Nervous–system–wise Functional Estimation of Directed Brain–Heart Interplay through Microstate Occurrences

Background: The quantification of functional brain–heart interplay (BHI) through analysis of the dynamics of the central and autonomic nervous systems provides effective biomarkers for cognitive, emotional, and autonomic state changes. Several computational models have been proposed to estimate BHI, focusing on a single sensor, brain region, or frequency activity. However, no models currently provide a directional estimation of such interplay at the organ level. Objective: This study proposes an analysis framework to estimate BHI that quantifies the directional information flow between whole–brain and heartbeat dynamics. Methods: System–wise directed functional estimation is performed through an ad-hoc symbolic transfer entropy implementation, which leverages on EEG-derived microstate series and on partition of heart rate variability series. The proposed framework is validated on two different experimental datasets: the first investigates the cognitive workload performed through mental arithmetic and the second focuses on an autonomic maneuver using a cold pressor test (CPT). Results: The experimental results highlight a significant bidirectional increase in BHI during cognitive workload with respect to the preceding resting phase and a higher descending interplay during a CPT compared to the preceding rest and following recovery phases. These changes are not detected by the intrinsic self entropy of isolated cortical and heartbeat dynamics. Conclusion: This study corroborates the literature on the BHI phenomenon under these experimental conditions and the new perspective provides novel insights from an organ–level viewpoint. Significance: A system–wise perspective of the BHI phenomenon may provide new insights into physiological and pathological processes that may not be completely understood at a lower level/scale of analysis

