Temporal Changes in Local Functional Connectivity Density Reflect the Temporal Variability of the Amplitude of Low Frequency Fluctuations in Gray Matter

Data-driven functional connectivity density (FCD) mapping is being increasingly utilized to assess brain connectomics at rest in the healthy brain and its disruption in neuropsychiatric diseases with the underlying assumption that the spatiotemporal hub distribution is stationary. However, recent studies show that functional connectivity is highly dynamic. Here we study the temporal variability of the local FCD (lFCD) at high spatiotemporal resolution (2-mm isotropic; 0.72s) using a sliding-window approach and ‘resting-state’ datasets from 40 healthy subjects collected under the Human Connectome Project. Prominent functional connectivity hubs in visual and posterior parietal cortices had pronounced temporal changes in local FCD. These dynamic patterns in the strength of the lFCD hubs occurred in cortical gray matter with high sensitivity (up to 85%) and specificity (> 85%) and showed high reproducibility (up to 72%) across sessions and high test-retest reliability (ICC(3,1) > 0.5). The temporal changes in lFCD predominantly occurred in medial occipitoparietal regions and were proportional to the strength of the connectivity hubs. The temporal variability of the lFCD was associated with the amplitude of the low frequency fluctuations (ALFF). Pure randomness did not account for the probability distribution of lFCD. Shannon entropy increased in proportion to the strength of the lFCD hubs suggesting high average flow of information per unit of time in the lFCD hubs, particularly in medial occipitoparietal regions. Thus, the higher dynamic range of the lFCD hubs is consistent with their role in the complex orchestration of interacting brain networks.     

Altered Functional Connectivity and Small-World in Mesial Temporal Lobe Epilepsy

Background

The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low–frequency coherent neuronal fluctuations that can be observed in a resting state condition. Little is known, so far, about the changes in functional connectivity and in the topological properties of functional networks, associated with different brain diseases.

Methodology/Principal Findings

In this study, we investigated alterations related to mesial temporal lobe epilepsy (mTLE), using resting state functional magnetic resonance imaging on 18 mTLE patients and 27 healthy controls. Functional connectivity among 90 cortical and subcortical regions was measured by temporal correlation. The related values were analyzed to construct a set of undirected graphs. Compared to controls, mTLE patients showed significantly increased connectivity within the medial temporal lobes, but also significantly decreased connectivity within the frontal and parietal lobes, and between frontal and parietal lobes. Our findings demonstrated that a large number of areas in the default-mode network of mTLE patients showed a significantly decreased number of connections to other regions. Furthermore, we observed altered small-world properties in patients, along with smaller degree of connectivity, increased n-to-1 connectivity, smaller absolute clustering coefficients and shorter absolute path length.

Conclusions/Significance

We suggest that the mTLE alterations observed in functional connectivity and topological properties may be used to define tentative disease markers.     

Organisms as Functional Machines: A Connectivity Explanation

Living organisms exist as a complex set of levels of organizationarranged in a pattern of strong ordering with none of theselevels being more important than others for a full understandingof life. Central to biological strong ordering is the organismallevel. Individual organisms are of special interest to biologistsbecause they are relevant to all biological processes regardlessof the operational level of the process. This is especiallytrue for investigations of the morphological-physiological propertiesof organisms. For such studies, living organisms must be consideredas complex machines with all of the sophisticated integrationand multifarious interactions of component parts typical ofcomplex systems. Understanding of the properties of any individualfeature in an organism depends as much, or possibly even more,on an appreciation of its connections and interactions withother features of that organism than on an understanding ofits intrinsic attributes. Learning the connectivity skills,including the modes of thinking, needed to comprehend the integrationof diverse components of any complex system requires a differenttraining than that needed to determine the detailed attributesof individual parts; both are necessary, however, to achieveproper advances in biological knowledge. Case studies of severalvertebrate features will be used to illustrate types of interactionswhich exist between structural/functional attributes, and howtheir recognition can lead to new and interesting questions.This "feeling for the organism" may be the major factor separatingthose biologists who are able to make important discoveriesfrom those who will only provide the subsequent, less excitingdetails of normal science.     

