Frequency-based approach to the study of semantic brain networks connectivity.

The interactions among cerebral regions involved in semantic word generations are explored through connectivity analysis based on fMRI data through multivariate autoregressive model (MVAR). Connections among the pars triangularis of the left inferior frontal gyrus (L45), the lateral fusiform girus (LFG) and the left medial fusiform girus (MFG) were investigated. Ten healthy subjects were asked to covertly generate nouns belonging to two semantic categories (Animals and Tools). Time series for each voxel were derived from fMRI images, averaged within each area and concatenated over all subjects. The MVAR model allowed estimating spectral power, coherence and partial coherence between pairs of time series, and causality relations assessed through direct directed transfer function (dDTF). Spectral power is mostly concentrated in the frequency range of the imposed stimulus and the activation in the specific areas is modulated by conditions as well as coherence and partial coherence. dDTF values revealed stronger connections between L45 and LFG in "Tools" conditions, while a stronger causality was found between L45 and MFG in "Animals" conditions. In addition, comparing the same connections in the two conditions, a mirror reversal of the two weights was observed, with stronger causality L45-LFG in "Tools" vs "Animals" and stronger causality L45-MFG in "Animals" vs "Tools". The present study confirms and extends previous results obtained by structural equation modeling analysis, suggesting the suitability of a data-driven Granger causality approach in identifying condition-dependent effective connectivity from BOLD signals. The proposed methodology completes and integrates other analysis procedures providing new tools to explore brain functions.

Exploring cortical attentional system by using fMRI during a Continuous Perfomance Test.

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' 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' CPT performance.

Using deconvolution to improve PET spatial resolution in OSEM iterative reconstruction.

OBJECTIVES: A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as "a priori" information to guide OSEM toward an improved solution. METHODS: Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included it in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this "recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. RESULTS: Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. CONCLUSIONS: Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.