Drawing from the poly-substance characteristics of the subjects considered in this study, one important objective of this work was to detect dFNC differences linked to combinations of the three commonly used substances. In general, the data were rich enough to allow for a match of substance use and dFNC characteristics acquired through appropriate linear combinations. These linear combinations do not point to a particular gray matter region, but represent the aggregated contribution of brain areas spanning the whole brain. Figure 4a–f illustrates 2 out of the 7 available data dimensions with significant information about differences in the 741 dFNC measures. These scatter plots show that each substance or their combined use has separate and identifiable effects in the brain. The difficulty in interpreting MANOVA results comes from its multivariate and higher dimensional features. As an outcome of the aggregation of connectivity through the whole brain, it is reasonable to compare the results with a global measure of connectivity. Figure 4h presents an interpretable visualization of the results based on global connectivity strength measure. Connectivity differences could be seen at the global level that we discuss in the following paragraphs along with more localized effects.

The overall effect of alcohol is a reduction of dFNC, especially among motor and sensorial areas. Figure 3 shows evidence that alcohol drinkers avert state 2, where there is a strong connectivity among sensorimotor, salience, and precuneus brain areas. The lack of preference for state 2 is confirmed by the negative regression coefficient that was found significant for the occupancy rate of state 2 in Table 2. The connectivity pattern in state 2 is in line with the hypothesis of an extrospective mind state within the resting state, providing readiness in case attention to outside stimuli is necessary (Fransson, 2005). In contrast, there is an inflated preference for state 4, where the mentioned brain areas are weakly connected. Not only the dFNC state preference is different for drinkers, but also connectivity strength. Figure 4h presents additional connectivity reduction in most states including states 2 and 4. In addition to whole brain connectivity data, univariate analyses identified reduced dFNC in drinkers between visual and motor areas, agreeing with the multivariate outcomes. Linear model results in this study provide previously missing evidence (Vergara et al, 2017a) of a significant link between alcohol effects in functional connectivity with alcohol use measures; specifically, the AUDIT score. This set of consistent results observed in dFNC also agrees with former studies of static functional connectivity. A previous study found a reduction of static functional connectivity primarily among the insula, precuneus, sensorimotor, and visual areas, but an increase on the putamen after testing for group differences against nondrinkers (Vergara et al, 2017a). These outcomes support both the ‘disconnection syndrome’ (Dupuy and Chanraud, 2016) and the reduced interoception effect (Çöl et al, 2016) related to alcohol use. Alcohol-related functional disconnection has been reported by several studies (Weiland et al, 2014) as associated with drinking, abstinence, and relapse (Camchong et al, 2013a, b) and loss of network efficiency in the brain (Sjoerds et al, 2017). This and the previously mentioned studies only presented evidence of functional disconnection, but structural studies have also indicated decreased white matter integrity (Jansen et al, 2015; Kril et al, 1997; Yeh et al, 2009); this helps explain the overall extent of the disconnection syndrome. One important point is that observed functional disconnection is more prominent in state 2, where salient brain areas in the insula (see the insula in Figure 1 and state 2 in Figure 2) are strongly connected with other brain regions. The insula is actively involved in interoception because it is a structure that processes the physiological condition of the rest of the body (Craig, 2003). The results are in accordance with the idea of a reduced interoception because of the disconnection produced by alcohol use. Evidence of reduced iteroception awareness related to alcohol use has been previously presented (Çöl et al, 2016) by means of a heartbeat perception performance method. In addition to alcohol, our data suggest that marijuana consumption may produce aberrant interoception patterns because of an increased occupancy rate in state 4 (see Figure 3) where the insula is weakly connected to the rest of the brain. Connectivity strength was also reduced in marijuana subjects within state 4 (see Figure 4); however, state 2 presents the opposite effect with different outcome than alcohol drinkers. It has been theorized that aberrant interoception is an effect that belongs to all addiction in general (Verdejo-Garcia et al, 2012). In this respect, our data suggest that alcohol may produce one of the largest aberrant interoception among the substances of abuse. We can turn our attention to state 1, where univariate analysis found a decrease in dFNC between a frontal area of the ECN and postcentral gyrus; both task-positive networks (TPNs). TPNs are brain networks elicited to perform demanding tasks (Fox et al, 2005). Aberrant connectivity in the ECN has been suggested as a contributing factor in sustaining alcohol addiction (Weiland et al, 2014). Group differences found in our analyses agree in part with predictions of the network model of addiction where resting state connectivity among TPNs and between salience and TPNs are reduced after substance use (Sutherland et al, 2012). In summary, alcohol use produces a general resting state functional disconnection that is harsh on TPNs (including sensorimotor and executive control areas) and the insula, a region important for interoceptive functions.

