Open Access
Numéro
Med Sci (Paris)
Volume 41, Numéro 5, Mai 2025
Enjeux et objectifs de la psychiatrie de précision
Page(s) 500 - 507
Section La psychiatrie de précision (PEPR PROPSY) : premiers succès
DOI https://doi.org/10.1051/medsci/2025061
Publié en ligne 26 mai 2025
  1. Schneckenburger R. La distinction entre neurologie et psychiatrie en France entre 1940 et 1968 : le point de vue de quelques neuropsychiatres. Les Cahiers du Centre Georges Canguilhem 2018 ; 7 : 33–54. [CrossRef] [Google Scholar]
  2. Blackman G, Neri G, Al-Doori O, et al. Prevalence of neuroradiological abnormalities in first-episode psychosis: a systematic review and meta-analysis. JAMA Psychiatry 2023 ; 80 : 1047–54. [CrossRef] [PubMed] [Google Scholar]
  3. Allen P, Modinos G, Hubl D, et al. Neuroimaging auditory hallucinations in schizophrenia: from neuroanatomy to neurochemistry and beyond. Schizophr Bull 2012 ; 38 : 695–703. [CrossRef] [PubMed] [Google Scholar]
  4. Jardri R, Favrod J, Laroi F. Psychothérapies des hallucinations. Issy-les-Moulineaux : Elsevier Masson SAS, 2016 : 352 p. [Google Scholar]
  5. Leichsenring F, Steinert C, Rabung S, et al. The efficacy of psychotherapies and pharmacotherapies for mental disorders in adults : an umbrella review and meta-analytic evaluation of recent meta-analyses. World Psychiatry 2022 ; 21 : 133–45. [CrossRef] [PubMed] [Google Scholar]
  6. Dhamala E, Yeo BTT, Holmes AJ. One size does not fit all: methodological considerations for brain-based predictive modeling in psychiatry. Biol Psychiatry 2023 ; 93 : 717–28. [CrossRef] [PubMed] [Google Scholar]
  7. Larivière S, Paquola C, Park B, et al. The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets. Nat Methods 2021 ; 18 : 698–700. [CrossRef] [PubMed] [Google Scholar]
  8. Zinkstok JR, Boot E, Bassett AS, et al. Neurobiological perspective of 22q11.2 deletion syndrome. Lancet Psychiatry 2019 ; 6 : 951–60. [CrossRef] [PubMed] [Google Scholar]
  9. Schneider M, Debbané M, Bassett AS, et al. Psychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the international consortium on brain and behavior in 22q11.2 deletion syndrome. AJP 2014 ; 171 : 627–39. [CrossRef] [PubMed] [Google Scholar]
  10. Sun D, Ching CRK, Lin A, et al. Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: convergence with idiopathic psychosis and effects of deletion size. Mol Psychiatry 2020 ; 25 : 1822–34. [CrossRef] [PubMed] [Google Scholar]
  11. Pierrefeu A de, Löfstedt T, Laidi C, et al. Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine learning with structured sparsity. Acta Psychiatr Scand 2018 ; 138 : 571–80. [CrossRef] [PubMed] [Google Scholar]
  12. Demazeux S, Pidoux V. Le projet RDoC — La classification psychiatrique de demain ? Med Sci (Paris) 2015 ; 31 : 792–6. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  13. Noble S, Spann MN, Tokoglu F, et al. Influences on the test-retest reliability of functional connectivity MRI and its relationship with behavioral utility. Cereb Cortex 2017 ; 27 : 5415–29. [CrossRef] [PubMed] [Google Scholar]
  14. Rutherford S, Barkema P, Tso IF, et al. Evidence for embracing normative modeling. Elife 2023 ; 12 : e85082. [CrossRef] [PubMed] [Google Scholar]
  15. Abi-Dargham A, Moeller SJ, Ali F, et al. Candidate biomarkers in psychiatric disorders: state of the field. World Psychiatry 2023 ; 22 : 236–62. [CrossRef] [PubMed] [Google Scholar]
