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Neurobiology and mechanisms

TL;DR — No single pathway explains MDD. The monoamine hypothesis organized drug discovery but a systematic umbrella review found no consistent evidence that depression is caused by low serotonin (Moncrieff 2023, PMID 35854107). Replicated group-level signals involve stress/HPA regulation, immune activation, synaptic plasticity, reward and control circuits, sleep/circadian biology, and metabolism, but none is diagnostic or uniquely causal. Ketamine's rapid effect shifted attention from transmitter concentration toward glutamatergic signaling and activity-dependent plasticity (Berman 2000, PMID 10686270; Zarate 2006, PMID 16894061). A defensible model is multilevel: polygenic liability and exposures alter interacting regulatory systems, producing diverse symptom states.

From monoamines to systems

The strong version of the monoamine hypothesis claims that deficient serotonin, noradrenaline, or dopamine causes depression. The weaker version says monoamine signaling participates in mood regulation and is a treatment target. These must not be conflated.

Evidence stream What it supports What it does not establish
Monoamine depletion Can provoke symptoms in susceptible/remitted groups Universal deficiency in untreated MDD
Transporter/receptor studies Group differences in some datasets A consistent diagnostic lesion
SSRI efficacy Manipulating monoaminergic systems can improve symptoms Illness is caused by low serotonin
Delayed response Downstream adaptation matters A simple concentration correction
Serotonin umbrella review No consistent support for deficiency as general cause Serotonin has no role in treatment or behavior

The 2022 online/2023 print umbrella review found no consistent evidence for reduced serotonin concentration or activity, altered receptors, or a causal role of the serotonin transporter genotype in depression (Moncrieff 2023, PMID 35854107). Earlier pro-monoamine synthesis argued for a deficiency model from pharmacology and depletion paradigms (Delgado 2000, PMID 10775018). The disagreement is partly about claim strength: pathway involvement is compatible with rejection of a universal chemical-imbalance account.

Stress and the HPA axis

Chronic stress can alter hypothalamic–pituitary–adrenal feedback, glucocorticoid signaling, sleep, immune activity, and hippocampal plasticity. Hypercortisolemia and impaired feedback occur in subsets, especially severe or melancholic presentations, but effects are heterogeneous. Prospective meta-analysis links cortisol measures with later depression in young people, while variability in sampling time, matrix, and stress context limits translation (Zajkowska 2022, PMID 34920399).

Level Candidate process Key uncertainty
Exposure adversity, chronic strain, illness exposure measurement and timing
Endocrine cortisol rhythm and feedback state vs trait; subtype specificity
Cellular glucocorticoid effects on neurons/glia human causal direction
Clinical sleep, appetite, cognition, arousal symptom-to-mechanism mapping

Neuroplasticity and BDNF

Neuroplasticity models propose that stress constrains synaptic remodeling and effective treatments restore a window for adaptive learning. BDNF–TrkB signaling links stress, inflammation, and synaptic change in preclinical models (Zhang 2016, PMID 26786147). Peripheral BDNF is not a direct readout of brain synaptic function, and group differences do not validate an individual biomarker.

Ketamine produced rapid antidepressant effects in the first controlled study (Berman 2000, PMID 10686270) and in a subsequent resistant-depression trial (Zarate 2006, PMID 16894061). Its hours-scale response is difficult to explain as gradual monoamine correction; current models emphasize NMDA/AMPA balance, downstream translation, and synaptogenesis. The exact necessary mechanism in humans remains unsettled.

Inflammation and immunometabolism

Meta-analytic and review literature finds higher inflammatory markers in subsets of depressed participants and links immune signaling with sickness behavior, reward, and stress circuits (Troubat 2021, PMID 32150310). Peripheral cytokines vary with adiposity, smoking, infection, sleep, medication, and assay conditions. A meta-analysis of cytokines and antidepressant response found candidate relationships but not a validated selector (Liu 2020, PMID 31427752).

Insulin resistance is also associated with depression at group level (Fernandes 2022, PMID 35777578). This supports an immunometabolic dimension, not the claim that MDD is one metabolic disease.

