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. Author manuscript; available in PMC: 2026 Mar 25.
Published in final edited form as: Drug Alcohol Depend. 2025 Jan 30;268:112577. doi: 10.1016/j.drugalcdep.2025.112577

Effects of alcohol and cannabis co-use on salience network resting state functional connectivity in individuals who drink alcohol heavily

Kaitlin R McManus 1, Malia Belnap 1,2, Dylan E Kirsch 1, Lara A Ray 1,3,4, Erica N Grodin 1,3,4
PMCID: PMC13011813  NIHMSID: NIHMS2154095  PMID: 39929057

Abstract

Introduction:

The salience network may be linked to addiction. Evidence suggests less salience network resting state functional connectivity (rsFC) from heavy alcohol use, but higher rsFC within and between brain networks from regular cannabis use. Given the rise in alcohol-cannabis co-use, the present study sought to elucidate rsFC between regions within the salience network and regions across the whole brain in individuals who use no drugs regularly, those who use alcohol only heavily, and those who co-use alcohol-cannabis.

Methods:

This is a secondary analysis of three clinical laboratory studies. A total of sixty individuals were classified into one of three groups based on their drug use: control (n=16), heavy alcohol use only (n=27), and heavy alcohol and regular cannabis co-use (n=17). All participants completed resting state fMRI scans. Seed regions from the salience network were used to examine group differences in rsFC.

Results:

Main effects of group on rsFC emerged between the anterior cingulate cortex, left and right anterior insula, and left supramarginal gyrus seeds and regions associated with motor, sensory, visual, and executive control functioning (all ps<0.05). Post-hoc analyses revealed less rsFC between alcohol-only and co-use groups as compared to controls (all ps<0.05), but no differences between alcohol-only and co-use groups (all ps>0.05).

Conclusions:

This preliminary study suggests that co-using alcohol-cannabis may not be associated with any additive or contrasting effects on rsFC compared to using alcohol alone. Thus, in individuals who co-use alcohol-cannabis, alcohol may drive neural alterations associated with inhibitory control and substance craving.

Keywords: Alcohol, Cannabis, Co-Use, Salience Network, Resting State

1. Introduction

Alcohol and cannabis co-use is rising in prevalence, with 10.7% of adults 18 years of age and over reporting use of both alcohol and cannabis in 2022 (“2022 National Survey on Drug Use and Health (NSDUH) Releases,” n.d.). Alcohol and cannabis co-use is associated with differences in neural circuitry. Drug-associated neural alterations are especially present within the salience network, which is a brain network that functions to detect and evaluate salient changes in internal and external environments and coordinate this information with other brain networks (Seeley et al., 2007). The incentive-sensitization theory of addiction posits that repeated consumption of a drug leads to neuroadaptation of the salience network, facilitating the transition from drug liking to drug craving or ‘wanting” (Robinson and Berridge, 1993). Indeed, in individuals experiencing an addiction, salience network activity and connectivity between the salience network and other brain regions may be lower in response to non-drug rewarding stimuli but higher in response to drug-related stimuli (Cushnie et al., 2023). Brain regions that are a part of the salience network include the anterior insula, prefrontal cortex, anterior cingulate cortex (ACC), and supramarginal gyrus (SMG; (Seeley et al., 2007; Sobczak et al., 2020)).

Resting state functional connectivity (rsFC) within and between the salience network has been investigated in individuals who drink alcohol and who use cannabis, separately. Within people who drink alcohol heavily (≥8 on Alcohol Use Disorders Identification Test (AUDIT); (Saunders et al., 1993)), there may be hypoconnectivity, or less rsFC, between insula, visual cortex, precuneus, and postcentral gyrus regions (Vergara et al., 2017). Less rsFC in these areas may be tied to decreased awareness of the negative consequences of alcohol, which may potentiate continued alcohol use (Camchong et al., 2013). However, in individuals who use cannabis heavily (>14 uses per week) or regularly (≥15 uses out of past 60 days), evidence suggests greater rsFC within the salience network and within the default mode network, but anticorrelated rsFC between the salience and default mode networks (Pujol et al., 2014; Vergara et al., 2018). This lack of synchronicity is contrasted by evidence that individuals who use cannabis at least weekly have greater rsFC between the left rostral ACC and insula compared to controls (Shollenbarger et al., 2019). Moreover, chronic cannabis use (≥4 times per week) as compared to occasional cannabis use (≤3 times per week) is associated with hyperconnectivity within nodes of the visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, default mode, subcortical, and cerebellar brain networks (Ramaekers et al., 2022). Chronic cannabis use is additionally associated with greater functional connectivity between the limbic network and DMN as well as between the attentional and somatomotor networks (Ramaekers et al., 2022). Therefore, evidence suggests less salience intra- and inter-network rsFC in individuals who drink alcohol heavily, but greater rsFC effects within and between brain networks from regular cannabis use.

There is mixed evidence regarding the effects of co-using alcohol and cannabis on rsFC in the salience network. One study suggests that co-using alcohol and cannabis at least monthly may lead to greater rsFC within the salience network compared to using alcohol alone (Morris et al., 2022), but did not include a control comparison group. One study with a control comparison group suggests that co-using both alcohol heavily (≥8 on AUDIT) and cannabis regularly (≥15 uses out of past 60 days) leads to connectivity patterns similar to those of control individuals who use no drugs (Vergara et al., 2018). In the same study, individuals who used cannabis alone had greater functional connectivity, while individuals who used alcohol alone had less functional connectivity among sensorimotor, salience, default, and executive control networks (Vergara et al., 2018), potentially indicating that the effects of alcohol and cannabis counteracted each other, resulting in comparable rsFC patterns in individuals who co-use and controls. Thus, these mixed findings warrant additional studies utilizing comparison control groups and clinically generalizable (i.e., heavy use) samples to investigate the effects of co-use on rsFC in the salience network.

