Biomedical subjects

Rajesh R Nandy

Publications and source records attributed to Rajesh R Nandy.

4 recordsLinked to original sources

PubMed2006

Low-frequency signal changes reflect differences in functional connectivity between good readers and dyslexics during continuous phoneme mapping.

The current fMRI study investigated correlations of low-frequency signal changes in the left inferior frontal gyrus, right inferior frontal gyrus and cerebellum in 13 adult dyslexic and 10 normal readers to examine functional networks associated with these regions. The extent of these networks to regions associated with phonological processing (frontal gyrus, occipital gyrus, angular gyrus, inferior temporal gyrus, fusiform gyrus, supramarginal gyrus and cerebellum) was compared between good and dyslexic readers. Analysis of correlations in low-frequency range showed that regions known to activate during an "on-off" phoneme-mapping task exhibit synchronous signal changes when the task is administered continuously (without any "off" periods). Results showed that three functional networks, which were defined on the basis of documented structural deficits in dyslexics and included regions associated with phonological processing, differed significantly in spatial extent between good readers and dyslexics. The methodological, theoretical and clinical significance of the findings for advancing fMRI research and knowledge of dyslexia are discussed.

Adult↗

PubMed2005

Estimation of the intrinsic dimensionality of fMRI data.

A new method based on an autoregressive noise model of order 1 is introduced to the problem of detecting the number of components in fMRI data. Unlike current information-theoretic criteria like AIC, MDL, and related PPCA, which do not incorporate autocorrelations in the noise, the new method leads to more consistent estimates of the model order, as illustrated in simulated and real fMRI resting-state data.

Adult↗

PubMed2003

Novel ROC-type method for testing the efficiency of multivariate statistical methods in fMRI.

The receiver operating characteristic (ROC) method is a useful and popular tool for testing the efficiency of various diagnostic tests applicable to functional MRI (fMRI) data. Typically, the diagnostic tests are applied on simulated and pseudo-human fMRI data, and the area under the ROC curve is used as a measure of the efficiency of the diagnostic test. The effectiveness of such a method depends on how well the simulated data approximate the real data. For multivariate statistical methods, however, this technique is usually inadequate, as the spatial dependence among voxels is ignored for simulated data. In this work a modified ROC method using real fMRI data with a broader scope is proposed. This method can be applied to most fMRI postprocessing techniques, including multivariate analyses such as canonical correlation analysis (CCA). Also, the relationship of the modified ROC method with the conventional ROC method is discussed in detail.

Algorithms↗

PubMed2003

Novel nonparametric approach to canonical correlation analysis with applications to low CNR functional MRI data.

Detection of activation in functional MRI (fMRI) is often complicated by the low contrast-to-noise ratio (CNR) in the data. The primary source of the difficulty is the fact that for activities that are subtle the signal can be hidden inside the inherent noise in the data. Classical univariate methods based on t-test or F-test are susceptible to noise, as they fail to harness systematic correlations in evoked responses within neighboring voxels. Here the power of a multivariate statistical analysis tool known as canonical correlation analysis (CCA) in fMRI studies is demonstrated where the CNR is low. As a further illustration of the power of the method, a comparative study of CCA and ordinary correlation analysis using simulated data under various noise levels is also performed. A novel nonparametric approach is introduced to calculate the P-values from the distribution of the complicated test statistic. The circumstances under which CCA is a better performer as well as when it is not the case are discussed. As an example, this method is applied to detect hippocampal activation from memory-related tasks.

Adult↗