Camillo Porcaro

An information theoretic approach to EEG-fMRI integration of visually evoked responses

The integration of signals from electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), acquired simultaneously from the same observer, holds great potential for the elucidation of the neurobiological underpinnings of human brain function. However, the most appropriate way in which to combine the data in order to achieve this goal is not clear. Here, we apply a novel route to the integration of simultaneously acquired multimodal brain imaging data. We adopt a theoretical framework developed in the study of neuronal population codes which explicitly takes into account the experimentally observed stimulus-response signal probability distributions using the concept of mutual information. We study the implications of this framework using simulated data sets generated from a set of linear Gaussian models, and apply the framework to EEG-fMRI data acquired during checkerboard stimulation of low and high contrast. We focus our evaluation on single-trial time-domain signal features from both modalities and provide evidence for the informativeness of a subset of these features with respect to the stimulus and each other. Specifically, the framework was able to identify the contrast dependency of the haemodynamic response and the P100 peak of the visual evoked potential, and showed that combining EEG and fMRI time-domain features by quantifying the information in their joint distribution was more informative than treating each one in isolation. In addition, the effect of different pre-processing strategies for EEG-fMRI data can be assessed quantitatively, indicating the improvements to be gained by more advanced methods. We conclude that the information theoretic framework is a promising methodology to quantify the relative importance of different response features in neural coding and neurovascular coupling, as well as the success of data pre-processing strategies.

Hand Somatosensory Subcortical and Cortical Sources Assessed by Functional Source Separation: An EEG Study

We propose a novel electroencephalographic application of a recently developed cerebral source extraction method (Functional Source Separation, FSS), which starts from extracranial signals and adds a functional constraint to the cost function of a basic independent component analysis model without requiring solutions to be independent. Five ad‐hoc functional constraints were used to extract the activity reflecting the temporal sequence of sensory information processing along the somatosensory pathway in response to the separate left and right median nerve galvanic stimulation. Constraints required only the maximization of the responsiveness at specific latencies following sensory stimulation, without taking into account that any frequency or spatial information. After source extraction, the reliability of identified FS was assessed based on the position of single dipoles fitted on its retroprojected signals and on a discrepancy measure. The FS positions were consistent with previously reported data (two early subcortical sources localized in the brain stem and thalamus, the three later sources in cortical areas), leaving negligible residual activity at the corresponding latencies. The high‐frequency component of the oscillatory activity (HFO) of the extracted component was analyzed. The integrity of the low amplitude HFOs was preserved for each FS. On the basis of our data, we suggest that FSS can be an effective tool to investigate the HFO behavior of the different neuronal pools, recruited at successive times after median nerve galvanic stimulation. As FSs are reconstructed along the entire experimental session, directional and dynamic HFO synchronization phenomena can be studied. Hum Brain Mapp, 2009.

Functional Source Separation from Magnetoencephalographic Signals

We propose a novel cerebral source extraction method (functional source separation, FSS) starting from extra‐cephalic magnetoencephalographic (MEG) signals in humans. It is obtained by adding a functional constraint to the cost function of a basic independent component analysis (ICA) model, defined according to the specific experiment under study, and removing the orthogonality constraint, (i.e., in a single‐unit approach, skipping decorrelation of each new component from the subspace generated by the components already found). Source activity was obtained all along processing of a simple separate sensory stimulation of thumb, little finger, and median nerve. Being the sources obtained one by one in each stage applying different criteria, the a posteriori “interesting sources selection” step is avoided. The obtained solutions were in agreement with the homuncular organization in all subjects, neurophysiologically reacting properly and with negligible residual activity. On this basis, the separated sources were interpreted as satisfactorily describing highly superimposed and interconnected neural networks devoted to cortical finger representation. The proposed procedure significantly improves the quality of the extraction with respect to a standard BSS algorithm. Moreover, it is very flexible in including different functional constraints, providing a promising tool to identify neuronal networks in very general cerebral processing. Hum Brain Mapp, 2006.