The present preliminary study sought to elucidate rsFC between regions within the salience network and regions across the whole brain in individuals who use no drugs regularly, those who use alcohol only heavily, and those who use both alcohol heavily and cannabis regularly. Given the proposed role of the salience network in addiction development and maintenance, we investigated the effects of co-use on rsFC between the salience network and the whole brain. In line with the limited evidence suggesting that alcohol and cannabis may have contrasting effects on one another, we hypothesized that rsFC in the salience network of individuals who co-use both alcohol and cannabis would be similar to rsFC patterns in control individuals, whereas individuals who use alcohol only would have less rsFC compared to control individuals.

2. Methods

2.1. Data source and sample

Data for this secondary analysis were culled from three separate clinical laboratory studies. All studies were conducted in the Addictions Laboratory at the University of California, Los Angeles (UCLA). Studies included a pharmacotherapy trial investigating the combination of naltrexone and varenicline (study 1; NCT02698215) (Grodin et al., 2021b; Ray et al., 2021), a pharmacotherapy trial investigating ibudilast (study 2; NCT03594435), and an experimental laboratory study investigating the role of stress and decision making in alcohol use (study 3). Despite the use of participant data from pharmacological studies that involved study medication, all demographic and clinical characteristics used in the present analyses, including co-use status, were collected at baseline prior to any medication administration procedures. Moreover, study medication use was at steady state during the MRI. Specifically, in study 1 (NCT02698215), participants were randomized to receive either the combination of varenicline (1mg twice daily) and naltrexone (50mg once daily) or varenicline (1mg twice daily) and placebo. The MRI in study 1 occurred two weeks after study medication use began. In study 2 (NCT03594435), participants were randomized to receive ibudilast (50mg twice daily) or placebo. The MRI in study 2 occurred four weeks after study medication or placebo use began. All study procedures were approved by the UCLA Institutional Review Board. All participants provided written informed consent. Participants were recruited from the greater Los Angeles area through online, radio, newspaper, and mass transit advertisements, as well as targeted recruitment through a database of previous participants willing to be recontacted for future studies.

All participants completed initial screening procedures via telephone to assess preliminary eligibility. Following, participants completed online or in-person formal screening procedures. Eligibility criteria for participants who drank alcohol heavily in all studies included (1) being between age 18 (study 2 and 3) or 21 (study 1) – 65 and (2) drinking at heavy levels as defined by either drinking >14 (males) or >7 drinks/week (females) in the 30 days prior to screening, meeting current Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) diagnosis of AUD (moderate-to-severe), or having an AUDIT score ≥ 8. Lastly, healthy control participants were recruited from study 3, and their eligibility criteria included (1) being between age 18 – 65, (2) AUDIT < 8, and (3) not meeting criteria for current AUD or previous (past 5-years or lifetime) moderate or severe AUD.

Exclusion criteria for all studies included: (1) DSM-5 lifetime history of a psychotic or bipolar disorder; (2) DSM-5 current drug use disorder (other than alcohol or nicotine); (3) clinically significant alcohol withdrawal, indicated by a score ≥10 on the Clinical Institute Withdrawal Assessment for Alcohol–Revised (CIWA-Ar) (Sullivan et al., 1989), (4) positive urine screen for illicit drugs (except cannabis) or psychoactive medications, and (5) pregnant or nursing (female). Exclusionary criteria for neuroimaging included (1) history of epilepsy, seizures, or severe head trauma, (2) claustrophobia, and (3) non-removable ferromagnetic objects in body.

2.2. Procedures

Participants in all studies completed questionnaires at baseline, prior to medication randomization (studies 1 and 2), that included demographic (age, sex, years of education, race and ethnicity) and clinical assessments. At baseline and all subsequent visits, participants were required to have a breath alcohol concentration (BrAC) of 0.000 g/dL, to have a urine toxicology screen negative for all drugs (except for THC), and to have a CIWA score <10 to indicate that they were not experiencing clinically significant alcohol withdrawal. Clinical assessments included the AUDIT to assess harmful and hazardous drinking, the Fagerstrom Test of Nicotine Dependence (FTND; (Heatherton et al., 1991)) to assess tobacco dependence, and the Cannabis Use Disorder Identification Test (CUDIT; (Adamson et al., 2010)) to assess cannabis use severity. A 30-day Timeline Followback (TLFB; (Sobell and Sobell, 1992)) interview-based assessment was used to capture alcohol, tobacco, and cannabis use over the past 30 days. Cannabis use was measured dichotomously as yes/no use per day. Total drinks, drinks per drinking day (DPDD), total cigarettes, cigarettes per smoking day, and total cannabis use days were calculated based on the TLFB. The Structured Clinical Interview of DSM-5 (SCID; (First, 2015)) was used to assess for current AUD and non-alcohol substance use disorders.

