Nonlinear Functional Connectivity Research Articles

Early MS Identification Using Non-linear Functional Connectivity and Graph-theoretic Measures of Cognitive Task-fMRI Data.

Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies have extracted invaluable information from the underlying substrate of the brain. However, cognitive deficiency has insufficiently been assessed by researchers in multiple sclerosis (MS). Therefore, extracting the brain network differences among relapsing-remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study. In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information based on the automated anatomical labeling atlas (AAL). Subsequently, a statistical test was carried out to determine the variation in brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with the Fisher score as a hybrid feature selection method. The results of the classification performance measures showed that the construction of a brain network using a new nonlinear connectivity measure in task-fMRI performs better than the linear connectivity measures in terms of classification. The Wilcoxon rank-sum test also demonstrated a superior result for clinical applications. We believe that non-linear connectivity measures, like KMI, outperform linear connectivity measures, like correlation coefficient in finding the biomarkers of MS disease according to classification performance metrics. The performance of some brain regions (the hippocampus, parahippocampus, cuneus, pallidum, and two segments of the cerebellum) is different between healthy and MS people.Non-linear connectivity measures, such as Kernel mutual information, perform better than linear connectivity measures, such as correlation coefficient, in finding the biomarkers of MS disease. Multiple sclerosis (MS) can disrupt the function of the central nervous system. The function of brain network is impaired in these patients. In this study, we evaluated the change in brain network based on a non-linear connectivity measure using cognitive task-based fMRI data between MS patients and healthy controls. We used Kernel mutual information (KMI) and designed a graph network based on the results of connectivity analysis. The the paced auditory serial addition test was used to activate cognitive functions of the brain. The classification was employed for the results using different decision tree -based technique and support vector machine. KMI can be considered a valid measure of connectivity over linear measures, like the correlation coefficient. KMI does not have the drawbacks of mutual information technique. However, further studies should be implemented on brain data of MS patients to draw more definite conclusions.

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer’s disease

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer’s disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

Nonlinear functional network connectivity in resting functional magnetic resonance imaging data.

In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting‐state fMRI data included 151 schizophrenia patients and 163 age‐ and gender‐matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a “boosted” approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects.

Early MS Identification Using Non-linear Functional Connectivity and Graph-theoretic Measures of Cognitive Task-fMRI Data

Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies extracted invaluable information from the underlying substrate of the brain. However, Cognitive deficiency has insufficiently been assessed by researchers in Multiple Sclerosis (MS). Therefore, extracting the brain network differences among Relapsing-Remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study. In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information (KMI) based on AAL atlas. Subsequently, a statistical test was carried out to determine the variation of brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with fisher score as a hybrid feature selection method. The results were then classified using multiple machine learning methods. Some regions, namely the Hippocampus, Para Hippocampal, Cuneus, Pallidum, and two segments of Cerebellum were considered to be discriminative between these groups. We achieved an accuracy of 95% using the support vector machine. The results of classification performance metrics showed that constructing a brain network using a novel non-linear connectivity measure in task-fMRI outperforms linear connectivity measures in terms of classification. The outcome of the Wilcoxon rank-sum test also demonstrated a superior result for clinical applications.

The Bilingual Lexicon, Back and Forth: Electrophysiological Signatures of Translation Asymmetry

Mainstream theories of first and second language (L1, L2) processing in bilinguals are crucially informed by word translation research. A core finding is the translation asymmetry effect, typified by slower performance in forward translation (FT, from L1 into L2) than in backward translation (BT, from L2 into L1). Yet, few studies have explored its neural bases and none has employed (de)synchronization measures, precluding the integration of bilingual memory models with neural (de)coupling accounts of word processing. Here, 27 proficient Spanish-English bilinguals engaged in FT and BT of single words as we obtained high-density EEG recordings to perform cluster-based oscillatory and non-linear functional connectivity analyses. Relative to BT, FT yielded slower responses, higher frontal theta (4–7 Hz) power in an early window (0–300 ms), reduced centro-posterior lower-beta (14–20 Hz) and centro-frontal upper-beta (21–30 Hz) power in a later window (300–600 ms), and lower fronto-parietal connectivity below 10 Hz in the early window. Also, the greater the behavioral difference between FT and BT, the greater the power of the early theta cluster for FT over BT. These results reveal key (de)coupling dynamics underlying translation asymmetry, offering frequency-specific constraints for leading models of bilingual lexical processing.