Concurrent nicotine and alcohol consumption did not have an effect in occupancy rates. The SAD group exhibited similar connectivity strength as the DRN group in states 2 and 4. There was no similar trend between SMK and SAD groups. In states 1 and 3, the two groups had connectivity strength similar to controls indicating the absence of an important effect. The similarity of effect between populations that drink and those that concurrently smoke and drink has been observed before in studying static FNC (Vergara et al, 2017a). In that static FNC study, there was a sparing of high visual areas in SAD subjects. A similar outcome was observed in Figure 5, where the connectivity between motor and a high visual processing area was reduced in drinkers, but was unaffected in SAD subjects. This outcome could be related to the activation enhancement of high visual areas produced by nicotine (Ghatan et al, 1998; Lawrence et al, 2002). The effect of nicotine on alcohol drinkers might have diminished dysfunctional connectivity because of alcohol, but future research is needed to verify the existence of this effect.

Nicotine outcomes were fewer overall, but with some relatively large effect sizes. Although no group differences in occupancy rate were found in relation to the SMK group, significant associations with FTQ were observed in the linear model. Higher occupancy rates in state 5 were linked to larger FTQ values. Lower occupancy rates in state 6 were associated with larger FTQ. The main difference between these two states is the higher connectivity of the ECN and the DMN in state 6 as compared with state 5, suggesting that nicotine reduces connectivity of the ECN and the DMN. Connectivity strength results in Figure 4 show a tendency for connectivity reduction in states 2, 5, and 6 when comparing SMK and CTR groups, favoring a reduced connectivity because of nicotine use. However, connectivity strength has a contrasting picture of increased connectivity in state 3 and no difference with controls in states 1 and 4. These contrasting results are not completely unexpected as nicotine has been found to produce both increased and decreased connectivity in some brain areas, including the frontoparietal network and the DMN (Pariyadath et al, 2014). Univariate analysis produced a more conclusive set of outcomes. ANOVA results in Figure 5 show a significant dFNC decrease in smokers compared with controls between sensorimotor and dorsal striatum areas in state 4. Reduced dFNC in nicotine users is further verified by significant links between FTQ and striatal-sensorimotor connectivity displayed in Table 3 with large effect sizes characterized by percentages of variance explained between 13% and 22%. To the best knowledge of the authors, this is one of the few times a strong effect of resting state functional connectivity has been observed in the dorsal striatum linked to nicotine. Static connectivity analysis using seed-based methods found that smokers during an abstinent period of 24 h exhibit decreased connectivity between dorsal striatum and cortical regions that include the supplementary motor area (Sweitzer et al, 2016). Although the ventral striatum is more frequently associated with nicotine addiction because of its role in the dopamine pathway (Brody et al, 2004; Okita et al, 2016), the dorsal striatum is thought to become a more important player as drug seeking transitions from voluntary to habitual behavior (Everitt and Robbins, 2005). This transition has been suggested to be present in abstinent nicotine smokers as an underlying mechanism that suppresses some automated habitual conduct in favor of diverting resources to craving and nicotine seeking behavior (Sweitzer et al, 2016; Tiffany and Conklin, 2000). There is also evidence that the dorsal, and not the ventral, striatum suffer morphological changes (volume and surface area) associated with nicotine craving (Janes et al, 2015). Our data and the previously mentioned studies support the existence of structural and functional connectivity changes in the dorsal striatum (putamen and caudate) linked to nicotine use and dependence.