Claim Current status
Inflammation is elevated in every MDD patient Unsupported
An inflammatory subgroup probably exists Plausible, boundaries unsettled
CRP/cytokines diagnose MDD Unsupported clinically
Immune-targeted treatment should be universal Unsupported
Biomarker-enriched anti-inflammatory trials are rational Supported as a research strategy

Circuits, cognition, and reward

Imaging implicates distributed networks rather than a depression center: frontoparietal control, default-mode/self-referential, salience, limbic, and reward circuits. Anhedonia can reflect altered reward anticipation, effort, learning, or consummation; the same symptom label may therefore conceal several computations. Integrated reviews place circuits alongside endocrine, immune, and synaptic processes (Dean 2017, PMID 28558878).

Group-average structural and functional differences overlap extensively with healthy distributions and other disorders. Scanner, preprocessing, medication, motion, comorbidity, and analytic flexibility constrain reproducibility. Circuit targeting is most clinically mature in TMS, where left dorsolateral prefrontal protocols are effective despite incomplete mechanistic specificity (Mutz 2019, PMID 30917990).

Genetics and environment

GWAS identified 102 independent depression-associated variants in 807,553 people, enriched in prefrontal and synaptic biology (Howard 2019, PMID 30718901). Each effect is tiny; genetic architecture is highly polygenic and shared across psychiatric diagnoses (Cross-Disorder Group 2013, PMID 23453885). Genes shape probabilities and sensitivity to environments, not a fixed episode.

A causal hierarchy

Level Examples Best current use
Distal liability polygenic risk, early adversity population research
Regulatory systems HPA, immune, circadian, metabolic subgroup hypotheses
Circuits/computation reward, threat, control, rumination mechanistic targets
Symptoms mood, anhedonia, sleep, cognition diagnosis and outcomes
Context/function relationships, work, care access prognosis and intervention design

The model prevents two common errors: treating a downstream correlate as a root cause, and assuming that a treatment target reveals the original cause of illness.

Mechanistic evidence deepening

Mechanistic evidence is strongest when it triangulates across human perturbation, longitudinal risk, imaging, and molecular data. Cross-sectional case–control differences alone cannot distinguish cause, consequence, medication exposure, episode state, or shared risk.

Mechanistic claim Evidence that supports it Evidence that limits it
A neuroimmune subgroup exists Drug-naïve and cumulative meta-analyses find higher selected inflammatory markers Marker distributions overlap controls and effects differ by marker, sex, adiposity, and assay
HPA dysregulation is causal Cortisol differences and stress-reactivity findings recur in subsets Treatment does not consistently normalize cortisol and cross-sectional elevation is nonspecific
Reward-circuit dysfunction explains anhedonia Meta-analysis finds ventral-striatal hypo-response with orbitofrontal hyper-response Circuit findings are group averages and classification accuracy is insufficient for diagnosis
Structural brain differences are disease lesions ENIGMA-scale analyses detect small reproducible subcortical/cortical differences Medication, recurrence, adversity, and developmental timing remain entangled
Rapid plasticity is a final common pathway Ketamine changes symptoms within hours and motivates synaptic models Human necessity of any proposed molecular cascade is not established

The live-search evidence therefore favors interacting, state-dependent systems over a single-lesion model. A clinically useful mechanism must predict prospective course or a treatment-by-marker interaction, not merely separate averaged groups.

Additional live-search evidence ledger

The records below were added after full PubMed E-utilities retrieval on 2026-08-30. The ledger states the evidentiary role of each record and preserves the design limitation that should travel with its citation.

  • Schmaal L 2016 — Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Schmaal L 2016, PMID 26122586)

  • Thompson PM 2014 — The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Primary or secondary empirical evidence; interpretation should follow its design and comparator rather than the headline alone. (Thompson PM 2014, PMID 24399358)

  • Shen X 2026 — Association between polygenic risk for Major Depression and brain structure in a mega-analysis of 50,975 participants across 11 studies. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Shen X 2026, PMID 40830579)

  • Wigmore EM 2017 — Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766). Longitudinal observational evidence; temporal ordering improves inference but residual confounding remains. (Wigmore EM 2017, PMID 28809859)

  • Whelan CD 2016 — Heritability and reliability of automatically segmented human hippocampal formation subregions. Primary or secondary empirical evidence; interpretation should follow its design and comparator rather than the headline alone. (Whelan CD 2016, PMID 26747746)

