Real time fMRI feedback and auditory processing in schizophrenia

描述(申请人提供)：听觉语言幻觉(AH)长期以来一直是精神分裂症(SZ)症状学的特征和主要诊断特征之一(Andreasen和Flaum，1991；DSM-IV)。已有研究表明，AH所涉及的脑区参与了语言加工的不同方面。这个大脑网络包括颞叶皮质，特别是颞叶上回(STG)、扣带前回(ACC)、额下回(IFG)和颞顶交界区(TPJ)。直到最近， 主要是用抗精神病药物控制，但往往无效。在这一应用中，我们建议使用一种尖端的生物反馈方法，即实时功能成像神经反馈(RT-fMRI)，以减少AH的频率，并更好地了解AH涉及的脑过程。我们采用了Hugdahl 2009年的认知障碍模型，根据该模型，认知障碍是由言语自我监控、选择性注意和听觉知觉加工的异常引起的。我们关注听觉皮质的作用，特别是STG，作为治疗深圳地区AH的神经反馈训练的靶区。在现有文献的基础上，我们假设STG中改进的功能将使AH所涉及的整个网络正常化。我们预测，由于从STG(以及形成AH网络的区域：IFG、ACC和TPJ)收到反馈，STG(以及形成AH网络的区域：IFG、ACC和TPJ)的激活将增加，这将导致在识别自我/他人语音的任务中表现更好，这是通过RT-fMRI训练前后的fMRI激活水平来衡量的，并通过减少阳性症状，特别是AH。RT-fMRI神经反馈依赖于计算机技术的最新进步，计算机技术允许受试者看到选定大脑区域的激活水平，并在提供培训后调节激活。受试者被教导在根据激活任务选择的特定大脑区域内上调或下调fMRI信号。重要的是，只有当受试者从他/她的目标区域接收到反馈时，报告的激活改变才是可能的，而不是当她/他从不同的受试者或与目标区域(假)不同的区域接收反馈时。最近已经证明，这种方法可能对许多临床情况有帮助，包括精神分裂症(Haller 2010；Sitaran 2011；Linden 2012；Veit 2009；Hartwell 2012；Li 2012，Ruiz 2011)。在这项研究中，我们建议研究30名慢性SZ患者和30名正常对照(NC)(其中一半将接受真实的fMRI反馈，另一半将接受假的fMRI反馈)。我们将同时使用功能磁共振成像(包括两个连接性测量)和临床测量RT-fMRI反馈的有效性。基于现有文献和我们的初步数据，我们预测我们将成功地改善STG内和靶向网络内的听觉处理，并将导致临床症状的改善，特别是AH的频率。

DESCRIPTION (provided by applicant): Auditory verbal hallucinations (AH) have long been a hallmark of schizophrenia (SZ) symptomatology and one of its major diagnostic features (Andreasen and Flaum, 1991; DSM-IV). It has been demonstrated that the brain regions implicated in AH are involved in different aspects of language processing. This brain network includes the temporal cortex and especially the superior temporal gyrus (STG), anterior cingulate (ACC), inferior frontal gyrus (IFG), and temporo-parietal junction (TPJ). Until recently, AH were primarily controlled with antipsychotic medication which was often ineffective. In this application, we propose to use a cutting-edge biofeedback method of real-time functional imaging neuro-feedback (rt-fMRI) to reduce the frequency of AH and to better understand brain processes involved in AH. We adopt the Hugdahl 2009 model of AH according to which AH result from abnormalities in self-monitoring of speech, in selective attention and in auditory perceptual processes. We focus on the role of auditory cortex, specifically the STG, as a target area of neuro-feedback training for the treatment of AH in SZ. Based on existing literature, we posit that that improved function in the STG will normalize the entire network involved in the experience of AH. We predict that the activation in the STG (and regions forming AH network: IFG, ACC, and TPJ) will increase as a result of receiving feedback from that region which will lead to a better performance on the task of recognizing self/other voice as measured by fMRI activation levels pre-and post-rt-fMRI training and by reduction in positive symptoms and especially AH. The rt-fMRI neuro-feedback relies on recent advances in computer technology that allows subjects to see the level of activation in the selected brain region and to regulate ths activation following the training provided. Subjects are taught to up-regulate or down-regulate fMRI signal within a specific brain region selected based on the activation task. Importantly, reported activation changes are possible only if the subject receives a feedback from his/her target area and not when she/he receives it from a different subject or a different area than the target one (sham). It has been recently demonstrated that this approach may be helpful in many clinical conditions, among them schizophrenia (Haller 2010; Sitaran 2011;Linden 2012;Veit 2009; Hartwell 2012; Li 2012, Ruiz 2011). In this study we propose to study 30 chronic SZ patients and 30 normal control (NC) individuals (half of them will receive real and half will receive a sham fMRI feedback). We will use both fMRI (including two measures of connectivity) and clinical measures of the effectiveness of the rt-fMRI feedback. Based on the existing literature and our preliminary data, we predict that we will successfully improve auditory processing within the STG and within the targeted network and that it will lead to improvement in the clinical symptoms and especially in the frequency of AH.