Analysis of functional magnetic resonance imaging data using self-organizing mapping with spatial connectivity

Abstract

Commonly used methods in analyzing functional magnetic resonance imaging (fMRI) data, such as the Student's t-test and cross-correlation analysis, are model-based approaches. Although these methods are easy to implement and are effective in analyzing data obtained with simple paradigms, they are not applicable in situations in which patterns of neuronal response are complicated and when fMRI response is unknown. In this work, Kohonen's self-organizing mapping (SOM), which is a model-free approach, is adapted for analyzing fMRI data. Because spatial connectivity is an important function in the identification of activation sites in functional brain imaging, it is incorporated into the SOM algorithm. Receiver operating characteristic analysis on simulated data shows that the new algorithm achieves measurable improvement over the standard algorithm. The applicability of the new algorithm is demonstrated on experimental data. Magn Reson Med 41:939

Wavelet transform-based Wiener filtering of event-related fMRI data

“…[13], the action by Eq. [18] on the raw data is to shrink each of its wavelet coefficients according to the ratio between the power density due to noise and the power density due to signal plus noise in the corresponding component. It should be noted that even though an epoch-to-epoch variation is present in the data, the estimation of the Wiener filter assumes that this variation is negligible.…”

“…Since the event-related design can provide the temporal information regarding the brain's response to events, enhancing the SNR of individual evoked responses will increase the accuracy of their temporal characterization, thereby improving the quantification of the commonalities and differences that may occur across repeated trials. In addition, using the filtering method as a preprocessing step to model-free algorithms, such as data clustering (17)(18)(19), may lead to improved activation detection. It should be noted that a model-free approach is more appropriate in this situation since the noise structure of the filtered data is complicated, making statistical analysis using conventional methods difficult.…”

Identifying distributed and overlapping clusters of hemodynamic synchrony in fMRI data sets

“…The a priori model, however, is limited in dealing with hemodynamic variations across subjects, brain regions, and even cortical layers [1,16]. As an alternative, data-driven methods group brain responses by temporal similarity [2,7,30,24] or distinguish brain response from various noise sources by data decomposition [4,11,27]. These methods are powerful in revealing multivariate patterns of brain activity independent of experimental conditions.…”

Detecting low‐frequency functional connectivity in fMRI using a self‐organizing map (SOM) algorithm

“…Recently, model-free analysis using self-organizing maps (SOMs) has been applied to functional MRI [Fischer and Hennig, 1999b;Ngan and Hu, 1999], and has shown promise in detecting activation patterns related to performing a cognitive task. We sought to assess whether the use of selforganizing maps (SOMs) can be extended to the detection of resting state functional connectivity.…”