Relationship between MEG global dynamic functional network connectivity measures and symptoms in schizophrenia.

An investigation of differences in dynamic functional network connectivity (dFNC) of healthy controls (HC) versus that of schizophrenia patients (SP) was completed, using eyes-open resting state MEG data. The MEG analysis utilized a source-space activity estimate (MNE/dSPM) whose result was the input to a group spatial independent component analysis (ICA), on which the networks of our MEG dFNC analysis were based. We have previously reported that our MEG dFNC revealed that SP change between brain meta-states (repeating patterns of network correlations which are allowed to overlap in time) significantly more often and to states which are more different, relative to HC. Here, we extend our previous work to investigate the relationship between symptomology in SP and four meta-state metrics. We found a significant correlation between positive symptoms and the two meta-state metrics which showed significant differences between HC and SP. These two statistics quantified 1) how often individuals change state and 2) the total distance traveled within the state-space. We additionally found that a clustering of the meta-state metrics divides SP into groups which vary in symptomology. These results indicate specific relationships between symptomology and brain function for SP.

Brain structure and verbal function across adulthood while controlling for cerebrovascular risks.

The development and decline of brain structure and function throughout adulthood is a complex issue, with cognitive aging trajectories influenced by a host of factors including cerebrovascular risk. Neuroimaging studies of age-related cognitive decline typically reveal a linear decrease in gray matter (GM) volume/density in frontal regions across adulthood. However, white matter (WM) tracts mature later than GM, particularly in regions necessary for executive functions and memory. Therefore, it was predicted that a middle-aged group (MC: 35-45 years) would perform best on a verbal working memory task and reveal greater regional WM integrity, compared with both young (YC: 18-25 years) and elder groups (EC: 60+ years). Diffusion tensor imaging (DTI) and magnetoencephalography (MEG) were obtained from 80 healthy participants. Objective measures of cerebrovascular risk and cognition were also obtained. As predicted, MC revealed best verbal working memory accuracy overall indicating some maturation of brain function between YC and MC. However, contrary to the prediction fractional anisotropy values (FA), a measure of WM integrity, were not greater in MC (i.e., there were no significant differences in FA between YC and MC but both groups showed greater FA than EC). An overall multivariate model for MEG ROIs showed greater peak amplitudes for MC and YC, compared with EC. Subclinical cerebrovascular risk factors (systolic blood pressure and blood glucose) were negatively associated with FA in frontal callosal, limbic, and thalamic radiation regions which correlated with executive dysfunction and slower processing speed, suggesting their contribution to age-related cognitive decline. Hum Brain Mapp 38:3472-3490, 2017. © 2017 Wiley Periodicals, Inc.

Characterization of a normal control group: are they healthy?

We examined the health of a control group (18-81years) in our aging study, which is similar to control groups used in other neuroimaging studies. The current study was motivated by our previous results showing that one third of the elder control group had moderate to severe white matter hyperintensities and/or cortical volume loss which correlated with poor performance on memory tasks. Therefore, we predicted that cardiovascular risk factors (e.g., hypertension, high cholesterol) within the control group would account for significant variance on working memory task performance. Fifty-five participants completed 4 verbal and spatial working memory tasks, neuropsychological exams, diffusion tensor imaging (DTI), and blood tests to assess vascular risk. In addition to using a repeated measures ANOVA design, a cluster analysis was applied to the vascular risk measures as a data reduction step to characterize relationships between conjoint risk factors. The cluster groupings were used to predict working memory performance. The results show that higher levels of systolic blood pressure were associated with: 1) poor spatial working memory accuracy; and 2) lower fractional anisotropy (FA) values in multiple brain regions. In contrast, higher levels of total cholesterol corresponded with increased accuracy in verbal working memory. An association between lower FA values and higher cholesterol levels were identified in different brain regions from those associated with systolic blood pressure. The conjoint risk analysis revealed that Risk Cluster Group 3 (the group with the greatest number of risk factors) displayed: 1) the poorest performance on the spatial working memory tasks; 2) the longest reaction times across both spatial and verbal memory tasks; and 3) the lowest FA values across widespread brain regions. Our results confirm that a considerable range of vascular risk factors are present in a typical control group, even in younger individuals, which have robust effects on brain anatomy and function. These results present a new challenge to neuroimaging studies both for defining a cohort from which to characterize 'normative' brain circuitry and for establishing a control group to compare with other clinical populations.

MEG-SIM: a web portal for testing MEG analysis methods using realistic simulated and empirical data.

MEG and EEG measure electrophysiological activity in the brain with exquisite temporal resolution. Because of this unique strength relative to noninvasive hemodynamic-based measures (fMRI, PET), the complementary nature of hemodynamic and electrophysiological techniques is becoming more widely recognized (e.g., Human Connectome Project). However, the available analysis methods for solving the inverse problem for MEG and EEG have not been compared and standardized to the extent that they have for fMRI/PET. A number of factors, including the non-uniqueness of the solution to the inverse problem for MEG/EEG, have led to multiple analysis techniques which have not been tested on consistent datasets, making direct comparisons of techniques challenging (or impossible). Since each of the methods is known to have their own set of strengths and weaknesses, it would be beneficial to quantify them. Toward this end, we are announcing the establishment of a website containing an extensive series of realistic simulated data for testing purposes ( http://cobre.mrn.org/megsim/ ). Here, we present: 1) a brief overview of the basic types of inverse procedures; 2) the rationale and description of the testbed created; and 3) cases emphasizing functional connectivity (e.g., oscillatory activity) suitable for a wide assortment of analyses including independent component analysis (ICA), Granger Causality/Directed transfer function, and single-trial analysis.

Development and decline of memory functions in normal, pathological and healthy successful aging.

Many neuroimaging studies of age-related memory decline interpret resultant differences in brain activation patterns in the elderly as reflecting a type of compensatory response or regression to a simpler state of brain organization. Here we review a series of our own studies which lead us to an alternative interpretation, and highlights a couple of potential confounds in the aging literature that may act to increase the variability of results within age groups and across laboratories. From our perspective, level of cognitive functioning achieved by a group of elderly is largely determined by the health of individuals within this group. Individuals with a history of hypertension, for example, are likely to have multiple white matter insults which compromise cognitive functioning, independent of aging processes. The health of the elderly group has not been well-documented in most previous studies and elderly participants are rarely excluded, or placed into a separate group, due to health-related problems. In addition, recent results show that white matter tracts within the frontal and temporal lobes, regions critical for higher cognitive functions, continue to mature well into the 4th decade of life. This suggests that a young age group may not be the best control group for understanding aging effects on the brain since development is ongoing within this age range. Therefore, we have added a middle-age group to our studies in order to better understand normal development across the lifespan as well as effects of pathology on cognitive functioning in the aging brain.