Triple Network Resting State Connectivity Predicts Distress Tolerance and Is Associated with Cocaine Use

Reese, Elizabeth D.; Yi, Jennifer Y.; McKay, Katlyn G.; Stein, Elliot A.; Ross, Thomas J.; Daughters, Stacey B.

doi:10.3390/jcm8122135

Cited by 22 publications

(19 citation statements)

References 46 publications

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“…In line with this perspective, elevated DI has been linked with acute stress potentiation of attention bias to negatively-valenced stimuli (Macatee, McDermott, et al, 2018), persistent negative emotional responding to an acute stressor (Albanese et al, 2021; Cougle et al, 2011), and cannabis craving in the context of acute stress (Buckner et al, 2019). Further, neurobiological studies in substance users have revealed altered activity in corticolimbic regions involved in attentional control/affective salience among those who persisted for less time on a distressing laboratory task and reported greater substance use after treatment (Daughters et al, 2017; Reese et al, 2019). Overall, these multimethod data suggest that DI is associated with acute stress modulation of motivated attention and that these processes may predict clinical outcome.…”

mentioning

confidence: 99%

Impact of a computerized distress intolerance intervention on electrocortical reactivity to cannabis and threat cues: A randomized controlled trial.

Macatee¹,

Preston²,

Afshar³

et al. 2022

Psychology of Addictive Behaviors

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Objective: Given increasing rates of daily cannabis use and Cannabis Use Disorder (CUD) in the United States, it is imperative to understand CUD mechanisms in high-risk groups. Cannabis users with high distress intolerance (DI) are at elevated risk for severe and chronic CUD, but neural mechanisms linking CUD and DI are unknown. Cross-sectional data suggests that acute stress modulation of the cannabis and threat cue-elicited late positive potential (LPP), a neurophysiological marker of motivated attention, are possible mechanisms. However, longitudinal research is needed to clarify the roles of these elicited LPPs in CUD maintenance. Method: Sixty cannabis users with high DI were randomized a brief computerized intervention targeting DI or a control intervention. Elicited LPPs were measured before and after a stressor at baseline and postintervention. Intervention effects on stress modulation of the cannabis and threat LPPs, as well as their prospective associations with CUD, were assessed. Results: Elicited LPPs did not significantly change in either intervention group. Acute stress enhancement of the cannabis LPP predicted more severe CUD and greater chronicity at 4-month follow-up. Conclusions: Cannabis and threat LPPs were not altered by a brief DI intervention despite improvement in DI and cannabis use outcomes. Given that acute stress enhancement of the cannabis LPP predicted poorer CUD outcome, it may be a fruitful intervention target in distress intolerant cannabis users. Public Health Significance StatementCannabis users with high distress intolerance are at heightened risk for Cannabis Use Disorder. This study demonstrates that a brief intervention for distress intolerant cannabis users did not affect the neurophysiological response to cannabis cues, but stress modulation of the cue-elicited response predicted worse intervention response. Stress modulation of cannabis cue reactivity may be a fruitful intervention target in this population.

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mentioning

confidence: 99%

Impact of a computerized distress intolerance intervention on electrocortical reactivity to cannabis and threat cues: A randomized controlled trial.