  • Jarkas DA 2024 — Sex differences in the inflammation-depression link: A systematic review and meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Jarkas DA 2024, PMID 39089535)

  • Haapakoski R 2015 — Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Haapakoski R 2015, PMID 26065825)

  • Zhang Y 2023 — Peripheral cytokine levels across psychiatric disorders: A systematic review and network meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Zhang Y 2023, PMID 36893912)

  • Bavaresco DV 2020 — Efficacy of infliximab in treatment-resistant depression: A systematic review and meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Bavaresco DV 2020, PMID 31837338)

  • Köhler CA 2018 — Peripheral Alterations in Cytokine and Chemokine Levels After Antidepressant Drug Treatment for Major Depressive Disorder: Systematic Review and Meta-Analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Köhler CA 2018, PMID 28612257)

  • Islam MR 2023 — Evaluation of inflammatory cytokines in drug-naïve major depressive disorder: A systematic review and meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Islam MR 2023, PMID 37625799)

  • Baxter L 2026 — An umbrella review of inflammatory biomarkers and their relationship to treatment response in MDD. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Baxter L 2026, PMID 42252042)

  • Li W 2024 — A meta-analysis and systematic review of the association between cortisol and the beginning of depression symptoms in adolescents and young adults. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Li W 2024, PMID 39963029)

  • McKay MS 2010 — The impact of treatment on HPA axis activity in unipolar major depression. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (McKay MS 2010, PMID 19747693)

  • Nelson EB 2012 — Psychotic depression--beyond the antidepressant/antipsychotic combination. Primary or secondary empirical evidence; interpretation should follow its design and comparator rather than the headline alone. (Nelson EB 2012, PMID 22936518)

  • Zorn JV 2017 — Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Zorn JV 2017, PMID 28012291)

  • Lopez-Duran NL 2009 — Hypothalamic-pituitary-adrenal axis dysregulation in depressed children and adolescents: a meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Lopez-Duran NL 2009, PMID 19406581)

  • Lombardo G 2019 — Baseline cortisol and the efficacy of antiglucocorticoid treatment in mood disorders: A meta-analysis. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Lombardo G 2019, PMID 31499391)

  • Machahary N 2025 — Association of endogenous hormones with major depressive disorder phenotype: A systematic review and meta-analysis of drug-free case and control cross-sectional study. Meta-analysis; pooled estimates depend on eligibility, heterogeneity, and reporting bias. (Machahary N 2025, PMID 41270487)

Open questions

  • Can inflammatory or metabolic enrichment prospectively identify benefit from a targeted intervention (Liu 2020, PMID 31427752; Fernandes 2022, PMID 35777578)?
  • Which ketamine-induced plasticity changes are necessary for response rather than correlates (Zarate 2006, PMID 16894061)?
  • Are circuit abnormalities stable traits, episode states, treatment scars, or mixtures (Dean 2017, PMID 28558878)?
  • How should symptom-level heterogeneity be integrated with multi-omic data without overfitting (Howard 2019, PMID 30718901)?

