• Renaud Jardri, Alexandre Pouchet, Delphine Pins, and Pierre Thomas.
• CNRS, University Lille North of France, France. renaud.jardri@chru-lille.fr
• Am J Psychiatry. 2011 Jan 1; 168 (1): 73-81.

ObjectiveAuditory verbal hallucinations (AVHs) constitute severe, incapacitating symptoms of schizophrenia. Despite increasing interest in the functional exploration of AVHs, the available findings remain difficult to integrate because of their considerable variability. The authors' aim was to perform a robust quantitative review of existing functional data in order to elucidate consistent patterns observed during the emergence of AVHs and to orient new pathophysiological models of hallucinations.MethodTen positron emission tomography or functional magnetic resonance imaging studies were selected for the meta-analysis after systematic review. A total of 68 patients with schizophrenia spectrum disorders experiencing AVHs during scanning were included. According to a random-effects activation likelihood estimation algorithm, stereotaxic coordinates of 129 foci, reported as significant in the source studies, were extracted and computed to estimate the brain locations most consistently associated with AVHs across studies (cluster-extent threshold: 200 mm³).ResultsPatients experiencing AVHs demonstrated significantly increased activation likelihoods in a bilateral neural network, including the Broca's area (activation likelihood estimation=1.84×10⁻³), anterior insula (1.78×10⁻³), precentral gyrus (1.46×10⁻³), frontal operculum (1.29×10⁻³), middle and superior temporal gyri (1.59×10⁻³), inferior parietal lobule (1.33×10⁻³), and hippocampus/parahippocampal region (1.90×10⁻³).ConclusionsThis meta-analysis demonstrated that experiencing AVHs is associated with increased activity in fronto-temporal areas involved in speech generation and speech perception, but also within the medial temporal lobe, a structure notably involved in verbal memory. Such findings support a model for AVHs in which aberrant cortical activations emerge within a distributed network involved at different levels of complexity in the brain architecture. Critical future directions are considered.

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