2.3. Co-Use Classification

Participants were classified into one of three groups based on their drug use: (1) control, (2) heavy alcohol use only, and (3) heavy alcohol and regular cannabis co-use. Heavy alcohol use was based on the National Institute on Alcohol Abuse and Alcoholism (NIAAA) heavy drinking definition of >14 (males) or >7 drinks/week (females; (“Drinking Levels Defined | National Institute on Alcohol Abuse and Alcoholism (NIAAA),” n.d.). Classification into each group was determined by the TLFB and corroborated by CUDIT and urine toxicology test for THC. Individuals in the control group reported no past 30-day cannabis use, drinking ≤14 (males) or ≤7 drinks/week (females), AUDIT scores <8, CUDIT scores <8, and negative THC urine toxicology tests. Individuals in the heavy alcohol use only group reported no past 30-day cannabis use and drinking >14 (males) or >7 drinks/week (females), AUDIT scores ≥8, CUDIT scores <8, and negative THC urine toxicology tests. Individuals in the heavy alcohol and regular cannabis co-use group reported ≥10 cannabis use days in the past 30 days and drinking >14 (males) or >7 drinks/week (females), AUDIT scores ≥8, and CUDIT scores ≥8. Though not required for group classification, THC urine toxicology tests were used as corroborating evidence of cannabis use. The AUDIT and CUDIT were not collected for all studies. Therefore, corroborating information regarding alcohol and cannabis use severity were used when available.

2.4. MRI acquisition

Scanning for all three studies took place at the UCLA Center for Cognitive Neuroscience on a 3.0T Siemens Magneton Prisma scanner using a 32-channel head coil. Prior to scanning, participants were required to have a BrAC of 0.000 g/dL and to have a urine toxicology screen negative for all drugs (except for THC). Additionally, females were required to have a negative pregnancy test. Each participant completed a structural scan for registration to resting state functional connectivity data. This structural scan consisted of a T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence (TR = 1,900 ms, TE = 2.26 ms, time to inversion = 900 ms, flip angle = 9°, voxel size = 1.0 mm3, FOV = 250 mm, ~6:50 minutes). Each participant additionally completed a functional resting state scan. Blood oxygenation level-dependent (BOLD) signal was measured with a T2* gradient-echo-planar image sequence (TR = 800 ms, TE= 37.00 ms, voxel size = 2.0 mm3, slice thickness = 2.00 mm, FOV = 208 mm, ~5:02–6:41 minutes). Participants were instructed to remain awake with their eyes open and view a cross hair on a screen during the resting state scan.

2.5. MRI Preprocessing

Structural and preliminary functional preprocessing of fMRI data were completed using fMRIPrep 23.1.4 (RRID:SCR_016216; (Esteban et al., 2019)). Functional preprocessing included generating a reference volume and its skull-stripped version. Following, the preprocessing pipeline included motion correction and nuisance estimation. BOLD reference images were co-registered to the T1w reference (Jenkinson and Smith, 2001)) with the boundary-based registration (Greve and Fischl, 2009) cost-function. The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. Motion outliers were identified for frames that exceeded a threshold of 0.5 mm framewise displacement or 1.5 standardized DVARS. Importantly, no participants were excluded from the analyses for exceeding thresholds (outlined in Supplementary Materials) or having unusable data.

Data were imported into the CONN toolbox (www.nitrc.org/projects/conn; (Whitfield-Gabrieli and Nieto-Castanon, 2012)) for SPM12 further processing (smoothing and denoising) and analysis. Smoothing was completed using a 6mm FWHM Hamming filter (Candemir, 2023). Denoising was completed using aCompCor (anatomical component analysis correction; (Behzadi et al., 2007)) regression followed by quadratic detrending and band-pass filtering (0.008 – 1 Hz). Regions from the salience network were chosen as seeds. These regions of interest included the left and right anterior insula, left and right rostral prefrontal cortex, anterior cingulate cortex, and left and right supramarginal gyrus, and were derived from the FSL Harvard Oxford Atlas in the CONN toolbox. A full description of preprocessing steps can be found in the supplementary materials.

2.6. Statistical Analyses

Resting state analyses were performed in the CONN Toolbox version 22.a and then extracted for further analysis in SAS version 9.4 statistical software. Using the CONN Toolbox, we performed seed-based bivariate correlation first-level analyses using functional connectivity weighted general linear models. At the second level, we used analyses of covariance (ANCOVAs) to identify a main effect of group (control, alcohol-only, co-use) on seed-based resting state functional connectivity by conducting contrasts comparing seed-based resting state functional connectivity between use groups while controlling for demeaned age, sex, and race. Given that individuals in the control group did not receive study medication or use cigarettes, study medication and cigarettes per day were not used as covariates. Additionally, we conducted secondary analyses at the second level to compare seed-based resting state functional connectivity between the alcohol-only and co-use groups while controlling for study medication and smoking status in addition to demeaned age, sex, and race. Smoking status was defined using the first question of the FTND which asks participants how often they smoke cigarettes (never, occasionally, often). Results were considered significant at a cluster forming threshold of voxel-level uncorrected p<0.001 and a cluster-level false discovery rate (FDR) correction of p-FDR<0.05. Fisher transformed correlation coefficients from seed-voxel clusters indicating a significant co-use group difference were exported from CONN for further analysis in SAS. SAS was used to conduct post-hoc analyses testing functional connectivity differences between the three use groups while still covarying for demeaned age, sex, and race. Planned post-hoc comparisons of significant omnibus tests were conducted using Tukey-Kramer t-tests.

Analyses of variance (ANOVAs) were used to compare co-use groups on baseline continuous demographic variables, while chi-square analyses were used to compare co-use groups on baseline categorical demographic variables. Post-hoc analyses for significant tests were conducted to observe group differences. A significance threshold of p<0.05 was used for all analyses.

