Functional Connectivity Metrics Research Articles

Altered executive control network connectivity in anti-NMDA receptor encephalitis.

ObjectiveThe goal of this study was to examine whether the static functional connectivity (FC) of the executive control network (ECN) and the temporal properties of dynamic FC states in the ECN can characterize the underlying nature of anti‐N‐methyl‐d‐aspartate (anti‐NMDA) receptor encephalitis and their correlations with cognitive functions.MethodsIn total, 21 patients with anti‐NMDA receptor encephalitis past the acute stage and 23 healthy controls (HCs) underwent a set of neuropsychological tests and participated in a resting‐state fMRI study to analyse the static FC of the ECN and the temporal properties of dynamic FC states in the ECN. In addition, correlation analyses were performed to determine the correlations between the FC metrics and cognitive performance.ResultsPatients with anti‐NMDA receptor encephalitis past the acute stage showed significant cognitive impairments compared to HCs. In accord with the results of neuropsychological tests, static intrinsic FC alterations and changed dynamic FC metrics of ECN were observed in the patients. Importantly, we observed significant correlations between altered ECN metrics and working memory, information processing speed, executive function performance in the patients.InterpretationOur findings suggest that cognitive impairments in patients with anti‐NMDA receptor encephalitis past the acute stage are likely related to altered static and dynamic ECN connectivity. These observations may enhance our understanding of the pathophysiological mechanisms underlying cognitive function in this population.

Sex differences in connectivity in the default mode network in healthy aging adults

AbstractBackgroundTwo‐thirds of individuals diagnosed with Alzheimer’s disease (AD) are women, but the reasons for women’s elevated risk are unknown, and sex differences in brain aging trajectories are poorly understood. Both symptomatic AD and aging are associated with decreased connectivity within the default mode network (DMN), which supports short‐term memory processing, while individuals with preclinical AD show increased connectivity in some DMN regions. We sought to identify sex‐based differences in DMN connectivity over the lifespan in a large cross‐sectional study of healthy adults.MethodWe examined functional MRI data from a cross‐sectional cohort of n=595 cognitively normal individuals (aged 36‐100 years; 249 females) drawn from the Human Connectome Project‐Aging (HCP‐A) dataset (Harms et al., 2018; Bookheimer et al., 2019). Preprocessing followed our previously described pipeline, with stringent motion thresholds applied. The four resting‐state scans for each individual were averaged and masked to a standard DMN parcellation (Yeo et al., 2011). We calculated within‐network connectivity of each DMN voxel using the intrinsic connectivity distribution (ICD) (Scheinost et al., 2012), a robust voxel‐based metric of functional connectivity, and used a general linear model to assess the main effect of sex on within‐network ICD with increasing age.ResultFemales showed relative hyperconnectivity in posterior DMN with age compared to males, but lower connectivity in anterior nodes (Fig 1). Specifically, z‐scores for female minus male contrasts for posterior clusters were 3.9 (posterior cingulate), 4.5 (R angular gyrus), and 4.7 (L), and for anterior clusters were ‐4.1 (R frontal pole) and ‐4.3 (L).ConclusionIn a large sample of healthy aging adults, women showed increased connectivity in posterior, and decreased connectivity in anterior, nodes of the DMN compared with their aging male counterparts. Interestingly, individuals with preclinical AD show a similar pattern of posterior hyperconnectivity, often interpreted as the compensatory response of a network under stress. These findings provide early clues to a potential role for network‐level sex differences in explaining women’s increased vulnerability to AD as they age.

Functional connectivity using high density EEG shows competitive reliability and agreement across test/retest sessions

BackgroundElectrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric. New methodsWe undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity. ResultsPower envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn’t strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals. ConclusionsThe competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.

Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation.

