The cross-frequency mediation mechanism of intracortical information transactions

In a seminal paper by von Stein and Sarnthein (2000), it was hypothesized that "bottom-up" information processing of "content" elicits local, high frequency (beta-gamma) oscillations, whereas "top-down" processing is "contextual", characterized by large scale integration spanning distant cortical regions, and implemented by slower frequency (theta-alpha) oscillations. This corresponds to a mechanism of cortical information transactions, where synchronization of beta-gamma oscillations between distant cortical regions is mediated by widespread theta-alpha oscillations. It is the aim of this paper to express this hypothesis quantitatively, in terms of a model that will allow testing this type of information transaction mechanism. The basic methodology used here corresponds to statistical mediation analysis, originally developed by (Baron and Kenny 1986). We generalize the classical mediator model to the case of multivariate complex-valued data, consisting of the discrete Fourier transform coefficients of signals of electric neuronal activity, at different frequencies, and at different cortical locations. The "mediation effect" is quantified here in a novel way, as the product of "dual frequency RV-coupling coefficients", that were introduced in (Pascual-Marqui et al 2016, http://arxiv.org/abs/1603.05343). Relevant statistical procedures are presented for testing the cross-frequency mediation mechanism in general, and in particular for testing the von Stein & Sarnthein hypothesis.Comment: https://doi.org/10.1101/119362 licensed as CC-BY-NC-ND 4.0 International license: http://creativecommons.org/licenses/by-nc-nd/4.0

The dual frequency RV-coupling coefficient: a novel measure for quantifying cross-frequency information transactions in the brain

Identifying dynamic transactions between brain regions has become increasingly important. Measurements within and across brain structures, demonstrating the occurrence of bursts of beta/gamma oscillations only during one specific phase of each theta/alpha cycle, have motivated the need to advance beyond linear and stationary time series models. Here we offer a novel measure, namely, the "dual frequency RV-coupling coefficient", for assessing different types of frequency-frequency interactions that subserve information flow in the brain. This is a measure of coherence between two complex-valued vectors, consisting of the set of Fourier coefficients for two different frequency bands, within or across two brain regions. RV-coupling is expressed in terms of instantaneous and lagged components. Furthermore, by using normalized Fourier coefficients (unit modulus), phase-type couplings can also be measured. The dual frequency RV-coupling coefficient is based on previous work: the second order bispectrum, i.e. the dual-frequency coherence (Thomson 1982; Haykin & Thomson 1998); the RV-coefficient (Escoufier 1973); Gorrostieta et al (2012); and Pascual-Marqui et al (2011). This paper presents the new measure, and outlines relevant statistical tests. The novel aspects of the "dual frequency RV-coupling coefficient" are: (1) it can be applied to two multivariate time series; (2) the method is not limited to single discrete frequencies, and in addition, the frequency bands are treated by means of appropriate multivariate statistical methodology; (3) the method makes use of a novel generalization of the RV-coefficient for complex-valued multivariate data; (4) real and imaginary covariance contributions to the RV-coherence are obtained, allowing the definition of a "lagged-coupling" measure that is minimally affected by the low spatial resolution of estimated cortical electric neuronal activity.Comment: technical report, pre-print, 2016-03-1

ECG-based estimation of respiratory modulation of AV nodal conduction during atrial fibrillation

Information about autonomic nervous system (ANS) activity may be valuable for personalized atrial fibrillation (AF) treatment but is not easily accessible from the ECG. In this study, we propose a new approach for ECG-based assessment of respiratory modulation in AV nodal refractory period and conduction delay. A 1-dimensional convolutional neural network (1D-CNN) was trained to estimate respiratory modulation of AV nodal conduction properties from 1-minute segments of RR series, respiration signals, and atrial fibrillatory rates (AFR) using synthetic data that replicates clinical ECG-derived data. The synthetic data were generated using a network model of the AV node and 4 million unique model parameter sets. The 1D-CNN was then used to analyze respiratory modulation in clinical deep breathing test data of 28 patients in AF, where a ECG-derived respiration signal was extracted using a novel approach based on periodic component analysis. We demonstrated using synthetic data that the 1D-CNN can predict the respiratory modulation from RR series alone ($\rho$ = 0.805) and that the addition of either respiration signal ($\rho$ = 0.830), AFR ($\rho$ = 0.837), or both ($\rho$ = 0.855) improves the prediction. Results from analysis of clinical ECG data of 20 patients with sufficient signal quality suggest that respiratory modulation decreased in response to deep breathing for five patients, increased for five patients, and remained similar for ten patients, indicating a large inter-patient variability.Comment: 20 pages, 7 figures, 5 table