Functional Connectivity Estimated from Intracranial EEG Predicts Surgical Outcome in Intractable Temporal Lobe Epilepsy

This project aimed to determine if a correlation-based measure of functional connectivity can identify epileptogenic zones from intracranial EEG signals, as well as to investigate the prognostic significance of such a measure on seizure outcome following temporal lobe lobectomy. To this end, we retrospectively analyzed 23 adult patients with intractable temporal lobe epilepsy (TLE) who underwent an invasive stereo-EEG (SEEG) evaluation between January 2009 year and January 2012. A follow-up of at least one year was required. The primary outcome measure was complete seizure-freedom at last follow-up. Functional connectivity between two areas in the temporal lobe that were sampled by two SEEG electrode contacts was defined as Pearson’s correlation coefficient of interictal activity between those areas. SEEG signals were filtered between 5 and 50 Hz prior to computing this correlation. The mean and standard deviation of the off diagonal elements in the connectivity matrix were also calculated. Analysis of the mean and standard deviation of the functional connections for each patient reveals that 90% of the patients who had weak and homogenous connections were seizure free one year after temporal lobectomy, whereas 85% of the patients who had stronger and more heterogeneous connections within the temporal lobe had recurrence of seizures. This suggests that temporal lobectomy is ineffective in preventing seizure recurrence for patients in whom the temporal lobe is characterized by weakly connected, homogenous networks. This pilot study shows promising potential of a simple measure of functional brain connectivity to identify epileptogenicity and predict the outcome of epilepsy surgery.     

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.     

人脑功能连通性研究进展

对人脑结构和功能的深入研究,已经要求脑成像技术不能仅仅局限于研究简单的脑功能定位问题,即寻找和定位与特定认知任务相关的某一块或者一组大脑皮层功能区,而必须研究分析各功能区间的动态功能连通和整合问题,即描述特定脑功能区域间的交互作用以及这些交互作用如何受认知任务的影响.已有几种非常规的脑成像技术和数据分析方法,包括时间相关性分析、心理生理交互作用(PPI)、结构方程模型(SEM)、动态因果模型(DCM)、弥散张量成像(DTI)等等,被成功用于人脑功能连通性和有效连通性的研究.脑功能连通性研究的发展,有利于深入理解人脑在系统水平上的动态运作方式,是今后认知神经科学发展的一个重要方向.     

NODDI and Tensor-Based Microstructural Indices as Predictors of Functional Connectivity

In Diffusion Weighted MR Imaging (DWI), the signal is affected by the biophysical properties of neuronal cells and their relative placement, as well as extra-cellular tissue compartments. Typically, microstructural indices, such as fractional anisotropy (FA) and mean diffusivity (MD), are based on a tensor model that cannot disentangle the influence of these parameters. Recently, Neurite Orientation Dispersion and Density Imaging (NODDI) has exploited multi-shell acquisition protocols to model the diffusion signal as the contribution of three tissue compartments. NODDI microstructural indices, such as intra-cellular volume fraction (ICVF) and orientation dispersion index (ODI) are directly related to neuronal density and orientation dispersion, respectively. One way of examining the neurophysiological role of these microstructural indices across neuronal fibres is to look into how they relate to brain function. Here we exploit a statistical framework based on sparse Canonical Correlation Analysis (sCCA) and randomised Lasso to identify structural connections that are highly correlated with resting-state functional connectivity measured with simultaneous EEG-fMRI. Our results reveal distinct structural fingerprints for each microstructural index that also reflect their inter-relationships.     

Whole-Brain Functional Connectivity Identification of Functional Dyspepsia

Recent neuroimaging studies have shown local brain aberrations in functional dyspepsia (FD) patients, yet little attention has been paid to the whole-brain resting-state functional network abnormalities. The purpose of this study was to investigate whether FD disrupts the patterns of whole-brain networks and the abnormal functional connectivity could reflect the severity of the disease. The dysfunctional interactions between brain regions at rest were investigated in FD patients as compared with 40 age- and gender- matched healthy controls. Multivariate pattern analysis was used to evaluate the discriminative power of our results for classifying patients from controls. In our findings, the abnormal brain functional connections were mainly situated within or across the limbic/paralimbic system, the prefrontal cortex, the tempo-parietal areas and the visual cortex. About 96% of the subjects among the original dataset were correctly classified by a leave one-out cross-validation approach, and 88% accuracy was also validated in a replication dataset. The classification features were significantly associated with the patients’ dyspepsia symptoms, the self-rating depression scale and self-rating anxiety scale, but it was not correlated with duration of FD patients (p>0.05). Our results may indicate the effectiveness of the altered brain functional connections reflecting the disease pathophysiology underling FD. These dysfunctional connections may be the epiphenomena or causative agents of FD, which may be affected by clinical severity and its related emotional dimension of the disease rather than the clinical course.     

