In the clinical treatment of mood disorders, the complex behavioral symptoms
presented by patients and variability of patient response to particular
medication classes can create difficulties in providing fast and reliable
treatment when standard diagnostic and prescription methods are used.
Increasingly, the incorporation of physiological information such as
neuroimaging scans and derivatives into the clinical process promises to
alleviate some of the uncertainty surrounding this process. Particularly, if
neural features can help to identify patients who may not respond to standard
courses of anti-depressants or mood stabilizers, clinicians may elect to avoid
lengthy and side-effect-laden treatments and seek out a different, more
effective course that might otherwise not have been under consideration.
Previously, approaches for the derivation of relevant neuroimaging features
work at only one scale in the data, potentially limiting the depth of
information available for clinical decision support. In this work, we show that
the utilization of multi spatial scale neuroimaging features - particularly
resting state functional networks and functional network connectivity measures
- provide a rich and robust basis for the identification of relevant medication
class and non-responders in the treatment of mood disorders. We demonstrate
that the generated features, along with a novel approach for fast and automated
feature selection, can support high accuracy rates in the identification of
medication class and non-responders as well as the identification of novel,
multi-scale biomarkers.

通过利用多空间尺度的神经影像特征，尤其是静息状态下的功能网络和功能网络连接度测量，本研究展示了这些特征对于在治疗情绪障碍中识别相关药物类别和非反应者具有丰富且可靠的基础。我们证明了所生成的特征，以及用于快速自动特征选择的新方法，可支持在药物类别和非反应者的识别以及多尺度生物标志物的识别方面具有高准确率。