RESUMEN
Much relevant information about activations and artifacts in a functional magnetic resonance imaging (fMRI) dataset can be obtained from an exploratory cluster analysis. In contrast to testing the significance of the measured experimental effect for a given model, unsupervised pattern recognition techniques, such as fuzzy clustering, often find unexpected behavior in addition to expected activations, allowing the exploitation of this element of surprise. The many artifact clusters often discovered might aid the experimenter in deciding whether the dataset is usable, whether some additional preprocessing step is required, or whether the one used has introduced spurious effects. However, clustering alone does not complete the analysis because the membership values that are generated are not indicative of the level of statistical significance with respect to the cluster activation patterns (centroids). This is of particular importance for fMRI datasets for which most time-series are "noise", with no activation patterns. We propose that an initial partition step should precede the clustering step. Only time-series that meet a certain statistical criterion (using a scaled version of Fisher's g-order statistic) are selected for clustering; this typically represents <5% of the whole brain region. The purpose of clustering is to generate a set of cluster centers that are the possible activation patterns; these are used in forming a linear model of all the time-series. The model parameter is tested for significance in both the time and frequency domains. We present a novel method of conducting these tests, which limits the number of false positives. We call the three-step process of initial partition, clustering and the two-domain significance test as exploring regions of interest with cluster analysis (EROICA).
Asunto(s)
Encéfalo/fisiología , Imagen por Resonancia Magnética , Análisis por Conglomerados , Interpretación Estadística de Datos , Humanos , Modelos Teóricos , Factores de TiempoRESUMEN
We examined whether cerebral activation to two different intense and painful stimuli could be detected using functional magnetic resonance imaging (fMRI) in alpha-chloralose anesthetized rats. Experiments were performed using a 9.4 T magnet and a surface coil centered over the forebrain. A set of gradient echo images were acquired and analyzed using our software based on fuzzy cluster analysis (EvIdent). Following the injection of 50 microl of formalin (5%) into the forepaw we observed a regional increase in signal intensity in the MR images in all animals. Anterior cingulate cortex, frontal cortex and sensory-motor cortex were some of the regions that activated frequently and often bilaterally. Surprisingly, activation appeared sequentially, often occurring first in either the right or the left hemisphere with a separation of seconds to minutes between peak activations. Morphine pre-treatment (1 mg/kg, i. v.) delayed and/or reduced the intensity of the activation resulting in a decrease in the overall response. Following episodes of intense electrical stimulation, produced by two brief stimulations (15 V, 0. 3 ms, 3 Hz) of the forepaw, activation was observed consistently in the sensory-motor cortex contralateral to the stimulation. Activation also occurred frequently in the anterior cingulate cortex, ipsilateral sensory-motor cortex and frontal cortical regions. All these regions of activation were markedly reduced during nitrous oxide inhalation. Treatment with morphine resulted in an inhibition of the activation response to electrical stimulation in most regions except for sensory-motor cortex. Thus, electrical and chemical noxious stimuli activated regions that are known to be involved in the central processing of pain and morphine modified the activation observed. fMRI combined with appropriate exploratory data analysis tools could provide an effective new tool with which to study novel analgesics and their effects on the CNS processing of pain in animal models.
Asunto(s)
Imagen por Resonancia Magnética , Dimensión del Dolor/métodos , Dolor/fisiopatología , Animales , Estimulación Eléctrica , Miembro Anterior , Dolor/inducido químicamente , Ratas , Ratas Sprague-Dawley , Organismos Libres de Patógenos Específicos , Estimulación QuímicaRESUMEN
The functional equivalence of overt movements and dynamic imagery is of fundamental importance in neuroscience. Here, we investigated the participation of the neocortical motor areas in a classic task of dynamic imagery, Shepard and Metzler's mental rotation task, by time-resolved single-trial functional Magnetic Resonance Imaging (fMRI). The subjects performed the mental-rotation task 16 times, each time with different object pairs. Functional images were acquired for each pair separately, and the onset times and widths of the activation peaks in each area of interest were compared to the response times. We found a bilateral involvement of the superior parietal lobule, lateral premotor area, and supplementary motor area in all subjects; we found, furthermore, that those areas likely participate in the very act of mental rotation. We also found an activation in the left primary motor cortex, which seemed to be associated with the right-hand button press at the end of the task period.
Asunto(s)
Mapeo Encefálico , Encéfalo/fisiología , Cognición , Neuronas/fisiología , Reconocimiento Visual de Modelos , Simulación por Computador , Femenino , Lateralidad Funcional , Humanos , Imagen por Resonancia Magnética/métodos , Masculino , Corteza Motora/fisiología , Lóbulo Parietal/fisiología , Tiempo de Reacción , Rotación , Factores de TiempoRESUMEN
Exploratory, data-driven analysis approaches such as cluster analysis, principal component analysis, independent component analysis, or neural network-based techniques are complementary to hypothesis-led methods. They may be considered as hypothesis generating methods. The representative time courses they produce may be viewed as alternative hypotheses to the null hypothesis, i.e., "no activation." We present here a resampling technique to validate the results of exploratory fuzzy clustering analysis. In this case an alternative hypothesis is represented by a cluster centroid. For both simulated and in vivo functional magnetic resonance imaging data, we show that by permutation-based resampling, statistical significance may be computed for each voxel belonging to a cluster of interest without parametric distributional assumptions.
Asunto(s)
Encéfalo/anatomía & histología , Imagen por Resonancia Magnética/métodos , Mapeo Encefálico , Análisis por Conglomerados , Humanos , Imagen por Resonancia Magnética/estadística & datos numéricos , Estimulación LuminosaRESUMEN
Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying "activation." The contrast-to-noise (CNR) ratio ranged between 1-10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.