A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing.

Seminar 1 impurity in the tomonaga-luttinger model:a functional integral approach

This chapter explains a functional integral approach about impurity in the Tomonaga–Luttinger model. The Tomonaga–Luttinger model of one-dimensional (1D) strongly correlates electrons gives a striking example of non-Fermi-liquid behavior. For simplicity, the chapter considers only a single-mode Tomonaga–Luttinger model, with one species of right- and left-moving electrons, thus, omitting spin indices and considering eventually the simplest linearized model of a single-valley parabolic electron band. The standard operator bosonization is one of the most elegant methods developed in theoretical physics. The main advantage of the bosonization, either in standard or functional form, is that including the quadric electron–electron interaction does not substantially change the free action. The chapter demonstrates the way to develop the formalism of bosonization based on the functional integral representation of observable quantities within the Keldysh formalism.

Virtual Instruments – Functional Model, Organization and Programming Architecture

∗ Thematic Harmonisation in Electrical and Information EngineeRing in Europe,Project Nr. 10063-CP-1-2000-1-PT-ERASMUS-ETNE.

An integrated functional mapping and source localization platform for brain research

The premise of this dissertation is to create a highly integrated platform that combines the most current recording technologies for brain research through the development of new algorithms for three-dimensional (3D) functional mapping and 3D source localization. The recording modalities that were integrated include: Electroencephalography (EEG), Optical Topographic Maps (OTM), Magnetic Resonance Imaging (MRI), and Diffusion Tensor Imaging (DTI). This work can be divided into two parts: The first part involves the integration of OTM with MRI, where the topographic maps are mapped to both the skull and cortical surface of the brain. This integration process is made possible through the development of new algorithms that determine the probes location on the MRI head model and warping the 2D topographic maps onto the 3D MRI head/brain model. Dynamic changes of the brain activation can be visualized on the MRI head model through a graphical user interface. The second part of this research involves augmenting a fiber tracking system, by adding the ability to integrate the source localization results generated by commercial software named Curry. This task involved registering the EEG electrodes and the dipole results to the MRI data. Such Integration will allow the visualization of fiber tracts, along with the source of the EEG, in a 3D transparent brain structure. The research findings of this dissertation were tested and validated through the participation of patients from Miami Children Hospital (MCH). Such an integrated platform presented to the medical professionals in the form of a user-friendly graphical interface is viewed as a major contribution of this dissertation. It should be emphasized that there are two main aspects to this research endeavor: (1) if a dipole could be situated in time at its different positions, its trajectory may reveal additional information on the extent and nature of the brain malfunction; (2) situating such a dipole trajectory with respect to the fiber tracks could ensure the preservation of these fiber tracks (axons) during surgical interventions, preserving as a consequence these parts of the brain that are responsible for information transmission.

Changes in Brain Resting-state Functional Connectivity Associated with Peripheral Nerve Block: A Pilot Study.

BACKGROUND: Limited information exists on the effects of temporary functional deafferentation (TFD) on brain activity after peripheral nerve block (PNB) in healthy humans. Increasingly, resting-state functional connectivity (RSFC) is being used to study brain activity and organization. The purpose of this study was to test the hypothesis that TFD through PNB will influence changes in RSFC plasticity in central sensorimotor functional brain networks in healthy human participants. METHODS: The authors achieved TFD using a supraclavicular PNB model with 10 healthy human participants undergoing functional connectivity magnetic resonance imaging before PNB, during active PNB, and during PNB recovery. RSFC differences among study conditions were determined by multiple-comparison-corrected (false discovery rate-corrected P value less than 0.05) random-effects, between-condition, and seed-to-voxel analyses using the left and right manual motor regions. RESULTS: The results of this pilot study demonstrated disruption of interhemispheric left-to-right manual motor region RSFC (e.g., mean Fisher-transformed z [effect size] at pre-PNB 1.05 vs. 0.55 during PNB) but preservation of intrahemispheric RSFC of these regions during PNB. Additionally, there was increased RSFC between the left motor region of interest (PNB-affected area) and bilateral higher order visual cortex regions after clinical PNB resolution (e.g., Fisher z between left motor region of interest and right and left lingual gyrus regions during PNB, -0.1 and -0.6 vs. 0.22 and 0.18 after PNB resolution, respectively). CONCLUSIONS: This pilot study provides evidence that PNB has features consistent with other models of deafferentation, making it a potentially useful approach to investigate brain plasticity. The findings provide insight into RSFC of sensorimotor functional brain networks during PNB and PNB recovery and support modulation of the sensory-motor integration feedback loop as a mechanism for explaining the behavioral correlates of peripherally induced TFD through PNB.

