Leandro Beltrachini

Cortical deficits of emotional face processing in adults with ADHD: Its relation to social cognition and executive function

Although it has been shown that adults with attention-deficit hyperactivity disorder (ADHD) have impaired social cognition, no previous study has reported the brain correlates of face valence processing. This study looked for behavioral, neuropsychological, and electrophysiological markers of emotion processing for faces (N170) in adult ADHD compared to controls matched by age, gender, educational level, and handedness. We designed an event-related potential (ERP) study based on a dual valence task (DVT), in which faces and words were presented to test the effects of stimulus type (faces, words, or face-word stimuli) and valence (positive versus negative). Individual signatures of cognitive functioning in participants with ADHD and controls were assessed with a comprehensive neuropsychological evaluation, including executive functioning (EF) and theory of mind (ToM). Compared to controls, the adult ADHD group showed deficits in N170 emotion modulation for facial stimuli. These N170 impairments were observed in the absence of any deficit in facial structural processing, suggesting a specific ADHD impairment in early facial emotion modulation. The cortical current density mapping of N170 yielded a main neural source of N170 at posterior section of fusiform gyrus (maximum at left hemisphere for words and right hemisphere for faces and simultaneous stimuli). Neural generators of N170 (fusiform gyrus) were reduced in ADHD. In those patients, N170 emotion processing was associated with performance on an emotional inference ToM task, and N170 from simultaneous stimuli was associated with EF, especially working memory. This is the first report to reveal an adult ADHD-specific impairment in the cortical modulation of emotion for faces and an association between N170 cortical measures and ToM and EF.

Size of cortical generators of epileptic interictal events and visibility on scalp EEG

Growing evidence indicates that fast oscillations (>80 Hz) can be recorded interictally in the scalp EEG of patients with epilepsy, and that they may point to the seizure-onset zone. However, mechanisms underpinning the emergence of scalp fast oscillations, and whether they differ from those of interictal epileptic discharges (IEDs), are yet to be understood. The visibility of cortical electric activity on scalp EEG recordings is dependent on two factors: the characteristics of the cortical generator and the background level. We studied this issue using scalp EEG recordings and detailed simulations, with a finite element model including 8 million elements and 8 different tissues. We observed an almost linear relationship between the amplitude of scalp electric potential and the extent of the generator on the cortex. However, this relationship is subject to substantial variability, with variations in factors greater than 3 occurring simply by changing the location on the cortex of generators of fixed extent. In addition, we showed that the background power in scalp EEG recordings decreases at higher frequency bands, being inversely proportional to a power of 2.5 of the frequency. In the specific case of fast oscillations, they can be detected within the lower noise level of the ripple band (80-200 Hz) even though their median amplitude on scalp EEG recordings is more than 10 times smaller than IEDs and consistent with cortical generators of approximately 1 cm(2). In conclusion, the physics governing the propagation of electrical activity from the brain to the scalp are consistent with the hypothesis that scalp fast oscillations and intracranial high-frequency oscillations (HFOs, 80-500 Hz) are expressions of common generators. Given the potential role of HFOs as biomarkers in epilepsy, the possibility to obtain some of the associated information from scalp EEG is of high clinical significance.

Neural Processing of Emotional Facial and Semantic Expressions in Euthymic Bipolar Disorder (BD) and Its Association with Theory of Mind (ToM)

Background Adults with bipolar disorder (BD) have cognitive impairments that affect face processing and social cognition. However, it remains unknown whether these deficits in euthymic BD have impaired brain markers of emotional processing. Methodology/Principal Findings We recruited twenty six participants, 13 controls subjects with an equal number of euthymic BD participants. We used an event-related potential (ERP) assessment of a dual valence task (DVT), in which faces (angry and happy), words (pleasant and unpleasant), and face-word simultaneous combinations are presented to test the effects of the stimulus type (face vs word) and valence (positive vs. negative). All participants received clinical, neuropsychological and social cognition evaluations. ERP analysis revealed that both groups showed N170 modulation of stimulus type effects (face > word). BD patients exhibited reduced and enhanced N170 to facial and semantic valence, respectively. The neural source estimation of N170 was a posterior section of the fusiform gyrus (FG), including the face fusiform area (FFA). Neural generators of N170 for faces (FG and FFA) were reduced in BD. In these patients, N170 modulation was associated with social cognition (theory of mind). Conclusions/Significance This is the first report of euthymic BD exhibiting abnormal N170 emotional discrimination associated with theory of mind impairments.

Extent of cortical generators visible on the scalp: Effect of a subdural grid

The effect of the non-conducting substrate of a subdural grid on the scalp electric potential distribution is studied through simulations. Using a detailed head model and the finite element method we show that the governing physics equations predict an important attenuation in the scalp potential for generators located under the grid, and an amplification for generators located under holes in the skull filled with conductive media. These effects are spatially localized and do not cancel each other. A 4 × 8 cm grid can produce attenuations of 2 to 3 times, and an 8 × 8 cm grid attenuation of up to 8 times. As a consequence, when there is no subdural grid, generators of 4 to 8 cm(2) produce scalp potentials of the same maximum amplitude as generators of 10 to 20 cm(2) under the center of a subdural grid. This means that the minimum cortical extents necessary to produce visible scalp activity determined from simultaneous scalp and subdural recordings can be overestimations.

Shrinkage Approach for Spatiotemporal EEG Covariance Matrix Estimation

The characterization of the background activity in electroencephalography (EEG) is of interest in many problems, such as in the study of the brain rhythms and in the solution of the inverse problem for source localization. In most cases the background activity is modeled as a random process, and a basic characterization is done via the second order moments of the process, i.e., the spatiotemporal covariance. The general spatiotemporal covariance matrix of the background activity in EEG is extremely large. To reduce its dimensionality it is generally decomposed as a Kronecker product of a spatial and a temporal covariance matrices. They are generally estimated from the data using sample estimators, which have numerical and statistical problems when the number of trials is small. We present a shrinkage estimator for both EEG spatial and temporal covariance matrices of the background activity. We show that this estimator outperforms the commonly used ones when the quantity of available data is low. We find sufficient conditions for the consistency of the shrinkage estimator and present some results concerning its numerical stability. We compare several shrinkage approaches and show how to improve the estimator by incorporating known structure in the covariance matrix based on background activity models. Results using simulated and real EEG data support our approach.

General bounds for electrode mislocation on the EEG inverse problem

We analyze the effect of electrode mislocation on the electroencephalography (EEG) inverse problem using the Cramér-Rao bound (CRB) for single dipolar source parameters. We adopt a realistic head shape model, and solve the forward problem using the Boundary Element Method; the use of the CRB allows us to obtain general results which do not depend on the algorithm used for solving the inverse problem. We consider two possible causes for the electrode mislocation, errors in the measurement of the electrode positions and an imperfect registration between the electrodes and the scalp surfaces. For 120 electrodes placed in the scalp according to the 10-20 standard, and errors on the electrode location with a standard deviation of 5mm, the lower bound on the standard deviation in the source depth estimation is approximately 1mm in the worst case. Therefore, we conclude that errors in the electrode location may be tolerated since their effect on the EEG inverse problem are negligible from a practical point of view.

Analysis of parametric estimation of head tissue conductivities using Electrical Impedance Tomography

We study the theoretical performance of using Electrical Impedance Tomography (EIT) to measure the conductivity of the main tissues of the head. The governing equations are solved using the Finite Element Method for realistically shaped head models with isotropic and anisotropic electrical conductivities. We focus on the Electroencephalography (EEG) signal frequency range since EEG source localization is the assumed application. We obtain the Cramer-Rao Lower Bound (CRLB) to find the minimum conductivity estimation error expected with EIT measurements. The more convenient electrode pairs selected for current injection from a typical EEG array are determined from the CRLB. Moreover, using simulated data, the Maximum Likelihood Estimator of the conductivity parameters is shown to be close to the CRLB for a relatively low number of measurements. The results support the idea of using EIT as a low-cost and practical tool for individually measure the conductivity of the head tissues, and to use them when solving the EEG source localization. Even when the conductivity of the soft tissues of the head is available from Diffusion Tensor Imaging, EIT can complement the electrical model with the estimation of the skull and scalp conductivities.

Error bounds in diffusion tensor estimation using multiple-coil acquisition systems

We extend the diffusion tensor (DT) signal model for multiple-coil acquisition systems. Considering the sum-of-squares reconstruction method, we compute the Cramér-Rao bound (CRB) assuming the widely accepted noncentral chi distribution. Within this framework, we assess the effect of noise in DT estimation and other measures derived from it, as a function of the number of acquisition coils, as well as other system parameters. We show the applications of CRB in many actual problems related to DT estimation: we compare different gradient field setup schemes proposed in the literature and show how the CRB can be used to choose a convenient one; we show that for fiber-type anisotropy tensors the ellipsoidal area ratio (EAR) can be estimated with less error than other scalar factors such as the fractional anisotropy (FA) or the relative anisotropy (RA), and that for this type of anisotropy tensors, increasing the number of coils is equivalent to increasing the signal-to-noise ratio, i.e., the information of the different coils can be regarded as independent. Also, we present results showing the CRB of several parameters for actual DT-MRI data. We conclude that the CRB is a valuable tool to optimal experiment design in DT-related studies.

Evaluation of wave delivery methodology for brain MRE: Insights from computational simulations

MR elastography (MRE) of the brain is being explored as a biomarker of neurodegenerative disease such as dementia. However, MRE measures for healthy brain have varied widely. Differing wave delivery methodologies may have influenced this, hence finite element‐based simulations were performed to explore this possibility.

Geometry Regularized Joint Dictionary Learning for Cross-Modality Image Synthesis in Magnetic Resonance Imaging

Multi-sequence MRI protocols are used in comprehensive examinations of various pathologies in both clinical diagnosis and medical research. Various MRI techniques provide complementary information about living tissue. However, a comprehensive examination covering all modalities is rarely achieved due to considerations of cost, patient comfort, and scanner time availability. This may lead to incomplete records owing to image artifacts or corrupted or lost data. In this paper, we explore the problem of synthesizing images for one MRI modality from an image of another MRI modality of the same subject using a novel geometry regularized joint dictionary learning framework for non-local patch reconstruction. Firstly, we learn a cross-modality joint dictionary from a multi-modality image database. Training image pairs are first co-registered. A cross-modality dictionary pair is then jointly learned by minimizing the cross-modality divergence via a Maximum Mean Discrepancy term in the objective function of the learning scheme. This guarantees that the distribution of both image modalities is taken jointly into account when building the resulting sparse representation. In addition, in order to preserve intrinsic geometrical structure of the synthesized image patches, we further introduced a graph Laplacian regularization term into the objective function. Finally, we present a patch-based non-local reconstruction scheme, providing further fidelity of the synthesized images. Experimental results demonstrate that our method achieves significant performance gains over previously published techniques.