The Impact of Combinations of Alcohol, Nicotine, and Cannabis on Dynamic Brain Connectivity

Abstract

Alcohol, nicotine, and cannabis are among the most commonly used drugs. A prolonged and combined use of these substances can alter normal brain wiring in different ways depending on the consumed cocktail mixture. Brain connectivity alterations and their change with time can be assessed using functional magnetic resonance imaging (fMRI) because of its spatial and temporal content. Here, we estimated dynamic functional network connectivity (dFNC) as derived from fMRI data to investigate the effects of single or combined use of alcohol, nicotine, and cannabis. Data from 534 samples were grouped according to their substance use combination as controls (CTR), smokers (SMK), drinkers (DRN), smoking-and-drinking subjects (SAD), marijuana users (MAR), smoking-and-marijuana users (SAM), marijuana-and-drinking users (MAD), and users of all three substances (ALL). The DRN group tends to exhibit decreased connectivity mainly in areas of sensorial and motor control, a result supported by the dFNC outcome and the alcohol use disorder identification test. This trend dominated the SAD group and in a weaker manner MAD and ALL. Nicotine consumers were characterized by an increment of connectivity between dorsal striatum and sensorimotor areas. Where possible, common and separate effects were identified and characterized by the analysis of dFNC data. Results also suggest that a combination of cannabis and nicotine have more contrasting effects on the brain than a single use of any of these substances. On the other hand, marijuana and alcohol might follow an additive effect trend. We concluded that all of the substances have an impact on brain connectivity, but the effect differs depending on the dFNC state analyzed.

Classification of cocaine‐dependent participants with dynamic functional connectivity from functional magnetic resonance imaging data

“…Alcohol-dose effects on brain activation were explored using ICA to isolate systematically nonoverlapping networks and their time courses (Calhoun, Pekar, & Pearlson, 2004). ICA and DFC analyses have been recently used to study alcohol, nicotine, and marijuana dependence (Vergara, Weiland, Hutchison, & Calhoun, 2018).…”

“…Alcohol‐dose effects on brain activation were explored using ICA to isolate systematically nonoverlapping networks and their time courses (Calhoun, Pekar, & Pearlson, ). ICA and DFC analyses have been recently used to study alcohol, nicotine, and marijuana dependence (Vergara, Weiland, Hutchison, & Calhoun, ). Networks found with ICA have been further used to study DFC characteristics in schizophrenia patients both with task (auditory oddball task) (Sakoglu & Calhoun, ; Sakoglu et al, ) and at rest (Sakoğlu & Calhoun, ; Sakoğlu, Michael, & Calhoun, ).…”

Differential associations of combined vs. isolated cannabis and nicotine on brain resting state networks

“…A widely utilized approach of looking at the brain’s resting state networks (RSNs) provides baseline information on the brain’s functional network architecture based on the temporal correlations of spatially distributed brain regions in the absence of a task (Biswal et al 1997). Existing studies in primarily cannabis and nicotine users indicate opposing effects of each substance (Subramaniam et al 2016), including in RSNs (Vergara et al 2018). For example, increased connectivity has been reported in cannabis users (Pujol et al 2014; Filbey et al 2014), whereas reduced connectivity has been observed in nicotine users (Weiland et al 2015).…”

An average sliding window correlation method for dynamic functional connectivity

“…The presence of artifacts in the form of spurious fluctuations was the main concern in this static case. Brain areas and dynamic states were extracted from previous work (Vergara et al, 2018). Although there is no ground truth, we can estimate quasi-static, quasi-sharp transitions and fluctuating dFNC using the dynamic state membership functions of each case.…”

“…Displayed coordinates are in MNI space. Brain areas and dynamic states were extracted from previous work (Vergara et al, 2018). Each row uses data from a different subject where the corresponding pattern was observed.…”