Mutual Information in the Frequency Domain for Application in Biological Systems

Biological systems are comprised of multiple components that typically interact nonlinearly and produce multiple outputs (time series/signals) with specific frequency characteristics. Although the exact knowledge of the underlying mechanism remains unknown, the outputs observed from these systems can provide the dependency relations through quantitative methods and increase our understanding of the original systems. The nonlinear relations at specific frequencies require advanced dependency measures to capture the generalized interactions beyond typical correlation in the time domain or coherence in the frequency domain. Mutual information from Information Theory is such a quantity that can measure statistical dependency between random variables. Herein, we develop a model–free methodology for detection of nonlinear relations between time series with respect to frequency, that can quantify dependency under a general probabilistic framework. Classic nonlinear dynamical system and their coupled forms (Lorenz, bidirectionally coupled Lorenz, and unidirectionally coupled Macky–Glass systems) are employed to generate artificial data and to test the proposed methodology. Comparisons between the performances of this measure and a conventional linear measure are presented from applications to the artificial data. This set of results indicates that the proposed methodology is better in capturing the dependency between the variables of the systems. This measure of dependency is also applied to a real–world electrophysiological dataset for emotion analysis to study brain stimuli–response functional connectivity. The results reveal distinct brain regions and specific frequencies that are involved in emotional processing

Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health

There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimer’s Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents List of Abbreviations ..................................................................................................................... i List of Figures .............................................................................................................................. ii Acknowledgements .....................................................................................................................iii 1 INTRODUCTION .....................................................................................................................1 1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3 1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4 1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4 1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6 1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8 1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8 1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10 2 PUBLICATIONS ....................................................................................................................12 2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks and memory performance in adults .................................................................................13 2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of human medial temporal lobe to sex hormone fluctuations ..............................................28 2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance in the healthy female brain ...............................................................................................51 2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated neurochemical changes across the female lifespan: implications for depression ..........65 2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in patients with premenstrual dysphoric disorder: a case-control longitudinal neuro- receptor ligand PET imaging study ..................................................................................82 3 SUMMARY ...........................................................................................................................100 References ..............................................................................................................................107 Supplementary Publications ...................................................................................................114 Author Contributions to Publication 1 .....................................................................................184 Author Contributions to Publication 2 .....................................................................................186 Author Contributions to Publication 3 .....................................................................................188 Author Contributions to Publication 4 .....................................................................................190 Author Contributions to Publication 5 .....................................................................................191 Declaration of Authenticity ......................................................................................................193 Curriculum Vitae ......................................................................................................................194 List of Publications ................................................................................................................195 List of Talks and Posters ......................................................................................................19

Cognitive style modulates semantic interference effects: evidence from field dependency

The so-called semantic interference effect is a delay in selecting an appropriate target word in a context where semantic neighbours are strongly activated. Semantic interference effect has been described to vary from one individual to another. These differences in the susceptibility to semantic interference may be due to either differences in the ability to engage in lexical-specific selection mechanisms or to differences in the ability to engage more general, top-down inhibition mechanisms which suppress unwanted responses based on task-demands. However, semantic interference may also be modulated by an individual’s disposition to separate relevant perceptual signals from noise, such as a field-independent (FI) or a field-dependent (FD) cognitive style. We investigated the relationship between semantic interference in picture naming and in an STM probe task and both the ability to inhibit responses top-down (measured through a Stroop task) and a FI/FD cognitive style measured through the embedded figures test (EFT). We found a significant relationship between semantic interference in picture naming and cognitive style—with semantic interference increasing as a function of the degree of field dependence—but no associations with the semantic probe and the Stroop task. Our results suggest that semantic interference can be modulated by cognitive style, but not by differences in the ability to engage top-down control mechanisms, at least as measured by the Stroop task