Passive Acoustic Monitoring of the Temporal Variability of Odontocete Tonal Sounds from a Long-Term Marine Observatory

The developments of marine observatories and automatic sound detection algorithms have facilitated the long-term monitoring of multiple species of odontocetes. Although classification remains difficult, information on tonal sound in odontocetes (i.e., toothed whales, including dolphins and porpoises) can provide insights into the species composition and group behavior of these species. However, the approach to measure whistle contour parameters for detecting the variability of odontocete vocal behavior may be biased when the signal-to-noise ratio is low. Thus, methods for analyzing the whistle usage of an entire group are necessary. In this study, a local-max detector was used to detect burst pulses and representative frequencies of whistles within 4.5–48 kHz. Whistle contours were extracted and classified using an unsupervised method. Whistle characteristics and usage pattern were quantified based on the distribution of representative frequencies and the composition of whistle repertoires. Based on the one year recordings collected from the Marine Cable Hosted Observatory off northeastern Taiwan, odontocete burst pulses and whistles were primarily detected during the nighttime, especially after sunset. Whistle usage during the nighttime was more complex, and whistles with higher frequency were mainly detected during summer and fall. According to the multivariate analysis, the diurnal variation of whistle usage was primarily related to the change of mode frequency, diversity of representative frequency, and sequence complexity. The seasonal variation of whistle usage involved the previous three parameters, in addition to the diversity of whistle clusters. Our results indicated that the species and behavioral composition of the local odontocete community may vary among seasonal and diurnal cycles. The current monitoring platform facilitates the evaluation of whistle usage based on group behavior and provides feature vectors for species and behavioral classification in future studies.     

Functional Connectivity Alterations in Epilepsy from Resting-State Functional MRI

The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.     

Disrupted Ipsilateral Network Connectivity in Temporal Lobe Epilepsy

ObjectiveThe current practice under which patients with refractory epilepsy are surgically treated is based mainly on the identification of specific cortical areas, mainly the epileptogenic zone, which is believed to be responsible for generation of seizures. A better understanding of the whole epileptic network and its components and properties is required before more effective and less invasive therapies can be developed. The aim of the present study was to partially characterize the evolution of the functional network during the preictal-ictal transition in partial seizures in patients with temporal lobe epilepsy (TLE).MethodsScalp and foramen ovale (FOE) recordings from twenty-two TLE patients were analyzed under the complex network perspective. The density of links, average path length, average clustering coefficient, and modularity were calculated during the preictal and the ictal stages. Both linear–Pearson correlation–and non-linear–phase synchronization–measures were used as proxies of functional connectivity between the electrode locations areas. The transition from one stage to the other was evaluated in the whole network and in the mesial sub-networks. The results were compared with a voltage-dependent measure, namely, the spectral entropy.ResultsChanges in the global functional network during the transition from the preictal to the ictal stage show, in the linear case, that in sixteen cases (72.7%) the density of the links increased during the seizure, with a decrease in the average path length in fifteen cases (68.1%). There was also a preictal and ictal imbalance in functional connectivity during both stages (77.2% to 86.3%). The SE dropped during the seizure in 95.4% of the cases, but did not show any tendency towards lateralization. When using the nonlinear measure of functional connectivity, the phase synchronization, similar results were obtained.ConclusionsIn TLE patients, the transition to the ictal stage is accompanied by increasing global synchronization and a more ordered spectral content of the signals, indicated by lower spectral entropy. The interictal connectivity imbalance (lower ipsilateral connectivity) is sustained during the seizure, irrespective of any appreciable imbalance in the spectral entropy of the mesial recordings.     

Time-Delayed Mutual Information of the Phase as a Measure of Functional Connectivity

We propose a time-delayed mutual information of the phase for detecting nonlinear synchronization in electrophysiological data such as MEG. Palus already introduced the mutual information as a measure of synchronization [1]. To obtain estimates on small data-sets as reliably as possible, we adopt the numerical implementation as proposed by Kraskov and colleagues [2]. An embedding with a parametric time-delay allows a reconstruction of arbitrary nonstationary connective structures – so-called connectivity patterns – in a wide class of systems such as coupled oscillatory or even purely stochastic driven processes [3]. By using this method we do not need to make any assumptions about coupling directions, delay times, temporal dynamics, nonlinearities or underlying mechanisms. For verifying and refining the methods we generate synthetic data-sets by a mutual amplitude coupled network of Rössler oscillators with an a-priori known connective structure. This network is modified in such a way, that the power-spectrum forms a power law, which is also observed in electrophysiological recordings. The functional connectivity measure is tested on robustness to additive uncorrelated noise and in discrimination of linear mixed input data. For the latter issue a suitable de-correlation technique is applied. Furthermore, the compatibility to inverse methods for a source reconstruction in MEG such as beamforming techniques is controlled by dedicated dipole simulations. Finally, the method is applied on an experimental MEG recording.     

Functional Brain Connectivity as a New Feature for P300 Speller

The brain is a large-scale complex network often referred to as the “connectome”. Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the ‘feature extraction’ methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of ‘P300 speller’. The proposed approach was compared to the well-known methods proposed in the state of the art of “P300 Speller”, mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.     

