Psychometric validity of the Montgomery and Åsberg Depression Rating Scale for Youths (MADRS-Y)

Vestin, Magnus; Åsberg, Marie; Wiberg, Marie; Blom, Eva Henje; Dennhag, Inga

doi:10.1080/08039488.2022.2135761

Cited by 3 publications

(1 citation statement)

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“…Major depressive disorder (MDD) is a widespread emotional disorder, which can lead to various psychological, physiological and cognitive disorders [1]. The traditional diagnosis include the hamilton depression rating scale-17 (HAMD-17) [2]- [4], montgomery asperger depression rating scale (MADRS) [5], patient health questionnaire-9 (PHQ-9) [6]. The above scales require a lot of domain knowledge, professional inquiry and filling in time.…”

Section: Introductionmentioning

confidence: 99%

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition

Sun,

Xu,

Zhao

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN.However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.

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