3. Results

3.1. Co-use classification

Sixty individuals that completed structural and functional MRI scans were included in the final analyses. In the overall sample, 16 (26.7%) individuals were classified as controls, 27 (45%) individuals were classified as alcohol-only users, and 17 (28.3%) individuals were classified as alcohol and cannabis co-users. On average, alcohol-only users endorsed 20.22 (SD=7.88) past-month drinking days, with 6.62 (SD=3.64) drinks per drinking day. The alcohol-only group endorsed an average AUDIT score of 18.23 (SD=5.69). The co-use group endorsed similar drinking characteristics, with an average of 21.00 (SD=6.96) past-month drinking days, 5.44 (SD=2.32) drinks per drinking day, and an average AUDIT score of 17.94 (5.67). In terms of their cannabis-use characteristics, the co-use group reported an average of 24.65 (SD=7.59) past-month cannabis use days and an average CUDIT score of 8.13 (SD=4.32; n=8). Thirteen (76.47%) individuals in the co-use group had a positive urine toxicology test for THC at baseline. Groups differed on age and race demographic variables (see Table 1).

Table 1.

Sample Demographics.

Variable Mean (SD) or n (%) Control (n=16) Alcohol Only (n=27) Alcohol + Cannabis (n=17) Statistic P-Value
Demographic Characteristics
 Ageb 30.88 (12.18) 37.74 (11.58) 40.82 (10.70) F=3.25 0.046
 Sex (no., %) X2=0.28 .868
  Male 10 (62.50) 18 (66.66) 10 (58.82)
  Female 6 (37.50) 9 (33.33) 7 (41.18)
 Race (no., %) X2=23.23 .026
  White 6 (37.50) 9 (33.33) 4 (23.53)
  Black/African American 1 (6.25) 8 (29.63) 4 (23.53)
  American Indian /Alaskan Native 0 (0.00) 1 (3.70) 0 (0.00)
  Asian/Asian American 5 (31.25) 1 (3.70) 0 (0.00)
  Pacific Islander 0 (0.00) 3 (11.11) 1 (5.88)
  Mixed Race 1 (6.25) 2 (7.41) 6 (35.29)
  Other 3 (18.75) 3 (11.11) 2 (11.76)
 Hispanic/Latinx (no., %) X2=1.17 .558
  Yes 3 (18.75) 7 (25.93) 6 (35.39)
  No 13 (81.25) 20 (74.07) 11 (64.71)
 Years of Education 15.69 (1.97) 14.88 (2.67) 14.29 (3.89) F=0.82 .448
 Study Medication (no., %)a,b 0 (0.00) 13 (48.15) 13 (76.47) X2=15.74 <.001
 Major Depressive Episode (no, %)2 0 (0.00) 4 (15.38) 0 (0.00) X2=5.45 .066
Drinking Characteristics
 Drinking days (past 30 days)a,b 3.69 (3.71) 20.22 (7.88) 21.00 (6.96) F=36.55 <.001
 DPDD (past 30 days)a,b 1.54 (0.98) 6.62 (3.64) 5.44 (2.32) F=17.01 <.001
 Drinks per week (past 30 days)a,b 1.34 (1.80) 29.27 (16.99) 25.12 (10.06) F=25.90 <.001
 AUDITa,b,1 2.31 (1.32) 18.23 (5.69) 17.94 (5.67) F=49.11 <.001
 Alcohol Use Disorder Symptom Counta,b 0.12 (0.34) 5.54 (2.08) 5.76 (1.92) F=58.50 <.001
 AUD (no., %)a,b,2 X2=50.85 <.001
  No use disorder 16 (100) 2 (7.69) 0 (0.00)
  Mild 0 (0.00) 2 (7.69) 1 (5.88)
  Moderate 0 (0.00) 9 (34.62) 8 (47.06)
  Severe 0 (0.00) 13 (50.00) 8 (47.06)
Cannabis Use Characteristics
 Cannabis use days (past 30 days)b,c 0 (0.00) 0 (0.00) 24.65 (7.59) F=228.81 <.001
 CUDIT-Rb,c,3 3.00 (2.65) 0.80 (0.45) 8.13 (4.32) F=7.95 .006
 THC at Baseline (no., %)b,c,4 0 (0.00) 0 (0.00) 13 (76.47) X2=44.61 <.001
 THC at MRI (no., %)b,c 0 (0.00) 0 (0.00) 10 (58.82) X2=30.35 <.001
 Cannabis use day prior to MRI (no., %)b,c 0 (0.00) 0 (0.00) 12 (70.59) X2=37.94 <.001
Cigarette Use Characteristics
 Cigarette days (past 30 days at baseline)b,c 0 (0.00) 8.48 (13.33) 16.18 (15.13) F=7.42 .001
 CPSD (past 30 days from baseline)b 0 (0.00) 4.95 (9.31) 9.14 (12.81) F=4.03 .023
 CPSD (past 30 days from MRI)a,b 0 (0.00) 4.42 (7.16) 4.66 (5.18) F=3.87 .026
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Note.

a

Control < Alcohol Only

b

Control < Alcohol+Cannabis

c

Alcohol Only < Alcohol+Cannabis

1

Control n=13, Alcohol Only n=26, Alcohol+Cannabis n=16

2

Control n=16, Alcohol Only n=26, Alcohol+Cannabis n=16

3

Control n=3, Alcohol Only n=5, Alcohol+Cannabis n=8

4

Control n=11, Alcohol Only n=23, Alcohol+Cannabis n=17

AUD = Alcohol Use Disorder; AUDIT = Alcohol Use Disorders Identification Test; CUDIT-R = Cannabis Use Disorders Identification Test–Revised; DPDD = drinks per drinking day, CPSD = cigarettes per smoking day (Assessed by the Timeline Follow-Back interview for the past 30 days); THC = Delta-9-tetrahydrocannabinol; MRI = Magnetic Resonance Imaging.