Measuring spectrally resolved information processing in neural data

Background: The human brain, an incredibly complex biological system comprising billions of neurons and trillions of synapses, possesses remarkable capabilities for information processing and distributed computations. Neurons, the fundamental building blocks, perform elementary operations on their inputs and collaborate extensively to execute intricate computations, giving rise to cognitive functions and behavior. Notably, distributed information processing in the brain heavily relies on rhythmic neural activity characterized by synchronized oscillations at specific frequencies. These oscillations play a crucial role in coordinating brain activity and facilitating communication between different neural circuits [1], effectively acting as temporal windows that enable efficient information exchange within specific frequency ranges. To understand distributed information processing in neural systems, breaking down its components, i.e., —information transfer, storage, and modification can be helpful, but requires precise mathematical definitions for each respective component. Thankfully, these definitions have recently become available [2]. Information theory is a natural choice for measuring information processing, as it offers a mathematically complete description of the concept of information and communication. The fundamental information-processing operations, are considered essential prerequisites for achieving universal information processing in any system [3]. By quantifying and analyzing these operations, we gain valuable insights into the brain’s complex computation and cognitive abilities. As information processing in the brain is intricately tied to rhythmic behavior, there is a need to establish a connection between information theoretic measures and frequency components. Previous attempts to achieve frequency-resolved information theoretic measures have mostly relied on narrowband filtering [4], which comes with several known issues of phase shifting and high false positive rate results [5], or simplifying the computation to few variables [6], that might result in missing important information in the analysed brain signals. Therefore, the current work aims to establish a frequency-resolved measure of two crucial components of information processing: information transfer and information storage. By proposing methodological advancements, this research seeks to shed light on the role of neural oscillations in information processing within the brain. Furthermore, a more comprehensive investigation was carried out on the communication between two critical brain regions responsible for motor inhibition in the frontal cortex (right Inferior Frontal gyrus (rIFG) and pre-Supplementary motor cortex (pre-SMA)). Here, neural oscillations in the beta band (12 − 30 Hz) have been proposed to have a pivotal role in response inhibition. A long-standing question in the field was to disentangle which of these two brain areas first signals the stopping process and drives the other [7]. Furthermore, it was hypothesized that beta oscillations carry the information transfer between these regions. The present work addresses the methodological problems and investigates spectral information processing in neural data, in three studies. Study 1 focuses on the critical role of information transfer, measured by transfer entropy, in distributed computation. Understanding the patterns of information transfer is essential for unraveling the computational algorithms in complex systems, such as the brain. As many natural systems rely on rhythmic processes for distributed computations, a frequency-resolved measure of information transfer becomes highly valuable. To address this, a novel algorithm is presented, efficiently identifying frequencies responsible for sending and receiving information in a network. The approach utilizes the invertible maximum overlap discrete wavelet transform (MODWT) to create surrogate data for computing transfer entropy, eliminating issues associated with phase shifts and filtering. However, measuring frequency-resolved information transfer poses a Partial information decomposition problem [8] that is yet to be fully resolved. The algorithm’s performance is validated using simulated data and applied to human magnetoencephalography (MEG) and ferret local field potential recordings (LFP). In human MEG, the study unveils a complex spectral configuration of cortical information transmission, showing top-down information flow from very high frequencies (above 100Hz) to both similarly high frequencies and frequencies around 20Hz in the temporal cortex. Contrary to the current assumption, the findings suggest that low frequencies do not solely send information to high frequencies. In the ferret LFP, the prefrontal cortex demonstrates the transmission of information at low frequencies, specifically within the range of 4-8 Hz. On the receiving end, V1 exhibits a preference for operating at very high frequency > 125 Hz. The spectrally resolved transfer entropy promises to deepen our understanding of rhythmic information exchange in natural systems, shedding light on the computational properties of oscillations on cognitive functions. In study 2, the primary focus lay on the second fundamental aspect of information processing: the active information storage (AIS). The AIS estimates how much information in the next measurements of the process can be predicted by examining its paste state. In processes that either produce little information (low entropy) or that are highly unpredictable, the AIS is low, whereas processes that are predictable but visit many different states with equal probabilities, exhibit high AIS [9]. Within this context, we introduced a novel spectrally-resolved AIS. Utilizing intracortical recordings of neural activity in anesthetized ferrets before and after loss of consciousness (LOC), the study reveals that the modulation of AIS by anesthesia is highly specific to different frequency bands, cortical layers, and brain regions. The findings reveal that the effects of anesthesia on AIS are prominent in the supragranular layers for the high/low gamma band, while the alpha/beta band exhibits the strongest decrease in AIS at infragranular layers, in accordance with the predictive coding theory. Additionally, the isoflurane impacts local information processing in a frequency-specific manner. For instance, increases in isoflurane concentration lead to a decrease in AIS in the alpha frequency but to an increase in AIS in the delta frequency range (<2Hz). In sum, analyzing spectrally-resolved AIS provides valuable insights into changes in cortical information processing under anesthesia. With rhythmic neural activity playing a significant role in biological neural systems, the introduction of frequency-specific components in active information storage allows a deeper understanding of local information processing in different brain areas and under various conditions. In study 3, to further verify the pivotal role of neural oscillations in information processing, we investigated the neural network mechanisms underlying response inhibition. A long-standing debate has centered around identifying the cortical initiator of response inhibition in the beta band, with two main regions proposed: the right rIFG and the pre-SMA. This third study aimed to determine which of these regions is activated first and exerts a potential information exchange on the other. Using high temporal resolution magnetoencephalography (MEG) and a relatively large cohort of subjects. A significant breakthrough is achieved by demonstrating that the rIFG is activated significantly earlier than the pre-SMA. The onset of beta band activity in the rIFG occurred at around 140 ms after the STOP signal. Further analyses showed that the beta-band activity in the rIFG was crucial for successful stopping, as evidenced by its predictive value for stopping performance. Connectivity analysis revealed that the rIFG sends information in the beta band to the pre-SMA but not vice versa, emphasizing the rIFG’s dominance in the response inhibition process. The results provide strong support for the hypothesis that the rIFG initiates stopping and utilizes beta-band oscillations for this purpose. These findings have significant implications, suggesting the possibility of spatially localized oscillation based interventions for response inhibition. Conclusion: In conclusion, the present work proposes a novel algorithm for uncovering the frequencies at which information is transferred between sources and targets in the brain, providing valuable insights into the computational dynamics of neural processes. The spectrally resolved transfer entropy was successfully applied to experimental neural data of intracranial recordings in ferrets and MEG recordings of humans. Furthermore, the study on active information storage (AIS) analysis under anesthesia revealed that the spectrally resolved AIS offers unique additional insights beyond traditional spectral power analysis. By examining changes in neural information processing, the study demonstrates how AIS analysis can deepen the understanding of anesthesia’s effects on cortical information processing. Moreover, the third study’s findings provide strong evidence supporting the critical role of beta oscillations in information processing, particularly in response inhibition. The research successfully demonstrates that beta oscillations in the rIFG functions as the key initiator of the response inhibition process, acting as a top-down control mechanism. The identification of beta oscillations as a crucial factor in information processing opens possibilities for further research and targeted interventions in neurological disorders. Taken together, the current work highlights the role of spectrally-resolved information processing in neural systems by not only introducing novel algorithms, but also successfully applying them to experimental oscillatory neural activity in relation to low-level cortical information processing (anesthesia) as well as high-level processes (cognitive response inhibition)

