Spatiotemporal dependency of age-related changes in brain signal variability.

Recent theoretical and empirical work has focused on the variability of network dynamics in maturation. Such variability seems to reflect the spontaneous formation and dissolution of different functional networks. We sought to extend these observations into healthy aging. Two different data sets, one EEG (total n = 48, ages 18-72) and one magnetoencephalography (n = 31, ages 20-75) were analyzed for such spatiotemporal dependency using multiscale entropy (MSE) from regional brain sources. In both data sets, the changes in MSE were timescale dependent, with higher entropy at fine scales and lower at more coarse scales with greater age. The signals were parsed further into local entropy, related to information processed within a regional source, and distributed entropy (information shared between two sources, i.e., functional connectivity). Local entropy increased for most regions, whereas the dominant change in distributed entropy was age-related reductions across hemispheres. These data further the understanding of changes in brain signal variability across the lifespan, suggesting an inverted U-shaped curve, but with an important qualifier. Unlike earlier in maturation, where the changes are more widespread, changes in adulthood show strong spatiotemporal dependence.

Hippocampal signal complexity in mesial temporal lobe epilepsy: a noisy brain is a healthy brain.

Patients with mesial temporal lobe epilepsy (mTLE) show structural and functional abnormalities in hippocampus and surrounding mesial temporal structures. Brain signal complexity appears to be a marker of functional integrity or capacity. We examined complexity in 8 patients with intracranial hippocampal electrodes during performance of memory tasks (scene encoding and recognition) known to be sensitive to mesial temporal integrity. Our patients were shown to have right mesial temporal seizure onsets, permitting us to evaluate both epileptogenic (right) and healthy (left) hippocampi. Using multiscale entropy (MSE) as a measure of complexity, we found that iEEG from the epileptogenic hippocampus showed less complexity than iEEG from the healthy hippocampus. This difference was reliable for encoding but not for recognition. Our results indicate that both functional integrity and cognitive demands influence hippocampal signal complexity.

Hippocampal-neocortical networks differ during encoding and retrieval of relational memory: functional and effective connectivity analyses.

Encoding and retrieval of relational information requires interaction between the hippocampus and various neocortical regions, but it is unknown whether the connectivity of hippocampal-neocortical networks is different at input and output stages. To examine this, we conducted a network analysis of event-related fMRI data collected during a face-recognition, remember/know paradigm. Directed analyses in the medial temporal lobe identified a small region in the left hippocampus that showed differential activation for encoding and retrieval of recollected versus familiar items. Multivariate seed partial least squares (PLS) analysis was used to identify brain regions that were functionally connected to this hippocampal region at encoding and retrieval of 'remembered' items. Anatomically based structural equation modeling (SEM) was then used to test for differences in effective connectivity of network nodes between these two memory stages. The SEM analysis revealed a reversal of directionality between the left hippocampus (LHC) and left inferior parietal cortex (LIPC) at encoding and retrieval. During encoding, activation of the LHC had a positive influence on the LIPC, whereas during retrieval the reverse pattern was found, i.e., the LIPC activation positively influenced LHC activation. These findings emphasize the importance of hippocampal-parietal connections and underscore the complexity of their interactions in initial binding and retrieval/reintegration of relational memory. We also found that, during encoding, the right hippocampus had a positive influence on the right retrospenial cortex, whereas during retrieval this influence was significantly weaker. We submit that examining patterns of connectivity can be important both to elaborate and constrain models of memory involving hippocampal-neocortical interactions.

Spatiotemporal analysis of event-related fMRI data using partial least squares.

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of spatiotemporal PLS (ST-PLS) to fMRI, explaining the theoretical foundation and application to an fMRI study of auditory and visual perceptual memory. Analysis of activation effects with ST-PLS was compared with conventional univariate random effects analysis, showing general consensus for both methods, but several unique observations by ST-PLS, including enhanced statistical power. The application of ST-PLS for assessment of task-dependent brain-behavior relationships is also presented. Singular features of ST-PLS include (1) no assumptions about the shape of the hemodynamic response functions (HRFs); (2) robust statistical assessment at the image level through permutation tests; (3) protection against outlier influences at the voxel level through bootstrap resampling; (4) flexible analytic configurations that allow assessment of activation difference, brain-behavior relations, and functional connectivity. These features enable ST-PLS to act as an important complement to other multivariate and univariate approaches used in neuroimaging research.