Cross-sample entropy for the study of coordinated brain activity in calm and distress conditions with electroencephalographic recordings

Traditionally, the brain has been studied as an ensemble of independent structures with determined functions. However, it has been demonstrated that the brain operates as a network in which all regions are interconnected. Apart from physical links, the brain presents functional associations between non-physically connected regions that work synchronized in a common mental process. For this reason, the study of functional connectivity is essential to reveal new insights about brain’s behavior. In this work, a nonlinear functional connectivity metric called cross-sample entropy is applied for the first time to emotions recognition. Concretely, it has been computed for the detection of distress because of being one of the most influencing emotions in developed societies with several negative implications for health. Results reveal a strong coordinated activity between channels in central, parietal and occipital areas in each brain hemisphere separately, and also in the inter-hemispheric interactions among the same regions. Moreover, an augmented amount of similar dynamics under distress conditions in all brain regions with respect to a calm state reveals an increase in self-coordination of brain activity in distressful situations.

Brain functional connectivity in chronic tic disorders and Gilles de la Tourette syndrome.

The pathophysiology of chronic tic disorder (cTD) and Gilles de la Tourette syndrome (GTS) is characterized by the dysfunction of both motor and non - motor cortico - striatal - thalamo - cortical (CSTC) circuitries, which leads to tic release and comorbids. A role of fronto - parietal network (FPN) connectivity breakdown has been postulated for tic pathogenesis, given that the FPN entertain connections with limbic, paralimbic, and CSTC networks. Our study was aimed at characterizing the FPN functional connectivity in cTD and GTS in order to assess the role of its deterioration in tic severity and the degree of comorbids. We recorded scalp EEG during resting state in patients with cTD and GTS. The eLORETA current source densities were analyzed, and the lagged phase synchronization (LPS) was calculated to estimate nonlinear functional connectivity between cortical areas. We found that the FPN functional connectivity in delta band was more detrimental in more severe GTS patients. Also, the sensorimotor functional connectivity in beta2 band was stronger in more severe cTD and GTS patients. FPN functional connectivity deterioration correlated with comorbids presence and severity in patients with GTS. Our data suggest that a FPN disconnection may contribute to the motoric symptomatology and comorbid severity in GTS, whereas sensorimotor disconnection may contribute to tic severity in cTD and GTS. Although preliminary, our study points out a differently disturbed brain connectivity between patients with cTD and GTS. This may serve as diagnostic marker and potentially interesting base to develop pharmacological and noninvasive neuromodulation trials aimed at reducing tic symptomatology.

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease.

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This article proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.

Frontal-temporal functional connectivity of EEG signal by standardized permutation mutual information during anesthesia.

Quantifying brain dynamics during anesthesia is an important challenge for understanding the neurophysiological mechanisms of anesthetic drug effect. Several single channel Electroencephalogram (EEG) indices have been proposed for monitoring anesthetic drug effect. The most commonly used single channel commercial index is the Bispectral index (BIS). However, this monitor has shown some drawbacks. In this study, a nonlinear functional connectivity measure named Standardized Permutation Mutual Information (SPMI) is proposed to describe communication between two-channel EEG signals at frontal and temporal brain regions during a controlled propofol-induced anesthesia and recovery design from eight subjects. The SPMI index has higher correlation with estimated propofol effect-site concentration and has better ability to distinguish three anesthetic states of patient than the other functional connectivity indexes (cross-correlation, coherence, phase analysis) and also the BIS index. Moreover, the SPMI index has a faster reaction to the effect of drug concentration, less variability at the consciousness state and better robustness to noise than BIS.

EEG Complexity and Functional Connectivity During Precise Timing Prediction.