Children with medically refractory epilepsy (MRE) require resective neurosurgery to achieve seizure freedom, whose success depends on accurate delineation of the epileptogenic zone (EZ). Functional connectivity (FC) can assess the extent of epileptic brain networks since intracranial EEG (icEEG) studies have shown its link to the EZ and predictive value for surgical outcome in these patients. Here, we propose a new noninvasive method based on magnetoencephalography (MEG) and high-density (HD-EEG) data that estimates FC metrics at the source level through an "implantation" of virtual sensors (VSs). We analyzed MEG, HD-EEG, and icEEG data from eight children with MRE who underwent surgery having good outcome and performed source localization (beamformer) on noninvasive data to build VSs at the icEEG electrode locations. We analyzed data with and without Interictal Epileptiform Discharges (IEDs) in different frequency bands, and computed the following FC matrices: Amplitude Envelope Correlation (AEC), Correlation (CORR), and Phase Locking Value (PLV). Each matrix was used to generate a graph using Minimum Spanning Tree (MST), and for each node (i.e., each sensor) we computed four centrality measures: betweenness, closeness, degree, and eigenvector. We tested the reliability of VSs measures with respect to icEEG (regarded as benchmark) via linear correlation, and compared FC values inside vs. outside resection. We observed higher FC inside than outside resection (p<0.05) for AEC [alpha (8-12 Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data with IEDs and AEC theta (4-8 Hz) on data without IEDs for icEEG, AEC broadband (1-50 Hz) on data without IEDs for MEG-VSs, as well as for all centrality measures of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG and VSs metrics presented high correlation (0.6-0.9, p<0.05). Our data support the notion that the proposed method can potentially replicate the icEEG ability to map the epileptogenic network in children with MRE.Clinical Relevance - The estimation of FC with noninvasive techniques, such as MEG and HD-EEG, via VSs is a promising tool that would help the presurgical evaluation by delineating the EZ without waiting for a seizure to occur, and potentially improve the surgical outcome of patients with MRE undergoing surgery.

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals.

The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

Resting state predicts neural activity during reward-guided decision making: An fMRI study on Balloon Analogue Risk Task

In our previous work [8], we have shown that resting state (RS) functional connectivity metrics are significantly related with behavioural performance at Balloon Analogue Risk Task (BART). In the present study we investigated the hypothesis of an association between RS metrics and neural activity evoked by BART execution. A group-level whole-brain regression was run to assess whether RS metrics predict brain activation during the BART, in a sample of 35 young healthy adults (mean age 23 ± 2 years, 25 F). Results complete the previous findings showing that RS is also significantly associated with the neural activity during BART execution. Specifically, ALFF is positively associated with the activity of both the right inferior parietal lobule and the left caudate. These new results are coherent with previous evidence indicating RS abnormalities in clinical conditions characterised by symptoms of impulse control disorders, and further suggest that RS might be a stable predictor of both behavioural indices and neural correlates of impulsivity and of reward-guided decision-making.

Kynurenines increase MRS metabolites in basal ganglia and decrease resting-state connectivity in frontostriatal reward circuitry in depression

Inflammation is associated with the development of anhedonia in major depression (MD), but the pathway by which inflammatory molecules gain access to the brain and lead to anhedonia is not clear. Molecules of the kynurenine pathway (KP), which is activated by inflammation, readily influx into the brain and generate end products that alter brain chemistry, disrupt circuit functioning, and result in the expression of inflammatory behaviors such as anhedonia. We examined the impact of plasma and CSF KP metabolites on brain chemistry and neural function using multimodal neuroimaging in 49 depressed subjects. We measured markers of glial dysfunction and distress including glutamate (Glu) and myo-inositol in the left basal ganglia using magnetic resonance spectroscopy (MRS); metrics of local activity coherence (regional homogeneity, ReHo) and functional connectivity from resting-state functional MRI measures; and anhedonia from the Inventory for Depressive Symptoms-Self Report Version (IDS-SR). Plasma kynurenine/tryptophan (KYN/TRP) ratio and cerebrospinal fluid (CSF) 3-hydroxykynurenine (3HK) were associated with increases in left basal ganglia myo-inositol. Plasma kynurenic acid (KYNA) and KYNA/QA were associated with decreases and quinolinic acid (QA) with increases in left basal ganglia Glu. Plasma and CSF KP were associated with decreases in ReHo in the basal ganglia and dorsomedial prefrontal regions (DMPFC) and impaired functional connectivity between these two regions. DMPFC-basal ganglia mediated the effect of plasma and CSF KP on anhedonia. These findings highlight the pathological impact of KP system dysregulation in mediating inflammatory behaviors such as anhedonia.

A Wavelet-Based Approach for Estimating Time-Varying Connectivity in Resting-State Functional Magnetic Resonance Imaging.

