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Toward a final common pathway of depression

2011· letter· en· W1573366726 on OpenAlexaff
Peter Williamson

Bibliographic record

VenueActa Psychiatrica Scandinavica · 2011
Typeletter
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsWestern UniversityUniversity Hospital
Fundersnot available
KeywordsOrbitofrontal cortexAnterior cingulate cortexPosterior cingulateCortex (anatomy)Cerebral cortexNeurosciencePerfusionCardiologyDepression (economics)Temporal cortexMedicinePsychologyCingulate cortexMagnetic resonance imagingPlaceboInternal medicineCentral nervous systemPathologyPrefrontal cortexRadiologyCognition

Abstract

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In this issue of Acta, Psychiatrica Scandinavica Järnum et al. (1) have reported a very ambitious longitudinal study of cortical thickness estimated with a sophisticated surface-based method, cerebral perfusion function assessed with pseudo-continuous arterial spin labeling and brain metabolites measured with magnetic resonance spectroscopy. Järnum et al. (1) reported thinner cortical regions in depressed patients compared to healthy controls. Some of these regions such as the inferior frontal region would be expected on the basis of regions known to be associated with emotional processing. However, the finding of cortical thinning in the superior and medial temporal regions was a bit surprising, suggesting that the networks associated with depression may be more complex than previously imagined. Another interesting finding was that there was a longitudinal increase in cortical thickness in the right superior temporal pole and left orbitofrontal cortex, perhaps indicating that some of the structural changes are reversible in keeping with other recent studies. Cerebral perfusion did not differ between groups as might be expected with patients on medication, but there were significant decreases in N-acetylaspartate, myoinositol, and glutamate in depressed patients at baseline in keeping with postmortem glial cell deficits in these patients. More interesting were findings in non-responders. Järnum et al. (1) reported significant cortical thinning in the posterior cingulate cortex and a decrease in perfusion in the anterior cingulate cortex in non-responders at baseline. Both placebo and fluoxetine have been reported to be associated with metabolic increases in these regions. The findings of Järnum et al. (1) add to the growing literature that suggests that rostral anterior cingulate activity may be associated with treatment response. However, cortical thinning in the posterior cingulate in non-responders suggests that structural changes in this region may also be associated with poor treatment response. Despite the obvious importance of genetic factors in mood disorders, after more than two decades of intensive search, no major genetic anomalies have arisen which account for more than a tiny fraction of cases. Psychiatric disorders are not alone. In fact, it is increasing clear that the ‘genomic bubble’ has been deflated (2). The challenge of our time is not to find the genetic anomalies associated with these disorders but to define the final common pathways that underlie major psychiatric disorders whatever the cause of the disorder (3). Finding the final common pathways that underlie psychiatric disorders is not going to be easy, but there is some reason to be optimistic in the case of mood disorders. The neural systems associated with emotional processing are beginning to be identified. These are a ventral system including the amygdala, insula, ventral striatum, ventral anterior cingulate gyrus, and prefrontal cortex involved in identification and autonomic regulation of emotional responses and a dorsal system including the hippocampus, dorsal anterior cingulate gyrus, and prefrontal cortex involved in effortful regulation of affectively mediated behaviors (4). Not surprisingly, many of regions involved in emotional processing are associated with brain imaging anomalies in mood disordered patients. However, the patterns tend to vary depending on the population studied and the techniques utilized. Very few of the studies in this area have attempted longitudinal studies and fewer still have used more than one technique to study brain structure and function in the same patients. Thus, the Järnum et al. (1) study is a welcome addition to the literature. While any number of genetic and/or environmental factors could lead to the structural and functional anomalies seen in these patients, an intriguing possibility offered by Järnum et al. (1) is that cortical thinning, decreased perfusion, and loss of glial cells might be consistent with chronic inflammatory changes associated with the hypersecretion of cortisol. Other explanations are possible but the idea that inflammation may be associated with depression is very appealing. Much like the suggestion that gastrointestinal ulcers might be caused by a bacterium, there has been some resistance to this idea, but there is some compelling evidence that inflammation and depression are linked. A particularly striking double-blind, randomized crossover studies demonstrated mood changes associated with subgenual cingulate activity in healthy volunteers given typhoid vaccinations (5). Clearly, there is a need to further explore this hypothesis with brain imaging studies in depressed patients which integrate cortisol and inflammatory markers. Some have argued that brain imaging studies have been no more successful than molecular genetic investigations in elucidating the pathophysiology of major psychiatric disorders. There is some truth in this argument, but the tide is changing. We are beginning to understand the nature of many brain networks (3), and it is not unreasonable to expect that this knowledge may translate into an understanding of the final common pathways of disorders like depression.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0070.010
Open science0.0010.005
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0200.004

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 head0.051
GPT teacher head0.286
Teacher spread0.235 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations1
Published2011
Admission routes1
Has abstractyes

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