The connectivity strength of marijuana subjects was larger than controls in states 1, 2, 3, and 5, but lower with a smaller magnitude in the other two states as displayed in Figure 4h. This result indicates that marijuana induces a stronger increment of connectivity through the brain, in selected dFNC states, than decrements. The effects were observed in a whole brain connectivity summary, but were not observable when selecting specific brain areas. Figure 5 illustrates this observation in the boxes comparing the CTR group with the rest. It can be argued that two factors contributed to the small number of marijuana results: (1) the large number of comparisons that were corrected and (2) the small number of marijuana subjects. Nevertheless, multivariate group results were observed as they are based on the linear combination of contributions from many group differences that were excluded if applying statistical multicomparison correction. Increased functional connectivity in cannabis users compared with controls has been previously reported in areas including the orbitofrontal cortex (Filbey et al, 2014); precentral, middle frontal, superior frontal, cingulate, inferior frontal, and fusiform giri (Cheng et al, 2014); and posterior cingulate and insula (Pujol et al, 2014). These observations of functional connectivity increments, including that in our data, are not expressions of beneficial effects. Structural studies found a series of axonal impairment in the hippocampus (fornix), splenium, commissural fibers (Zalesky et al, 2012), and morphological changes in the amygdala (Cousijn et al, 2012), cerebellum (Cousijn et al, 2012; Medina et al, 2010), and prefrontal cortex (Medina et al, 2009). One hypothesis that can explain why increased functional connectivity might point to an actual dysfunction suggests interference of some brain network in the normal function of others (Sutherland et al, 2012). This idea has been used to explain increased connectivity in key areas of the default mode network as a detrimental effect on the brain (Pujol et al, 2014).

As previously explained, both marijuana and alcohol consumptions are linked to changes of structural connectivity (Jansen et al, 2015; Zalesky et al, 2012), but affect functional connectivity in opposite directions as alcohol decreases (Camchong et al, 2013b; Vergara et al, 2017a; Weiland et al, 2014) and marijuana increases (Cheng et al, 2014; Filbey et al, 2014; Pujol et al, 2014) overall connectivity. Thinking in an additive way, the effect of concurrent use of these substances may subtract each other and this trend should be observable in the MAD sample group. Connectivity strength measures in Figure 4h support this view, giving the trend of the MAD group to be closer to controls (CTR group) in states 2 and 5, where the MAR group has increased but the DRN group decreased connectivity. Univariate ANOVA results in Figure 5 show a MAD group with higher connectivity than the DRN group in state 1, suggesting that alcohol reduced connectivity between postcentral and inferior frontal gyrus, but mixed alcohol and marijuana diminished the effect of alcohol. A similar trend in higher connectivity in MAD vs DRN groups is observed in state 4. Increased connectivity in MAD compared with DRN groups could in part be explained by reported increments of structural connectivity in the prefrontal cortex linked to marijuana use (Filbey et al, 2014). More evidence can be found comparing the brain of adolescents, where subjects that binge drink and consume marijuana exhibited less white matter alterations than those who only consumed alcohol (Jacobus et al, 2009). Even though a subtractive effect is plausible, the consequences do not translate into beneficial outcomes. Comorbid alcohol and marijuana consumption does have a toll in neurocognitive abilities including verbal learning, memory, attention, processing speed, visuospatial functioning, and cognitive control (Squeglia and Gray, 2016). The opposite trend between alcohol and cannabis is not an indication that detrimental neurocognitive effects will diminish because of concurrent use.

