The spatial coherence of functional activity is an intrinsic property of brain function that can be used for hemispheric lateralization of function within functionally relevant ROI. In this paper, an fMRI data coherence based laterality index (CLI) was evaluated as a tool to assess function laterality in both sensorimotor cortex and the mesial temporal lobe.
In sensorimotor cortex during a unilateral motor task, the CLI within MC correctly lateralized to the activated hemisphere. Although functional stimulation generally increased the coherence in brain regions regardless of whether those regions were involved in the functional task or not, markedly increased coherence laterality was only observed in the task-associated functional regions (MC and FC for the sensorimotor task), suggesting a task-specific effect. This effect was also subtly detected within the hemispheric ROI.
CLI calculated within HPF in control subjects using both resting data and sensorimotor data showed a symmetric distribution, suggesting no prominent coherence laterality in HPF of the normal brain. A symmetrical distribution was also found in the HPF CLI calculation using normal subjects’ scene-encoding fMRI data, suggesting no functional lateralization in HPF of the normal brain during the scene-encoding task.
Using scene-encoding data acquired from patients with unilateral TLE, HPF CLI showed functional memory lateralization away from the seizure side (or the IAT memory laterality since they were the same for the patient cohort assessed in this paper) for both the left side and the right side unilateral epilepsy patients. CLI in HPF of the right side patient group was significantly greater than 0, meaning the brain activity coherence in the left side of HPF was significantly larger than the right side (seizure side). As compared to GLMLI, CLI provided improved correlations with IAT memory laterality for the right side seizure group and for differentiating the right sided group from the left sided group. CLI performance for the left sided patient group was dominated by one outlier. Similar results were also obtained when comparing CLI with a probability weighted GLMLI (where the T value of each voxels within the functional ROI was weighted by 1-2P (P is the p value of that voxel), and the laterality was then determined by comparing the area under the weighted T value distribution curve from the left and right side of functional region) (Branco et al., 2006
). For simplicity of description, we only showed the comparison results with the ratio-based GLMLI (Rabin et al., 2004a
) in this paper. The improved performance of CLI as compared to GLMLI may be attributable to its data-driven property. The coherence measure does not require prior brain response modeling based on an a priori hemodynamic response function that may be somewhat inaccurate for pathological brain.
Variations of the functional ROI are a potential source of variability for CLI calculation. Although CLI could be altered by the size of ROIs, additional CLI calculations using the normal controls’ sensorimotor data with different sizes of MC ROIs demonstrated that CLI could still yield a correct function laterality, even when using a hemispheric ROI. We selected HPF ROI for CLI calculation during scene encoding mainly because we have empirically found this larger medial temporal ROI provides more reproducible GLMLI for memory lateralization for TLE patients (Detre et al., 1998
; Rabin et al., 2004a
). Our results showed that CLI with HPF was able to predict the side of seizure laterality and corresponding IAT memory laterality. As with GLMLI, performance of CLI for predicting seizure laterality and IAT memory laterality was markedly degraded when using data from a much smaller hippocampal (HIPP) ROI, likely reflecting inadequate sensitivity in this small ROI which is further degraded by susceptibility artifacts (Ojemann et al., 1997
). In a control brain region, the basal ganglia, CLI did not show a biased distribution for all patients and controls. For all assessed target functional or control regions, CLI yielded symmetric distributions for the normal controls performing the same scene-encoding fMRI task.
Note that in our patient cohort, IAT memory laterality was always contralateral to seizure laterality. Therefore, it was impossible to assess the relative sensitivity of CLI for lateralizing the seizure focus versus memory activity. The effects of seizure laterality independent of memory activity would be ideally assessed in resting fMRI data, though mesial temporal memory activity may persist during a resting task (Stark and Squire, 2000
). In this study, removal of task effects from scene-encoding fMRI data was used to assess the utility of CLI for memory lateralization from pseudo-resting data, since true resting data was not available from the epilepsy patient cohort. Interestingly, CLI still correctly lateralized the seizure side for all but one patient of the right side group, and it was significantly greater than 0 (P=0.039) in the right side group. Further verification of this potential using actual resting data and a larger sample size is acquired.
Spatial smoothing applied during data preprocessing could alter measured spatial coherences. To minimize this effect, a modest isotropic smoothing kernel with an FWHM of 6 mm was used in this paper to improve the spatial SNR of fMRI data. As previously demonstrated (Kriegeskorte et al., 2006
), this kernel size should not alter the coherence measurement. Furthermore, this smoothing kernel is much smaller than any of the ROIs assessed in this paper.
As a pure data-driven method, CLI may vary with the length of the acquired fMRI time series. The calculated temporal CLI convergence curve showed that an fMRI data series longer than 100 timepoints is enough to get a robust estimation of CLI, which subsequently guaranteed an effective coherence laterality determination with the data used in this paper, since all of them were longer than 100. Our simulations with 84 fMRI images showed that CLI could still indicate the correct size of the lateralized motor task for normal subjects and determine memory laterality for patients with seizure lateralized to the left (data not shown); GLMLI had better performance for memory lateralization for epilepsy patients with left sided focal seizures but worse performance for the right sided patient group and for differentiating the left side group from the right side group.
Since CLI and GLMGI reflect different properties of the data, it is possible if not likely that they can be combined to increase specificity. Indeed, in clinical practice lateralization of seizures or brain function is typically based on consideration of a broad range of complementary data. Our data demonstrated that improved lateralization could be potentially achieved by combining GLMLI and CLI.
Although these preliminary findings are promising, the application to memory lateralization for epilepsy patients was limited by the small number of patients assessed and the lack of a true “gold standard” for memory lateralization. CLI was more effective than GLMLI only for the right sided TLE group, and its performance for the left side patient group was largely affected by one outlier. A much larger sample size of TLE patients including careful documentation of post-surgical memory change following temporal lobectomy would be required to further verify the usefulness of CLI for memory lateralization. Resting fMRI data from unilateral epilepsy patients would also be required to assess the feasibility of using CLI to determine the memory (or other) functional laterality from the resting brain. The effects of smoothing on CLI calculations should also be assessed in future work.