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WHAT NEUROBIOLOGY TELLS US ABOUT ADDICTION

2010· letter· en· W2129170345 on OpenAlexaboutno aff
Martin Y. Iguchi, Christopher J. Evans

Bibliographic record

VenueAddiction · 2010
Typeletter
Languageen
FieldNeuroscience
TopicNeurotransmitter Receptor Influence on Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsAddictionAddiction medicinePsychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

The paper by Harold Kalant, ‘What neurobiology can not tell us about addiction’1, rehashes arguments regarding the limitation of neurobiology research in understanding addiction. We agree that addiction research is limited by complexity at all levels of analysis, but we respectfully disagree with several key issues raised by the author. Let us start with the author's first question: ‘What is addiction?’. The author moves quickly past the DSM-IV-TR and other definitions to a statement by an ad hoc committee of the Royal Society of Canada that: ‘the only elements common to all definitions of addiction are a strongly established pattern of repeated self-administration of a drug in doses that reliably produce reinforcing psychoactive effects, and great difficulty in achieving voluntary long-term cessation of such use, even when the user is strongly motivated to stop’. The author concludes that the key phrase is ‘self-administration’ and that ‘addiction is not produced by a drug, but by self-administration of a drug’. He supports his conviction by contrasting the relatively low likelihood that a pain patient will become addicted when administered opioids by a health care professional, with the anecdote that Civil War veterans allowed to self-administer opiates often developed ‘soldier's disease’. 2 He acknowledges differences between pain patients and the soldiers, but his conclusion remains that the only important difference is self-administration. He concludes from this that models of addiction not based on self-administration are basically ‘flawed’. That addiction does not occur in all patients administered opioids by health care professionals is clear, but some do become addicted. At the same time, a great majority of patients who self-administer opioids do not become addicted. That said, we point to the many important factors that account for such differences beyond self-administration such as environmental contexts (e.g. war veterans often experienced severe traumas during and following the war, experienced high unemployment and possibly mental health problems), the likelihood and importance of co-administered substances, high levels of social disruption and displacement, as well as other pre-existing vulnerabilities (genetic and otherwise). We also point to conceptual/integrative models such as proposed by Koob and colleagues that place such factors into a wider context 3. We agree on the need to look at genetic and environmentally engraved phenotypes as drivers of addictive behavior. Differences between modes of drug administration have been researched extensively at the molecular, cellular and behavioral levels, and researchers are far from naive. For example, Robinson and colleagues demonstrated marked differences between experimenter and self-administered opioids in alteration of neuronal spine density in several brain regions 4. That experimenter-administered drug, which results in sensitization and place preference, is completely ‘flawed’ for understanding aspects of addiction is not our opinion. We agree that study of the genetics of addiction has the best chance in the areas of socially accepted drugs such as alcohol and nicotine. Recent identification of a region of chromosome 15 (containing the α5-α3-β4 nicotinic receptor genes) separately in smokers and lung-cancer patients provides evidence that genetic tools will have power to identify aspects of susceptibility 5. Also, success of treatment options has gained insight from genetics with the finding that naltrexone appears to be more successful in alcoholics with selective alleles of the mu-opioid receptor 6 The numbers needed for genome-wide association studies for relatively straightforward phenotypes has been greater than expected because the effect size for specific genes is often exceedingly small 7. The implication is that it will be extremely difficult to identify the genetic vulnerability to illicit drugs and complex psychological endophenotypes underlying susceptibility to addictive behavior 8. The author makes the important point that the control of proteins are at the heart of the issue, and the opportunity for genetic influence at multiple loci to modulate proteins has increased markedly as we begin to understand the complexity of epigenetics, promoters, protein trafficking/turnover and microRNA regulation. Finally, we agree that reductionistic or analytical approaches are placed into a more appropriate context when corralled within appropriate and integrated conceptual frames. This is true for all areas of science. The challenge is certainly one of making the science ‘fly’, as nothing brings focus like a plane about to crash. We agree that the field needs to think more conceptually and integratively, and we look forward to flying with Dr Kalant in the future. None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.526
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0030.004

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.

Opus teacher head0.024
GPT teacher head0.269
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations3
Published2010
Admission routes1
Has abstractyes

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