Effective Connectivity Measures Research Articles

Changes in Brain Functional and Effective Connectivity After Treatment for Breast Cancer and Implications for Intervention Targets.

Background: Patients with breast cancer frequently report cognitive impairment both during and after completion of therapy. Evidence suggests that cancer-related cognitive impairments are related to widespread neural network dysfunction. The default mode network (DMN) is a large conserved network that plays a critical role in integrating the functions of various neural systems. Disruption of the network may play a key role in the development of cognitive impairment. Methods: We compared neuroimaging and neurocognitive data from 43 newly diagnosed primary breast cancer patients (mean age = 48, standard deviation [SD] = 8.9 years) and 50 frequency-matched healthy female controls (mean age = 50, SD = 10 years) before treatment and 1 year after treatment completion. Functional and effective connectivity measures of the DMN were obtained using graph theory and Bayesian network analysis methods, respectively. Results: Compared with healthy females, the breast cancer group displayed higher global efficiency and path length post-treatment (p < 0.03, corrected). Breast cancer survivors showed significantly lower performance on measures of verbal memory, attention, and verbal fluency (p < 0.05) at both time points. Within the DMN, local brain network organization, as measured by edge-betweenness centralities, was significantly altered in the breast cancer group compared with controls at both time points (p < 0.0001, corrected), with several connections showing a significant group-by-time effect (p < 0.003, corrected). Effective connectivity demonstrated significantly altered patterns of neuronal coupling in patients with breast cancer (p < 0.05). Significant correlations were seen between hormone blockade therapy, radiation therapy, chemotherapy cycles, memory, and verbal fluency test and edge-betweenness centralities. Discussion: This pattern of altered network organization in the default mode is believed to result in reduced network efficiency and disrupted communication. Subregions of the DMN, the orbital prefrontal cortex and posterior memory network, appear to be at the center of this disruption and this could inform future interventions. Impact statement This prospective study is the first to investigate how post-treatment changes in functional and effective connectivity in the regions of default mode network are related to cancer therapy and measures of memory and verbal learning in breast cancer patients. We demonstrate that the interactions between treatment, brain connectivity, and neurocognitive outcomes coalesce around a subgroup of brain structures in the orbital frontal and parietal lobe. This would suggest that interventions that target these regions may improve neurocognitive outcomes in breast cancer survivors.

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. &#x0D; Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA).&#x0D; Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy.&#x0D; Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

Local and distant responses to single pulse electrical stimulation reflect different forms of connectivity

Measuring connectivity in the human brain involves innumerable approaches using both noninvasive (fMRI, EEG) and invasive (intracranial EEG or iEEG) recording modalities, including the use of external probing stimuli, such as direct electrical stimulation. To examine how different measures of connectivity correlate with one another, we compared ‘passive’ measures of connectivity during resting state conditions to the more ‘active’ probing measures of connectivity with single pulse electrical stimulation (SPES). We measured the network engagement and spread of the cortico-cortico evoked potential (CCEP) induced by SPES at 53 out of 104 total sites across the brain, including cortical and subcortical regions, in patients with intractable epilepsy (N=11) who were undergoing intracranial recordings as a part of their clinical care for identifying seizure onset zones. We compared the CCEP network to functional, effective, and structural measures of connectivity during a resting state in each patient. Functional and effective connectivity measures included correlation or Granger causality measures applied to stereoEEG (sEEGs) recordings. Structural connectivity was derived from diffusion tensor imaging (DTI) acquired before intracranial electrode implant and monitoring (N=8). The CCEP network was most similar to the resting state voltage correlation network in channels near to the stimulation location. In contrast, the distant CCEP network was most similar to the DTI network. Other connectivity measures were not as similar to the CCEP network. These results demonstrate that different connectivity measures, including those derived from active stimulation-based probing, measure different, complementary aspects of regional interrelationships in the brain.