3.2. Between-group resting state functional connectivity with anterior cingulate cortex seed

There was a main effect of group on functional connectivity between the anterior cingulate cortex seed and clusters with peak locations in the cerebellum (F(2,54)=30.77, pFDR<0.001), right thalamus, (F(2,54)=23.60, pFDR<0.001; see Figure 1A), right putamen (F(2,54)=14.69, pFDR<0.001), and left dorsal prefrontal cortex (PFC) (F(2,54)=17.20, pFDR<0.001; see Figure 1B) when controlling for age, sex, and race (see Table 2). Post-hoc analyses to determine between-group differences revealed that the control group had significantly greater rsFC between the ACC and the cerebellum, right thalamus, right putamen, and left dorsal PFC compared to both the alcohol only group (all ps<0.001) and co-use group (all ps<0.001), but there was no difference in rsFC between the alcohol-only and co-use groups (all ps>0.05; see Table 3 for post-hoc analyses).

Figure 1.

Figure 1.

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Mean Fisher’s corrected correlation coefficients between salience network seed regions and select peak cluster locations. (A) Correlated rsFC between ACC and right thalamus. Control group had significantly higher rsFC compared to the alcohol only (t(54)=6.53, pFDR<0.001) and co-use groups (t(54)=5.56, pFDR<0.001). (B) Correlated rsFC between ACC and left dorsal PFC. Control group had significantly higher rsFC compared to the alcohol only (t(54)=5.60, pFDR<0.001) and co-use groups (t(54)=4.70, pFDR<0.001). (C) Correlated rsFC between left anterior insula and right superior temporal gyrus. Control group had significantly higher rsFC compared to the alcohol only (t(54)=5.45, pFDR<0.001) and co-use groups (t(54)=4.74, pFDR<0.001). (D) Correlated rsFC between right anterior insula and right thalamus. Control group had significantly higher rsFC compared to the alcohol only (t(54)=6.71, pFDR<0.001) and co-use groups (t(54)=6.29, pFDR<0.001).

Table 2.

Peak cluster locations with significant functional connectivity to seed region.

Salience Network Seed Peak Cluster Region Peak Cluster Location (x, y, z) Number of Voxels
Anterior Cingulate Cortex Cerebellum −6 −54 −30 297
Right Thalamus +10 −12 +4 212
Right Putamen +28 −2 −2 120
Left Dorsal Prefrontal Cortex −6 +46 +28 99
Left Anterior Insula Right Superior Temporal Gyrus +60 −30 +12 258
Cerebellum −6 −50 −32 238
Right Thalamus +6 −16 +4 157
Right Visual Association +38 −86 −14 66
Right Anterior Insula Right Thalamus +8 −16 +4 236
Cerebellum −2 −48 −32 226
Left Supramarginal Gyrus Right Primary Auditory +56 −28 +8 524
Planum Temporale −68 −24 +12 134
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Table 3.

Group differences in mean correlated functional connectivity between the anterior cingulate cortex seed region and peak cluster locations.

Cluster F (2,54) p Group Mean (SE) t (54) p Pairwise Comparisons
Cerebellum 30.77 <0.001 Control (C) 0.11 (0.01) 9.90 <0.001 C v. AO: t 54 =7.48, p<0.001
Alcohol Only (AO) 0.01 (0.01) 0.66 0.512 C v. AC: t 54 =6.30, p<0.001
Alcohol + Cannabis (AC) 0.01 (0.01) 0.95 0.347 AO v AC: t54=−0.34, p=0.733
Right Thalamus 23.60 <0.001 Control (C) 0.17 (0.01) 12.39 <0.001 C v. AO: t 54 =6.53, p<0.001
Alcohol Only (AO) 0.06 (0.01) 5.57 <0.001 C v. AC: t 54 =5.56, p<0.001
Alcohol + Cannabis (AC) 0.06 (0.01) 4.61 <0.001 AO v AC: t54=−0.23, p=0.818
Right Putamen 14.69 <0.001 Control (C) 0.16 (0.02) 9.44 <0.001 C v. AO: t 54 =4.78, p<0.001
Alcohol Only (AO) 0.06 (0.01) 4.57 <0.001 C v. AC: t 54 =4.87, p<0.001
Alcohol + Cannabis (AC) 0.04 (0.02) 2.57 0.013 AO v AC: t54=0.77, p=0.447
Left Dorsal Prefrontal Cortex 17.20 <0.001 Control (C) 0.27 (0.03) 9.65 <0.001 C v. AO: t 54 =5.60, p<0.001
Alcohol Only (AO) 0.07 (0.02) 3.48 0.001 C v. AC: t 54 =4.70, p<0.001
Alcohol + Cannabis (AC) 0.08 (0.03) 3.04 0.004 AO v AC: t54=−0.27, p=0.786
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3.3. Between-group resting state functional connectivity with anterior insula seed

There was a main effect of group on functional connectivity between the left anterior insula seed and clusters with peak locations in the right superior temporal gyrus (F(2,54)=16.69, pFDR<0.001; see Figure 1C), cerebellum, (F(2,54)=32.09, pFDR<0.001), right thalamus (F(2,54)=16.40, pFDR<0.001), and right visual association (F(2,54)=13.15, pFDR<0.001) when controlling for age, sex, and race (see Table 2). Similarly to the ACC seed, between-group post-hoc analyses revealed that the control group had significantly greater rsFC between the left anterior insula and the right superior temporal gyrus, cerebellum, right thalamus, and right visual association compared to both the alcohol-only group (all ps<0.001) and co-use group (all ps<0.001), but there was no difference in rsFC between the alcohol only and co-use groups (all ps>0.05; see Table 4 for post-hoc analyses).

