Overcoming confounds of stimulus blocking: an event-related fMRI design of semantic processing.

The way in which meaning is represented and processed in the brain is a key issue in cognitive neuroscience, which can be usefully addressed by functional imaging techniques. In contrast to previous imaging studies of semantic knowledge, which have primarily used blocked designs, in this study we use an event-related fMRI (erfMRI) design, which has the advantage of enabling events to be presented pseudorandomly, thus reducing strategic processes and enabling more direct comparison with psychological behavioral studies. We used a semantic categorization task in which events were words representing either artifact or natural kinds concepts. Significant areas of activation for semantic processing included inferior frontal lobe bilaterally (BA 47) and left temporal regions, both inferior (BA 36 and 20) and middle (BA 21). These are areas that have been identified in previous neuroimaging studies of semantic knowledge. However, there were no significant differences between artifact and natural kinds concepts. These results are consistent with our previous imaging studies using blocked designs and suggest that conceptual knowledge is represented in a unitary, distributed neural system undifferentiated by domain of knowledge. These findings demonstrate that event-related designs can generate activations that are similar to those seen in blocked designs investigating semantics and, moreover, offer a greater capacity for interpretation free from the confounds of block effects.

The neural representation of nouns and verbs: PET studies.

Neuropsychological studies of patients with selective deficits for nouns or verbs have been taken as evidence for the neural specialization of different word classes. Noun deficits are associated with lesions in anterior temporal regions while verb deficits arise from left inferior frontal lesions. However, neuroimaging studies do not unequivocally support this account, with only some studies supporting claims for regional specialization. We carried out two PET studies to determine whether there is any regional specialization for the processing of nouns and verbs. One study used the lexical decision task and the other used a more semantically demanding task, i.e. semantic categorization. We found robust activation of a semantic network extending from left inferior frontal cortex into the inferior temporal lobe, but no differences as a function of word class. We interpret these data within the framework of cognitive accounts in which conceptual knowledge is represented within a non-differentiated distributed system.

Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains.

Even in the absence of an experimental effect, functional magnetic resonance imaging (fMRI) time series generally demonstrate serial dependence. This colored noise or endogenous autocorrelation typically has disproportionate spectral power at low frequencies, i.e., its spectrum is (1/f)-like. Various pre-whitening and pre-coloring strategies have been proposed to make valid inference on standardised test statistics estimated by time series regression in this context of residually autocorrelated errors. Here we introduce a new method based on random permutation after orthogonal transformation of the observed time series to the wavelet domain. This scheme exploits the general whitening or decorrelating property of the discrete wavelet transform and is implemented using a Daubechies wavelet with four vanishing moments to ensure exchangeability of wavelet coefficients within each scale of decomposition. For (1/f)-like or fractal noises, e.g., realisations of fractional Brownian motion (fBm) parameterised by Hurst exponent 0 < H < 1, this resampling algorithm exactly preserves wavelet-based estimates of the second order stochastic properties of the (possibly nonstationary) time series. Performance of the method is assessed empirically using (1/f)-like noise simulated by multiple physical relaxation processes, and experimental fMRI data. Nominal type 1 error control in brain activation mapping is demonstrated by analysis of 13 images acquired under null or resting conditions. Compared to autoregressive pre-whitening methods for computational inference, a key advantage of wavelet resampling seems to be its robustness in activation mapping of experimental fMRI data acquired at 3 Tesla field strength. We conclude that wavelet resampling may be a generally useful method for inference on naturally complex time series.

Effect of slice orientation on reproducibility of fMRI motor activation at 3 Tesla.

The effect of slice orientation on reproducibility and sensitivity of 3T fMRI activation using a motor task has been investigated in six normal volunteers. Four slice orientations were used; axial, oblique axial, coronal and sagittal. We applied analysis of variance (ANOVA) to suprathreshold voxel statistics to quantify variability in activation between orientations and between subjects. We also assessed signal detection accuracy in voxels across the whole brain by using a finite mixture model to fit receiver operating characteristic (ROC) curves to the data. Preliminary findings suggest that suprathreshold cluster characteristics demonstrate high motor reproducibility across subjects and orientations, although a significant difference between slice orientations in number of activated voxels was demonstrated in left motor cortex but not cerebellum. Subtle inter-orientation differences are highlighted in the ROC analyses, which are not obvious by ANOVA; the oblique axial slice orientation offers the highest signal detection accuracy, whereas coronal slices give the lowest.

Brain tissue classification of magnetic resonance images using partial volume modeling.

This paper presents a fully automatic three-dimensional classification of brain tissues for Magnetic Resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three main types of brain tissue: gray matter, white matter, and cerebro spinal fluid, called pure classes, but also mixtures, called mixclasses. A statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to assess the risk alpha of the approximation. In order to classify a brain into three types of brain tissue and deal with the problem of partial volume effects, the proposed algorithm uses two steps: 1) segmentation of the brain into pure and mixclasses using the mixture model; 2) reclassification of the mixclasses into the pure classes using knowledge about the obtained pure classes. Both steps use Markov random field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRFs to improve discrimination of the mixclasses. The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.