Improved temporal clustering analysis method for detecting multiple response peaks in fMRI

Detection of irregular, transient fMRI activity in normal controls using 2dTCA: comparison to event-related analysis using known timing

When events occur spontaneously during the acquisition of a series of images, traditional modeling methods for detecting functional MRI activation detection cannot be employed. The two-dimensional temporal clustering algorithm, 2dTCA, has been shown to accurately detect random, transient activations in computer simulations without the use of known event timings. In this study we applied the 2dTCA technique to detect the timings and spatial locations of sparse, irregular, transient activations of the visual, auditory, and motor cortices in 12 normal controls. Experiments with one and two independent types of stimuli were employed. Event-related activation using known timing was compared with event-related activation using 2dTCA-detected timing in individuals and across groups. The 2dTCA algorithm detected the activation from all presented stimuli in every subject. When compared with block-design results using a measure of correlation between activation maps, no significant difference was found between the 2dTCA activation maps and the event-related maps using known timing across all subjects. Therefore, 2dTCA has the potential to be an accurate and more practical method for detection of spontaneous, transient events using fMRI.

Development of 2dTCA for the detection of irregular, transient BOLD activity

The temporal clustering algorithm (TCA) has been developed in order to detect irregular, transient functional MRI (fMRI) activation signals when the timings of the stimuli are unknown. Unfortunately, such methods are also sensitive to signal changes caused by motion and physiological noise. We have developed a modified TCA technique, 2dTCA, which can detect multiple different timing patterns within a dataset so that signals of interest can be separated from artifacts and those of no interest. The objective of this work was to further develop the 2dTCA methods and evaluate their performance in simulated functional MRI datasets. Comparisons were made with TCA and a freely-distributed independent component analysis algorithm (ICA). We created two different sets of six computer-generated phantoms with one and two different simulated activation time courses present in 10 regions of interest. The phantoms also contained real subject rigid and nonrigid body motion and noise. Sensitivity of detection, defined as the true-positive activation rate at false-positive activation rates varying between 0.0001 and 0.01, was compared between methods. Additionally, specificity of detection of the irregular, transient signal of interest was assessed by comparing the number of signal time courses detected by each algorithm. The results suggest that the increased sensitivity of 2dTCA over TCA in detecting this particular signal of interest is comparable to the detection with ICA, but with fewer other signals detected. A few examples of the successful application of 2dTCA to the localization of interictal activity in preliminary studies of temporal lobe epilepsy are also described.

Cluster analysis detection of functional MRI activity in temporal lobe epilepsy

PURPOSE

The objective of this pilot study was to evaluate the ability of a novel method of cluster analysis, 2dTCA, for identifying and characterizing peak fluctuations in fMRI BOLD signals in the temporal lobes and the default-mode network in temporal lobe epilepsy (TLE) without EEG.

METHODS

BOLD fMRI images were acquired in 17 TLE patients and compared to EEG. The timing of significant transient BOLD peaks was estimated by 2dTCA, and activation maps were determined.

RESULTS

Sixteen subjects (94%) showed apparent temporal lobe activation. Mesial temporal activation was present in 76.4% (13 patients). Temporal lobe or insula activations were detected ipsilateral to the EEG focus in 64.7% (11 patients), bilaterally with no predominance in 29.4% (5 patients), and exclusively contralateral to the EEG focus in none. Eleven subjects showed activation in the so-called default-mode network including posterior cingulate, bilateral posterior parietal cortex, and sometimes anterior cingulate cortex.

CONCLUSIONS

These results demonstrate significant positive BOLD fluctuations in the temporal lobes and default-mode regions in a higher percentage of TLE patients than previously reported using other methods. These fluctuations appear physiologically relevant and suggest increased neural activity which may not be detected on scalp EEG, but which may be important in understanding the mechanisms and origins of epileptic discharges.