The results obtained for combined nicotine and marijuana show connectivity effects that contrast with single substance use. Connectivity strength in state 4 is dramatically increased compared with all of the other samples groups. In states 1 and 6, the connectivity strength of the SAM group is lower than all other groups. These connectivity differences do not follow an obvious trend, nor an additive effect, when compared with the MAR and SMK groups. The chemistry of combined marijuana nicotine use is characterized by an increase of nicotinic acetylcholine receptor (nAChR) availability in the prefrontal cortex and the thalamus as compared with single nicotine use (Brody et al, 2016). In the same work, this interaction thought to occur at the cell molecular level was also found in mixed nicotine caffeine consumption. Availability of nAChRs modulate whole brain connectivity measures such as global network efficiency that measures the efficiency of information transfer through the brain (Wylie et al, 2012). In similar fashion, chemical interactions of nicotine and marijuana may have potentiated the variety of global connectivity strength effects that are seen in Figure 4h. These outcomes must be interpreted in the context of whole brain analysis and cannot be used to describe more specific effects of each substance. Univariate ANOVA outcomes of state 4 (see Figure 5) are compatible with a difference; specifically, an increment of connectivity in comorbid marijuana and nicotine use as compared with single substance use. However, the SAM group did not show differences with controls, indicating that observed combined vs single use effects are not simple to explain.

Up to this point, we can observe that some states are more affected by certain substances. For example, alcohol has a consistently strong influence in state 2. Marijuana produced a large increase of connectivity strength in states 1, 2, and 3. Nicotine produced a large effect size between dorsal striatum and sensorimotor areas in state 4. We can observe that the ALL group influenced by all three substances might follow the trend of one of the three substances on different states. In state 1, ALL and MAR groups had a similar connectivity strength. This increment in the ALL could also be seen in the univariate results for state 1 (shown in Figure 5) and is an opposite effect to the decrease connectivity in the DRN group in that state. If the interaction of marijuana and alcohol can be thought of as additive, then marijuana could have a stronger influence than alcohol in that state. Both multivariate and univariate results agree that alcohol is the stronger influence in state 2. In Figure 4h, the ALL group may not have achieved the same decrement of connectivity strength as that seen with the SAD and DRN groups because of the influence of marijuana. It is noteworthy that the MAR group had a very strong increment of connectivity strength. Note that ALL and MAD groups showed similar connectivity strength. The univariate results for state 2 in Figure 5 are consistent with a decrement of connectivity in the DRN and ALL groups, suggesting that alcohol was the most influencing substance. With respect to nicotine, only the univariate results show a consistent similarity between ALL and SMK groups in state 4, suggesting that nicotine was the most influential substance. The effect was strong in specific areas of the brain, dorsal striatum, and sensorimotor areas, but was not observed when analyzing the connectivity strength. We observed effects that were more focused than global effects related to nicotine.

An important limitation of this study was the disparity on the number of samples, where there is a relatively large number of alcohol users, but a low number of marijuana users. Although the low number of marijuana users allowed the observation of effects using MANOVA, the low statistical power was more evident when analyzing single dFNCs. Many univariate results with marijuana effects were excluded after multicomparison correction, but the P-values were close to being significant after FDR. Unfortunately, subjects suitable for single marijuana consumption are difficult to find and, along with the need for correcting over a large amount of comparisons, causes a considerable limitation of statistical power. Nevertheless, multivariate analysis picked up strong signals from whole brain marijuana effects because it combined the available information. The MAR group shows similar reduction of occupancy rate with the DRN and MAD subjects in state 2, but this reduction did not achieve significance mainly because of the small number of subjects in MAR. However, the observed trend is compatible among these three sample groups, indicating that alcohol and cannabis might have the same effects on the occupancy rate of state 2. In state 4, the MAR group exhibits an even higher mean occupancy than DRN (group that differ from CTR), but high variability of dFNC from MAR samples decremented detection power. The second important limitation was the lack of covariate measures for the CTR group. Although AUDIT was missing from this group, several studies indicate that AUDIT and DSM-IV have similar specificity (Dawson et al, 2012; Foxcroft et al, 2015), providing evidence that subjects in the CTR group were correctly classified. However, the same cannot be inferred for the other important measures of BID, BAI, ImpSS, and Income. For this reason, linear correlation analysis was limited to the substance user. Third, the small number of single dFNC findings likely reflects only the strongest effects. Other existing effects, such as those observed by hypotheses-driven techniques (Chanraud et al, 2011; Janes et al, 2012), may be missed. The small number within the MAR group plays a role in this limitation.