 and 1.43 ind./km2 among the 38 surveyed stations in 2018 and 2019, respectively. The species distribution model accurately predicted juvenile presence in mudflats and adult presence offshore, and it identified 43 potential areas that are suitable for the establishment of marine protected areas. The marine protected areas that ranked higher in importance contributed significantly to maintaining the integrity of regional connectivity. The network connectivity shows a law of diminishing returns as the number of marine protected areas increases. The framework developed in this study will provide decision support for building and optimizing marine conservation networks.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer’s using machine learning

BackgroundThe pathological process of Alzheimer’s disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.MethodsThe main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.ResultsOverall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.ConclusionsOur study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Difference in functional connectivity between end-stage renal disease patients with and without restless legs syndrome: A prospective study.

The purpose of this study was to examine differences in functional connectivity between patients with end-stage renal disease (ESRD) with and without restless legs syndrome (RLS). In addition, the study aimed to identify any potential associations between RLS severity and functional connectivity. We enrolled patients with ESRD who had been undergoing hemodialysis. Patients with and without RLS were separated into two groups. Using functional near-infrared spectroscopy (fNIRS) and a graph theory approach, we determined the functional connectivity of patients with ESRD. The data were collected during a 300-s resting state evaluation performed in the dialysis room prior to dialysis. Eighteen of 48 patients with ESRD were diagnosed with RLS, whereas 30 patients did not exhibit RLS symptoms. Notably, functional connectivity metrics differed significantly between patients with and without RLS. Specifically, patients withESRD and RLS displayed higher values for mean clustering coefficient (0.474 vs. 0.352, p = 0.001), global efficiency (0.520 vs. 0.414, p = 0.001), strength (6.538 vs. 4.783, p = 0.001), and transitivity (0.714 vs. 0.521, p = 0.001), while values for diameter (5.451 vs. 7.338, p = 0.002), eccentricity (4.598 vs. 5.985, p = 0.004), and characteristic path length (2.520 vs. 3.271, p = 0.002) were lower in patientswithESRD and RLS compared to those without RLS. In addition, there were correlations between the RLS severity score and the assortative coefficient (r = 0.479, p = 0.044), the small-worldness index (r = -0.475, p = 0.046), and transitivity (r = 0.500, p = 0.034). We demonstrated differences in functional connectivity between patients withESRD with and without RLS, which may shed light on the pathophysiology of RLS. Notably, a number of functional connectivity metrics demonstrated strong associations with RLS severity. Our study also confirmed the applicability of fNIRS as a tool for investigating functional connectivity in patients with RLS.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

Characterizing multiple sclerosis disease progression using a combined structural and functional connectivity metric

PurposeA combined resting state functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) metric called structural and functional connectivity index (SFCI) was recently proposed for tracking disease status and progression in multiple sclerosis (MS). The metric combines fcMRI and transverse diffusivity (TD) along different functional pathways involved in principle symptomatic domains of MS.In a longitudinal study of patients with MS receiving the same MS therapy, initial worsening of transcallosal (TC) motor and frontoparietal (FP) cognitive networks, as measured by fcMRI and DTI over the first year was followed by stabilization in the second year of follow-up. In this study we have (i) probed relationships between individual and composite neurological measures of MS with SFCI and its individual components along TC motor and FP cognitive pathways and (ii) compared sensitivity of SFCI to treatment-induced longitudinal changes with each individual imaging measure. MethodsTwenty five patients with MS (15 female, age 42 ± 8 y) participated in this study and were scanned at 3 T whole body MRI scanner with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at baseline and 6, 12, 18 and 24 months after starting fingolimod. fcMRI and TD along TC and FP pathways were combined to form structural and functional connectivity index (SFCI) at each time point. Correlations between individual/combined neurological measures and individual imaging components/SFCI at baseline and were evaluated and compared. In addition, efficacies of individual and combined imaging metrics in tracking network integrity were compared. ResultsIndividual TD along the TC pathway was significantly inversely correlated with all individual/composite neurological scores. There were moderate correlations of TC and FP components of SFCI with most of the neurological scores, and the pathway-combined SFCI was significantly correlated with all neurological scores. Trend-level increases of both TC and FP fcMRI were observed during the second year of follow-up, both TC and FP TD increased significantly in the first year and then stabilized during the second year. A trend toward a decrease in combined imaging metrics along TC and FP were observed during the first year, followed by a trend toward an increase in these metrics during the second year, while a significant decrease in SFCI during the first year followed by a significant increase during the second year was observed. ConclusionsSFCI was more effective in tracking network integrity/disease progression than individual pathway-specific components, which supports its use as an imaging marker for MS disease status and progression.

Functional brain connectivity indexes derived from low-density EEG of pre-implanted patients as VNS outcome predictors

Objective. In 1/3 of patients, anti-seizure medications may be insufficient, and resective surgery may be offered whenever the seizure onset is localized and situated in a non-eloquent brain region. When surgery is not feasible or fails, vagus nerve stimulation (VNS) therapy can be used as an add-on treatment to reduce seizure frequency and/or severity. However, screening tools or methods for predicting patient response to VNS and avoiding unnecessary implantation are unavailable, and confident biomarkers of clinical efficacy are unclear. Approach. To predict the response of patients to VNS, functional brain connectivity measures in combination with graph measures have been primarily used with respect to imaging techniques such as functional magnetic resonance imaging, but connectivity graph-based analysis based on electrophysiological signals such as electroencephalogram, have been barely explored. Although the study of the influence of VNS on functional connectivity is not new, this work is distinguished by using preimplantation low-density EEG data to analyze discriminative measures between responders and non-responder patients using functional connectivity and graph theory metrics. Main results. By calculating five functional brain connectivity indexes per frequency band upon partial directed coherence and direct transform function connectivity matrices in a population of 37 refractory epilepsy patients, we found significant differences (p < 0.05) between the global efficiency, average clustering coefficient, and modularity of responders and non-responders using the Mann–Whitney U test with Benjamini–Hochberg correction procedure and use of a false discovery rate of 5%. Significance. Our results indicate that these measures may potentially be used as biomarkers to predict responsiveness to VNS therapy.