Diagnosing Schizophrenia Using Effective Connectivity of Resting-State EEG Data

Schizophrenia is a serious mental illness associated with neurobiological deficits. Even though the brain activities during tasks (i.e., P300 activities) are considered as biomarkers to diagnose schizophrenia, brain activities at rest have the potential to show an inherent dysfunctionality in schizophrenia and can be used to understand the cognitive deficits in these patients. In this study, we developed a machine learning algorithm (MLA) based on eyes closed resting-state electroencephalogram (EEG) datasets, which record the neural activity in the absence of any tasks or external stimuli given to the subjects, aiming to distinguish schizophrenic patients (SCZs) from healthy controls (HCs). The MLA has two steps. In the first step, symbolic transfer entropy (STE), which is a measure of effective connectivity, is applied to resting-state EEG data. In the second step, the MLA uses the STE matrix to find a set of features that can successfully discriminate SCZ from HC. From the results, we found that the MLA could achieve a total accuracy of 96.92%, with a sensitivity of 95%, a specificity of 98.57%, precision of 98.33%, F1-score of 0.97, and Matthews correlation coefficient (MCC) of 0.94 using only 10 out of 1900 STE features, which implies that the STE matrix extracted from resting-state EEG data may be a promising tool for the clinical diagnosis of schizophrenia.

Association between emotional intelligence and effective brain connectome: A large-scale spectral DCM study

IntroductionEmotional Intelligence (EI) is a well-documented aspect of social and interpersonal functioning, but the underlying neural mechanisms for this capacity remain poorly understood. Here we used advanced brain connectivity techniques to explore the associations between EI and effective connectivity (EC) within four functional brain networks. MethodsThe Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was used to collect EI data from 55 healthy individuals (mean age = 30.56±8.3 years, 26 males). The MSCEIT comprises two area cores – experiential EI (T1) and strategic EI (T2). The T1 core included two sub-scales – perception of emotions (S1) and using emotions to facilitate thinking (S2), and the T2 core included two sub-scales – understanding of emotions (S3) and management of emotions (S4). All participants underwent structural and resting-state functional magnetic resonance imaging (rsfMRI) scans. The spectral dynamic causal modeling approach was implemented to estimate EC within four networks of interest – the default-mode network (DMN), dorsal attention network (DAN), control-execution network (CEN) and salience network (SN). The strength of EC within each network was correlated with the measures of EI, with correlations at pFDR < 0.05 considered as significant. ResultsThere was no significant association between any of the measures of EI and EC strength within the DMN and DAN. For CEN, however, we found that there were significant negative associations between EC strength from the right anterior prefrontal cortex (RAPFC) to the left anterior prefrontal cortex (LAPFC) and both S2 and T1, and significant positive associations between EC strength from LAPFC to RAPFC and S2. EC strength from the right superior parietal cortex (SPC) to RAPFC also showed significant negative association with S4 and T2. For the SN, S3 showed significant negative association with EC strength from the right insula to RAPFC and significant positive association with EC strength from the left insula to dorsal anterior cingulate cortex (DACC). ConclusionsWe provide evidence that the negative ECs within the right hemisphere, and from the right to left hemisphere, and positive ECs within the left hemisphere and from the left to right hemisphere of CEN (involving bilateral frontal and right parietal region) and SN (involving right frontal, anterior cingulate and bilateral insula) play a significant role in regulating and processing emotions. These findings also suggest that measures of EC can be utilized as important biomarkers to better understand the underlying neural mechanisms of EI.

Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal.

Introduction:Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process.Methods:In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes.Results:Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia.Conclusion:TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

Assessment of Directional Connectivity Between Neural Sources Using Effective Connectivity Measures and Particle Filters

Electrical neural activity monitoring and recording will increase our understanding of how the human brain works. Tracking mechanisms of neural activity led to better diagnosis and management of severe neurological conditions such as Parkinson’s disease and epilepsy. More importantly, these approaches were used to distinguish between various types of seizures based on the location and direction of the seizure foci, thereby increasing the outcomes of epilepsy surgery. A detailed study was carried out on the role of neural synchrony in brain functions with Electroencephalography (EEG). Most studies had been conducted on EEG connectivity analysis at sensor level. It is not easy to evaluate the connected networks because the volume conductive effect significantly distorts signals because of the electrical conductiveness of the head and often scalp electrodes derive input from the same sources in the brain. These factors help to estimate the real connectivity between brain regions inaccurately. The suggested approach is referred to as EEG source connectivity. The inverse problem is the estimation of the localized current dipole model from the EEG measurements. In order to solve the inverse EEG problem, advanced signal-processing algorithms such as the efficient implementation of PF have been built to facilitate direct exposure to neural dipole sources in real time and measure the connectivity of neural sources time courses using functional and effective connectivity measures.