Introduction: The selection of an appropriate window size, window function, and functional connectivity (FC) metric in the sliding window method is not straightforward due to the absence of ground truth. Methods: A previously proposed wavelet-based method was accordingly adjusted for estimating time-varying FC (TVFC) and was applied to a large high-quality, low-motion dataset of 400 resting-state functional magnetic resonance imaging data. Specifically, the wavelet coherence magnitude and relative phase were averaged across wavelet (frequency) scales to yield TVFC and synchronization patterns. To assess whether the observed fluctuations in TVFC were statistically significant (dynamic FC [dFC]; the distinction between TVFC and dFC is intentional), surrogate data were generated using the multivariate phase randomization (MVPR) and multivariate autoregressive randomization (MVAR) methods to define the null hypothesis of dFC absence. Results: By averaging across all frequencies, core regions of the default mode network (DMN; medial prefrontal and posterior cingulate cortices, inferior parietal lobes, hippocampal formation) were found to exhibit dFC (test-retest reproducibility of 90%) and were also synchronized in activity (-15° ≤ phase ≤15°). When averaging across distinct frequency bands, the same dynamic connections were identified, with the majority of them identified in the frequency range (0.01, 0.198) Hz, though with lower test-retest reproducibility (<66%). Additional analysis suggested that MVPR method better preserved properties (p < 10-10), including time-averaged coherence, of the original data compared with MVAR approach. Conclusions: The wavelet-based approach identified dynamic associations between the core DMN regions with fewer choices in parameters, compared with sliding window method. Impact statement We employed a wavelet-based method, previously used in the literature, and proposed modifications to assess time-varying functional connectivity in resting-state functional magnetic resonance imaging. With this approach, dynamic connections within the default mode network were identified, involving the medial prefrontal and posterior cingulate cortices, inferior parietal lobes, and hippocampal formation, which were also highly consistent in test-retest analysis (test-retest reproducibility of 90%), without the need to select window size, window function, and functional connectivity metric as with the sliding window method, whereby no consensus on the appropriate choices of hyperparameters currently exists in the literature.

Individualized tDCS modeling predicts functional connectivity changes within the working memory network in older adults

BackgroundWorking memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task. These regions were part of the working memory network. The present study is the first study that utilizes individualized finite element models derived from older adults’ MRI to predict significant changes of functional connectivity observed from an acute tDCS application. MethodsIndividualized head models from 15 healthy older adults (mean age = 71.3 years) were constructed to create current density maps. Each head model was segmented into 11 tissue types: white matter, gray matter, CSF, muscle, blood vessels, fat, eyes, air, skin, cancellous, and cortical bone. Electrodes were segmented from T1-weighted images and added to the models. Computed median and maximum current density values in the left DLPFC and left VLPFC regions of interest (ROIs) were correlated with beta values as functional connectivity metrics measured in different timepoint (baseline, during stimulation) and stimulation condition (active and sham). Main resultsPositive significant correlations (R2 = 0.523 for max J, R2 = 0.367 for median J, p < 0.05) were found between the beta values and computed current densities in the left DLPFC ROIs for active stimulation, but no significant correlation was found during sham stimulation. We found no significant correlation between connectivity and current densities computed in the left VLPFC for both active and sham stimulation. ConclusionsThe amount of current within the left DLPFC ROIs was found positively correlated with changes in functional connectivity between left DLPFC and left VLPFC during active 2 mA stimulation. Future work may include expansion of number of participants to further test the accuracy of tDCS models used to predict tDCS-induced functional connectivity changes within the working memory network.

Spinal Cord Resting State Activity in Individuals With Fibromyalgia Who Take Opioids.

Chronic pain coincides with myriad functional alterations throughout the brain and spinal cord. While spinal cord mechanisms of chronic pain have been extensively characterized in animal models and in vitro, to date, research in patients with chronic pain has focused only very minimally on the spinal cord. Previously, spinal cord functional magnetic resonance imaging (fMRI) identified regional alterations in spinal cord activity in patients (who were not taking opioids) with fibromyalgia, a chronic pain condition. Here, in patients with fibromyalgia who take opioids (N = 15), we compared spinal cord resting-state fMRI data vs. patients with fibromyalgia not taking opioids (N = 15) and healthy controls (N = 14). We hypothesized that the opioid (vs. non-opioid) patient group would show greater regional alterations in spinal cord activity (i.e., the amplitude of low frequency fluctuations or ALFF, a measure of regional spinal cord activity). However, we found that regional spinal cord activity in the opioid group was more similar to healthy controls, while regional spinal cord activity in the non-opioid group showed more pronounced differences (i.e., ventral increases and dorsal decreases in regional ALFF) vs. healthy controls. Across patient groups, self-reported fatigue correlated with regional differences in spinal cord activity. Additionally, spinal cord functional connectivity and graph metrics did not differ among groups. Our findings suggest that, contrary to our main hypothesis, patients with fibromyalgia who take opioids do not have greater alterations in regional spinal cord activity. Thus, regional spinal cord activity may be less imbalanced in patients taking opioids compared to patients not taking opioids.