Table 4.

Group differences in mean correlated functional connectivity between the left anterior insula seed region and peak cluster locations.

Cluster F (2,54) p Group Mean (SE) t (54) p Pairwise Comparisons
Cerebellum 32.09 <0.001 Control (C) 0.09 (0.01) 8.86 <0.001 C v. AO: t 54 =7.43, p<0.001
Alcohol Only (AO) −0.01 (0.01) −0.65 0.520 C v. AC: t 54 =6.78, p<0.001
Alcohol + Cannabis (AC) −0.01 (0.01) −0.85 0.401 AO v AC: t54=0.27, p=0.788
Right Superior Temporal Gyrus 16.69 <0.001 Control (C) 0.26 (0.02) 10.35 <0.001 C v. AO: t 54 =5.45, p<0.001
Alcohol Only (AO) 0.09 (0.02) 4.66 <0.001 C v. AC: t 54 =4.74, p<0.001
Alcohol + Cannabis (AC) 0.09 (0.02) 3.71 <0.001 AO v AC: t54=−0.07, p=0.943
Right Thalamus 16.40 <0.001 Control (C) 0.15 (0.02) 9.42 <0.001 C v. AO: t 54 =5.13, p<0.001
Alcohol Only (AO) 0.05 (0.01) 3.95 <0.001 C v. AC: t 54 =5.06, p<0.001
Alcohol + Cannabis (AC) 0.03 (0.02) 2.26 0.028 AO v AC: t54=0.63, p=0.534
Right Visual Association 13.15 <0.001 Control (C) 0.11 (0.03) 4.33 <0.001 C v. AO: t 54 =4.81, p<0.001
Alcohol Only (AO) −0.04 (0.02) −2.29 0.030 C v. AC: t 54 =4.26, p<0.001
Alcohol + Cannabis (AC) −0.05 (0.02) −1.81 0.080 AO v AC: t54=0.02, p=0.981
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There was a main effect of group on rsFC between the right anterior insula seed and clusters with peak locations in the right thalamus (F(2,54)=26.73, pFDR<0.001; see Figure 1D) and cerebellum (F(2,54)=33.79, pFDR<0.001) when controlling for age, sex, and race (see Table 2). Similarly to the ACC and left anterior insula seeds, post-hoc analyses to identify between-group differences revealed that the control group has significantly greater rsFC between the right anterior insula and the right thalamus and cerebellum compared to both the alcohol-only group (all ps<0.001) and co-use group (all ps<0.001), but there was no difference in rsFC between the alcohol only and co-use groups (all ps>0.05; see Table 5 for post-hoc analyses).

Table 5.

Group differences in in mean correlated functional connectivity between the right anterior insula seed region and peak cluster locations.

Cluster F (2,54) p Group Mean (SE) t (54) p Pairwise Comparisons
Cerebellum 33.79 <0.001 Control (C) 0.09 (0.01) 9.37 <0.001 C v. AO: t 54 =8.04, p<0.001
Alcohol Only (AO) −0.01 (0.01) −1.00 0.323 C v. AC: t 54 =6.09, p<0.001
Alcohol + Cannabis (AC) 0.01 (0.01) 0.70 0.486 AO v AC: t54=−1.16, p=0.251
Right Thalamus 26.73 <0.001 Control (C) 0.13 (0.01) 11.61 <0.001 C v. AO: t 54 =6.71, p<0.001
Alcohol Only (AO) 0.04 (0.01) 4.23 <0.001 C v. AC: t 54 =6.29, p<0.001
Alcohol + Cannabis (AC) 0.03 (0.01) 2.73 0.009 AO v AC: t54=0.44, p=0.664
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3.4. Between-group resting state functional connectivity with supramarginal gyrus seed

Group significantly influenced rsFC between the left SMG seed and clusters with peak locations in the right primary auditory cortex (F(2,54)=18.91, pFDR<0.001) and planum temporale (F(2,54)=12.56, pFDR<0.001) when controlling for age, sex, and race (see Table 2). Between-group post-hoc analyses revealed that the control group had significantly higher rsFC between the left SMG and the right primary auditory and planum temporale compared to both the alcohol only group (all ps<0.001) and co-use group (all ps<0.001), but there was no difference in rsFC between the alcohol only and co-use groups (all ps>0.05; see Table 6 for post-hoc analyses). Group did not significantly influence rsFC between the right SMG seed region and any clusters in the brain.

Table 6.

Group differences in mean correlated functional connectivity between the left supramarginal gyrus seed region and peak cluster locations.

