Identification of activated regions during a language task.

Functional magnetic resonance imaging (fMRI) techniques are based on the assumption that changes in neural activity are accompanied by modulation in the blood-oxygenation-level-dependent (BOLD) signal. In addition to conventional increases in BOLD signals, sustained negative BOLD signal changes are occasionally observed in many fMRI experiments, which show regions of cortex that seem to respond in antiphase with primary stimulus. The existence of this so-called negative BOLD response (NBR) has been observed and investigated in many functional studies. Several theoretical mechanisms have been proposed to account for it, but its origin has never been fully explained. In this study, the variability of fMRI activation, including the sources of the negative BOLD signal, during phonological and semantic language tasks, was investigated in six right-handed healthy subjects. We found significant activations in the brain regions, mainly in the left hemisphere, involved in the language stimuli [prominent in the inferior frontal gyrus, approximately Brodmann Areas (BA)7, BA44, BA45 and BA47, and in the precuneus]. Moreover, we observed activations in motor regions [precentral gyrus and supplementary motor area (SMA)], a result that suggests a specific role of these areas (particularly the SMA) in language processing. Functional analysis have also shown that certain brain regions, including the posterior cingulate cortex and the anterior cingulate cortex, have consistently greater activity during resting states compared to states of performing cognitive tasks. In our study, we observed diffuse NBR at the cortical level and a stronger negative response in correspondence to the main sinuses. These phenomena seem to be unrelated to a specific neural activity, appearing to be expressions of a mechanical variation in hemodynamics. We discussed about the importance of these responses that are anticorrelated with the stimulus. Our data suggest that particular care must be considered in the interpretation of fMRI findings, especially in the case of presurgical studies.

An independent component analysis-based approach on ballistocardiogram artifact removing.

Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.