<i>Background</i>. Magnetic Resonance (MR) diffusion
tensor imaging (DTI) is able to quantify in vivo tissue
microstructure properties and to detect disease related pathology
of the central nervous system. Nevertheless, DTI is limited by low
spatial resolution associated with its low signal-to-noise-ratio
(SNR). <i>Aim</i>. The aim is to select a DTI sequence
for brain clinical studies, optimizing SNR and resolution.
<i>Methods and Results</i>. We applied 6 methods for SNR
computation in 26 DTI sequences with different parameters using 4
healthy volunteers (HV). We choosed two DTI sequences for their
high SNR, they differed by voxel size and b-value. Subsequently,
the two selected sequences were acquired from 30 multiple
sclerosis (MS) patients with different disability and lesion load
and 18 age matched HV. We observed high concordance between mean
diffusivity (MD) and fractional anysotropy (FA), nonetheless the
DTI sequence with smaller voxel size displayed a better
correlation with disease progression, despite a slightly lower
SNR. The reliability of corpus callosum (CC) fiber tracking with
the chosen DTI sequences was also tested.
<i>Conclusion</i>. The sensitivity of DTI-derived
indices to MS-related tissue abnormalities indicates that the
optimized sequence may be a powerful tool in studies aimed at
monitoring the disease course and severity.

M. Lagan&#224;

M. Rovaris

A. Ceccarelli

C. Venturelli

S. Marini

G. Baselli

DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application

The use of modern brain imaging techniques could be useful to understand what brain areas are involved in the observation of video clips related to commercial advertising, as well as for the support of political campaigns, and also the areas of Public Service Announcements (PSAs). In this paper we describe the capability of tracking brain activity during the observation of commercials, political spots, and PSAs with advanced high-resolution EEG statistical techniques in time and frequency domains in a group of normal subjects. We analyzed the statistically significant cortical spectral power activity in different frequency bands during the observation of a commercial video clip related to the use of a beer in a group of 13 normal subjects. In addition, a TV speech of the Prime Minister of Italy was analyzed in two groups of swing and &#8220;supporter&#8221; voters. Results suggested that the cortical activity during the observation of commercial spots could vary consistently across the spot. This fact suggest the possibility to remove the parts of the spot that are not particularly attractive by using those cerebral indexes. The cortical activity during the observation of the political speech indicated a major cortical activity in the supporters group when compared to the swing voters. In this case, it is possible to conclude that the communication proposed has failed to raise attention or interest on swing voters. In conclusions, high-resolution EEG statistical techniques have been proved to able to generate useful insights about the particular fruition of TV messages, related to both commercial as well as political fields.

Giovanni Vecchiato

Laura Astolfi

Alessandro Tabarrini

Serenella Salinari

Donatella Mattia

Febo Cincotti

Luigi Bianchi

Domenica Sorrentino

Fabio Aloise

Ramon Soranzo

Fabio Babiloni

EEG Analysis of the Brain Activity during the Observation of Commercial, Political, or Public Service Announcements

The brain activity during perception or cognition is mostly examined by functional magnetic resonance imaging (fMRI). However, the cause of the detected activity relies on the anatomy. Diffusion tensor magnetic resonance imaging (DTMRI) as a noninvasive modality providing in vivo anatomical information allows determining neural fiber connections which leads to brain mapping. Still a complete map of fiber paths representing the human brain is missing in literature. One of the main drawbacks of reliable fiber mapping is the correct detection of the orientation of multiple fibers within a single imaging voxel. In this study a method based on linear data structures is proposed to define the fiber paths regarding their diffusivity. Another advantage of the proposed method is that the analysis is applied on entire brain diffusion tensor data. The implementation results are promising, so that the method will be developed as a rapid fiber tractography algorithm for the clinical use as future study.

