Information Flow Between Resting-State Networks

Extending the spectral decomposition of Granger causality to include instantaneous influences: application to the control mechanisms of heart rate variability

Assessing Granger causality (GC) intended as the influence, in terms of reduction of variance of surprise, that a driver variable exerts on a given target, requires a suitable treatment of 'instantaneous' effects, i.e. influences due to interactions whose time scale is much faster than the time resolution of the measurements, due to unobserved confounders or insufficient sampling rate that cannot be increased because the mechanism of generation of the variable is inherently slow (e.g. the heartbeat). We exploit a recently proposed framework for the estimation of causal influences in the spectral domain and include instantaneous interactions in the modelling, thus obtaining (i) a novel index of undirected instantaneous causality and (ii) a novel measure of GC including instantaneous effects. An effective procedure to speed up the optimization of parameters in this frame is also presented. After illustrating the proposed formalism in a theoretical example, we apply it to two datasets of cardiovascular and respiratory time series and compare the values obtained within the frequency bands of physiological interest by the proposed total measure of causality with those derived from the standard GC analysis. We find that the inclusion of instantaneous causality allows us to correctly disentangle the baroreflex mechanism from the effects related to cardiorespiratory interactions. Moreover, studying how controlling the respiratory rhythm acts on cardiovascular interactions, we document an increase of the direct (non-baroreflex mediated) influence of respiration on the heart rate in the respiratory frequency band when switching from spontaneous to paced breathing. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

A Controlled Thermoalgesic Stimulation Device for Exploring Novel Pain Perception Biomarkers

OBJECTIVE

To develop a new device for identifying physiological markers of pain perception by reading the brain's electrical activity and hemodynamic interactions while applying thermoalgesic stimulation.

METHODS

We designed a compact prototype that generates well-controlled thermal stimuli using a computer-driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females). We first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increased the temperature. Next, we defined the painful condition as the one occurring at temperature equal to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation.

RESULTS

Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals differentiated the condition of pain. In particular, the SE for PPG was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG.

CONCLUSION

Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception.

SIGNIFICANCE

The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.

Brain connectivity and cognitive functioning in individuals six months after multiorgan failure

Multiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear.

OBJECTIVE

To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC).

METHODS

22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed.

RESULTS

There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities.

CONCLUSIONS

In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients. Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.

Interaction Information Along Lifespan of the Resting Brain Dynamics Reveals a Major Redundant Role of the Default Mode Network

Interaction Information (II) generalizes the univariate Shannon entropy to triplets of variables, allowing the detection of redundant (R) or synergetic (S) interactions in dynamical networks. Here, we calculated II from functional magnetic resonance imaging data and asked whether R or S vary across brain regions and along lifespan. Preserved along lifespan, we found high overlapping between the pattern of high R and the default mode network, whereas high values of S were overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, we have found a robust balance between R and S among different age intervals, indicating informational compensatory mechanisms in brain networks.

Structure-function multi-scale connectomics reveals a major role of the fronto-striato-thalamic circuit in brain aging

Physiological aging affects brain structure and function impacting morphology, connectivity, and performance. However, whether some brain connectivity metrics might reflect the age of an individual is still unclear. Here, we collected brain images from healthy participants (N = 155) ranging from 10 to 80 years to build functional (resting state) and structural (tractography) connectivity matrices, both data sets combined to obtain different connectivity features. We then calculated the brain connectome age-an age estimator resulting from a multi-scale methodology applied to the structure-function connectome, and compared it to the chronological age (ChA). Our results were twofold. First, we found that aging widely affects the connectivity of multiple structures, such as anterior cingulate and medial prefrontal cortices, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus, and temporal pole. Second, we found that the connectivity between basal ganglia and thalamus to frontal areas, also known as the fronto-striato-thalamic (FST) circuit, makes the major contribution to age estimation. In conclusion, our results highlight the key role played by the FST circuit in the process of healthy aging. Notably, the same methodology can be generally applied to identify the structural-functional connectivity patterns correlating to other biomarkers than ChA.

Enhanced prefrontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Traumatic brain injury (TBI) affects structural connectivity, triggering the reorganization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain network reorganization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severe TBI, comparing them to young, typically developing control participants. TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: (1) a subcortical network, including parts of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulate gyrus, and precuneus; and (2) a task-positive network, involving regions of the dorsal attention system, together with dorsolateral and ventrolateral prefrontal regions. We also found that the increased prefrontal connectivity in TBI patients was correlated with some postural control indices, such as the amount of body sway, whereby patients with worse balance increased their connectivity in frontal regions more strongly. The increased prefrontal connectivity found in TBI patients may provide the structural scaffolding for stronger cognitive control of certain behavioral functions, consistent with the observations that various motor tasks are performed less automatically following TBI and that more cognitive control is associated with such actions.