Robotic Motion Compensation for Beating Heart Intracardiac Surgery

3D ultrasound imaging has enabled minimally invasive, beating heart intracardiac procedures. However, rapid heart motion poses a serious challenge to the surgeon that is compounded by significant time delays and noise in 3D ultrasound. This paper investigates the concept of using a one-degree-of-freedom motion compensation system to synchronize with tissue motions that may be approximated by 1D motion models. We characterize the motion of the mitral valve annulus and show that it is well approximated by a 1D model. The subsequent development of a motion compensation instrument (MCI) is described, as well as an extended Kalman filter (EKF) that compensates for system delays. The benefits and robustness of motion compensation are tested in user trials under a series of non-ideal tracking conditions. Results indicate that the MCI provides an approximately 50% increase in dexterity and 50% decrease in force when compared with a solid tool, but is sensitive to time delays. We demonstrate that the use of the EKF for delay compensation restores performance, even in situations of high heart rate variability. The resulting system is tested in an in vitro 3D ultrasound-guided servoing task, yielding accurate tracking (1.15 mm root mean square) in the presence of noisy, time-delayed 3D ultrasound measurements.Engineering and Applied Science

Dynamic coupling between whisking, barrel cortex, and hippocampus during texture discrimination: A role for slow rhythms

Increasing amounts of work have demonstrated that brain rhythms might constitute clocking mechanisms against which to coordinate sequences of neural firing; such rhythms may be essential to the coding operations performed by the local networks. The sequence of operations underlying a tactile discrimination task in rats requires the animal to integrate two streams of information, those coming from the environment and, from reference memory the rules that dictate the correct response. The current study is a follow up on the work which has described the hippocampal representation of the tactile guided task. We have used a well-established texture discrimination task, in which rats have to associate two stimuli with two different reward locations. We placed microelectrodes in primary somatosensory cortex and the CA1 region of hippocampus to perform recordings of spiking activity and local field potentials when the animal touched the discriminandum as well as when he was in a resting state. We also performed recording on an arena in which the animal moved freely and did not perform any task. Earlier work has demonstrated that tactile signals reach the hippocampus during texture discrimination, presumably through the somatosensory cortex. We predicted that neurons in the primary somatosensory cortex (S1) are entrained to the oscillatory theta rhythm that permeates the hippocampus. Our expectation is that such coherence could serve to increase the reliability of synaptic transmission, linking the acquisition of new sensory information with associative processes. We addressed the following issues: Is the timing of action potentials in S1 modulated by the ongoing hippocampal theta rhythm? If so, is the occurrence of this modulation aligned in time to the period in which the hippocampus acquires tactile signals? We also predicted that the 10-Hz whisking that characterizes the acquisition of texture information would be more strongly phase locked to theta rhythm than the whisking in the air that is not accompanied by any explicit tactile task. We speculate that such phase locking could be a means to synchronize sensory and hippocampal processing. The notion that the coordination between brain areas might be related to the rhythmic of sensorimotor cycles is particularly appealing. We have found that the firing of 18% of barrel cells was significantly modulated by hippocampal theta during the half-second period of active tactile discrimination. Importantly, we found that during periods of rest interleaved in the session, neurons significantly decreased the degree of phase-locking with respect to touch. We hypothesize that areas involved with motivational processes as basal ganglia could gate the entrainment during task related epochs. S1 neurons were classified as those excited by contact with the discriminandum, and those not excited by contact. The firing of both sorts of neurons was modulated by CA1 theta rhythm during exploration of the texture. However the theta phase to which they fired preferentially was opposite; contact-responsive neurons tended to fire in the upward phases of the cycle whereas contact non-responsive neurons tended to fire in the downward phase of the cycle suggesting that theta rhythm might have the function of temporally separating sensory cortical neurons according to their functional properties and the information they carry. By clustering touch-sensitive neurons to a certain time window and separating them from \u2018non-informative\u2019 neurons, theta rhythm could increase the efficiency not only of information tranfer to hippocampus but also the efficiency of information encoding/decoding. We also found phase and amplitude relationships between whisking and hippocampal theta during the goal-directed tactile task; the relationships disappear when the animal moves along an open arena, still actively whisking but not engaged in the texture discrimination task. We were able to show, for the first time to our knowledge, that CA1 theta rhythm can exert a behavioral state-dependent modulatory effect on sensory cortex. S1 neuron firing and whisking activity are entrained to hippocampal theta rhythm when the animal collects meaningful tactile information from the environment