An entropy-based investigation of underpinnings and impact of oscillations in a model of PD

The involvement of the basal ganglia in motor control has been highlighted in studies of Parkinson’s disease (PD) and other movement disorders. The loss of dopaminergic neurons in the substantia nigra pars compacta and subsequent decrease of the dopamine level in the basal ganglia is recognized as the hallmark of PD. The classical view of the architecture of the dopamine depleted basal ganglia-thalamo-cortical circuit identifies changes in firing rates as the probable cause for the motor impairments in PD. Yet, more recent findings have shown that disturbances in other intrinsic dynamical properties of these networks may also contribute to motor deficits. Electrophysiological recordings in the basal ganglia of deep brain stimulation (DBS) patients (when OFF stimulation) have found pathological oscillations at beta frequency (13-30 Hz). This abnormal oscillatory activity has also been found in basal ganglia nuclei of animal models of PD. Additionally, the beta frequency oscillations were found to decrease when the patients are on dopamine replacement therapies and as they initiate movement. Beta frequency oscillations have been identified in the firing of single neurons and in the coupling of discharges between neurons. Within the framework of information theory, we proposed a time series model to analyse and relate the effects of changes in the dynamics of individual factors – such as alterations in firing rates, oscillations and synchrony (or auto and cross-correlations) caused by dopamine depletion – on the coding capacity (i.e., entropy) of a network. We estimated the entropy of a neural network based on the probabilities of current spiking conditioned on the observation of firing rate and spiking history of the current neuron and of neighbouring neurons. Moreover, we could estimate entropies for each of these factors separately, in healthy and dopamine depleted conditions, and assess their relative contribution to the decrease of coding capacity in disease. Furthermore, the causal characteristics of the model made it possible to compare the synaptic connectivity of neuronal populations in health with that in disease, by measuring the amount of directed information transferred between populations. We employed the model to study the external globus pallidus (GPe) network in control and 6-hydroxydopamine (6-OHDA) lesioned rats – a model for PD. We found a significant decrease in the coding capacity in lesioned animals, compared to controls, and that this decrease was predominantly on account of a reduction in the GPe firing rates. Although to a lesser extent, the amplification of the oscillatory activity (mainly in the beta frequency range) observed in the lesioned animals had also a significant impact on the reduction of their coding capacity. The higher synchrony found in the 6-OHDA rats had the least effect. We also found that the levels of coding capacity in the GPe were restored to levels close to control when the lesioned animals were treated with the dopamine agonist apomorphine. In addition, we detected a stronger coupling between the subthalamic nucleus (STN) and the GPe in the dopamine depleted rats, pointing to an abnormally exaggerated transfer of information within this network. We have shown that the GPe and the STN-GPe networks in the dopamine depleted rat exhibit information processing irregularities. We believe these deficits in processing and relaying information may also be present in other structures of the basal ganglia-thalamo-cortical circuit and that they may underlie the motor impairment in PD

Development and application of an optogenetic platform for controlling and imaging a large number of individual neurons