A Machine Learning Approach Using Effective Connectivity to Predict Response to Clozapine Treatment

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps: 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.

Connectivity of the Cerebello-Thalamo-Cortical Pathway in Survivors of Childhood Leukemia Treated With Chemotherapy Only

Treatment with contemporary chemotherapy-only protocols is associated with risk for neurocognitive impairment among survivors of childhood acute lymphoblastic leukemia (ALL). To determine whether concurrent use of methotrexate and glucocorticoids is associated with interference with the antioxidant system of the brain and damage and disruption of glucocorticoid-sensitive regions of the cerebello-thalamo-cortical network. This cross-sectional study was conducted from December 2016 to July 2019 in a single pediatric cancer tertiary care center. Participants included survivors of childhood ALL who were more than 5 years from cancer diagnosis, age 8 years or older, and treated on an institutional chemotherapy-only protocol. Age-matched community members were recruited as a control group. Data were analyzed from August 2017 to August 2020. ALL treatment using chemotherapy-only protocols. This study compared brain volumes between survivors and individuals in a community control group and examined associations among survivors of methotrexate and dexamethasone exposure with neurocognitive outcomes. Functional and effective connectivity measures were compared between survivors with and without cognitive impairment. The Rey-Osterrieth complex figure test, a neurocognitive evaluation in which individuals are asked to copy a figure and then draw the figure from memory, was scored according to published guidelines and transformed into age-adjusted z scores based on nationally representative reference data and used to measure organization and planning deficits. β values for neurocognitive tests represented the amount of change in cerebellar volume or chemotherapy exposure associated with 1 SD change in neurocognitive outcome by z score (mm3/1 SD in z score for cerebellum, mm3/[g×hr/L] for dexamethasone and methotrexate AUC, and mm3/intrathecal count for total intrathecal count). Among 302 eligible individuals, 218 (72%) participated in the study and 176 (58%) had usable magnetic resonance imaging (MRI) results. Among these, 89 (51%) were female participants and the mean (range) age was 6.8 (1-18) years at diagnosis and 14.5 (8-27) years at evaluation. Of 100 community individuals recruited as the control group, 82 had usable MRI results; among these, 35 (43%) were female individuals and the mean (range) age was 13.8 (8-26) years at evaluation. There was no significant difference in total brain volume between survivors and individuals in the control group. Survivors of both sexes showed decreased mean (SD) cerebellar volumes compared with the control population (female: 70 568 [6465] mm3 vs 75 134 [6780] mm3; P < .001; male: 77 335 [6210] mm3 vs 79 020 [7420] mm3; P < .001). In female survivors, decreased cerebellar volume was associated with worse performance in Rey-Osterrieth complex figure test (left cerebellum: β = 55.54; SE = 25.55; P = .03; right cerebellum: β = 52.57; SE = 25.50; P = .04) and poorer dominant-hand motor processing speed (ie, grooved pegboard performance) (left cerebellum: β = 82.71; SE = 31.04; P = .009; right cerebellum: β = 91.06; SE = 30.72; P = .004). In female survivors, increased number of intrathecal treatments (ie, number of separate injections) was also associated with Worse Rey-Osterrieth test performance (β = -0.154; SE = 0.063; P = .02), as was increased dexamethasone exposure (β = -0.0014; SE = 0.0005; P = .01). Executive dysfunction was correlated with increased global efficiency between smaller brain regions (Pearson r = -0.24; P = .01) compared with individuals without dysfunction. Anatomical connectivity showed differences between impaired and nonimpaired survivors. Analysis of variance of effective-connectivity weights identified a significant interaction association (F = 3.99; P = .02) among the direction and strength of connectivity between the cerebellum and DLPFC, female sex, and executive dysfunction. Finally, no effective connectivity was found between the precuneus and DLPFC in female survivors with executive dysfunction. These findings suggest that dexamethasone exposure was associated with smaller cerebello-thalamo-cortical regions in survivors of ALL and that disruption of effective connectivity was associated with impairment of executive function in female survivors.

