23 research outputs found
Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification
Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson’s correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN “features” that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality
The neural architecture of semantic retrieval with and without cues: evidence from neuropsychology and neuroimaging
Everyday situations are conceptually rich, but not all of this knowledge is relevant at a given time. At the heart of adaptive cognition is flexibility, which allows us to focus on particular mental representations in a way that suits the changing context and goals. Previous work has highlighted the importance of semantic control mechanisms in retrieval, which allow cognition to diverge from dominant associations (Lambon Ralph et al., 2016). However, a clear understanding of the cognitive and neural substrates of semantic flexibility is currently lacking. This work collects evidence from different methods and experimental populations to tackle this broad question. We use novel multimodal semantic cues (i.e. affect and spatial locations) to examine the mechanisms that support flexible patterns of retrieval when the context is helpful or unhelpful. The first two empirical chapters examine behavioural effects of cues and miscues in patients with semantic aphasia (Chapter 2) and investigate whether patients with SA show greater benefits of coherent cue combinations compared to minimal levels of cueing (Chapter 3). The third chapter explores the neural bases of cued semantic retrieval, and tests the predictions of another recent framework which situates the default mode network at the top of a cortical hierarchy of abstraction (Margulies et al., 2016). The final chapter investigates whether the intrinsic connectivity of the brain at rest is predictive of the behavioural efficiency in cued semantic retrieval. Our findings provide evidence for the existence of two qualitatively distinct mechanisms for semantic flexibility, one driven by control processes (impaired in SA) and one driven by the integration of contextual information with long-term semantic knowledge (relatively intact in SA). In line with a growing body of work suggesting a role of default mode network in information integration, we show that more coherent patterns of retrieval which are driven by the context recruit this network. In contrast, multiple-demand regions appear to support more executive aspects of cued retrieval required for the maintenance of cue information. Finally, this thesis provide evidence that affect and location cues are both effective at shaping the activation of semantic knowledge. In summary, this thesis suggests that semantic flexibility is a complex and multi-faceted process which requires an interplay of different cognitive and neural components