A Molecular-scale Programmable Stochastic Process Based On Resonance Energy Transfer Networks: Modeling And Applications

While molecular and cellular processes are often modeled as stochastic processes, such as Brownian motion, chemical reaction networks and gene regulatory networks, there are few attempts to program a molecular-scale process to physically implement stochastic processes. DNA has been used as a substrate for programming molecular interactions, but its applications are restricted to deterministic functions and unfavorable properties such as slow processing, thermal annealing, aqueous solvents and difficult readout limit them to proof-of-concept purposes. To date, whether there exists a molecular process that can be programmed to implement stochastic processes for practical applications remains unknown.

In this dissertation, a fully specified Resonance Energy Transfer (RET) network between chromophores is accurately fabricated via DNA self-assembly, and the exciton dynamics in the RET network physically implement a stochastic process, specifically a continuous-time Markov chain (CTMC), which has a direct mapping to the physical geometry of the chromophore network. Excited by a light source, a RET network generates random samples in the temporal domain in the form of fluorescence photons which can be detected by a photon detector. The intrinsic sampling distribution of a RET network is derived as a phase-type distribution configured by its CTMC model. The conclusion is that the exciton dynamics in a RET network implement a general and important class of stochastic processes that can be directly and accurately programmed and used for practical applications of photonics and optoelectronics. Different approaches to using RET networks exist with vast potential applications. As an entropy source that can directly generate samples from virtually arbitrary distributions, RET networks can benefit applications that rely on generating random samples such as 1) fluorescent taggants and 2) stochastic computing.

By using RET networks between chromophores to implement fluorescent taggants with temporally coded signatures, the taggant design is not constrained by resolvable dyes and has a significantly larger coding capacity than spectrally or lifetime coded fluorescent taggants. Meanwhile, the taggant detection process becomes highly efficient, and the Maximum Likelihood Estimation (MLE) based taggant identification guarantees high accuracy even with only a few hundred detected photons.

Meanwhile, RET-based sampling units (RSU) can be constructed to accelerate probabilistic algorithms for wide applications in machine learning and data analytics. Because probabilistic algorithms often rely on iteratively sampling from parameterized distributions, they can be inefficient in practice on the deterministic hardware traditional computers use, especially for high-dimensional and complex problems. As an efficient universal sampling unit, the proposed RSU can be integrated into a processor / GPU as specialized functional units or organized as a discrete accelerator to bring substantial speedups and power savings.

Model-driven development of sensor network applications with optimization of non-functional constraints

Wireless sensor networks (WSNs) differ from conventional distributed systems in many aspects. The resource limitation of sensor nodes, the ad-hoc communication and topology of the network, coupled with an unpredictable deployment environment are difficult non-functional constraints that must be carefully taken into account when developing software systems for a WSN. Thus, more research needs to be done on designing, implementing and maintaining software for WSNs. This thesis aims to contribute to research being done in this area by presenting an approach to WSN application development that will improve the reusability, flexibility, and maintainability of the software. Firstly, we present a programming model and software architecture aimed at describing WSN applications, independently of the underlying operating system and hardware. The proposed architecture is described and realized using the Model-Driven Architecture (MDA) standard in order to achieve satisfactory levels of encapsulation and abstraction when programming sensor nodes. Besides, we study different non-functional constrains of WSN application and propose two approaches to optimize the application to satisfy these constrains. A real prototype framework was built to demonstrate the developed solutions in the thesis. The framework implemented the programming model and the multi-layered software architecture as components. A graphical interface, code generation components and supporting tools were also included to help developers design, implement, optimize, and test the WSN software. Finally, we evaluate and critically assess the proposed concepts. Two case studies are provided to support the evaluation. The first case study, a framework evaluation, is designed to assess the ease at which novice and intermediate users can develop correct and power efficient WSN applications, the portability level achieved by developing applications at a high-level of abstraction, and the estimated overhead due to usage of the framework in terms of the footprint and executable code size of the application. In the second case study, we discuss the design, implementation and optimization of a real-world application named TempSense, where a sensor network is used to monitor the temperature within an area.

Functional Magnetic Resonance Imaging Neurofeedback and Motor training for Parkinson’s Disease: Randomised Trial

Objective: Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) uses feedback of the patient’s own brain activity to self-regulate brain networks which in turn could lead to a change in behaviour and clinical symptoms. The objective was to determine the effect of neurofeedback and motor training and motor training (MOT) alone on motor and non-motor functions in Parkinson’s disease (PD) in a 10-week small Phase I randomised controlled trial. Methods: 30 patients with PD (Hoehn & Yahr I-III) and no significant comorbidity took part in the trial with random allocation to two groups. Group 1 (NF: 15 patients) received rt-fMRI-NF with motor training. Group 2 (MOT: 15 patients) received motor training alone. The primary outcome measure was the Movement Disorder Society – Unified Parkinson’s Disease Rating Scale-Motor scale (MDS-UPDRS-MS), administered pre- and post-intervention ‘off-medication’. The secondary outcome measures were the ‘on-medication’ MDS-UPDRS, the Parkinson’s disease Questionnaire-39, and quantitative motor assessments after 4 and 10 weeks. Results: Patients in the NF group were able to upregulate activity in the supplementary motor area by using motor imagery. They improved by an average of 4.5 points on the MDS-UPDRS-MS in the ‘off-medication’ state (95% confidence interval: -2.5 to -6.6), whereas the MOT group improved only by 1.9 points (95% confidence interval +3.2 to -6.8). However, the improvement did not differ significantly between the groups. No adverse events were reported in either group. Interpretation: This Phase I study suggests that NF combined with motor training is safe and improves motor symptoms immediately after treatment, but larger trials are needed to explore its superiority over active control conditions. Clinical Trial website : Unique Identifier: NCT01867827 URL: https://clinicaltrials.gov/ct2/show/NCT01867827?term=NCT01867827&rank=1