Transfer entropy in continuous time, with applications to jump and neural spiking processes

Transfer entropy has been used to quantify the directed flow of information between source and target variables in many complex systems. While transfer entropy was originally formulated in discrete time, in this paper we provide a framework for considering transfer entropy in continuous time systems, based on Radon-Nikodym derivatives between measures of complete path realizations. To describe the information dynamics of individual path realizations, we introduce the pathwise transfer entropy, the expectation of which is the transfer entropy accumulated over a finite time interval. We demonstrate that this formalism permits an instantaneous transfer entropy rate. These properties are analogous to the behavior of physical quantities defined along paths such as work and heat. We use this approach to produce an explicit form for the transfer entropy for pure jump processes, and highlight the simplified form in the specific case of point processes (frequently used in neuroscience to model neural spike trains). Finally, we present two synthetic spiking neuron model examples to exhibit the pertinent features of our formalism, namely, that the information flow for point processes consists of discontinuous jump contributions (at spikes in the target) interrupting a continuously varying contribution (relating to waiting times between target spikes). Numerical schemes based on our formalism promise significant benefits over existing strategies based on discrete time formalisms.

Top-Down Network Effective Connectivity in Abstinent Substance Dependent Individuals

Objective

We hypothesized that compared to healthy controls, long-term abstinent substance dependent individuals (SDI) will differ in their effective connectivity between large-scale brain networks and demonstrate increased directional information from executive control to interoception-, reward-, and habit-related networks. In addition, using graph theory to compare network efficiencies we predicted decreased small-worldness in SDI compared to controls.

Methods

50 SDI and 50 controls of similar sex and age completed psychological surveys and resting state fMRI. fMRI results were analyzed using group independent component analysis; 14 networks-of-interest (NOI) were selected using template matching to a canonical set of resting state networks. The number, direction, and strength of connections between NOI were analyzed with Granger Causality. Within-group thresholds were p<0.005 using a bootstrap permutation. Between group thresholds were p<0.05, FDR-corrected for multiple comparisons. NOI were correlated with behavioral measures, and group-level graph theory measures were compared.

Results

Compared to controls, SDI showed significantly greater Granger causal connectivity from right executive control network (RECN) to dorsal default mode network (dDMN) and from dDMN to basal ganglia network (BGN). RECN was negatively correlated with impulsivity, behavioral approach, and negative affect; dDMN was positively correlated with impulsivity. Among the 14 NOI, SDI showed greater bidirectional connectivity; controls showed more unidirectional connectivity. SDI demonstrated greater global efficiency and lower local efficiency.

Conclusions

Increased effective connectivity in long-term abstinent drug users may reflect improved cognitive control over habit and reward processes. Higher global and lower local efficiency across all networks in SDI compared to controls may reflect connectivity changes associated with drug dependence or remission and requires future, longitudinal studies to confirm.

Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain

One of the grand challenges faced by neuroscience is to delineate the determinants of interindividual variation in the comprehensive structural and functional connection matrices that comprise the human connectome. At present, this endeavor appears most tractable at the macroanatomic scale, where intrinsic brain activity exhibits robust patterns of synchrony that recapitulate core functional circuits at the individual level. Here, we use a classical twin study design to examine the heritability of intrinsic functional network properties in 101 twin pairs, including network activity (i.e., variance of a network's specific temporal fluctuations) and internetwork coherence (i.e., correlation between networks' specific temporal fluctuations). Five of 7 networks exhibited significantly heritable (23.3–65.2%) network activity, 6 of the 21 internetwork coherences were significantly heritable (25.6–42.0%), and 11 of the 21 internetwork coherences were significantly influenced by common environmental factors (18.0–47.1%). These results suggest that the source of interindividual variation in functional connectome has a modular architecture: individual modules represented by intrinsic connectivity networks are genetic controlled, while environmental factors influence the interplays between the modules. This work further provides network-specific hypotheses for discovery of the specific genetic and environmental factors influencing functional specialization and integration of the human brain.

Lagged and instantaneous dynamical influences related to brain structural connectivity

Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Indeed, different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2^* signal, providing functional connectivity (FC). Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by three different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; in particular, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C, nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.