  16. Nunes A, Schnack HG, Ching CRK, et al. Using structural MRI to identify bipolar disorders — 13 site machine learning study in 3020 individuals from the ENIGMA bipolar disorders working group. Mol Psychiatry 2020 ; 25 : 2130–43. [CrossRef] [PubMed] [Google Scholar]
  17. Collin G, Seidman LJ, Keshavan MS, et al. Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program. Mol Psychiatry 2020 ; 25 : 2431–40. [CrossRef] [PubMed] [Google Scholar]
  18. Brucar LR, Feczko E, Fair DA, et al. Current approaches in computational psychiatry for the data-driven identification of brain-based subtypes. Biol Psychiatry 2022 ; 93 : 704–16. [Google Scholar]
  19. Scott J, Hidalgo-Mazzei D, Strawbridge R, et al. Prospective cohort study of early biosignatures of response to lithium in bipolar-I-disorders: overview of the H2020-funded R-LiNK initiative. Int J Bipolar Disord 2019 ; 7 : 20. [CrossRef] [PubMed] [Google Scholar]
  20. Poulet E, Bubrovszky M, Bulteau S, et al. Stimulation magnétique transcrânienne répétitive : Applications en psychiatrie. Tours : PUFR, 2019 : 390. [Google Scholar]
  21. Boyer M, Baudin P, Stengel C, et al. In vivo low-intensity magnetic pulses durably alter neocortical neuron excitability and spontaneous activity. J Physiol 2022 ; 600 : 4019–37. [CrossRef] [PubMed] [Google Scholar]
  22. Bradley C, Nydam AS, Dux PE, et al. State-dependent effects of neural stimulation on brain function and cognition. Nat Rev Neurosci 2022 ; 23 : 459–75. [CrossRef] [PubMed] [Google Scholar]
  23. Caulfield KA, Fleischmann HH, Cox CE, et al. Neuronavigation maximizes accuracy and precision in TMS positioning: Evidence from 11,230 distance, angle, and electric field modeling measurements. Brain Stimulation 2022 ; 15 : 1192–205. [CrossRef] [PubMed] [Google Scholar]
  24. Fox MD, Buckner RL, White MP, et al. Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry 2012 ; 72 : 595–603. [CrossRef] [PubMed] [Google Scholar]
  25. Cash RFH, Cocchi L, Lv J, et al. Functional magnetic resonance imaging-guided personalization of transcranial magnetic stimulation treatment for depression. JAMA Psychiatry 2021 ; 78 : 337–9. [CrossRef] [PubMed] [Google Scholar]
  26. Siddiqi SH, Weigand A, Pascual-Leone A, et al. Identification of personalized transcranial magnetic stimulation targets based on subgenual cingulate connectivity: an independent replication. Biol Psychiatry 2021 ; 90 : e55–e56. [CrossRef] [PubMed] [Google Scholar]
  27. Elbau IG, Lynch CJ, Downar J, et al. Functional connectivity mapping for rTMS target selection in depression. Am J Psychiatry 2023 ; 180 : 230–40. [CrossRef] [PubMed] [Google Scholar]
  28. Cole EJ, Stimpson KH, Bentzley BS, et al. Stanford accelerated intelligent neuromodulation therapy for treatment-resistant depression. AJP 2020 ; 177 : 716–26. [CrossRef] [PubMed] [Google Scholar]
  29. Cole EJ, Phillips AL, Bentzley BS, et al. Stanford neuromodulation therapy (SNT): a double-blind randomized controlled trial. AJP 2022 ; 179 : 132–41. [CrossRef] [PubMed] [Google Scholar]
  30. Talou J, Sayous R, Bouaziz N, et al. Mondor-stim: an open-source pipeline to optimize the target for transcranial magnetic stimulation, based on fMRI Functional connectivity. 2024 ; 10.5281/zenodo.13170889. [Google Scholar]
