Abstract Introduction Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows the detection of specific cerebral functions in humans based on hemodynamic changes. The contrast changes are about 5%, making visual inspection impossible. Thus, statistic strategies are applied to infer which brain region is engaged in a task. However, the traditional methods like general linear model and cross-correlation utilize voxel-wise calculation, introducing a lot of false-positive data. So, in this work we tested post-processing cluster algorithms to diminish the false-positives. Methods In this study, three clustering algorithms (the hierarchical cluster, k-means and self-organizing maps) were tested and compared for false-positive removal in the post-processing of cross-correlation analyses. Results Our results showed that the hierarchical cluster presented the best performance to remove the false positives in fMRI, being 2.3 times more accurate than k-means, and 1.9 times more accurate than self-organizing maps. Conclusion The hierarchical cluster presented the best performance in false-positive removal because it uses the inconsistency coefficient threshold, while k-means and self-organizing maps utilize a priori cluster number (centroids and neurons number); thus, the hierarchical cluster avoids clustering scattered voxels, as the inconsistency coefficient threshold allows only the voxels to be clustered that are at a minimum distance to some cluster.

O Brasil é o maior produtor mundial de café e os preços dessa mercadoria são diretamente influenciados pela qualidade dos grãos. A atividade antioxidante do café foi relacionada com a sua qualidade de bebida através de Espectroscopia de Ressonância de spin eletrônico (ESR), como uma tentativa de estabelecer um método não subjetivo para classificar a qualidade do grão. O teste IC50 e o monitoramento temporal da fração do radical DPPH não reduzida pelas três qualidades de bebida Mole (Alta), Dura (Média) e Rio (Baixa) foram determinados. Uma solução metanólica de 2,2-difenil-1-picril-hidrazila (DPPH), que tem um radical estável e um espectrômetro JEOL FA-200 (banda-X) foram utilizados para os ensaios. O monitoramento temporal da reação entre o radical e o café foi registrado por espectros de ESR. A taxa de radicais reduzidos ou antioxidados foi determinada no tempo e para cada qualidade da bebida do café foram encontrados diferentes inclinações da curva: Mole (0,32 ± 0,02), Dura (0,47 ± 0,02) e Rio (0,60 ± 0,02). O resultado de IC50 de qualidade Baixa (2,7 ± 0,9) foi diferente dos valores de Mole (7,8 ± 1,9) e Dura (6,5 ± 1,5), mas não existiu nenhuma diferença entre os resultados de Mole e Dura, em razão da incerteza associada. Portanto, os resultados encontrados, principalmente para o monitoramento temporal, estabelecem uma nova metodologia quantitativa para classificar a qualidade de bebida dos grãos de café.

Brazil is the largest producer of coffee in the world and coffee prices are directly linked to grain quality. In this work, the antioxidant activity of coffee was related to its quality through Electron Spin Resonance Spectroscopy (ESR), as an attempt to establish a non-subjective method to classify the grain quality. For that purpose, the IC50 and temporal monitoring of its non-oxidized fraction were determined for three bean qualities: Soft (High), Hard (Medium) and Rio (Low). Methanolic solution of 2,2-difenil-1-picril-hidrazila (DPPH), that has a stable radical and a JEOL FA-200 (X-Band) spectrometer were used for these tests. The temporal monitoring of reaction between radical and coffee was performed. The rate of reduced or of antioxidated radicals was determined on time and for each coffee beverage quality were found different slopes of curve: Soft (0.32±0.02), Hard (0.47±0.02) and Rio (0.60±0.02). The IC50 result of Rio quality (2.7 ± 0.9) was different from the Soft (7.8 ± 1.9) and Hard (6.5 ± 1.5) values, but there was no difference between the High and Medium results due to the uncertainty associated. Therefore the results found, mainly for monitoring temporal, establish a new quantitative methodology for classifying the coffee beverage quality.

The Kullback-Leibler distance (or relative entropy) is applied in the analysis of functional magnetic resonance (fMRI) data series. Our study is designed for event-related (ER) experiments, where a brief stimulus is presented and a long period of rest is followed. In particular, this relative entropy is used as a measure of the "distance" between the probability distributions p1 and p2 of the signal levels related to stimulus and non-stimulus. In order to avoid undesirable divergences of the Kullback-Leibler distance, a small positive parameter delta is introduced in the definition of the probability functions in such a way that it does not bias the comparison between both distributions. Numerical simulations are performed so as to determine the probability densities of the mean Kullback-Leibler distance $\overline{D}$ (throughout the N epochs of the whole experiment). For small values of N (N < 30), such probability densities $f(\overline{D})$ are found to be fitted very well by Gamma distributions (chi2 < 0.0009). The sensitivity and specificity of the method are evaluated by construction of the receiver operating characteristic (ROC) curves for some values of signal-to-noise ratio (SNR). The functional maps corresponding to real data series from an asymptomatic volunteer submitted to an ER motor stimulus is obtained by using the proposed technique. The maps present activation in primary and secondary motor brain areas. Both simulated and real data analyses indicate that the relative entropy can be useful for fMRI analysis in the information measure scenario.