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

Abstract

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.

Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation Across Mental Disorders

“…We included 110 studies, encompassing a total of 4820 participants . The majority of participants were male (2959 [61.4%] compared with 1861 females [38.6%]), and the mean (SD) age was 43 (8.8) years.…”

Uncorking the limitation—improving dual tasking using transcranial electrical stimulation and task training in the elderly: a systematic review

“… Patel et al (2019) noted that the improvement in cognitive and behavioral skills induced after tDCS over M1 could be attributed to cortical plasticity, potentially triggered by a decrease in gamma-aminobutyric acid concentration, which has an important role during motor learning. The higher performance after tDCS over DLPFC may be due to the increased cortical excitability in the executive control and ventral attention networks within the brain ( Soleimani et al, 2022 ).…”

Exploring anterior thalamus functional connectivity with cortical regions in prospective memory with ultra-high-field fMRI

“…Inferences were performed at the cluster level, based on parametric statistics from Gaussian Random Field theory 56,70 . Results were thresholded using a combination of a cluster-forming p < 0.001 voxel-level threshold, and a p-FWE < 0.05 cluster size threshold 71 , as recommended 72,73 and commonly applied [74][75][76] . A left ANT seed, a mirror location in the right ANT, and a parvocellular division of left mediodorsal thalamic nucleus seed for the DMNT were used for seed-to-voxel functional connectivity analysis (Table 1).…”