Macatee¹,

Preston²,

Afshar³

et al. 2022

Psychology of Addictive Behaviors

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“…The bilaterally reduced RAIs observed among PLWH are similar to those previously observed across substance use and neuropsychiatric disorders ( Alexopoulos et al, 2012 ; Bartova et al, 2015 ; Bednarski et al, 2011 ; Bonavita et al, 2017 ; Chang et al, 2014 ; Gauffin et al, 2013 ; Høgestøl et al, 2019 ; Lee et al, 2017 ; Liddle et al, 2011 ; Liu et al, 2018 ; Oyegbile et al, 2019 ; Peterson et al, 2009 ; Schilbach et al, 2016 ; Sutherland et al, 2012 ; Verfaillie et al, 2018 ; K. Wang et al, 2019 ; Y. Wang et al, 2013 ; Whitfield-Gabrieli & Ford, 2012 ; Wu et al, 2011 ; Yin et al, 2016 ; R. Zhang & Volkow, 2019 ; Zhou et al, 2016 ). Nicotine and other drug-dependent individuals have displayed reduced RAIs during acute withdrawal ( Lerman et al, 2014 ; Reese et al, 2019 ). However, Moradi et al’s (2020) recent work questioned the RAI as a reliable biomarker for substance use disorders following null effects among stimulant and/or opiate users that had been abstinent for, in some cases, multiple months (mean of 108 days, ranging from 4 to 365 days).…”

Section: Discussionmentioning

confidence: 99%

“…Networks in this functional atlas were identified by applying independent component analysis (ICA; MELODIC, FSL) to resting-state data and visually identifying 14 canonical intrinsic functional connectivity networks based on prior work out of the 30 generated; the creation of this atlas is described in detail elsewhere ( Shirer et al, 2012 ). While we did not have specific hypotheses regarding CEN laterality, we calculated separate RAIs for the left and right hemisphere consistent with prior work ( Lerman et al, 2014 ; Reese et al, 2019 ; J. T. Zhang et al, 2017 ). We note that the RAI metric in these prior studies was calculated using network masks derived via an ICA-based (as opposed to an atlas-based) approach ( Lerman et al, 2014 ; Moradi et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…The resource allocation index (RAI) is a previously developed metric quantifying network-level interactions based on the theorized role of the SN in toggling activity between the DMN and CEN such that higher RAI values indicate increased synchrony between the SN and CEN and/or decreased synchrony between the SN and DMN ( Lerman et al, 2014 ). The RAI has been used to evaluate dysfunction in this relative balance between SN-DMN and SN-CEN coupling ( Choi et al, 2013 ; Lerman et al, 2014 ; Reese et al, 2019 ; J. T. Zhang et al, 2017 ). For example, the attentional and cognitive deficits characterizing nicotine withdrawal have been linked to reduced activation in brain regions comprising the CEN and less suppression of activity in regions comprising the DMN ( Falcone et al, 2014 ; Hahn et al, 2007 ; Lerman et al, 2014 ; Loughead et al, 2010 ; Tanabe et al, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

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Altered large-scale brain network interactions associated with HIV infection and error processing

Flannery

Riedel

Hill-Bowen

et al. 2022

Network Neuroscience

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Altered activity within and between large-scale brain networks has been implicated across various neuropsychiatric conditions. However, patterns of network dysregulation associated with human immunodeficiency virus (HIV), and further impacted by cannabis (CB) use, remain to be delineated. We examined the impact of HIV and CB on resting-state functional connectivity (rsFC) between brain networks and associations with error awareness and error-related network responsivity. Participants (N = 106), stratified into four groups (HIV+/CB+, HIV+/CB−, HIV−/CB+, HIV−/CB−), underwent fMRI scanning while completing a resting-state scan and a modified Go/NoGo paradigm assessing brain responsivity to errors and explicit error awareness. We examined separate and interactive effects of HIV and CB on resource allocation indexes (RAIs), a measure quantifying rsFC strength between the default mode network (DMN), central executive network (CEN), and salience network (SN). We observed reduced RAIs among HIV+ (vs. HIV−) participants, which was driven by increased SN-DMN rsFC. No group differences were detected for SN-CEN rsFC. Increased SN-DMN rsFC correlated with diminished error awareness, but not with error-related network responsivity. These outcomes highlight altered network interactions among participants with HIV and suggest such rsFC dysregulation may persist during task performance, reflecting an inability to disengage irrelevant mental operations, ultimately hindering error processing.