Ecological Connectivity in Agricultural Green Infrastructure: Suggested Criteria for Fine Scale Assessment and Planning

In promoting biodiversity conservation and ecosystem service capacity, landscape connectivity is considered a critical feature to counteract the negative effects of fragmentation. Under a Green Infrastructure (GI) perspective, this is especially true in rural and peri-urban areas where a high degree of connectivity may be associated with the enhancement of agriculture multifunctionality and sustainability. With respect to GI planning and connectivity assessment, the role of dispersal traits of tree species is gaining increasing attention. However, little evidence is available on how to select plant species to be primarily favored, as well as on the role of landscape heterogeneity and habitat quality in driving the dispersal success. The present work is aimed at suggesting a methodological approach for addressing these knowledge gaps, at fine scales and for peri-urban agricultural landscapes, by means of a case study in the Metropolitan City of Rome. The study area was stratified into Environmental Units, each supporting a unique type of Potential Natural Vegetation (PNV), and a multi-step procedure was designed for setting priorities aimed at enhancing connectivity. First, GI components were defined based on the selection of the target species to be supported, on a fine scale land cover mapping and on the assessment of land cover type naturalness. Second, the study area was characterized by a Morphological Spatial Pattern Analysis (MSPA) and connectivity was assessed by Number of Components (NC) and functional connectivity metrics. Third, conservation and restoration measures have been prioritized and statistically validated. Notwithstanding the recognized limits, the approach proved to be functional in the considered context and at the adopted level of detail. Therefore, it could give useful methodological hints for the requalification of transitional urban–rural areas and for the achievement of related sustainable development goals in metropolitan regions.

Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

Machine Learning based Classification of Meditators using Functional Connectivity over Resting State Networks

Meditation has several health benefits and is also used as a complementary treatment for various ailments. Neuroimaging studies have shed light on the effects of meditation, especially on the brain. Functional Magnetic Resonance Imaging, a powerful non-invasive imaging technique is used in this study to determine the functional connectivity in meditator’s brain. In this study, long-term effects of Rajayoga Meditation practice were considered where the difference in functional connectivity between two groups of subjects one with long duration and the other with short duration of Rajayogameditation practice was found. Two groups of subjects with long-term and short-term practice of Rajayoga meditation were recruited. Task-based fMRI was acquired as the subject performed a Neurocognitive task. Functional connectivity among the regions of Resting-State Networks was performed and four functional connectivity metrics were derived. Machine learning algorithms were used to classify these two groups based on functional connectivity metrics used as features. It was found that the…… classifier could differentiate the two groups with …. Accuracy.

Evaluation of a connectivity-based imaging metric that reflects functional decline in Multiple Sclerosis.

Cognitive impairment is a common symptom in individuals with Multiple Sclerosis (MS), but meaningful, reliable biomarkers relating to cognitive decline have been elusive, making evaluation of the impact of therapeutics on cognitive function difficult. Here, we combine pathway-based MRI measures of structural and functional connectivity to construct a metric of functional decline in MS. The Structural and Functional Connectivity Index (SFCI) is proposed as a simple, z-scored metric of structural and functional connectivity, where changes in the metric have a simple statistical interpretation and may be suitable for use in clinical trials. Using data collected at six time points from a 2-year longitudinal study of 20 participants with MS and 9 age- and sex-matched healthy controls, we probe two common symptomatic domains, motor and cognitive function, by measuring structural and functional connectivity in the transcallosal motor pathway and posterior cingulum bundle. The SFCI is significantly lower in participants with MS compared to controls (p = 0.009) and shows a significant decrease over time in MS (p = 0.012). The change in SFCI over two years performed favorably compared to measures of brain parenchymal fraction and lesion volume, relating to follow-up measures of processing speed (r = 0.60, p = 0.005), verbal fluency (r = 0.57, p = 0.009), and score on the Multiple Sclerosis Functional Composite (r = 0.67, p = 0.003). These initial results show that the SFCI is a suitable metric for longitudinal evaluation of functional decline in MS.

Insights on the Relationship Between Hippocampal Connectivity and Memory Performances at the Early Stage of Multiple Sclerosis.