Cluster F (2,54) p Group Mean (SE) t (54) p Pairwise Comparisons
Right Primary Auditory Cortex 18.91 <0.001 Control (C) 0.28 (0.03) 9.71 <0.001 C v. AO: t 54 =5.60, p<0.001
Alcohol Only (AO) 0.08 (0.02) 3.57 0.001 C v. AC: t 54 =5.35, p<0.001
Alcohol + Cannabis (AC) 0.06 (0.03) 2.15 0.036 AO v AC: t54=0.48, p=0.633
Left Planum Temporale 12.56 <0.001 Control (C) 0.29 (0.03) 8.77 <0.001 C v. AO: t 54 =4.30, p<0.001
Alcohol Only (AO) 0.11 (0.03) 4.49 <0.001 C v. AC: t 54 =4.61, p<0.001
Alcohol + Cannabis (AC) 0.07 (0.03) 2.27 0.027 AO v AC: t54=0.95, p=0.344
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3.5. Between-group resting state functional connectivity with rostral prefrontal cortex seed

Group did not significantly influence rsFC between the left or right rostral PFC region and any areas in the brain.

3.6. Secondary analysis of between-group resting state functional connectivity in alcohol-only and co-use groups

There was no significant difference in rsFC between the specified seed regions and any area of the brain between alcohol-only and co-use groups when controlling for study medication status, smoking status, age, sex, and race (all ps>0.05).

4. Discussion

This preliminary study examined the effect of co-using alcohol and cannabis on resting state functional connectivity between regions in the salience network and the whole brain in individuals who drink alcohol heavily and use cannabis regularly. Alcohol only and alcohol-cannabis co-use groups had significantly less rsFC between salience network seed regions and regions across the brain as compared to controls. Specifically, we identified lowered rsFC between the salience network seed regions and regions associated with motor, sensory, visual, and executive control functioning as compared to controls. Moreover, there were no significant rsFC group differences between the alcohol only and alcohol-cannabis co-use groups. This lack of significant rsFC group differences between alcohol-only and co-use groups was further supported by secondary analyses controlling for study medication and smoking status between the two groups.

Thus, our hypothesis that rsFC between the salience network and whole brain in the alcohol-cannabis co-use group would be comparable to the control group due was not supported by our results. Rather, the regular use of cannabis in addition to heavy alcohol use did not appear to alter salience network rsFC compared to alcohol-only rsFC. Previous research indicates that co-use of both alcohol and cannabis leads to higher rsFC within the salience network compared to using alcohol alone (Morris et al., 2022), and leads to similar connectivity patterns compared to control individuals who use no drugs (Vergara et al., 2018). Our findings that co-use resulted in similar rsFC patterns compared to the alcohol-alone group and less rsFC compared to the control group may differ from previous co-use and rsFC studies with respect to the methods and study samples utilized. The present study examined rsFC between salience network seed regions and the whole brain, whereas previous studies have focused on rsFC within the salience network itself (Morris et al., 2022) or among dynamic network states (Vergara et al., 2018). Moreover, our study leveraged individuals who drink alcohol heavily for both the alcohol-only and co-use groups, as determined by drinks per week and scores above 8 on the AUDIT. These drinking characteristics are similar to the sample utilized by Vergara et al. (2018) but are more severe than Morris et al. (2022). Individuals in our co-use group additionally reported a higher frequency of cannabis use compared to the co-use samples utilized by Vergara et al. (2018) and Morris et al. (2022).

Our finding that regular cannabis use did not have an additive or contrasting effect on rsFC when used with alcohol heavily suggests that alcohol use may be driving some of these neurobiological alterations. These findings align with evidence that suggests there are no differences in gray matter volume between individuals who only use alcohol and those who co-use alcohol and cannabis (Grodin et al., 2021a; reviewed in Karoly et al., 2020; reviewed in Lees et al., 2021). Indeed, our findings suggest that alcohol use is associated with less rsFC between the salience network and regions across the whole brain in individuals who drink alcohol heavily, and regular cannabis use may not have an effect over and above that of alcohol in people who are already drinking heavily. These alcohol-driven alterations may be associated with the negative behavioral and physiological consequences of alcohol and cannabis co-use that include greater likelihood of alcohol use disorder (Midanik et al., 2007), more harms to relationships, finances, and health (Subbaraman and Kerr, 2015), and increased hangover symptoms (Gunn et al., 2022; Sokolovsky et al., 2020).

In support of our hypothesis that individuals who use alcohol only would have less rsFC compared to control individuals, we found less rsFC between the salience network seed regions (ACC, left and right insula, left supramarginal gyrus) and regions associated with motor (cerebellum, right putamen, right thalamus), sensory (right thalamus, right superior temporal gyrus, right primary auditory cortex, left planum temporale), visual (right visual association), and executive control (left dorsal PFC) functioning in the alcohol-only group. This lowered rsFC observation in the alcohol-only group compared to control individuals is consistent with previous evidence that suggests heavy alcohol lowers rsFC among motor and sensory areas (Vergara et al., 2018, 2017), as well as among the insula and visual regions (Vergara et al., 2017) and executive control network (Weiland et al., 2014). Decreases in rsFC between salience network seed regions and motor, sensory, visual, and executive control areas can be interpreted through the function of the salience network. The salience network integrates information from other brain networks, such as the central executive network (CEN), which is involved with external attentional processing and goal-directed cognition (Menon and Uddin, 2010). The left dorsolateral PFC is a key region in the CEN (Menon and Uddin, 2010) and is recruited by the ACC for increased cognitive demand (Johnston et al., 2007). In addition, the putamen and thalamus are typically associated with the motor and limbic networks to control movement and behavior related to alcohol and external drug-related cues (Everitt and Robbins, 2016; Lanciego et al., 2012; Shokri-Kojori et al., 2017). Our results indicate less rsFC between salience network seed regions and left dorsal PFC and motor regions in individuals who use alcohol alone compared to controls. Less rsFC within executive control, visual, and reward networks in individuals with alcohol use disorder is associated with less inhibitory control and higher risk of relapse and alcohol use (Camchong et al., 2013). Therefore, a finding of less activity between the salience network and these executive control/motor regions could suggest an inability to inhibit behavior that may contribute to the compulsion to seek out drugs (Robinson and Berridge, 1993).

