Marco Leite

Transfer Function between EEG and BOLD Signals of Epileptic Activity

Simultaneous electroencephalogram (EEG)-functional Magnetic Resonance Imaging (fMRI) recordings have seen growing application in the evaluation of epilepsy, namely in the characterization of brain networks related to epileptic activity. In EEG-correlated fMRI studies, epileptic events are usually described as boxcar signals based on the timing information retrieved from the EEG, and subsequently convolved with a hemodynamic response function to model the associated Blood Oxygen Level Dependent (BOLD) changes. Although more flexible approaches may allow a higher degree of complexity for the hemodynamics, the issue of how to model these dynamics based on the EEG remains an open question. In this work, a new methodology for the integration of simultaneous EEG-fMRI data in epilepsy is proposed, which incorporates a transfer function from the EEG to the BOLD signal. Independent component analysis of the EEG is performed, and a number of metrics expressing different models of the EEG-BOLD transfer function are extracted from the resulting time courses. These metrics are then used to predict the fMRI data and to identify brain areas associated with the EEG epileptic activity. The methodology was tested on both ictal and interictal EEG-fMRI recordings from one patient with a hypothalamic hamartoma. When compared to the conventional analysis approach, plausible, consistent, and more significant activations were obtained. Importantly, frequency-weighted EEG metrics yielded superior results than those weighted solely on the EEG power, which comes in agreement with previous literature. Reproducibility, specificity, and sensitivity should be addressed in an extended group of patients in order to further validate the proposed methodology and generalize the presented proof of concept.

Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG–fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level‐dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological–haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG‐informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG–fMRI mapping), or exploring a range of EEG‐derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG–fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG‐informed fMRI. Our review is focused on EEG‐informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG‐informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG–fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations. Hum Brain Mapp, 36:391–414, 2015.

Tracking slow modulations in synaptic gain using dynamic causal modelling: Validation in epilepsy

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space — identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory–inhibitory balance.

On conductance-based neural field models.

This technical note introduces a conductance-based neural field model that combines biologically realistic synaptic dynamics—based on transmembrane currents—with neural field equations, describing the propagation of spikes over the cortical surface. This model allows for fairly realistic inter-and intra-laminar intrinsic connections that underlie spatiotemporal neuronal dynamics. We focus on the response functions of expected neuronal states (such as depolarization) that generate observed electrophysiological signals (like LFP recordings and EEG). These response functions characterize the models transfer functions and implicit spectral responses to (uncorrelated) input. Our main finding is that both the evoked responses (impulse response functions) and induced responses (transfer functions) show qualitative differences depending upon whether one uses a neural mass or field model. Furthermore, there are differences between the equivalent convolution and conductance models. Overall, all models reproduce a characteristic increase in frequency, when inhibition was increased by increasing the rate constants of inhibitory populations. However, convolution and conductance-based models showed qualitatively different changes in power, with convolution models showing decreases with increasing inhibition, while conductance models show the opposite effect. These differences suggest that conductance based field models may be important in empirical studies of cortical gain control or pharmacological manipulations.

Ballistocardiogram artifact correction taking into account physiological signal preservation in simultaneous EEG-fMRI

The ballistocardiogram (BCG) artifact is currently one of the most challenging in the EEG acquired concurrently with fMRI, with correction invariably yielding residual artifacts and/or deterioration of the physiological signals of interest. In this paper, we propose a family of methods whereby the EEG is decomposed using Independent Component Analysis (ICA) and a novel approach for the selection of BCG-related independent components (ICs) is used (PROJection onto Independent Components, PROJIC). Three ICA-based strategies for BCG artifact correction are then explored: 1) BCG-related ICs are removed from the back-reconstruction of the EEG (PROJIC); and 2-3) BCG-related ICs are corrected for the artifact occurrences using an Optimal Basis Set (OBS) or Average Artifact Subtraction (AAS) framework, before back-projecting all ICs onto EEG space (PROJIC-OBS and PROJIC-AAS, respectively). A novel evaluation pipeline is also proposed to assess the methods performance, which takes into account not only artifact but also physiological signal removal, allowing for a flexible weighting of the importance given to physiological signal preservation. This evaluation is used for the group-level parameter optimization of each algorithm on simultaneous EEG-fMRI data acquired using two different setups at 3T and 7T. Comparison with state-of-the-art BCG correction methods showed that PROJIC-OBS and PROJIC-AAS outperformed the others when priority was given to artifact removal or physiological signal preservation, respectively, while both PROJIC-AAS and AAS were in general the best choices for intermediate trade-offs. The impact of the BCG correction on the quality of event-related potentials (ERPs) of interest was assessed in terms of the relative reduction of the standard error (SE) across trials: 26/66%, 32/62% and 18/61% were achieved by, respectively, PROJIC, PROJIC-OBS and PROJIC-AAS, for data collected at 3T/7T. Although more significant improvements were achieved at 7T, the results were qualitatively comparable for both setups, which indicate the wide applicability of the proposed methodologies and recommendations.

Agile manufacturing practices for new product development: industrial case studies

Purpose – For decades multiple management philosophies directed toward lean production and mass were assumed as to respond to process inefficiencies and rampant consumerism, optimizing operation costs. However, new customization and flexible productions philosophies have been gaining ground in some industries, such as the agile manufacturing. From a literature review that addresses the history of this philosophy, it is clear that agile manufacturing is not fully comprehended, with very scarce information about practical cases. The paper aims to discuss these issues. Design/methodology/approach – In this paper the authors describe an exploratory methodology approach, with three semi-structured case study interviews. The goal is to study which of agile manufacturing practices are being applied in the studied companies and what is the perceived effect that these have on operational performance. Since most of these companies develop highly customized products, the role of agility on new product development ca...