The understanding and treatment of brain disorders as well as the development of intelligent machines is hampered by the lack of knowledge of how the brain fundamentally functions. Over the past century, we have learned much about how individual neurons and neural networks behave, however new tools are critically needed to interrogate how neural networks give rise to complex brain processes and disease conditions. Recent innovations in molecular techniques, such as optogenetics, have enabled neuroscientists unprecedented precision to excite, inhibit and record defined neurons. The impressive sensitivity of currently available optogenetic sensors and actuators has now enabled the possibility of analyzing a large number of individual neurons in the brains of behaving animals. To promote the use of these optogenetic tools, this thesis integrates cutting edge optogenetic molecular sensors which is ultrasensitive for imaging neuronal activity with custom wide field optical microscope to analyze a large number of individual neurons in living brains. Wide-field microscopy provides a large field of view and better spatial resolution approaching the Abbe diffraction limit of fluorescent microscope. To demonstrate the advantages of this optical platform, we imaged a deep brain structure, the Hippocampus, and tracked hundreds of neurons over time while mouse was performing a memory task to investigate how those individual neurons related to behavior. In addition, we tested our optical platform in investigating transient neural network changes upon mechanical perturbation related to blast injuries. In this experiment, all blasted mice show a consistent change in neural network. A small portion of neurons showed a sustained calcium increase for an extended period of time, whereas the majority lost their activities. Finally, using optogenetic silencer to control selective motor cortex neurons, we examined their contributions to the network pathology of basal ganglia related to Parkinson’s disease. We found that inhibition of motor cortex does not alter exaggerated beta oscillations in the striatum that are associated with parkinsonianism. Together, these results demonstrate the potential of developing integrated optogenetic system to advance our understanding of the principles underlying neural network computation, which would have broad applications from advancing artificial intelligence to disease diagnosis and treatment

Assessing brain connectivity through electroencephalographic signal processing and modeling analysis

Brain functioning relies on the interaction of several neural populations connected through complex connectivity networks, enabling the transmission and integration of information. Recent advances in neuroimaging techniques, such as electroencephalography (EEG), have deepened our understanding of the reciprocal roles played by brain regions during cognitive processes. The underlying idea of this PhD research is that EEG-related functional connectivity (FC) changes in the brain may incorporate important neuromarkers of behavior and cognition, as well as brain disorders, even at subclinical levels. However, a complete understanding of the reliability of the wide range of existing connectivity estimation techniques is still lacking. The first part of this work addresses this limitation by employing Neural Mass Models (NMMs), which simulate EEG activity and offer a unique tool to study interconnected networks of brain regions in controlled conditions. NMMs were employed to test FC estimators like Transfer Entropy and Granger Causality in linear and nonlinear conditions. Results revealed that connectivity estimates reflect information transmission between brain regions, a quantity that can be significantly different from the connectivity strength, and that Granger causality outperforms the other estimators. A second objective of this thesis was to assess brain connectivity and network changes on EEG data reconstructed at the cortical level. Functional brain connectivity has been estimated through Granger Causality, in both temporal and spectral domains, with the following goals: a) detect task-dependent functional connectivity network changes, focusing on internal-external attention competition and fear conditioning and reversal; b) identify resting-state network alterations in a subclinical population with high autistic traits. Connectivity-based neuromarkers, compared to the canonical EEG analysis, can provide deeper insights into brain mechanisms and may drive future diagnostic methods and therapeutic interventions. However, further methodological studies are required to fully understand the accuracy and information captured by FC estimates, especially concerning nonlinear phenomena

