Commentary on Song <i>et al</i>: Brain stimulation for addictions‐ optimizing impact via strategic interleaving with pharmacotherapy, cognitive behavioral therapy, and restructuring the micro‐environment
Bibliographic record
Abstract
Song and colleagues[1] may be helping us to finally answer basic questions about efficacy of brain stimulation methods and move toward equally pressing questions as to how we can obtain optimal synergies between brain stimulation methods and other modalities, including the restructuring of the microenvironment. How does one go about strategically combining brain stimulation with pharmacotherapy, psychotherapy and other addictions treatment modalities? Issues related to risks and benefits, optimum timing and parameter selection of brain stimulation in relation to medical and psychosocial addiction intervention need to be considered carefully. Some psychotropic medications used in the context of addiction can lower the seizure threshold, and while this does not seem to add major risk of seizures when rTMS is used to treat depression [2], the risk of seizure might increase in the context of acute toxicity or withdrawal from substances of abuse [3]. On the other hand, some psychotropics used to manage addiction (e.g. benzodiazepine) can reduce efficacy of brain stimulation by interfering with the underlying mechanism of treatment [4]. Increasingly there are attempts to integrate rTMS with cognitive behavioral therapy. Indeed this happens incidentally in clinical practice on a routine basis, by virtue of the fact that many individuals undergoing brain stimulation treatment are also receiving ongoing psychotherapeutic intervention, either in group or individual format. What is largely missing is the strategic timing of sessions and phases of therapy with stimulation such as to maximize their impact. For instance, early in CBT treatment it might be important to bolster brain networks supporting self-regulatory processes in craving control, long enough for the individual to gain a foothold and benefit from the skills offered for craving control by the therapist. Likewise, an argument could be made that stimulation sessions should be timed such that peak benefit coincides with the most taxing phase of therapy, wherein self-regulation of emotional responses is especially important; in the case of cue exposure sessions, this might mean that stimulation should reach its maximum impact before exposure is attempted. The same logic could support timing the sessions so that maximum benefit is received before reintegration back into a risky environment, following release from an inpatient treatment program. The primary point is that strategic interleaving of rTMS and psychotherapeutic intervention seems to be potentially very important, and yet we know very little about it. Only careful empirical research can inform questions about optimal timing of stimulation in relation to psychotherapeutic interventions like CBT. Finally, an important future avenue for intervention is pairing brain stimulation with strategic changes in the micro-environment (i.e., the context in which the craving object is routinely encountered in everyday life). Using the example of eating, we have found that indulgent food consumption is jointly impacted by both stimulation type and the nature of cues in the eating environment [5]. Engineering the micro-environment to reduce cued cravings may optimize the impact of strengthening of brain networks using brain stimulation methods. Beyond this, the wider macro-environment supporting or mitigating indulgence of various types is the province of public health, meaning that multi-level interventions—from brain to society—may be critical for managing addictions of all types. Ultimately, brain stimulation research and public health research may be mutually reinforcing. The first author wishes to acknowledge funding support from the Natural Sciences and Engineering Research Council of Canada (NSERC). None. PAH wrote the initial draft; PAH and AMB contributed to the final draft.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".