Artificial shifting of fMRI activation localized by volume- and surface-based analyses

Spatial smoothing is an important post-processing procedure that is used to increase the signal-to-noise ratio (SNR) of blood oxygenation level-dependent signals (BOLD) in common functional magnetic resonance imaging (fMRI) applications. However, recent studies have shown that smoothing artificially shifts probabilistic local maxima of fMRI activations. In this study, we show shifting of the localization of functional centers in hand motor areas of the cerebral cortex by three-dimensional isotropic Gaussian kernel smoothing or two-dimensional heat kernel smoothing in volume- and surface-based fMRI analyses. Activation maps derived from smoothed echo planar imaging (EPI) data by volume- and surface-based analyses were assigned to the nodes of individual cortical surface models, and local maxima in the primary motor area (M1) and the primary somatosensory cortex (S1) were compared with those derived from non-smoothed risk map analysis, which is commonly used in presurgical applications. For each analysis, the Euclidean and geodesic distances between the correlation coefficients of local maxima derived from smoothed and non-smoothed EPI data were measured. The results show that the correlation coefficients derived from the volume- and surface-based analyses were about 29.4% and 42.9% higher for smoothed than for non-smoothed risk map analyses, and show minimum shifting of localizations by 12.1 mm and 6.9 mm on average in Euclidean distance, respectively, and about 9.5 mm and 5.7 mm on average in geodesic distance, respectively.This work was supported by the Korea Science and Engineerign Foundation (KOSEF) grant funded by the Korea government (MOST) (R0A-2007-000-20068-0)

Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

Oscillatory Mechanisms Supporting Interval Timing in Cortical-Striatal Circuits

<p>Numerous studies have explored brain activities in relation to timing behaviors from spike firing rates to human neuroimaging signals; however, not many studies have explored functional relations between neural oscillation and timing behavior. Neural oscillations are recently being considered as a fundamental aspect of brain function that modulate broad ranges of cognitive processes. Striatal-beat frequency model (SBF) also proposed that oscillatory properties of neurons are the critical feature that underlies timing behavior. In order to reveal the functional relations between neural oscillations and timing behavior, multiple timing paradigms have trained in groups of rats, and the neural oscillatory patterns during the timing tasks were recorded and analyzed. More specifically, oscillatory patterns that are involved in duration encoding and comparison have been identified using ordinal temporal comparison task. Then, the patterns of theta and delta rhythms have been explored in relation to duration judgment and production. Also, oscillatory patterns underlying interval timing have been compared to the patterns of working memory. The major target areas for those electrophysiological experiments were the cortical-striatal circuits which known for their critical role in timing behavior. Finally, excitatory and inhibitory oscillators (EIO) model has been proposed in order to address oscillatory features underlying interval timing and working memory.</p>Dissertatio