Bridging structural and functional hydrological connectivity in dryland ecosystems

On dryland hillslopes, vegetation water availability is often subsidized by the redistribution of rainfall runoff from bare soil (sources) to vegetation patches (sinks). In regions where rainfall volumes are too low to support spatially continuous plant growth, such functional connectivity between bare soil and vegetated areas enables the establishment and persistence of dryland ecosystems. Increasing the connectivity within bare soil areas can intensify runoff and increase water losses from hillslopes, disrupting this redistribution and reducing the water available to sustain ecosystem function. Inferring functional connectivity (from bare to vegetated, or within bare areas) from structural landscape features is an attractive approach to enable rapid, scalable characterization of dryland ecosystem function from remote observations. Such inference, however, would rely on metrics of structural connectivity, which describe the contiguity of bare soil areas. Several studies have observed non-stationarity in the relations between functional and structural connectivity metrics as rainfall conditions vary. Consequently, the suitability of using structural connectivity to provide a reliable proxy for functional connectivity remains uncertain and motivates the work here.Relations between structural and functional connectivity metrics are established based on model simulations of rainfall-runoff on hillslopes with varying soil properties and vegetation patterns. These relations vary between two hydrologic limits – a ‘local’ (patch-scale) limit, in which functional connectivity is related to structural connectivity, and a ‘global’ (hillslope-scale) limit, in which functional connectivity is most related to the hillslope vegetation fraction regardless of the structural connectivity of bare soil areas. The transition between these limits within the simulations depends on rainfall intensity and duration, and soil permeability. While the local limit may strengthen positive feedbacks between vegetation and water availability, the implications of these limits for dryland functioning need further exploration, particularly considering the timescale separation between storm runoff production and vegetation growth.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear.In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear phase-to-phase FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting phase-to-phase FC.Throughout all simulated scenarios, lowest performance is obtained with pipelines involving the absolute value of coherency. Further, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. Pipelines that show promising results with our simulated pseudo-EEG data involve the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected phase-to-phase FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed phase-to-phase FC. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies.We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis of EEG data recorded during motor imagery.

Integrating seascape resistances and gene flow to produce area-based metrics of functional connectivity for marine conservation planning

ContextPrioritizing regions that facilitate connectivity among populations is an essential principle for conservation planning. However, the lack of conspicuous geographical and environmental features that constrain dispersal and gene flow throughout life history challenges the characterization of dispersal pathways within a three-dimensional marine realm.ObjectivesTo elucidate regions of high connectivity value in the marine environment, we develop a novel approach that integrates estimates of spatial genetic structure with representation of regions of high dispersal potential for meroplankton, incorporating elements of pelagic larval and benthic adult life history.MethodsSpatial patterns of connectivity were characterized using circuit theory as an inverse function oceanographic- and habitat-based resistance to movement. We integrate emergent spatial patterns of connectivity with population genetic data to account for realized patterns of gene flow across a seascape. We apply this approach to four broadly distributed species in the Northwest Atlantic.ResultsEstimates of resistance to gene flow revealed multiple connectivity barriers not observed in oceanographic or habitat models. Comparison of isolation-by-distance versus isolation-by-resistance revealed genetic variation was best explained by seascape resistance in three of four species, supporting the resistance-based assessments of connectivity. Our approach identified areas of high and low connectivity value for each species, with overlap generally associated with geographic pinch points and areas of low genetic exchange.ConclusionsBy integrating spatial interpolations of gene flow and estimated pathways for dispersal, we develop a novel area-based metric of connectivity that considers life-history based structural constraints to dispersal and observed genetic variation. Outputs from this workflow can reveal regions of connectivity for conservation planning.

Different MRI structural processing methods do not impact functional connectivity computation

Resting-state functional magnetic resonance imaging (rs-fMRI) has become an increasingly popular technique. This technique can assess several features of brain connectivity, such as inter-regional temporal correlation (functional connectivity), from which graph measures of network organization can be derived. However, these measures are prone to a certain degree of variability depending on the analytical steps during preprocessing. Many studies have investigated the effect of different preprocessing steps on functional connectivity measures; however, no study investigated whether different structural reconstructions lead to different functional connectivity metrics. Here, we evaluated the impact of different structural segmentation strategies on functional connectivity outcomes. To this aim, we compared different metrics computed after two different registration strategies. The first strategy used structural information from the 3D T1-weighted image (unimodal), while the second strategy implemented a multimodal approach, where an additional registration step used the information from the T2-weighted image. The impact of these different approaches was evaluated on a sample of 58 healthy adults. As expected, different approaches led to significant differences in structural measures (i.e., cortical thickness, volume, and gyrification index), with the maximum impact on the insula cortex. However, these differences were only slightly translated to functional metrics. We reported no differences in graph measures and seed-based functional connectivity maps, but slight differences in the insula when we compared the mean functional strength for each parcel. Overall, these results suggested that functional metrics are only slightly different when using a unimodal compared to a multimodal approach, while the structural output can be significantly affected.