Statistical Analysis of EEG Phase Shift Events

This thesis develops statistical methods for the identification, and analysis of phase shift events, i.e. sudden changes in the timing relationship between coupled oscillators. Phase shifts events occur in many complex systems but here the primary interest is the analysis of electroencephalogram (EEG) recordings where they have been identified as markers of information transmission in the brain; as a secondary example we analyze systems of weakly coupled Rossler attractors. The main result, found in Chapter 2, is a novel method for estimating neural connectivity from EEG recordings based on spatio-temporal patterns of phase shift events. Phase shift events are modelled as a multivariate point process, and the ideas of Granger causality are used to motivate a directed measure of connectivity. The method is demonstrated on EEG recordings from 18 participants during three task conditions; resting, visual vigilance and auditory vigilance. Likelihood ratios are used to test the hypothesis of no Granger causal interaction between signals, and network patterns are analyzed using graph theory. In Chapter 3 the problem of phase shift identification is formulated as a change point in the instantaneous phase. Two estimators are considered, based on the cumulative summation and the instantaneous phase derivative. Block bootstrapping techniques are used to capture the dependency structure in the signals and determine critical values for shift identification. Estimators are evaluated both on their accuracy, and temporal resolution. Finally, detailed simulation studies are performed using realistic head models to investigate the effect of volume conduction (linear spread of electrical activity at the scalp) on phase shift analysis. Specifically, Chapter 4 investigates the effect of volume conduction on the analysis, in order to understand the limitations of the phase shift Granger causality method. Chapter 5 then investigates an approach for reducing the effect of volume conduction by using EEG source reconstruction techniques to estimate neural source activity and then identifying phase shifts with-in the brain directly from the reconstructed sources. The primary impact is the novel method for estimating neural connectivity. Each chapter investigates a different aspect of EEG phase analysis, and together they form a complete package for estimation and interpretation of neural connectivity. Two other areas of impact are in statistical change point analysis, and behavioural psychology

Anesthetic-induced unresponsiveness: Electroencephalographic correlates and subjective experiences

Anesthetic drugs can induce reversible alterations in responsiveness, connectedness and consciousness. The measures based on electroencephalogram (EEG) have marked potential for monitoring the anesthetized state because of their relatively easy use in the operating room. In this study, 79 healthy young men participated in an awake experiment, and 47 participants continued to an anesthesia experiment where they received either dexmedetomidine or propofol as target-controlled infusion with stepwise increments until the loss of responsiveness. The participants were roused during the constant drug infusion and interviewed. The drug dose was increased to 1.5-fold to achieve a deeper unresponsive state. After regaining responsiveness, the participants were interviewed. EEG was measured throughout the experiment and the N400 event-related potential component and functional and directed connectivity were studied. Prefrontal-frontal connectivity in the alpha frequency band discriminated the states that differed with respect to responsiveness or drug concentration. The net direction of connectivity was frontal-to-prefrontal during unresponsiveness and reversed back to prefrontal-to-frontal upon return of responsiveness. The understanding of the meaning of spoken language, as measured with the N400 effect, was lost along with responsiveness but, in the dexmedetomidine group, the N400 component was preserved suggesting partial preservation of the processing of words during anesthetic-induced unresponsiveness. However, the N400 effect could not be detected in all the awake participants and the choice of analysis method had marked impact on its detection rate at the individual-level. Subjective experiences were common during unresponsiveness induced by dexmedetomidine and propofol but the experiences most often suggested disconnectedness from the environment. In conclusion, the doses of dexmedetomidine or propofol minimally sufficient to induce unresponsiveness do not render the participants unconscious and dexmedetomidine does not completely abolish the processing of semantic stimuli. The local anterior EEG connectivity in the alpha frequency band may have potential in monitoring the depth of dexmedetomidine- and propofol-induced anesthesia.Anesteettien aiheuttama vastauskyvyttömyys: aivosähkökäyräpohjaiset korrelaatit ja subjektiiviset kokemukset Anestesialääkkeillä voidaan saada aikaan palautuvia muutoksia vastauskykyisyydessä, kytkeytyneisyydessä ja tajunnassa. Aivosähkökäyrään (EEG) pohjautuvat menetelmät tarjoavat lupaavia mahdollisuuksia mitata anestesian vaikutusta aivoissa, sillä niitä on suhteellisen helppo käyttää leikkaussalissa. Tässä tutkimuksessa 79 tervettä nuorta miestä osallistui valvekokeeseen ja 47 heistä jatkoi anestesiakokeeseen. Anestesiakokeessa koehenkilöille annettiin joko deksmedetomidiinia tai propofolia tavoiteohjattuna infuusiona nousevia annosportaita käyttäen, kunnes he menettivät vastauskykynsä. Koehenkilöt herätettiin tasaisen lääkeinfuusion aikana ja haastateltiin. Koko kokeen ajan mitattiin EEG:tä, josta tutkittiin N400-herätevastetta sekä toiminnallista ja suunnattua konnektiivisuutta. Prefrontaali-frontaalivälillä mitattu konnektiivisuus alfa-taajuuskaistassa erotteli toisistaan tilat, jotka erosivat vastauskykyisyyden tai lääkepitoisuuden suhteen. Konnektiivisuuden vallitseva suunta oli frontaalialueilta prefrontaalialueille vastauskyvyttömyyden aikana, mutta se kääntyi takaisin prefrontaalisesta frontaaliseen kulkevaksi koehenkilöiden vastauskyvyn palatessa. N400-efektillä mitattu puhutun kielen ymmärtäminen katosi vastauskyvyn menettämisen myötä. Deksmedetomidiiniryhmässä N400-komponentti säilyi, mikä viittaa siihen, että anesteettien aiheuttaman vastauskyvyttömyyden aikana sanojen prosessointi voi säilyä osittain. Yksilötasolla N400-efektiä ei kuitenkaan havaittu edes kaikilla hereillä olevilla henkilöillä, ja analyysimenetelmän valinnalla oli suuri vaikutus herätevasteen havaitsemiseen. Subjektiiviset kokemukset olivat yleisiä deksmedetomidiinin ja propofolin aiheuttaman vastauskyvyttömyyden aikana, mutta kokemukset olivat usein ympäristöstä irtikytkeytyneitä. Yhteenvetona voidaan todeta, että deksmedetomidiini- ja propofoliannokset, jotka juuri ja juuri riittävät aikaansaamaan vastauskyvyttömyyden, eivät aiheuta tajuttomuutta. Deksmedetomidiini ei myöskään täysin estä merkityssisällöllisten ärsykkeiden käsittelyä. Frontaalialueen sisällä EEG:llä mitattu konnektiivisuus alfataajuuskaistassa saattaa olla tulevaisuudessa hyödyllinen menetelmä deksmedetomidiini- ja propofolianestesian syvyyden mittaamiseksi

