Background

Functional magnetic resonance imaging (fMRI) capture aims at detecting auditory-verbal hallucinations (AVHs) from continuously recorded brain activity. Establishing efficient capture methods with low computational cost that easily generalize between patients remains a key objective in precision psychiatry. To address this issue, we developed a novel automatized fMRI-capture procedure for AVHs in patients with schizophrenia (SCZ).

Methods

We used a previously validated but labor-intensive personalized fMRI-capture method to train a linear classifier using machine learning techniques. We benchmarked the performances of this classifier on 2320 AVH periods versus resting-state periods obtained from SCZ patients with frequent symptoms (n = 23). We characterized patterns of blood oxygen level–dependent activity that were predictive of AVH both within and between subjects. Generalizability was assessed with a second independent sample gathering 2000 AVH labels (n = 34 patients with SCZ), while specificity was tested with a nonclinical control sample performing an auditory imagery task (840 labels, n = 20).

Results

Our between-subject classifier achieved high decoding accuracy (area under the curve = 0.85) and discriminated AVH from rest and verbal imagery. Optimizing the parameters on the first schizophrenia dataset and testing its performance on the second dataset led to an out-of-sample area under the curve of 0.85 (0.88 for the converse test). We showed that AVH detection critically depends on local blood oxygen level–dependent activity patterns within Broca's area.

Conclusions

Our results demonstrate that it is possible to reliably detect AVH states from fMRI blood oxygen level–dependent signals in patients with SCZ using a multivariate decoder without performing complex preprocessing steps. These findings constitute a crucial step toward brain-based treatments for severe drug-resistant hallucinations.

中文翻译：

---

布罗卡区的解码活动预测受试者幻听的发生

背景

功能性磁共振成像 (fMRI) 捕获旨在从连续记录的大脑活动中检测听觉幻觉 (AVH)。建立具有低计算成本且易于在患者之间推广的有效捕获方法仍然是精准精神病学的关键目标。为了解决这个问题，我们为精神分裂症 (SCZ) 患者的 AVH 开发了一种新的自动化 fMRI 捕获程序。

方法

我们使用了一种先前经过验证但劳动密集型的个性化 fMRI 捕获方法来使用机器学习技术训练线性分类器。我们将该分类器在 2320 个 AVH 周期与从具有频繁症状的 SCZ 患者（n  = 23）获得的静息状态周期的性能进行了基准测试。我们描述了可预测受试者内部和受试者之间 AVH 的血氧水平依赖性活动模式。使用收集 2000 个 AVH 标签（n  = 34 名 SCZ 患者）的第二个独立样本评估普遍性，而使用执行听觉图像任务的非临床对照样本（840 个标签，n  = 20）测试特异性。

结果

我们的主体间分类器实现了高解码精度（曲线下面积 = 0.85），并将 AVH 与休息和语言图像区分开来。优化第一个精神分裂症数据集的参数并测试其在第二个数据集上的性能导致曲线下的样本外面积为 0.85（相反测试为 0.88）。我们发现 AVH 检测严重依赖于 Broca 区域内的局部血氧水平依赖性活动模式。

结论

我们的结果表明，无需执行复杂的预处理步骤，就可以使用多变量解码器从 SCZ 患者的 fMRI 血氧水平依赖信号中可靠地检测 AVH 状态。这些发现是朝着基于大脑的严重耐药幻觉治疗迈出的关键一步。