Towards linking diffusion MRI based macro- and microstructure measures with cortico-cortical transmission in brain tumor patients

We aimed to link macro- and microstructure measures of brain white matter obtained from diffusion MRI with effective connectivity measures based on a propagation of cortico-cortical evoked potentials induced with intrasurgical direct electrical stimulation. For this, we compared streamline lengths and log-transformed ratios of streamlines computed from presurgical diffusion-weighted images, and the delays and amplitudes of N1 peaks recorded intrasurgically with electrocorticography electrodes in a pilot study of 9 brain tumor patients. Our results showed positive correlation between these two modalities in the vicinity of the stimulation sites (Pearson coefficient 0.54±0.13 for N1 delays, and 0.47±0.23 for N1 amplitudes), which could correspond to the neural propagation via U-fibers. In addition, we reached high sensitivities (0.78±0.07) and very high specificities (0.93±0.03) in a binary variant of our comparison. Finally, we used the structural connectivity measures to predict the effective connectivity using a multiple linear regression model, and showed a significant role of brain microstructure-related indices in this relation.

Dorsolateral prefrontal circuit effective connectivity mediates the relationship between white matter structure and PASAT-3 performance in multiple sclerosis.

Three decades ago a series of parallel circuits were described involving the frontal cortex and deep grey matter structures, with putative roles in control of motor and oculomotor function, cognition, behaviour and emotion. The circuit comprising the dorsolateral prefrontal cortex, caudate, globus pallidus and thalamus has a putative role in regulating executive functions. The aim of this study is to investigate effective connectivity (EC) of the dorsolateral‐prefrontal circuit and its association with PASAT‐3 performance in people with multiple sclerosis(MS). We use Granger causality analysis of resting‐state functional MRI from 52 people with MS and 36 healthy people to infer that reduced EC in the afferent limb of the dorsolateral prefrontal circuit occurs in the people with MS with cognitive dysfunction (left: p = .006; right: p = .029), with bilateral EC reductions in this circuit resulting in more severe cognitive dysfunction than unilateral reductions alone (p = .002). We show that reduced EC in the afferent limb of the dorsolateral prefrontal circuit mediates the relationship between cognitive performance and macrostrucutral and microstructural alterations of white matter tracts in components of the circuit. Specificity is shown by the absence of any relationship between cognition and EC in the analogous and anatomically proximal motor circuit. We demonstrate good stability of the EC measures in people with MS over an interval averaging 8‐months. Key positive and negative results are replicated in an independent cohort of people with MS. Our findings identify the dorsolateral prefrontal circuit as a potential target for therapeutic strategies aimed at improving cognition in people with MS.

Cortical connectivity and spectral perturbations underlying TENS stimulation of hand nerves: a case study.

The major challenge in upper limbs neuroprosthetic improvement is the implementation of effective sensory feedback. Transcutaneous electrical nerve stimulation (TENS) of the median and ulnar nerves confirmed, with electroencephalographic (EEG) recordings, the presence of appropriate responses in relevant cortical areas with induced sensation successfully located in the innervation regions of each nerve. The characterization of these elicited responses could be used to recreate precise somatotopic feedback from hand protheses. Using TENS and EEG, the purpose of this study was to detect distinctions in time-frequency cortical dynamics and connectivity occurring after stimulation of hand nerves. Region of interest (ROI) were selected according to topographical distributions and Somatosensory Evoked Potentials (SEP) localization and were named Contralateral Parietal (Cont P), Central Frontal (Cent F) and Superior Parietal (Sup P). The analysis of cortical oscillations showed spectral inflections in theta [4-7 Hz] and alpha [7.5-12.5 Hz] band which occurred at 60 ms in Cont P and 300 ms in Sup P and prominent for the ulnar condition over the median one. The beta band decrease [16-30 Hz] which occurred in the same ROIs was especially significant after ulnar stimulation too. Effective connectivity measures did not differ significantly across conditions but exhibited some slight difference in the alpha-band causal flow coming from Cent F in direction to Cont P and Sup P. Although pending completion of multiple-subjects study, these results already suggest magnitude differences in somatosensory spectral fluctuations and sensorimotor interactions flows.

