Spatiotemporal group ICA applied to fMRI datasets

Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment

Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients.

Mapping a lateralization gradient within the ventral stream for auditory speech perception

Recent models on speech perception propose a dual-stream processing network, with a dorsal stream, extending from the posterior temporal lobe of the left hemisphere through inferior parietal areas into the left inferior frontal gyrus, and a ventral stream that is assumed to originate in the primary auditory cortex in the upper posterior part of the temporal lobe and to extend toward the anterior part of the temporal lobe, where it may connect to the ventral part of the inferior frontal gyrus. This article describes and reviews the results from a series of complementary functional magnetic resonance imaging studies that aimed to trace the hierarchical processing network for speech comprehension within the left and right hemisphere with a particular focus on the temporal lobe and the ventral stream. As hypothesized, the results demonstrate a bilateral involvement of the temporal lobes in the processing of speech signals. However, an increasing leftward asymmetry was detected from auditory-phonetic to lexico-semantic processing and along the posterior-anterior axis, thus forming a "lateralization" gradient. This increasing leftward lateralization was particularly evident for the left superior temporal sulcus and more anterior parts of the temporal lobe.

Unbiased group-level statistical assessment of independent component maps by means of automated retrospective matching

This report presents and validates a method for the group-level statistical assessment of independent component analysis (ICA) outcomes. The method is based on a matching of individual component maps to corresponding aggregate maps that are obtained from concatenated data. Group-level statistics are derived that include an explicit correction for selection bias. Outcomes were validated by means of calculations with artificial null data. Although statistical inferences were found to be incorrect if bias was neglected, the use of the proposed bias correction sufficed to obtain valid results. This was further confirmed by extensive calculations with artificial data that contained known effects of interest. While uncorrected statistical assessments systematically violated the imposed confidence level thresholds, the corrected method was never observed to exceed the allowed false positive rate. Yet, bias correction was found to result in a reduced sensitivity and a moderate decrease in discriminatory power. The method was also applied to analyze actual fMRI data. Various effects of interest that were detectable in the aggregate data were similarly revealed by the retrospective matching method. In particular, stimulus-related responses were extensive. Nevertheless, differences were observed regarding their spatial distribution. The presented findings indicate that the proposed method is suitable for neuroimaging analyses. Finally, a number of generalizations are discussed. It is concluded that the proposed method provides a framework that may supplement many of the currently available group ICA methods with validated unbiased group inferences.