Real-time functional magnetic resonance imaging neurofeedback in motor neurorehabilitation

Purpose of review Recent developments in functional magnetic resonance imaging (fMRI) have catalyzed a new field of translational neuroscience. Using fMRI to monitor the aspects of task-related changes in neural activation or brain connectivity, investigators can offer feedback of simple or complex neural signals/patterns back to the participant on a quasireal-time basis [real-time-fMRI-based neurofeedback (rt-fMRI-NF)]. Here, we introduce some background methodology of the new developments in this field and give a perspective on how they may be used in neurorehabilitation in the future. Recent findings The development of rt-fMRI-NF has been used to promote self-regulation of activity in several brain regions and networks. In addition, and unlike other noninvasive techniques, rt-fMRI-NF can access specific subcortical regions and in principle any region that can be monitored using fMRI including the cerebellum, brainstem and spinal cord. In Parkinson’s disease and stroke, rt-fMRI-NF has been demonstrated to alter neural activity after the self-regulation training was completed and to modify specific behaviours. Summary Future exploitation of rt-fMRI-NF could be used to induce neuroplasticity in brain networks that are involved in certain neurological conditions. However, currently, the use of rt-fMRI-NF in randomized, controlled clinical trials is in its infancy.

A functional specification and validation model for networks on chip in the ACL2 logic

International audience

MRI indices of functional recovery following intracerebral implantation of a human neural stem cell line in a pre-clinical model of ischaemic stroke

Stem cell therapy for ischaemic stroke is an emerging field in light of an increasing number of patients surviving with permanent disability. Several allogenic and autologous cells types are now in clinical trials with preliminary evidence of safety. Some clinical studies have reported functional improvements in some patients. After initial safety evaluation in a Phase 1 study, the conditionally immortalised human neural stem cell line CTX0E03 is currently in a Phase 2 clinical trial (PISCES-II). Previous pre-clinical studies conducted by ReNeuron Ltd, showed evidence of functional recovery in the Bilateral Asymmetry test up to 6 weeks following transplantation into rodent brain, 4 weeks after middle cerebral artery occlusion. Resting-state fMRI is increasingly used to investigate brain function in health and disease, and may also act as a predictor of recovery due to known network changes in the post-stroke recovery period. Resting-state methods have also been applied to non-human primates and rodents which have been found to have analogous resting-state networks to humans. The sensorimotor resting-state network of rodents is impaired following experimental focal ischaemia of the middle cerebral artery territory. However, the effects of stem cell implantation on brain functional networks has not previously been investigated. Prior studies assessed sensorimotor function following sub-cortical implantation of CTX0E03 cells in the rodent post-stroke brain but with no MRI assessments of functional improvements. This thesis presents research on the effect of sub-cortical implantation of CTX0E03 cells on the resting- state sensorimotor network and sensorimotor deficits in the rat following experimental stroke, using protocols based on previous work with this cell line. The work in this thesis identified functional tests of appropriate sensitivity for long-term dysfunction suitable for this laboratory, and investigated non-invasive monitoring of physiological variables required to optimize BOLD signal stability within a high-field MRI scanner. Following experimental stroke, rats demonstrated expected sensorimotor dysfunction and changes in the resting-state sensorimotor network. CTX0E03 cells did not improve post-stroke functional outcome (compared to previous studies) and with no changes in resting-state sensorimotor network activity. However, in control animals, we observed changes in functional networks due to the stereotaxic procedure. This illustrates the sensitivity of resting-state fMRI to stereotaxic procedures. We hypothesise that the damage caused by cell or vehicle implantation may have prevented functional and network recovery which has not been previously identified due to the application of different functional tests. The findings in this thesis represent one of few pre-clinical studies in resting-state fMRI network changes post-stroke and the only to date applying this technique to evaluate functional outcomes following a clinically applicable human neural stem cell treatment for ischaemic stroke. It was found that injury caused by stereotaxic injection should be taken into account when assessing the effectiveness of treatment.

Analysis and control of transient spectra using time-dependent density functional theory.

215 p.