References

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  2. Delgado PL. Depression: the case for a monoamine deficiency. Journal of Clinical Psychiatry. 2000. PMID 10775018
  3. Zajkowska Z, et al. Cortisol and development of depression in adolescence and young adulthood: a systematic review and meta-analysis. Psychoneuroendocrinology. 2022. PMID 34920399
  4. Zhang JC, et al. BDNF-TrkB Signaling in Inflammation-related Depression and Potential Therapeutic Targets. Current Neuropharmacology. 2016. PMID 26786147
  5. Berman RM, et al. Antidepressant effects of ketamine in depressed patients. Biological Psychiatry. 2000. PMID 10686270
  6. Zarate CA Jr, et al. A randomized trial of an NMDA antagonist in treatment-resistant major depression. Archives of General Psychiatry. 2006. PMID 16894061
  7. Troubat R, et al. Neuroinflammation and depression: A review. European Journal of Neuroscience. 2021. PMID 32150310
  8. Liu JJ, et al. Peripheral cytokine levels and response to antidepressant treatment in depression. Molecular Psychiatry. 2020. PMID 31427752
  9. Fernandes BS, et al. Insulin resistance in depression: a large meta-analysis. Neuroscience and Biobehavioral Reviews. 2022. PMID 35777578
  10. Dean J, Keshavan M. The neurobiology of depression: An integrated view. Asian Journal of Psychiatry. 2017. PMID 28558878
  11. Mutz J, et al. Comparative efficacy and acceptability of non-surgical brain stimulation for major depressive episodes. BMJ. 2019. PMID 30917990
  12. Howard DM, et al. Genome-wide meta-analysis of depression identifies 102 independent variants. Nature Neuroscience. 2019. PMID 30718901
  13. Cross-Disorder Group of the Psychiatric Genomics Consortium. Shared genetic risk loci across five psychiatric disorders. Lancet. 2013. PMID 23453885
  14. Schmaal L, et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Molecular psychiatry. 2016;21:806-12. PMID 26122586
  15. Thompson PM, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain imaging and behavior. 2014;8:153-82. PMID 24399358
  16. Shen X, et al. Association between polygenic risk for Major Depression and brain structure in a mega-analysis of 50,975 participants across 11 studies. Molecular psychiatry. 2026;31:611-621. PMID 40830579
  17. Wigmore EM, et al. Do regional brain volumes and major depressive disorder share genetic architecture? A study of Generation Scotland (n=19 762), UK Biobank (n=24 048) and the English Longitudinal Study of Ageing (n=5766). Translational psychiatry. 2017;7:e1205. PMID 28809859
  18. Whelan CD, et al. Heritability and reliability of automatically segmented human hippocampal formation subregions. NeuroImage. 2016;128:125-137. PMID 26747746
  19. Jarkas DA, et al. Sex differences in the inflammation-depression link: A systematic review and meta-analysis. Brain, behavior, and immunity. 2024;121:257-268. PMID 39089535
  20. Haapakoski R, et al. Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Brain, behavior, and immunity. 2015;49:206-15. PMID 26065825
  21. Zhang Y, et al. Peripheral cytokine levels across psychiatric disorders: A systematic review and network meta-analysis. Progress in neuro-psychopharmacology & biological psychiatry. 2023;125:110740. PMID 36893912
  22. Bavaresco DV, et al. Efficacy of infliximab in treatment-resistant depression: A systematic review and meta-analysis. Pharmacology, biochemistry, and behavior. 2020;188:172838. PMID 31837338
  23. Köhler CA, et al. Peripheral Alterations in Cytokine and Chemokine Levels After Antidepressant Drug Treatment for Major Depressive Disorder: Systematic Review and Meta-Analysis. Molecular neurobiology. 2018;55:4195-4206. PMID 28612257
  24. Islam MR, et al. Evaluation of inflammatory cytokines in drug-naïve major depressive disorder: A systematic review and meta-analysis. International journal of immunopathology and pharmacology. 2023;37:3946320231198828. PMID 37625799
  25. Baxter L, et al. An umbrella review of inflammatory biomarkers and their relationship to treatment response in MDD. Neuroscience and biobehavioral reviews. 2026;188:106806. PMID 42252042
  26. Li W, et al. A meta-analysis and systematic review of the association between cortisol and the beginning of depression symptoms in adolescents and young adults. Folia neuropathologica. 2024;62:335-347. PMID 39963029
  27. McKay MS, et al. The impact of treatment on HPA axis activity in unipolar major depression. Journal of psychiatric research. 2010;44:183-92. PMID 19747693
  28. Nelson EB. Psychotic depression--beyond the antidepressant/antipsychotic combination. Current psychiatry reports. 2012;14:619-23. PMID 22936518
  29. Zorn JV, et al. Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analysis. Psychoneuroendocrinology. 2017;77:25-36. PMID 28012291
  30. Lopez-Duran NL, et al. Hypothalamic-pituitary-adrenal axis dysregulation in depressed children and adolescents: a meta-analysis. Psychoneuroendocrinology. 2009;34:1272-83. PMID 19406581
  31. Lombardo G, et al. Baseline cortisol and the efficacy of antiglucocorticoid treatment in mood disorders: A meta-analysis. Psychoneuroendocrinology. 2019;110:104420. PMID 31499391
  32. Machahary N, et al. Association of endogenous hormones with major depressive disorder phenotype: A systematic review and meta-analysis of drug-free case and control cross-sectional study. Biochemical and biophysical research communications. 2025;793:153007. PMID 41270487