Predictive timing in millisecond-scale is crucial for human beings to efficiently accomplish ongoing perceptions and actions. It is believed that the predictive timing process is an implementation of the predictive coding model in time domain, which can flexibly deal with both the matching and mismatching predictions. However, it's still far from being understood how the neural signature differs between the two conditions during precise timing prediction. As the brain is a complex system, it is necessary to use nonlinear measures and functional connectivity to investigate the brain function of timing prediction. Here, we probe into the EEG signatures during predictive timing process by the sample entropy (SampEn), Lempel-Ziv complexity (LZC) and partial directed coherence methods. Significant lower EEG complexity and stronger brain functional connectivity were observed when the stimulus matches the timing prediction. The current observation may shed light on the modeling of the precise predictive timing process.

Normalized Transfer Entropy as a Tool to Identify Multisource Functional Epileptic Networks.

Epilepsy is a major health problem worldwide. A significant proportion of patients develop medication-refractory epilepsy (MRE); they are of ten evaluated for possible surgery where the focus of epileptogenic zones (EZ) are removed from the brain. Hence, prior to epilepsy surgery, insertion of depth electrodes into the brain is necessary to identify the EZs. These depth electrodes have multiple contacts that monitor the neuronal activity in multiple locations within the brain along each electrode trajectory. In the present study, we show that normalized transfer entropy measurements demonstrate functional connectivity across multiple sites within the brain of an MRE patient who did not demonstrate a clear EZ using conventional EEG criteria. Interestingly, linear measures of functional connectivity were not predictive of such an epileptic network. Our results suggest that routine evaluation of both linear and non-linear functional connectivity including normalized transfer entropy from depth electrode recordings may be useful to identify multisource epileptogenic networks in MRE patients. Identification of networks that contribute to epilepsy in such patients could potentially allow the clinician to avoid resective surgery and adopt alternate therapies such as vagal nerve stimulation or other emergent alternatives.

Investigating Changes in Resting-State Connectivity from Functional MRI Data in Patients with HIV Associated Neurocognitive Disorder Using MCA and Machine Learning.

Infection of the brain by the Human Immunodeficiency Virus (HIV) causes irreversible damage to the synaptic connections resulting in cognitive impairment. Patients with HIV infection, showing signs of impairment in multiple cognitive domains, as assessed by neuropsychological testing, are said to exhibit symptoms of HIV Associated Neurocognitive Disorder (HAND). In this study, we use resting-state functional MRI (fMRI) data to distinguish between healthy subjects and subjects with symptoms of HAND. To this end, we first establish a measure of interaction between pairs of regional time-series by quantifying their non-linear functional connectivity using Mutual Connectivity Analysis (MCA). Subsequently, we use a classifier to distinguish patterns of interaction between healthy and diseased individuals. Our results, quantified as the mean Area under the ROC curve (AUC) over 75 iterations, indicate that, using fMRI data, we can discriminate between the two cohorts well (AUC > 0.8). Specifically, we find that MCA (mean AUC = 0.89) based connectivity features perform significantly better (p < 0.05) when compared to cross-correlation (mean AUC = 0.82) at the classification task. A higher AUC using our approach suggests that such a nonlinear approach is better able to capture connectivity changes between brain regions and has potential for the development of novel neuro-imaging biomarkers.

Classification of Cognitive Level of Patients with Leukoaraiosis on the Basis of Linear and Non-Linear Functional Connectivity.

Leukoaraiosis (LA) describes diffuse white matter abnormalities apparent in computed tomography (CT) or magnetic resonance (MR) brain scans. Patients with LA generally show varying degrees of cognitive impairment, which can be classified as cognitively normal (CN), mild cognitive impairment (MCI), and dementia. However, a consistent relationship between the degree of LA and the level of cognitive impairment has not yet been established. We used functional magnetic resonance imaging (fMRI) to explore possible neuroimaging biomarkers for classification of cognitive level in LA. Functional connectivity (FC) between brain regions was calculated using Pearson’s correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC). Next, FCs with high discriminative power for different cognitive levels in LA were used as features for classification based on support vector machine. CN and MCI were classified with accuracies of 75.0, 61.9, and 91.1% based on features from PCC, MIC, and eMIC, respectively. MCI and dementia were classified with accuracies of 80.1, 86.2, and 87.4% based on features from PCC, MIC, and eMIC, respectively. CN and dementia were classified with accuracies of 80.1, 89.9, and 94.4% based on features from PCC, MIC, and eMIC, respectively. Our results suggest that features extracted from fMRI were efficient for classification of cognitive impairment level in LA, especially, when features were based on a non-linear method (eMIC).