Formal Comparison of Dual-Parameter Temporal Discounting Models in Controls and Pathological Gamblers

Temporal or delay discounting refers to the phenomenon that the value of a reward is discounted as a function of time to delivery. A range of models have been proposed that approximate the shape of the discount curve describing the relationship between subjective value and time. Recent evidence suggests that more than one free parameter may be required to accurately model human temporal discounting data. Nonetheless, many temporal discounting studies in psychiatry, psychology and neuroeconomics still apply single-parameter models, despite their oftentimes poor fit to single-subject data. Previous comparisons of temporal discounting models have either not taken model complexity into account, or have overlooked particular models. Here we apply model comparison techniques in a large sample of temporal discounting datasets using several discounting models employed in the past. Among the models examined, an exponential-power model from behavioural economics (CS model, Ebert & Prelec 2007) provided the best fit to human laboratory discounting data. Inter-parameter correlations for the winning model were moderate, whereas they were substantial for other dual-parameter models examined. Analyses of previous group and context effects on temporal discounting with the winning model provided additional theoretical insights. The CS model may be a useful tool in future psychiatry, psychology and neuroscience work on inter-temporal choice.     

Resting-State Functional Connectivity Bias of Middle Temporal Gyrus and Caudate with Altered Gray Matter Volume in Major Depression

Magnetic resonance imaging (MRI) studies have indicated that the structure deficits and resting-state functional connectivity (FC) imbalances in cortico-limbic circuitry might underline the pathophysiology of MDD. Using structure and functional MRI, our aim is to investigate gray matter abnormalities in patients with treatment-resistant depression (TRD) and treatment-responsive depression (TSD), and test whether the altered gray matter is associated with altered FC. Voxel-based morphometry was used to investigate the regions with gray matter abnormality and FC analysis was further conducted between each gray matter abnormal region and the remaining voxels in the brain. Using one-way analysis of variance, we found significant gray matter abnormalities in the right middle temporal cortex (MTG) and bilateral caudate among the TRD, TSD and healthy controls. For the FC of the right MTG, we found that both the patients with TRD and TSD showed altered connectivity mainly in the default-mode network (DMN). For the FC of the right caudate, both patient groups showed altered connectivity in the frontal regions. Our results revealed the gray matter reduction of right MTG and bilateral caudate, and disrupted functional connection to widely distributed circuitry in DMN and frontal regions, respectively. These results suggest that the abnormal DMN and reward circuit activity might be biomarkers of depression trait.     

Interindividual Variability and Idiosyncrasy in Social Behaviours in the Ant Camponotus vagus Scop

Large intraspecific variability was found in interactive ant behaviours. However, a time study of callow and mature Camponotus vagus workers showed some stability in relationships within pairs of workers or in given individuals. Individual idiosyncrasies were observed as tendencies to simultaneously display a single level of activity for several behaviours. Thus some mature workers reverse the food flow during trophallactic exchanges, spend more time exchanging food and are more aggressive toward intruders. The time spent by callows in their main activity, brood-tending, allows discrimination on two activity levels. Moreover, callows deprived of any relationship with older nestmates show different levels of activity in the duration and intensity of aggressive and non-aggressive behaviours. The causality of these behavioural idiosyncrasies could possibly be explained in part by the social experience of each individual.     

Functional Connectivity of the Posteromedial Cortex

As different areas within the PMC have different connectivity patterns with various cortical and subcortical regions, we hypothesized that distinct functional modules may be present within the PMC. Because the PMC appears to be the most active region during resting state, it has been postulated to play a fundamental role in the control of baseline brain functioning within the default mode network (DMN). Therefore one goal of this study was to explore which components of the PMC are specifically involved in the DMN. In a sample of seventeen healthy volunteers, we performed an unsupervised voxelwise ROI-based clustering based on resting state functional connectivity. Our results showed four clusters with different network connectivity. Each cluster showed positive and negative correlations with cortical regions involved in the DMN. Progressive shifts in PMC functional connectivity emerged from anterior to posterior and from dorsal to ventral ROIs. Ventral posterior portions of PMC were found to be part of a network implicated in the visuo-spatial guidance of movements, whereas dorsal anterior portions of PMC were interlinked with areas involved in attentional control. Ventral retrosplenial PMC selectively correlated with a network showing considerable overlap with the DMN, indicating that it makes essential contributions in self-referential processing, including autobiographical memory processing. Finally, ventral posterior PMC was shown to be functionally connected with a visual network.The paper represents the first attempt to provide a systematic, unsupervised, voxelwise clustering of the human posteromedial cortex (PMC), using resting-state functional connectivity data. Moreover, a ROI-based parcellation was used to confirm the results.     

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis

Background

Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS.

Methods

18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity.

Results

Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05).

Discussion

There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS.