Laura Astolfi

Andrzej Cichocki

Fabio Babiloni

NeuroMath: Advanced Methods for the Estimation of Human Brain Activity and Connectivity

An important field of blood oxygen level dependent (BOLD) functional
magnetic resonance imaging (fMRI) is the investigation of effective connectivity, that is, the actions that a given set of regions exert on one another. We recently proposed a data-driven method based on the partial correlation matrix that could provide some insight regarding the pattern of functional interaction between brain regions as represented by structural equation modeling (SEM). So far, the efficiency of this approach was mostly based on empirical
evidence. In this paper, we provide theoretical fundaments explaining why and in what measure structural equation modeling and partial correlations are related. This gives better insight regarding  what parts of SEM can be retrieved by partial correlation analysis and what remains inaccessible. We illustrate the different results with real data.

Guillaume Marrelec

Habib Benali

A Theoretical Investigation of the Relationship between Structural Equation Modeling and Partial Correlation in Functional MRI Effective Connectivity

We present the four key areas of research—preprocessing, the volume conductor, the forward problem, and the inverse problem—that affect the performance of EEG and MEG source
imaging. In each key area we identify prominent approaches and methodologies that have open
issues warranting further investigation within the community, challenges associated with certain
techniques, and algorithms necessitating clarification of their implications. More than providing
definitive answers we aim to identify important open issues in the quest of source localization.

Katrina Wendel

Outi V&#xE4;is&#xE4;nen

Jaakko Malmivuo

Nevzat G. Gencer

Bart Vanrumste

Piotr Durka

Ratko Magjarevi&#x107;

Selma Supek

Mihail Lucian Pascu

Hugues Fontenelle

Rolando Grave de Peralta Menendez

EEG/MEG Source Imaging: Methods, Challenges, and Open Issues

Event-Related Potentials (ERPs) or Event-Related 
                  Oscillations (EROs) have been widely used to 
                  study emotional processing, mainly on the theta and gamma 
                  frequency bands. However, the role of the slow 
                  (delta) waves has been largely ignored. The aim 
                  of this study is to provide a framework that 
                  combines EROs with Event-Related 
                  Desynchronization (ERD)/Event-Related 
                  Synchronization (ERS), and peak amplitude 
                  analysis of delta activity, evoked by the 
                  passive viewing of emotionally evocative 
                  pictures. Results showed that this kind of 
                  approach is sensitive to the effects of gender, 
                  valence, and arousal, as well as, the study of 
                  interhemispherical disparity, as the two-brain 
                  hemispheres interplay roles in the detailed 
                  discrimination of gender. Valence effects are 
                  recovered in both the central electrodes as well 
                  as in the hemisphere interactions. These 
                  findings suggest that the temporal patterns of 
                  delta activity and the alterations of delta 
                  energy may contribute to the study of emotional 
                  processing. Finally the results depict the 
                  improved sensitivity of the proposed framework 
                  in comparison to the traditional ERP techniques, 
                  thereby delineating the need for further 
                  development of new methodologies to study slow 
                  brain frequencies.

Manousos A. Klados

Christos Frantzidis

Ana B. Vivas

Christos Papadelis

Chrysa Lithari

Costas Pappas

Panagiotis D. Bamidis

A Framework Combining Delta Event-Related
                 Oscillations (EROs) and Synchronisation Effects (ERD/ERS) to Study Emotional Processing

The aim of this study is to propose a method for analyzing measured signal obtained
from functional Near-Infrared Spectroscopy (fNIRS), which is applicable for
neuroimaging studies for car drivers. We developed a signal processing method by
multiresolution analysis (MRA) based on discrete wavelet transform. Statistical group
analysis using Z-score is conducted after the extraction of task-related signal using
MRA. Brain activities of subjects with different level of mental calculation are
measured by fNIRS and fMRI. Results of mental calculation with nine subjects by using
fNIRS and fMRI showed that the proposed methods were effective for the evaluation of
brain activities due to the task. Finally, the proposed method is applied for evaluating
brain function of car driver with and without adaptive cruise control (ACC) system for
demonstrating the effectiveness of the proposed method. The results showed that frontal
lobe was less active when the subject drove with ACC.

Hitoshi Tsunashima

Kazuki Yanagisawa

Measurement of Brain Function of Car Driver Using Functional Near-Infrared Spectroscopy (fNIRS)

This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models as well as nonlinear dynamic models. Some examples involving EEG-data are presented.

Milka Culic

Biljana Gjoneska

Hiie Hinrikus

Magnus Jändel

Wlodzimierz Klonowski

Hans Liljenström

Nada Pop-Jordanova

Dan Psatta

Dietrich von Rosen

Björn Wahlund

Signatures of Depression in Non-Stationary Biometric Time Series

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs)  during a  foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. 
The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by the <svg style="vertical-align:-1.70624pt;width:55.962502px;" id="M1" height="13.55" version="1.1" viewBox="0 0 55.962502 13.55" width="55.962502" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg"><g transform="matrix(.017,-0,0,-.017,.062,11.362)"><path id="x43" d="M614 175l29 -10q-33 -109 -57 -154q-121 -26 -184 -26q-90 0 -160.5 29t-112.5 77t-63.5 105.5t-21.5 119.5q0 157 108 253t277 96q36 0 71.5 -5t69 -13.5t36.5 -8.5q15 -102 20 -150l-29 -8q-20 79 -66.5 114t-128.5 35q-119 0 -187.5 -86t-68.5 -207&#xA;q0 -140 73.5 -227.5t188.5 -87.5q73 0 119.5 37.5t86.5 116.5z" /></g><g transform="matrix(.017,-0,0,-.017,11.367,11.362)"><path id="x4D" d="M861 0h-263v28q61 5 73 22t12 97l-4 408h-4l-244 -548h-20l-224 534h-2l-15 -285q-5 -98 -3 -166q1 -34 19.5 -45t72.5 -17v-28h-233v28q45 5 62.5 17t22.5 43q11 65 19 183l14 208q3 36 3.5 55t-2.5 37.5t-6.5 25t-16.5 13t-25.5 8t-39.5 4.5v28h164l228 -501l232 501&#xA;h170v-28q-68 -6 -81 -21.5t-11 -91.5l9 -362q2 -80 15 -97t78 -22v-28z" /></g><g transform="matrix(.017,-0,0,-.017,26.513,11.362)"><path id="x41" d="M673 0h-245v28q47 6 58 16t1 39l-55 154h-213q-31 -87 -36 -129q-16 -47 -5 -61t68 -19v-28h-226v28q50 7 68.5 23.5t43.5 80.5l214 524l24 9l193 -536q23 -63 41.5 -79.5t68.5 -21.5v-28zM418 280l-81 255h-2q-74 -197 -101 -255h184z" /></g><g transform="matrix(.017,-0,0,-.017,38.259,11.362)"><path id="x5F" d="M500 -125h-500v50h500v-50z" /></g><g transform="matrix(.017,-0,0,-.017,46.758,11.362)"><path id="x4C" d="M495 163l30 -6q-19 -99 -36 -157h-453v28q63 5 77.5 20t14.5 78v400q0 63 -13 77t-75 19v28h261v-28q-61 -5 -74.5 -19t-13.5 -77v-395q0 -41 5 -59t18 -25q24 -13 96 -13q85 0 112 31q30 33 51 98z" /></g></svg> during the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed.

Filippo Cona

Melissa Zavaglia

Mauro Ursino

Changes in EEG Power Spectral Density and Cortical Connectivity in Healthy and Tetraplegic Patients during a Motor Imagery Task

We estimate cortical activity in normal subjects during the observation of TV commercials inserted within a movie by using high-resolution EEG techniques. The brain activity was evaluated in both time and frequency domains by solving the associate inverse problem of EEG with the use of realistic head models. In particular, we recover statistically significant information about cortical areas engaged by particular scenes inserted within the TV commercial proposed with respect to the brain activity estimated while watching a documentary. Results obtained in the population investigated suggest that the statistically significant brain activity during the observation of the TV commercial was mainly concentrated in frontoparietal cortical areas, roughly coincident with the Brodmann areas 8, 9, and 7, in the analyzed population.

Giovanni Vecchiato

Fabrizio De Vico Fallani

Serenella Salinari

Febo Cincotti

Fabio Aloise

Donatella Mattia

Maria Grazia Marciani

Luigi Bianchi

Ramon Soranzo

The Track of Brain Activity during the Observation of TV Commercials with the High-Resolution EEG Technology

A tomography of neural sources could be constructed from EEG/MEG recordings once the neuroelectromagnetic inverse problem (NIP) is solved. Unfortunately the NIP lacks a unique solution and therefore additional constraints are needed to achieve uniqueness. Researchers are then confronted with the dilemma of choosing one solution on the basis of the advantages publicized by their authors. This study aims to help researchers to better guide their choices by clarifying what is hidden behind inverse solutions oversold by their apparently optimal properties to localize single sources. Here, we introduce an inverse solution (ANA) attaining perfect localization of single sources to illustrate how spurious sources emerge and destroy the reconstruction of simultaneously active sources. Although ANA is probably the simplest and robust alternative for data generated by a single dominant source plus noise, the main contribution of this manuscript is to show that zero localization error of single sources is a trivial and largely uninformative property unable to predict the performance of an inverse solution in presence of simultaneously active sources. We recommend as the most logical strategy for solving the NIP the incorporation of sound additional a priori information about neural generators that supplements the information contained in the data.

Rolando Grave de Peralta

Olaf Hauk

Sara L. Gonzalez

The Neuroelectromagnetic Inverse Problem and the Zero Dipole Localization Error

<i  >Objectives</i>. Examine frequency distributions of ictal EEG after ECT stimulation in diagnostic subgroups of depression. <i  >Methods</i>. EEG registration was consecutively monitored in 33 patients after ECT stimulation. Patients were diagnosed according to DSM IV and subdivided into: (1) major depressive disorder with psychotic features <svg     style="vertical-align:-2.3205pt;width:48px;" id="M1" height="15.0875" version="1.1" viewBox="0 0 48 15.0875" width="48"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg>, (2) unipolar depression <svg     style="vertical-align:-2.3205pt;width:56.162498px;" id="M2" height="15.0875" version="1.1" viewBox="0 0 56.162498 15.0875" width="56.162498"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
			<g transform="matrix(.017,-0,0,-.017,.062,12.138)"><use xlink:href="#x28"/></g><g transform="matrix(.017,-0,0,-.017,5.944,12.138)"><use xlink:href="#x1D45B"/></g><g transform="matrix(.017,-0,0,-.017,19.186,12.138)"><use xlink:href="#x3D"/></g><g transform="matrix(.017,-0,0,-.017,33.89,12.138)"><path id="x32" d="M412 140l28 -9q0 -2 -35 -131h-373v23q112 112 161 170q59 70 92 127t33 115q0 63 -31 98t-86 35q-75 0 -137 -93l-22 20l57 81q55 59 135 59q69 0 118.5 -46.5t49.5 -122.5q0 -62 -29.5 -114t-102.5 -130l-141 -149h186q42 0 58.5 10.5t38.5 56.5z" /></g><g transform="matrix(.017,-0,0,-.017,42.049,12.138)"><path id="x30" d="M241 635q53 0 94 -28.5t63.5 -76t33.5 -102.5t11 -116q0 -58 -11 -112.5t-34 -103.5t-63.5 -78.5t-94.5 -29.5t-95 28t-64.5 75t-34.5 102.5t-11 118.5q0 58 11.5 112.5t34.5 103t64.5 78t95.5 29.5zM238 602q-32 0 -55.5 -25t-35.5 -68t-17.5 -91t-5.5 -105&#xA;q0 -76 10 -138.5t37 -107.5t69 -45q32 0 55.5 25t35.5 68.5t17.5 91.5t5.5 105t-5.5 105.5t-18 92t-36 68t-56.5 24.5z" /></g><g transform="matrix(.017,-0,0,-.017,50.209,12.138)"><use xlink:href="#x29"/></g>

		
	
</svg>, and (3) bipolar depression <svg     style="vertical-align:-2.3205pt;width:48px;" id="M3" height="15.0875" version="1.1" viewBox="0 0 48 15.0875" width="48"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg>. <i  >Results</i>. Results indicate that the diagnostically subgroups differ in their ictal EEG frequency spectrumml: (1) psychotic depression has a high occurrence of delta and theta waves, (2) unipolar depression has high occurrence of delta, theta and gamma waves, and (3) bipolar depression has a high occurrence of gamma waves. A linear discriminant function separated the three clinical groups with an accuracy of 94&#x25;. <i  >Conclusion</i>. Psychotic depressed patients differ from bipolar depression in their frequency based on probability distribution of ictal EEG. Psychotic depressed patients show more prominent slowing of EEG than nonpsychotic depressed patients. Thus the EEG results may be supportive in classifying subgroups of depression already at the start of the ECT treatment.

Paolo Piazza

Benny Liberg

Seizure (Ictal)—EEG Characteristics in Subgroups of Depressive Disorder in Patients Receiving Electroconvulsive Therapy (ECT)—A Preliminary Study and Multivariate Approach

About this journal

<p><em>Computational Intelligence and Neuroscience</em> is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.</p>


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<p>Chief Editor, Professor Cichocki, engages in world-leading research in the field of artificial intelligence and biomedical applications of advanced data analytics technologies.</p>


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Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence. The journal’s focus is on intelligent systems for computational neuroscience.

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