# New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning Source: [New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity Using Dictionary Learning]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:44:10.915Z Authors: Fateme Ghayem, Hanlu Yang, Furkan Kantar, Seung-Jun Kim, Vince D. Calhoun, Tulay Adali Published: 2022-11-10T19:49:16.000Z ## Abstract Independent component analysis (ICA) of multi-subject functional magnetic resonance imaging (fMRI) data has proven useful in providing a fully multivariate summary that can be used for multiple purposes. ICA can identify patterns that can discriminate between healthy controls (HC) and patients with various mental disorders such as schizophrenia (Sz). Temporal functional network connectivity (tFNC) obtained from ICA can effectively explain the interactions between brain networks. On the other hand, dictionary learning (DL) enables the discovery of hidden information in data using learnable basis signals through the use of sparsity. In this paper, we present a new method that leverages ICA and DL for the identification of directly interpretable patterns to discriminate between the HC and Sz groups. We use multi-subject resting-state fMRI data from $358$ subjects and form subject-specific tFNC feature vectors from ICA results. Then, we learn sparse representations of the tFNCs and introduce a new set of sparse features as well as new interpretable patterns from the learned atoms. Our experimental results show that the new representation not only leads to effective classification between HC and Sz groups using sparse features, but can also identify new interpretable patterns from the learned atoms that can help understand the complexities of mental diseases such as schizophrenia. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Disorder Diagnosis (HC vs. MDD) on U center](): Disorder Diagnosis (HC vs. MDD); U center; Accuracy: 0.711. - [Disorder Diagnosis (HC vs. ASD) on ABIDE (NYU site)](): Disorder Diagnosis (HC vs. ASD); ABIDE (NYU site); Accuracy: 66.9. - [Disorder Diagnosis (HC vs. BD) on U center](): Disorder Diagnosis (HC vs. BD); U center; Accuracy (ACC): 70.9. ## Original sources and implementation [Original paper]() [PDF]()