Functional source separation and hand cortical representation for a brain–computer interface feature extraction

A brain–computer interface (BCI) can be defined as any system that can track the persons intent which is embedded in his/her brain activity and, from it alone, translate the intention into commands of a computer. Among the brain signal monitoring systems best suited for this challenging task, electroencephalography (EEG) and magnetoencephalography (MEG) are the most realistic, since both are non‐invasive, EEG is portable and MEG could provide more specific information that could be later exploited also through EEG signals. The first two BCI steps require set up of the appropriate experimental protocol while recording the brain signal and then to extract interesting features from the recorded cerebral activity. To provide information useful in these BCI stages, our aim is to provide an overview of a new procedure we recently developed, named functional source separation (FSS). As it comes from the blind source separation algorithms, it exploits the most valuable information provided by the electrophysiological techniques, i.e. the waveform signal properties, remaining blind to the biophysical nature of the signal sources. FSS returns the single trial source activity, estimates the time course of a neuronal pool along different experimental states on the basis of a specific functional requirement in a specific time period, and uses the simulated annealing as the optimization procedure allowing the exploit of functional constraints non‐differentiable. Moreover, a minor section is included, devoted to information acquired by MEG in stroke patients, to guide BCI applications aiming at sustaining motor behaviour in these patients. Relevant BCI features – spatial and time‐frequency properties – are in fact altered by a stroke in the regions devoted to hand control. Moreover, a method to investigate the relationship between sensory and motor hand cortical network activities is described, providing information useful to develop BCI feedback control systems. This review provides a description of the FSS technique, a promising tool for the BCI community for online electrophysiological feature extraction, and offers interesting information to develop BCI applications to sustain hand control in stroke patients.

Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.

Intrinsic variability in the human response to pain is assembled from multiple, dynamic brain processes

The stimulus-evoked response is the principle measure used to elucidate the timing and spatial location of human brain activity. Brain and behavioural responses to pain are influenced by multiple intrinsic and extrinsic factors and display considerable, natural trial-by-trial variability. However, because the neuronal sources of this variability are poorly understood the functional information it contains is under-exploited for understanding the relationship between brain function and behaviour. We recorded simultaneous EEG-fMRI during rest and noxious thermal stimulation to characterise the relationship between natural fluctuations in behavioural pain-ratings, the spatiotemporal dynamics of brain network responses and intrinsic connectivity. We demonstrate that fMRI response variability in the pain network is: dependent upon its resting-state functional connectivity; modulated by behaviour; and correlated with EEG evoked-potential amplitude. The pre-stimulus default-mode network (DMN) fMRI signal predicts the subsequent magnitude of pain ratings, evoked-potentials and pain network BOLD responses. Additionally, the power of the ongoing EEG alpha oscillation, an index of cortical excitability, modulates the DMN fMRI response to pain. The complex interaction between alpha-power, DMN activity and both the behavioural report of pain and the brains response to pain demonstrates the neurobiological significance of trial-by-trial variability. Furthermore, we show that multiple, interconnected factors contribute to both the brains response to stimulation and the psychophysiological emergence of the subjective experience of pain.

Cortico-muscular coherence as an index of fatigue in multiple sclerosis

Background: Highly common in multiple sclerosis (MS), fatigue severely impacts patients’ daily lives. Previous findings of altered connectivity patterns led to the hypothesis that the distortion of functional connections within the brain-muscle circuit plays a crucial pathogenic role. Objective: The objective of this paper is to identify markers sensitive to fatigue in multiple sclerosis. Methods: Structural (magnetic resonance imaging with assessment of thalamic volume and cortical thickness of the primary sensorimotor areas) and functional (cortico-muscular coherence (CMC) from simultaneous electroencephalo- and surface electromyographic recordings during a weak handgrip task) measures were used on 20 mildly disabled MS patients (relapsing–remitting course, Expanded Disability Status Scale score ≤ 2) who were recruited in two fatigue-dependent groups according to the Modified Fatigue Index Scale (MFIS) score. Results: The two groups were similar in terms of demographic, clinical and imaging features, as well as task execution accuracy and weariness. In the absence of any fatigue-dependent brain and muscular oscillatory activity alterations, CMC worked at higher frequencies as fatigue increased, explaining 67% of MFIS variance (p=.002). Conclusion: Brain-muscle functional connectivity emerged as a sensitive marker of phenomena related to the origin of MS fatigue, impacting central-peripheral communication well before the appearance of any impairment in the communicating nodes.

Functional source separation improves the quality of single trial visual evoked potentials recorded during concurrent EEG-fMRI.

EEG quality is a crucial issue when acquiring combined EEG-fMRI data, particularly when the focus is on using single trial (ST) variability to integrate the data sets. The most common method for improving EEG data quality following removal of gross MRI artefacts is independent component analysis (ICA), a completely blind source separation technique. In the current study, a different approach is proposed based on the functional source separation (FSS) algorithm. FSS is an extension of ICA that incorporates prior knowledge about the signal of interest into the data decomposition. Since in general the part of the EEG signal that will contain the most relevant information is known beforehand (i.e. evoked potential peaks, spectral bands), FSS separates the signal of interest by exploiting this prior knowledge without renouncing the advantages of using only information contained in the original signal waveforms. A reversing checkerboard stimulus was used to generate visual evoked potentials (VEPs) in healthy control subjects. Gradient and ballistocardiogram artefacts were removed with template subtraction techniques to form the raw data, which were then subjected to ICA denoising and FSS. The resulting EEG data sets were compared using several metrics derived from average and ST data and correlated with fMRI data. In all cases, ICA was an improvement on the raw data, but the most obvious improvement was provided by FSS, which consistently outperformed ICA. The results show the benefit of FSS for the recovery of good quality single trial evoked potentials during concurrent EEG-fMRI recordings.

Hand sensory–motor cortical network assessed by functional source separation

The functional source separation procedure (FSS) was applied to identify the activities of the primary sensorimotor areas (SM1) devoted to hand control. FSS adds a functional constraint to the cost function of the basic independent component analysis, and obtains source activity all along different processing states. Magnetoencephalographic signals from the left SM1 were recorded in 14 healthy subjects during a simple sensorimotor paradigm—galvanic right median nerve stimuli intermingled with submaximal isometric thumb opposition. Two functional sources related to the sensory flow in the primary cortex were extracted requiring maximal responsiveness to the nerve stimulation at around 20 and 30 ms (S1a, S1b). Maximal cortico‐muscular coherence was required for the extraction of the motor source (M1). Sources were multiplied by the Euclidean norm of their corresponding weight vectors, allowing amplitude comparisons among sources in a fixed position. In all subjects, S1a, S1b, M1 were successfully obtained, positioned consistently with the SM1 organization, and behaved as physiologically expected during the movement and processing of the sensory stimuli. The M1 source reacted to the nerve stimulation with higher intensity at latencies around 30 ms than around 20 ms. The FSS method was demonstrated to be able to obtain the dynamics of different primary cortical network activities, two devoted mainly to sensory inflow, and the other to the motor control of the contralateral hand. It was possible to observe each source both during pure sensory processing and during motor tasks. In all conditions, a direct comparison of source intensities can be achieved. Hum Brain Mapp, 2008.

Somatosensory dynamic gamma-band synchrony: a neural code of sensorimotor dexterity.

To investigate neural coding characteristics in the human primary somatosensory cortex, two fingers with different levels of functional skill were studied. Their dexterity was scored by the Fingertip writing test. Each finger was separately provided by a passive simple sensory stimulation and the responsiveness of each finger cortical representation was studied by a novel source extraction method applied to magnetoencephalographic signals recorded in a 14 healthy right handed subject cohort. In the two hemispheres, neural oscillatory activity synchronization was analysed in the three characteristic alpha, beta and gamma frequency bands by two dynamic measures, one isolating the phase locking between neural network components, the other reflecting the total number of synchronous recruited neurons. In the dominant hemisphere, the gamma band phase locking was higher for the thumb than for the little finger and it correlated with the contra-lateral finger dexterity. Neither in the dominant nor in the non-dominant hemisphere, any effect was observed in the alpha and beta bands. In the gamma band, the amplitude showed similar tendency to the phase locking, without reaching statistical significance. These findings suggest the dynamic gamma band phase locking as a code for finger dexterity, in addition to the magnification of somatotopic central maps.