Détection automatisée des hallucinations auditives en IRM fonctionnelle et perspectives thérapeutiques dans la schizophrénie

Abstract : Hallucination is a transient subjective experience perceived as real, but occurring in the absence of an appropriate stimulation coming from the external environment. Hallucinatory events, which can occur across every sensory modality, are observed in various neurological and psychiatric disorders but also among “non-clinical” populations. The most frequent disorder associated with hallucinations in the field of psychiatry is schizophrenia. Auditory-verbal experiences are particularly frequent, with a lifetime-prevalence of 60 to 80% in patients suffering from schizophrenia. Hallucinations may cause long-term disability and poorer quality of life.In this context, the management of auditory-verbal hallucinations in patients with schizophrenia constitutes a major challenge. However, despite the increasing sophistication of biological and psychosocial research methods in the field, no significant therapeutic breakthrough has occurred in the last decade and a consensus exists that a significant proportion of patients with schizophrenia (i.e., around 25 %), exhibit drug-resistant auditory-verbal hallucinations. Non-pharmacological treatments, such as repetitive transcranial magnetic stimulation (rTMS) or transcranial direct current stimulation (tDCS) have been proposed as an option for addressing the unmet medical needs described above. However, these neuromodulation techniques show a moderate effect in alleviating drug-resistant auditory-verbal hallucinations and the development of innovative therapeutic strategies remains a major challenge.In recent years, the number of brain imaging studies in the field of auditory-verbal hallucinations has grown substantially, leading to a better pathophysiological understanding of this subjective phenomenon. Recent progress in deciphering the neural underpinnings of AVHs has strengthened transdiagnostic neurocognitive models that characterize auditory-verbal hallucinations, but more specifically these findings built the bases for new therapeutic strategies. In this regards the development of auditory hallucinations “capture" brain-imaging studies (i.e. the identification of functional patterns associated with the occurrence of auditory hallucinations), was the main topic of this thesis.The first part of this work is devoted to the automatized detection of auditory-verbal hallucinations using functional MRI (fMRI). The identification of hallucinatory periods occurring during a fMRI session is now possible using a semi-automatized procedure based on an independent component analysis applied to resting fMRI data combined with a post-fMRI interview (i.e. the patient is asked to report auditory-verbal hallucinations immediately after acquisition). This “two-steps method” allows for the identification of hallucination periods (ON) and non-hallucination ones (OFF). However, the time-consuming nature of this a posteriori labelling procedure considerably limits its use. In these regards, we show how machine-learning, especially support vector machine (SVM), allows the automation of hallucinations capture. We present new results of accurate and generalizable classifiers which could be used in real-time because of their low computational-cost. We also highlight that algorithms able to identify the "pre-hallucinatory" period exhibit significant performances. Finally, we propose the use of an alternative learning-machine strategy, based on TV-Elastic-net, which achieves slightly better performances and more interpretable discriminative maps than SVM [...]
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Thomas Fovet. Détection automatisée des hallucinations auditives en IRM fonctionnelle et perspectives thérapeutiques dans la schizophrénie. Médecine humaine et pathologie. Université du Droit et de la Santé - Lille II, 2017. Français. ⟨NNT : 2017LIL2S036⟩. ⟨tel-01956591v2⟩

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