Moreover, our finding of lowered rsFC between the insula and visual brain regions in the alcohol-only group replicates previous alcohol-only findings (Vergara et al., 2017) and may be interpreted through the critical role of the salience network, and the insula specifically, in drug craving (Naqvi et al., 2014) The insula is a key region of the salience network that alters the subjective awareness, or interoceptive awareness, of one’s craving state (Craig, 2002, 2009; Khalsa et al., 2018; Naqvi and Bechara, 2009). Enhanced interoceptive awareness is correlated with higher connectivity between the anterior and midbrain/thalamus, and cerebellar regions (Chong et al., 2017; Smith et al., 2022). However, our findings and previous findings on alcohol use and rsFC (Vergara et al., 2018, 2017) suggest that individuals who use alcohol have less rsFC between salience network regions and motor, visual, and sensory regions. These lowered connections may be associated with a possible reduction in attention towards integrating salient stimuli, thereby altering interoceptive processes. Clinically, disrupted interoception may lead to further cravings for alcohol and emotion dysregulation (ATEŞ ÇÖL et al., 2016; Wiśniewski et al., 2021). Therefore, our findings replicate less synchrony in rsFC between salience network seed regions and regions across the brain to support the neuroadaptation of the salience network as described by the incentive-sensitization theory of addiction.

Study strengths include the utilization of a control group comprised of individuals who did not regularly use drugs. Utilizing a comparison control group without regular drug use allows us identify differences in neural processes that are likely due to the regular use of drugs. Moreover, a strength of our study is that it included a heavy alcohol use and regular cannabis use sample, contributing to greater clinical generalizability. A primary study limitation is the absence of a cannabis-only group. Future studies should recruit individuals who use only cannabis heavily. Relatedly, no information was collected on type of cannabis product or quantity of cannabis used by individuals in the co-use group. Individuals in the co-use group reported regular frequency of cannabis use, but the quantity of cannabis use was unknown. There may be dose-dependent effects of cannabis on rsFC (Pujol et al., 2014); therefore, future investigations should collect detailed cannabis-use information to ascertain cannabinoid-specific impacts on rsFC. The small overall sample size and unbalanced group sample sizes were limitations as well. Current findings are considered preliminary given the small sample size; therefore, larger sample sizes should be utilized to replicate findings. In terms of sample characteristics, an additional limitation is that individuals in the control group were younger, on average, than individuals in the alcohol-only and co-use groups. Given this difference, we controlled for age in all analyses, but future studies should seek to balance groups on age and other demographic characteristics. In addition, nearly half of the individuals in the alcohol-only group, and more than half of the individuals in the co-use group, were treatment-seeking for substance use. This high prevalence of treatment-seeking status is not generalizable in comparison to the low treatment seeking rates observed in the population (Venegas et al., 2021). Lastly, we were not able to covary for cigarette use or study medication use in our primary analyses given that the there was no cigarette use or study medication use in the control group. The groups did differ on these characteristics, with individuals in the co-use group smoking significantly more cigarettes compared to both the alcohol-only and control groups. There is evidence that nicotine use affects functional connectivity in the thalamus and dorsal striatum, specifically the putamen (Vergara et al., 2018, 2017). Given this evidence, we conducted secondary analyses with just the alcohol-only and co-use groups to control for study medication and smoking status and supported our primary finding of no differences in rsFC between salience network seed regions and regions across the whole brain. However, future studies should still strive to incorporate a cigarette-only and alcohol-cannabis-nicotine tri-use group.

Taken together, our findings preliminarily suggest that alcohol alone and alcohol-cannabis co-use are associated with lower rsFC between the salience network and regions associated with motor, sensory, visual, and executive control functioning. Follow-up studies should seek to replicate these findings in a larger sample. Our findings extended the literature on salience network rsFC in drug use by identifying that there may be no additive or contrasting effects on rsFC from co-using alcohol and cannabis together. These findings suggest that alcohol may drive neural alterations between the salience network and brain regions that impact inhibitory control and substance craving in both alcohol-only and cannabis-alcohol users, thereby characterizing the incentive-sensitization theory of addiction.

Supplementary Material

Supplementary materials

Highlights.

  • Salience network resting state functional connectivity (rsFC) is linked to addiction.

  • Effects of alcohol and cannabis co-use on salience network rsFC are not well known.

  • Alcohol use is associated with lower rsFC compared to control.

  • Alcohol-cannabis co-use is associated with lower rsFC compared to control.

  • rsFC is not significantly different between alcohol and alcohol-cannabis co-users.

Funding:

Funding for this study was provided by the National Institute on Alcohol Abuse and Alcoholism (K01AA029712 to ENG; K24AA025704, R21AA030643, R01AA026190, and R01DA041226 to LAR; F32AA031425 to DEK). The funders had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Footnotes

Conflict of Interest: The authors declare no conflicting interests.

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