Dilek Göksel Duru

Mehmed Özkan

Determination of Neural Fiber Connections Based on Data Structure Algorithm

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, <svg style="vertical-align:-4.37271pt;width:78.050003px;" id="M1" height="16.137501" version="1.1" viewBox="0 0 78.050003 16.137501" width="78.050003"  xmlns="http://www.w3.org/2000/svg">
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</svg> interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma (<svg style="vertical-align:-2.34499pt;width:8.0625px;" id="M4" height="9.9375" version="1.1" viewBox="0 0 8.0625 9.9375" width="8.0625"  xmlns="http://www.w3.org/2000/svg">
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</svg>) band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward <svg style="vertical-align:-4.37271pt;width:70.3125px;" id="M5" height="16.137501" version="1.1" viewBox="0 0 70.3125 16.137501" width="70.3125"  xmlns="http://www.w3.org/2000/svg">
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</svg> interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of <svg style="vertical-align:-2.34499pt;width:8.0625px;" id="M6" height="9.9375" version="1.1" viewBox="0 0 8.0625 9.9375" width="8.0625"  xmlns="http://www.w3.org/2000/svg">
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</svg> rhythms and to provide better understanding of cortical EEG spectra.

M. Zavaglia

F. Cona

M. Ursino

A Neural Mass Model to Simulate Different Rhythms in a Cortical Region

Functional magnetic resonance imaging (fMRI) was performed in eight healthy subjects to identify the localization, magnitude, and volume extent of activation in brain regions that are involved in blood oxygen level-dependent (BOLD) response during the performance of Conners&#39; Continuous Performance Test (CPT). An extensive brain network was activated during the task  including frontal, temporal, and occipital cortical areas and left cerebellum. The more activated cluster in terms of volume extent and magnitude was located in the right anterior cingulate cortex (ACC). Analyzing the dynamic trend of the activation in the identified areas during the entire duration of the sustained attention test, we found a progressive decreasing of BOLD response probably due to a habituation effect without any deterioration of the performances. The observed brain network is consistent with existing models of visual object processing and attentional control and may serve as a basis for fMRI studies in clinical populations with neuropsychological deficits in Conners&#39; CPT performance.

M. G. Tana

E. Montin

S. Cerutti

A. M. Bianchi

Exploring Cortical Attentional System by Using fMRI during a Continuous Perfomance Test

Many studies have revealed that attention operates across different sensory modalities, to facilitate the selection of relevant information in the multimodal situations of every-day life. Cross-modal links have been observed either when attention is directed voluntarily (endogenous) or involuntarily (exogenous). The neural basis of cross-modal attention presents a significant challenge to cognitive neuroscience. Here, we used a neural network model to elucidate the neural correlates of visual-tactile interactions in exogenous and endogenous attention. The model includes two unimodal (visual and tactile) areas connected with a bimodal area in each hemisphere  and a competition between the two hemispheres. The model is able to explain cross-modal facilitation both in exogenous and endogenous attention, ascribing it to an advantaged  activation of the bimodal area on the attended side (via a top-down or bottom-up biasing), with concomitant inhibition towards the opposite side. The model suggests that a competitive/cooperative interaction with biased competition may mediate both forms of cross-modal attention.

Elisa Magosso

Andrea Serino

Giuseppe di Pellegrino

Mauro Ursino

Crossmodal Links between Vision and Touch in Spatial Attention: A Computational Modelling Study

The aim of this study is to present electrooculogram signals that can be used for human computer interface efficiently. Establishing an efficient alternative channel for communication without overt speech and hand movements is important to increase the quality of life for patients suffering from Amyotrophic Lateral Sclerosis or other illnesses that prevent correct limb and facial muscular responses. We have made several experiments to compare the P300-based BCI speller and EOG-based new system. A five-letter word can be written on average in 25 seconds and in 105 seconds with the EEG-based device. Giving message such as &#8220;clean-up&#8221; could be performed in 3 seconds with the new system. The new system is more efficient than P300-based BCI system in terms of accuracy, speed, applicability, and cost efficiency. Using EOG signals, it is possible to improve the communication abilities of those patients who can move their eyes.

A. B. Usakli

S. Gurkan

F. Aloise

G. Vecchiato

F. Babiloni

On the Use of Electrooculogram for Efficient Human Computer Interfaces

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<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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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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