Investigating the mechanism of action of Deep Brain Stimulation using functional magnetic resonance imaging

Depleted of dopamine, the dynamics of the Parkinsonian brain impact on both “action” and “resting” motor behaviour. Subthalamic nucleus deep brain stimulation (STN DBS) has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Functional magnetic resonance imaging (fMRI) using the blood oxygen level dependent (BOLD) contrast provides the opportunity to study the human brain in vivo, collecting indirect measures of neural activity across the whole brain. To date, technical difficulties and safety concerns have precluded the use of fMRI in DBS patients. Previous work from this department has demonstrated that scanning patients with certain DBS systems and MRI equipment is both safe and feasible. This thesis explores the neuromodulatory actions of STN DBS on both action and resting motor behaviours in patients with Parkinson’s disease (PD) using fMRI. In brief, I present two fMRI studies conducted on STN DBS patients, one task-based, and one resting, collected under a previously approved protocol. I then present experiments exploring the safety of scanning DBS patients using an improved protocol, and then detail two further experiments collected under this new protocol, again one task-based, and one resting. Specifically, I employ statistical parametric mapping to determine DBS-induced changes in motor evoked responses. Using dynamic causal modelling (DCM) and Bayesian model selection, I compare generative models of cortico-subcortical interactions to explain the observed data, inferring which connections DBS may be affecting, and which modulations predict efficacy. I proceed to use stochastic DCM to model the modulatory effects of DBS on endogenous (resting-state) dynamics. Abstract | 4 4 This work casts DBS in terms of modulating effective connectivity within the cortico-basal ganglia motor loops. I discuss how this may explain its current usage in PD, as well as exploratory uses to treat other pathological brain states