PERCEPTUAL SUPPRESSION REVEALED BY ADAPTIVE MULTI-SCALE ENTROPY ANALYSIS OF LOCAL FIELD POTENTIAL IN MONKEY VISUAL CORTEX

Generalized flash suppression (GFS), in which a salient visual stimulus can be rendered invisible despite continuous retinal input, provides a rare opportunity to directly study the neural mechanism of visual perception. Previous work based on linear methods, such as spectral analysis, on local field potential (LFP) during GFS has shown that the LFP power at distinctive frequency bands are differentially modulated by perceptual suppression. Yet, the linear method alone may be insufficient for the full assessment of neural dynamic due to the fundamentally nonlinear nature of neural signals. In this study, we set forth to analyze the LFP data collected from multiple visual areas in V1, V2 and V4 of macaque monkeys while performing the GFS task using a nonlinear method - adaptive multi-scale entropy (AME) - to reveal the neural dynamic of perceptual suppression. In addition, we propose a new cross-entropy measure at multiple scales, namely adaptive multi-scale cross-entropy (AMCE), to assess the nonlinear functional connectivity between two cortical areas. We show that: (1) multi-scale entropy exhibits percept-related changes in all three areas, with higher entropy observed during perceptual suppression; (2) the magnitude of the perception-related entropy changes increases systematically over successive hierarchical stages (i.e. from lower areas V1 to V2, up to higher area V4); and (3) cross-entropy between any two cortical areas reveals higher degree of asynchrony or dissimilarity during perceptual suppression, indicating a decreased functional connectivity between cortical areas. These results, taken together, suggest that perceptual suppression is related to a reduced functional connectivity and increased uncertainty of neural responses, and the modulation of perceptual suppression is more effective at higher visual cortical areas. AME is demonstrated to be a useful technique in revealing the underlying dynamic of nonlinear/nonstationary neural signal.

Discriminative analysis of non-linear brain connectivity in schizophrenia: an fMRI Study

Background: Dysfunctional integration of distributed brain networks is believed to be the cause of schizophrenia, and resting-state functional connectivity analyses of schizophrenia have attracted considerable attention in recent years. Unfortunately, existing functional connectivity analyses of schizophrenia have been mostly limited to linear associations.Objective: The objective of the present study is to evaluate the discriminative power of non-linear functional connectivity and identify its changes in schizophrenia.Method: A novel measure utilizing the extended maximal information coefficient was introduced to construct non-linear functional connectivity. In conjunction with multivariate pattern analysis, the new functional connectivity successfully discriminated schizophrenic patients from healthy controls with relative higher accuracy rate than the linear measure.Result: We found that the strength of the identified non-linear functional connections involved in the classification increased in patients with schizophrenia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain.Conclusion: The classification results suggest that the non-linear functional connectivity provided useful discriminative power in diagnosis of schizophrenia, and the inverse but similar spatial distributed changes between the non-linear and linear measure may indicate the underlying compensatory mechanism and the complex neuronal synchronization underlying the symptom of schizophrenia.

A Volterra kernel approach to non-linear functional connectivity

A Volterra kernel approach to non-linear functional connectivity

IC-P3-211: Increased randomness of functional brain networks in AD: ‘Small-world' network analysis of non-linear functional connectivity

IC-P3-211: Increased randomness of functional brain networks in AD: ‘Small-world' network analysis of non-linear functional connectivity

P2-070: Increased randomness of functional brain networks in Alzheimer's disease: ‘small-world' network analysis of non-linear functional connectivity

P2-070: Increased randomness of functional brain networks in Alzheimer's disease: ‘small-world' network analysis of non-linear functional connectivity