  31. Morriss R, Briley PM, Webster L, et al. Connectivity-guided intermittent theta burst versus repetitive transcranial magnetic stimulation for treatment-resistant depression: a randomized controlled trial. Nat Med 2024 ; 30 : 403–13. [CrossRef] [PubMed] [Google Scholar]
  32. Hoffman RE, Boutros NN, Hu S, et al. Transcranial magnetic stimulation and auditory hallucinations in schizophrenia. Lancet 2000 ; 355 : 1073–5. [CrossRef] [PubMed] [Google Scholar]
  33. Demeulemeester M, Amad A, Bubrovszky M, et al. What is the real effect of 1-Hz repetitive transcranial magnetic stimulation on hallucinations? Controlling for publication bias in neuromodulation trials. Biol Psychiatry 2012 ; 71 : e15–6. [CrossRef] [PubMed] [Google Scholar]
  34. Amad A, Jardri R, Rousseau C, et al. Excess significance sias in repetitive transcranial magnetic stimulation literature for neuropsychiatric disorders. Psychother Psychosom 2019 ; 1–8. [Google Scholar]
  35. Plaze M, Paillère-Martinot ML, Penttilä J, et al. “Where do auditory hallucinations come from?” - A brain morphometry study of schizophrenia patients with inner or outer space hallucinations. Schizophr Bull 2011 ; 37 : 212–21. [CrossRef] [PubMed] [Google Scholar]
  36. Saxe R, Brett M, Kanwisher N. Divide and conquer: a defense of functional localizers. Neuroimage 2006 ; 30 : 1088–1096 ; discussion 1097-9. [CrossRef] [PubMed] [Google Scholar]
  37. Jardri R, Thomas P, Delmaire C, et al. The neurodynamic organization of modality-dependent hallucinations. Cereb Cortex 2013 ; 23 : 1108–17. [CrossRef] [PubMed] [Google Scholar]
  38. Jardri R, Pouchet A, Pins D, et al. Cortical activations during auditory verbal hallucinations in schizophrenia: a coordinate-based meta-analysis. Am J Psychiatry 2011 ; 168 : 73–81. [CrossRef] [PubMed] [Google Scholar]
  39. Sommer IEC, Diederen KMJ, Blom JD, et al. Auditory verbal hallucinations predominantly activate the right inferior frontal area. Brain 2008 ; 131 : 3169–77. [CrossRef] [PubMed] [Google Scholar]
  40. Gill K, Percival C, Roes M, et al. Real-time symptom capture of hallucinations in schizophrenia with fMRI: absence of duration-dependent activity. Schizophr Bull Open 2022 ; sgac050. [CrossRef] [PubMed] [Google Scholar]
  41. Leroy A, Foucher JR, Pins D, et al. fMRI capture of auditory hallucinations: validation of the two-steps method. Hum Brain Mapp 2017 ; 38 : 4966–79. [CrossRef] [PubMed] [Google Scholar]
  42. Fovet T, Yger P, Lopes R, et al. Decoding activity in Broca’s area predicts the occurrence of auditory hallucinations across subjects. Biol Psychiatry 2022 ; 91 : 194–201. [CrossRef] [PubMed] [Google Scholar]
  43. Jardri R, Lucas B, Delevoye-Turrell Y, et al. An 11-year-old boy with drug-resistant schizophrenia treated with temporo-parietal rTMS. Mol Psychiatry 2007 ; 12 : 320. [CrossRef] [PubMed] [Google Scholar]
  44. Pindi P, Houenou J, Piguet C, et al. Real-time fMRI neurofeedback as a new treatment for psychiatric disorders: A meta-analysis. Prog Neuropsychopharmacol Biol Psychiatry 2022 ; 119 : 110605. [CrossRef] [PubMed] [Google Scholar]
  45. Donantueno C, Yger P, Cabestaing F, et al. fMRI-based neurofeedback strategies and the way forward to treating phasic psychiatric symptoms. Front Neurosci 2023 ; 17 : 1275229. [CrossRef] [PubMed] [Google Scholar]
  46. Hardelin JP. Facteur « confondant » ou de confusion. Med Sci (Paris) 2024 ; 40 : 381. [CrossRef] [EDP Sciences] [Google Scholar]

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