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“…Based on the computational head models ( Datta et al, 2009 ), conventional tDCS produces diffuse brain current flow, and stimulation outcomes may be understood as modulation of global networks ( Soleimani et al, 2021a ). Previous fMRI studies have revealed that three networks—frontoparietal executive control network (ECN) for processing of exogenous stimuli, default mode network (DMN) involved in internally relevant stimuli as well as the self-monitoring process, and salience ventral attention network (VAN) implicated in attentional resource allocation between ECN and DMN—have received the most attention in SUDs ( Reese et al, 2019 ; Bolton et al, 2020 ), which can be considered as stimulation targets in tDCS studies ( Peña-Gómez et al, 2012 ; Kunze et al, 2016 ). One crucial mechanism underlying addiction is the coupling between the main nodes of these large-scale networks in response to drug-related cues, and applying stimulation over the DLPFC can modulate the interaction (activity/connectivity) between these network nodes ( Peña-Gómez et al, 2012 ; Shahbabaie et al, 2018a ; Abellaneda-Pérez et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

DLPFC stimulation alters large-scale brain networks connectivity during a drug cue reactivity task: A tDCS-fMRI study

Soleimani

Towhidkhah²,

Oghabian³

et al. 2022

Front. Syst. Neurosci.

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Transcranial direct current stimulation (tDCS) is a promising intervention for reducing craving/consumption in individuals with substance use disorders. However, its exact mechanism of action has not yet been well explored. We aimed to examine the network-based effects of tDCS while people with methamphetamine use disorders (MUDs) were exposed to drug cues. In a randomized, double-blind sham-controlled trial with a crossover design, 15 participants with MUDs were recruited to receive 20 min of active/sham tDCS with an anode/cathode over F4/F3. MRI data, including structural and task-based functional MRI during a standard drug cue-reactivity task, were collected immediately before and after stimulation sessions. Craving scores were also recorded before and after MRI scans. Individualized head models were generated to determine brain regions with strong electric fields (EFs). Using atlas-based parcellation of head models, averaged EFs were extracted from the main nodes of three large-scale networks that showed abnormalities in MUDs; executive control (ECN), default mode (DMN), and ventral attention (VAN) networks. Main nodes with high EF intensity were used as seed regions for task-based functional connectivity (FC) [using generalized psychophysiological interaction (gPPI)] and activity [using a general linear model (GLM)] calculations. Subjective craving showed a significant reduction in immediate craving after active (–15.42 ± 5.42) compared to sham (–1 ± 2.63). In seed-to-whole brain results, the PFC node in ECN showed an enhanced PPI connectivity with precuneus and visual cortex; the cluster center in MNI (6, –84, –12); the PFC node in DMN showed a decreased PPI connectivity with contralateral parietal cortex;(–48, –60, 46). ROI-to-ROI results showed increased PPI connectivity within/between ECN-VAN while connectivity between ECN-DMN decreased. In line with connectivity, functional activity in the right PFC node in DMN decreased after tDCS while activity in PFC nodes of ECN/VAN increased. EF calculations in PFC nodes revealed that EF in DMN was outward, while the direction of EFs was inward in ECN/VAN. This study provides new insight into neural circuitry underlying MUDs that can be modulated by tDCS at the network level and specifically suggests that bilateral tDCS increases cortical excitability in ECN and VAN, while it has opposite effects on DMN that may be related to the direction of EFs.

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Triple Network Resting State Connectivity Predicts Distress Tolerance and Is Associated with Cocaine Use

Cited by 22 publications

References 46 publications

Impact of a computerized distress intolerance intervention on electrocortical reactivity to cannabis and threat cues: A randomized controlled trial.

Impact of a computerized distress intolerance intervention on electrocortical reactivity to cannabis and threat cues: A randomized controlled trial.

Altered large-scale brain network interactions associated with HIV infection and error processing

DLPFC stimulation alters large-scale brain networks connectivity during a drug cue reactivity task: A tDCS-fMRI study

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