Accelerated Cognitive Ageing in epilepsy: exploring the effective connectivity between resting-state networks and its relation to cognitive decline

ObjectiveThis study aims at understanding the dynamic functional brain organization in Accelerated Cognitive Ageing (ACA) in epilepsy. We also assess to which extend the (abnormal) effective connectivity between brain networks correlates with the (estimated) decline in IQ scores observed in the ACA patients. Material and methodsTwo multi-echo resting-state fMRI scans of 10 ACA patients and 14 age- and education-matched healthy controls were acquired. A task-based fMRI was acquired in-between those two scans, for possible cognitive fatigue effects on reserve capacity. Granger causality (GC), a measure of effective connectivity between brain regions, was applied on 7 major cognitive networks, and group-wise compared, using permutation testing statistics. This was performed on each of the resting-state sessions independently. We assessed the correlation between the cognitive deterioration scores (representing cognitive decline), and the paired-networks granger causality values. ResultsThe cingulate cortex appeared to be more engaged in ACA patients. Its dynamics towards the right fronto-parietal cortex, salience network, and the dorsal attention networks (DAN) was stronger than in controls, only in the first resting-state scan session. The Granger causality from the DAN to the default mode network (DMN) and from the ventral attention network (VAN) to the left fronto-parietal network (FPL) was also stronger in ACA patients and again only in the first scans. In the second resting-state scans, only the DMN was more strongly connected with the cingulate cortex in ACA patients. A weaker GC from DMN to FPL, and stronger GC from the salience network to cingulate cortex were associated with more decline in the Full-scale IQ and more GC from DMN to VAN would lead to more decline in the Perceptual Reasoning Index in ACA. ConclusionThe results are in line with the hypothesis of over-recruitment at low cognitive load, and exhaustion at higher cognitive load, as shown by the compensation-related utilization of neural circuits hypothesis (CRUNCH) model for ageing. Moreover, the DMN to VAN directed connectivity strongly correlates with the (estimated) decline in the Perceptual Reasoning Index, which is also in line with a recent study on ageing with mild cognitive impairment in elderly, and the posterior-anterior shift in aging (PASA) model. This study therefore supports the idea that the cognitive decline in our patients resembles the decline observed in healthy ageing, but in an accelerated mode. This study also sheds light on the directions of the impaired connectivity between the main networks involved in the deterioration process, which can be helpful for future development of treatment solutions.

Graph theory analysis of directed functional brain networks in major depressive disorder based on EEG signal

Objective. Analysis of functional and structural brain networks has suggested that major depressive disorder (MDD) is associated with a disruption in brain networks. This paper aims to investigate the abnormalities of brain networks in MDD. Approach. To this aim, we constructed weighted directed functional networks based on electroencephalography (EEG) signals of 26 MDD patients and 23 normal (N) subjects. The nodes of networks were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a model-free, phase-based effective connectivity measure that is relatively robust to noise and linear mixing. Since the correct instantaneous phase of a signal is computed for narrow frequency bands, the networks were analyzed in eight frequency sub-bands including delta, theta, alpha1, alpha2, beta1, beta2, beta3, and beta4. To assess the alteration in the topology of brain networks in MDD patients, graph theory metrics consisting of global efficiency, local efficiency, node betweenness centrality, node degree, and node strength were analyzed by statistical tests and classification. Furthermore, directed differential connectivity graphs (dDCGs) for the MDD and N groups were studied. Main results. These analyses revealed a higher node degree and strength in the dDCGs of the MDD group than the normal group. It was also found that MDD brain networks have a more randomized structure than the N group. Moreover, our results indicated that the out-degree of networks classified MDD and N subjects with an accuracy of 92%; thus, our method can be considered as a powerful tool for depression detection. Significance. Our analysis may provide new insights into developing biomarkers for depression detection based on brain networks.

Directed Connectivity Analysis of the Neuro-Cardio- and Respiratory Systems Reveals Novel Biomarkers of Susceptibility to SUDEP.

Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality and its pathophysiological mechanisms remain unknown. Goal: We set to record and analyze for the first time concurrent electroencephalographic (EEG), electrocardiographic (ECG), and unrestrained whole-body plethysmographic (Pleth) signals from control (WT - wild type) and SUDEP-prone mice (KO- knockout Kcna1 animal model). Methods: Employing multivariate autoregressive models (MVAR) we measured all tri-organ effective directional interactions by the generalized partial directed coherence (GPDC) in the frequency domain over time (hours). Results: When compared to the control (WT) animals, the SUDEP-prone (KO) animals exhibited (p < 0.001) reduced afferent and efferent interactions between the heart and the brain over the full frequency spectrum (0-200Hz), enhanced efferent interactions from the brain to the lungs and from the heart to the lungs at high (>90 Hz) frequencies (especially during periods with seizure activity), and decreased feedback from the lungs to the brain at low (<40 Hz) frequencies. Conclusions: These results show that impairment in the afferent and efferent pathways in the holistic neuro-cardio-respiratory network could lead to SUDEP, and effective connectivity measures and their dynamics could serve as novel biomarkers of susceptibility to SUDEP and seizures respectively.

Multimodal alterations of directed connectivity profiles in patients with attention-deficit/hyperactivity disorders

Functional and effective connectivity measures for tracking brain region interactions that have been investigated using both electroencephalography (EEG) and magnetoencephalography (MEG) bringing up new insights into clinical research. However, the differences between these connectivity methods, especially at the source level, have not yet been systematically studied. The dynamic characterization of coherent sources and temporal partial directed coherence, as measures of functional and effective connectivity, were applied to multimodal resting EEG and MEG data obtained from 11 young patients (mean age 13.2 ± 1.5 years) with attention-deficit/hyperactivity disorder (ADHD) and age-matched healthy subjects. Additionally, machine-learning algorithms were applied to the extracted connectivity features to identify biomarkers differentiating the two groups. An altered thalamo-cortical connectivity profile was attested in patients with ADHD who showed solely information outflow from cortical regions in comparison to healthy controls who exhibited bidirectional interregional connectivity in alpha, beta, and gamma frequency bands. We achieved an accuracy of 98% by combining features from all five studied frequency bands. Our findings suggest that both types of connectivity as extracted from EEG or MEG are sensitive methods to investigate neuronal network features in neuropsychiatric disorders. The connectivity features investigated here can be further tested as biomarkers of ADHD.

A Data-Driven Measure of Effective Connectivity Based on Renyi's α-Entropy.

Transfer entropy (TE) is a model-free effective connectivity measure based on information theory. It has been increasingly used in neuroscience because of its ability to detect unknown non-linear interactions, which makes it well suited for exploratory brain effective connectivity analyses. Like all information theoretic quantities, TE is defined regarding the probability distributions of the system under study, which in practice are unknown and must be estimated from data. Commonly used methods for TE estimation rely on a local approximation of the probability distributions from nearest neighbor distances, or on symbolization schemes that then allow the probabilities to be estimated from the symbols' relative frequencies. However, probability estimation is a challenging problem, and avoiding this intermediate step in TE computation is desirable. In this work, we propose a novel TE estimator using functionals defined on positive definite and infinitely divisible kernels matrices that approximate Renyi's entropy measures of order α. Our data-driven approach estimates TE directly from data, sidestepping the need for probability distribution estimation. Also, the proposed estimator encompasses the well-known definition of TE as a sum of Shannon entropies in the limiting case when α → 1. We tested our proposal on a simulation framework consisting of two linear models, based on autoregressive approaches and a linear coupling function, respectively, and on the public electroencephalogram (EEG) database BCI Competition IV, obtained under a motor imagery paradigm. For the synthetic data, the proposed kernel-based TE estimation method satisfactorily identifies the causal interactions present in the data. Also, it displays robustness to varying noise levels and data sizes, and to the presence of multiple interaction delays in the same connected network. Obtained results for the motor imagery task show that our approach codes discriminant spatiotemporal patterns for the left and right-hand motor imagination tasks, with classification performances that compare favorably to the state-of-the-art.

Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI

Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fMRI) is a prominent modality for investigating the human brain's neural substrate, especially when cognitive impairment occurs. The present study was an attempt to investigate the brain network's differences between MS patients and HCs using graph-theoretic measures constructed by an effective connectivity measure through statistical tests. The results of the significant measures were then evaluated through machine learning methods. To this end, we gathered blood-oxygen level dependent (BOLD) fMRI data of the participants during the execution of paced auditory serial addition test (PASAT). Granger causality analysis (GCA) was then employed between brain regions' time series on each subject in order to construct a brain network. Afterward, the Wilcoxon rank-sum test was implemented to find the alteration of brain networks between the mentioned groups. According to the results, Global flow coefficient was significantly different between HCs and patients. Moreover, MS disease impacted several areas of the brain including Hippocampus, Para Hippocampal, Thalamus, Cuneus, Superior temporal gyrus, Heschl, Caudate, Medial Frontal Superior Gyrus, Fusiform, Pallidum, and several parts of Cerebellum in centrality measures and local flow coefficient. Most of the obtained regions were related to the cognitive impacts of the disease. We also found the best subset of graph features by means of Fisher score, and classified them to evaluate the features strength for the discrimination of MS patients from HCs via several machine learning methods. Having used the combination of Wilcoxon rank-sum test and Fisher score, we were able to classify MS patients from HCs using linear support vector machine (SVM) with an accuracy of 95%. With regard to the few existing studies on brain network of MS patients, especially during a cognitive task execution, our findings showed that the selected graph measures by Wilcoxon rank-sum test and Fisher score from the GCA-based brain networks resulted in a promising classification accuracy.

Reconfiguring Motor Circuits for a Joint Manual and BCI Task.

Designing brain-computer interfaces (BCIs) that can be used in conjunction with ongoing motor behavior requires an understanding of how neural activity co-opted for brain control interacts with existing neural circuits. For example, BCIs may be used to regain lost motor function after stroke. This requires that neural activity controlling unaffected limbs is dissociated from activity controlling the BCI. In this study we investigated how primary motor cortex accomplishes simultaneous BCI control and motor control in a task that explicitly required both activities to be driven from the same brain region (i.e. a dual-control task). Single-unit activity was recorded from intracortical, multi-electrode arrays while a non-human primate performed this dual-control task. Compared to activity observed during naturalistic motor control, we found that both units used to drive the BCI directly (control units) and units that did not directly control the BCI (non-control units) significantly changed their tuning to wrist torque. Using a measure of effective connectivity, we observed that control units decrease their connectivity. Through an analysis of variance we found that the intrinsic variability of the control units has a significant effect on task proficiency. When this variance is accounted for, motor cortical activity is flexible enough to perform novel BCI tasks that require active decoupling of natural associations to wrist motion. This study provides insight into the neural activity that enables a dual-control brain-computer interface.

Characteristic changes in the EEG signals between microsleeps and preceding responsive states.

This work aims at identifying characteristic features of EEG to demarcate a microsleep from preceding responsive states. The EEG signals, after reference electrode standardization technique (REST) re-referencing, were processed through a time-varying general linear Kalman filter (TVGLKF) to derive time-varying auto-regressive (TVAR) parameters. The time-varying effective connectivity measure of orthogonal partial directed coherence (OPDC) was obtained for every instant at 256 Hz. Effective connectivity matrices formed using these OPDC measures, with the scalp electrodes as nodes were processed further using graph theory. Community-based measures were investigated and statistical significances compared. Non-parametric Wilcoxon signed rank test was used for significance analysis, with Cohen-type and Common Language effect size (CLES) as measures of effect sizes. The results showed a decrease in directional modularity from anterior to posterior, in theta, alpha, and beta bands in microsleeps. The alpha band showed the highest significance with a Cohen-type effect size of 1.25 and a median percentage difference of 23% across subjects, with a range of 13-28%. Flexibility and integration also decreased with average percentage of 25% (17-35%) and 20% (16-32%), respectively, while recruitment increased on an average of 11% (3-16%), wherever significant across all bands. These community-based measures can help characterize and explain changes in brain mechanisms, and can also serve as potential biomarkers for microsleep detection.