While memory impairment in multiple sclerosis (MS) is known to be associated with hippocampal alterations, whether hippocampal networks could dynamically reorganize as a compensation mechanism is still a matter of debate. In this context, our aim was to identify the patterns of structural and functional connectivity between the hippocampus and the rest of the brain and their possible relevance to memory performances in early MS. Thirty-two patients with a first episode suggestive of MS together with 10 matched healthy controls were prospectively explored at baseline, 1 and 5 years follow up. They were scanned with MRI and underwent a neuropsychological battery of tests that included the Selective Reminding Test and the Brief Visual Memory Test Revised to assess verbal and visuo-spatial memory, respectively. Hippocampal volume was computed together with four graph theory metrics to study the structural and functional connectivity of both hippocampi with the rest of the brain. Associations between network parameters and memory performances were assessed using linear mixed-effects (LME) models. Considering cognitive abilities, verbal memory performances of patients decreased over time while visuo-spatial memory performances were maintained. In parallel, hippocampal volumes decreased significantly while structural and functional connectivity metrics were modified, with an increase in hippocampal connections over time. More precisely, these modifications were indicating a reinforcement of hippocampal short-distance connections. LME models revealed that the drop in verbal memory performances was associated with hippocampal volume loss, while the preservation of visuo-spatial memory performances was linked to decreased hippocampal functional shortest path length. In conclusion, we demonstrated a differential impairment in memory performances in the early stages of MS and an important interplay between hippocampal-related structural and functional networks and those performances. As the structural damage increases, functional reorganization seems to be able to maintain visuo-spatial memory performances with strengthened short-distance connections.

Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses.

Premature birth affects the developmental trajectory of the brain during a period of intense maturation with possible lifelong consequences. To better understand the effect of prematurity on brain structure and function, we performed blood‐oxygen‐level dependent (BOLD) and anatomical magnetic resonance imaging (MRI) at 40 weeks of postmenstrual age on 88 newborns with variable gestational age (GA) at birth and no evident radiological alterations. We extracted measures of resting‐state functional connectivity and activity in a set of 90 cortical and subcortical brain regions through the evaluation of BOLD correlations between regions and of fractional amplitude of low‐frequency fluctuation (fALFF) within regions, respectively. Anatomical information was acquired through the assessment of regional volumes. We performed univariate analyses on each metric to examine the association with GA at birth, the spatial distribution of the effects, and the consistency across metrics. Moreover, a data‐driven multivariate analysis (i.e., Machine Learning) framework exploited the high dimensionality of the data to assess the sensitivity of each metric to the effect of premature birth. Prematurity was associated with bidirectional alterations of functional connectivity and regional volume and, to a lesser extent, of fALFF. Notably, the effects of prematurity on functional connectivity were spatially diffuse, mainly within cortical regions, whereas effects on regional volume and fALFF were more focal, involving subcortical structures. While the two analytical approaches delivered consistent results, the multivariate analysis was more sensitive in capturing the complex pattern of prematurity effects. Future studies might apply multivariate frameworks to identify premature infants at risk of a negative neurodevelopmental outcome.

Functional connectivity of the attention networks is altered and relates to neuropsychological outcomes in children with prenatal alcohol exposure

Cognitive and functional brain alterations can occur in children with prenatal alcohol exposure (PAE). We examined the functional connectivity (FC) among regions within and between attention networks, and whether inter- and intranetwork FC moderated cognition in children with PAE (n = 37; age 12.8 ± 2.8 years) and nonexposed controls (n = 40; age 13.2 ± 2.8 years). Participants completed standardized attention and executive functioning tasks and resting state functional MRI. Inter- and intra-network FC and graph-theoretical metrics were calculated among attention network regions. Relative to controls, PAE was associated with reduced FC between the left temporoparietal junction and left ventral frontal cortex and anterior insula/frontal operculum (aI/fO), and between the left intraparietal sulcus and bilateral aI/fO. PAE was associated with increased FC between the right precuneus and intraparietal lobes, the right anterior prefrontal cortex and left ventral frontal cortex and aI/fO, and the left thalamus and dorsal frontal cortex. Graph-theoretical metrics did not differ by group. FC predicted cognitive performance, negatively in the children with PAE and positively in controls. Increased intra-network together with reduced internetwork FC suggests inefficient network specialization and impaired long-range FC among attention network regions after PAE. Results further suggest that those alterations may underlie attention and executive dysfunction in children with PAE.

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity.

To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

Functional brain connectivity related to surgical skill dexterity in physical and virtual simulation environments.

.Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent.Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill.Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively.Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, , . Partial eta squared effect size for the inter-regional WCO metrics was found to be highest between the central prefrontal cortex (CPFC) and SMA, CPFC-SMA (). Two-tailed Mann–Whitney U tests with a 95% confidence interval showed baseline equivalence and a statistically significant () difference in the CPFC-SMA WPCO metrics for the physical simulator training group () versus the untrained control group () following training for 10 consecutive days in addition to the pretest and posttest days.Conclusion: We show that brain functional connectivity WCO metric corresponds to surgical motor skills in the laparoscopic physical simulators. Functional connectivity between the CPFC and the SMA is lower for subjects that exhibit expert surgical motor skills than untrained subjects in laparoscopic physical simulators.