Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses.

Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise.

Splendor in the Grass? A Pilot Study Assessing the Impact of Medical Marijuana on Executive Function.

Currently, 25 states and Washington DC have enacted full medical marijuana (MMJ) programs while 18 states allow limited access to MMJ products. Limited access states permit low (or zero) tetrahydrocannabinol (THC) and high cannabidiol (CBD) products to treat specified conditions such as uncontrolled epilepsy. Although MMJ products are derived from the same plant species as recreational MJ, they are often selected for their unique cannabinoid constituents and ratios, not typically sought by recreational users, which may impact neurocognitive outcomes.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals.

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain.

Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type.

Blood-oxygen-level dependent (BOLD) signals are widely used in functional magnetic resonance imaging (fMRI) as a proxy measure of brain activation. However, because these signals are blood-related, they are also influenced by other physiological processes. This is especially true in resting state fMRI, during which no experimental stimulation occurs.