Phase–amplitude coupling and the BOLD signal: A simultaneous intracranial EEG (icEEG) - fMRI study in humans performing a finger-tapping task

Abstract Although it has been consistently found that local blood‐oxygen‐level‐dependent (BOLD) changes are better modelled by a combination of the power of multiple EEG frequency bands rather than by the power of a unique band alone, the local electro‐haemodynamic coupling function is not yet fully characterised. Electrophysiological studies have revealed that the strength of the coupling between the phase of low‐ and the amplitude of high‐ frequency EEG activities (phase–amplitude coupling ‐ PAC) has an important role in brain function in general, and in preparation and execution of movement in particular. Using electrocorticographic (ECoG) and functional magnetic resonance imaging (fMRI) data recorded simultaneously in humans performing a finger‐tapping task, we investigated the single‐trial relationship between the amplitude of the BOLD signal and the strength of PAC and the power of &agr;, &bgr;, and &ggr; bands, at a local level. In line with previous studies, we found a positive correlation for the &ggr; band, and negative correlations for the PAC&bgr;&ggr; strength, and the &agr; and &bgr; bands. More importantly, we found that the PAC&bgr;&ggr; strength explained variance of the amplitude of the BOLD signal that was not explained by a combination of the &agr;, &bgr;, and &ggr; band powers. Our main finding sheds further light on the distinct nature of PAC as a functionally relevant mechanism and suggests that the sensitivity of EEG‐informed fMRI studies may increase by including the PAC strength in the BOLD signal model, in addition to the power of the low‐ and high‐ frequency EEG bands. HighlightsFirst study of single‐trial correlations between the phase amplitude coupling strength and BOLD.Intracranial EEG and fMRI data simultaneously recorded in humans during a motor task.PAC&bgr;&ggr; strength explains variance of BOLD in addition a combination of &agr;, &bgr;, and &ggr; band powers.

Objective selection of epilepsy-related independent components from EEG data.

BACKGROUND Independent Component Analysis (ICA) is commonly used for the identification of sources of interest in electroencephalographic (EEG) data, but the selection of the relevant components remains an open issue depending on the specific application. NEW METHOD We propose a novel approach for the objective selection of epilepsy-related independent components (ICs) from EEG data collected during functional Magnetic Resonance Imaging (fMRI) acquisitions, called PROJection onto Independent Components (PROJIC). Inter-ictal epileptiform discharges (IEDs) are identified on a reference EEG dataset collected outside the MRI scanner by an expert neurophysiologist, and the resulting average IED is projected onto the IC space of the EEG data collected simultaneously with fMRI. The power of the IED projection is then used to inform a k-means clustering algorithm of the ICs, allowing for the classification of epilepsy-related ICs. COMPARISON WITH EXISTING METHODS The performance of PROJIC was compared with two methods previously proposed for the objective selection of EEG ICs of interest, which are based on the explicit similarity of the ICs with spatio-temporal templates of the events of interest, instead of the projection power. RESULTS The proposed PROJIC method outperformed the others for both artificial and real data (19 datasets collected from 6 patients with drug-refractory focal epilepsy), with an average accuracy of 98.6%. CONCLUSIONS The ability of our method to accurately and objectively select epilepsy-related ICs makes it an important contribution for simultaneous EEG-fMRI epilepsy studies, with potential applications in the analysis of event-related EEG activity more generally, and also in EEG artefact correction.

Design and development of a customised knee positioning orthosis using low cost 3D printers

ABSTRACT Off-the-shelf orthoses used in rehabilitation medicine present challenges in the lack of individual comfort offered and in the support of the different motor conditions of the affected individuals. Although custom-made orthoses address these issues, their current fabrication method consists of a laborious and material wasteful process performed by skilled orthotists. Combining 3D scanning, 3D modelling and 3D printing, this paper proposes and assesses a method for the fabrication of custom-made products for rehabilitation medicine. In collaboration with CMRA, a Hospital dedicated to rehabilitation, the method is applied to the design of a customised knee positioning orthosis for a patient with cerebral palsy. This project is exploratory on the capabilities of the use of 3D printing in patient specific customisation of orthoses. Having developed, prototyped and tested four different concepts with the patient, from which one was successfully accepted, it is possible to demonstrate the potential of industrialisation of these technologies.

A CAD-based approach for measuring volumetric error in layered manufacturing

Rapid prototyping uses layered manufacturing technology to produce functional parts directly from 3D computer-aided design model without involving any tools and human intervention. Due to layer by layer deposition, volumetric error remains in the part which is basically the volumetric difference between computer-aided design model and the fabricated part. This volumetric error causes poor dimensional accuracy and surface finish, which has limited the widespread applications of rapid prototyping. Although rapid prototyping is able to produce functional parts in less build time with less material wastage, today many industries are looking for better surface quality associated with these parts. Literature discloses that the part quality can be improved by selecting proper build orientation that corresponds to minimum volumetric error. In support of this, current study presents a computer-aided design-based novel methodology to precisely measure the volumetric error in layered manufacturing process, in particular fused deposition modeling process. The proposed method accepts computer-aided design model of the part in .CAT format and automatically calculates volumetric error for different build orientations. An Excel function is integrated with it to determine optimum build orientation based on minimum volumetric error. Several simple and complex examples verified the robustness of our proposed methodology. We anticipate that the current invention will help future rapid prototyping users in producing high-quality products through an intelligent process planning.