Antipsychotics are thought to improve schizophrenia symptoms through the antagonism of dopamine D2 receptors, which are abundant mainly in subcortical regions. By introducing functional gradient, a novel approach to identify hierarchy alterations by capturing the similarity of whole brain fucntional connectivity (FC) profiles between two voxels, the present study aimed to characterize how the subcortical gradient is associated with treatment effects and response in first-episode schizophrenia in vivo. Two independent samples of first-episode schizophrenia (FES) patients with matched healthy controls (HC) were obtained: the discovery dataset included 71 patients (FES_0W) and 64 HC at baseline, and patients were re-scanned after either 6 weeks (FES_6W, N = 33) or 12 months (FES_12M, N = 57) of antipsychotic treatment, of which 19 patients finished both 6-week and 12-month evaluation. The validation dataset included 22 patients and 24 HC at baseline and patients were re-scanned after 6 weeks. Gradient metrics were calculated using BrainSpace Toolbox. Voxel-based gradient values were generated and group-averaged gradient values were further extracted across all voxels (global), three systems (thalamus, limbic and striatum) and their subcortical subfields. The comparisons were conducted separately between FES_0W and HC for investigating illness effects, and between FES_6W/FES_12M and FES_0W for treatment effects. Correlational analyses were then conducted between the longitudinal gradient alterations and the improvement of clinical ratings. Before treatment, schizophrenia patients exhibited an expanded range of global gradient scores compared to HC which indicated functional segregation within subcortical systems. The increased gradient in limbic system and decreased gradient in thalamic and striatal system contributed to the baseline abnormalities and led to the disruption of the subcortical functional integration. After treatment, these disruptions were normalized and the longitudinal changes of gradient scores in limbic system were significantly associated with symptom improvement. Similar illness and treatment effects were also observed in the validation dataset. By measuring functional hierarchy of subcortical organization, our findings provide a novel imaging marker that is sensitive to treatment effects and may make a promising indicator of treatment response in schizophrenia.

White matter (WM) consists of fibers that transmit information from one brain region to another, and functional fiber clustering that combines diffusion and functional MRI provides a novel perspective for exploring the functional architecture of axonal fibers. However, existing methods only concern functional signals in gray matter (GM), whereas the connecting fibers may not transmit relevant functional signals. There has been growing evidence that neural activity is encoded in WM BOLD signals as well, which provides rich multimodal information for fiber clustering. In this paper, we develop a comprehensive Riemannian framework for functional fiber clustering using WM BOLD signals along fibers. Specifically, we derive a novel metric that is highly discriminative of different functional classes while reducing the variability within classes and, in the meantime, enables low-dimensional coding of high-dimensional data. Our in vivo experiments show that the proposed framework is able to achieve clustering results with inter-subject consistency and functional homogeneity. In addition, we develop an atlas of WM functional architecture for standardizable yet flexible use and exemplify a machine-learning-based application for the classification of autism spectrum disorders, which further demonstrates the great potential of our approach in practical applications.

Denoising is the primary preprocessing step for subsequent application of MRI. However, most commonly-used patch-based denoising methods are heavily dependent on the degree of patch matching. Due to the large number of voxels in the 3D MRI dataset, the procedure of searching sufficient similarity patches was limited by the empirical compromising between computational efficiency and estimation accuracy, and cannot fulfill the application in multimodal MRI dataset with different SNR and resolutions. In this study, we propose a modified global filtering framework for 3D MRI. For each denoising voxel, the similarity weighting matrix is computed using the reference patch and other patches from the whole dataset. This large weighting matrix is then approximated using the k-means clustering Nyström method to achieve computational viability. Experiments on both synthetic and in vivo MRI datasets demonstrated that the proposed adaptive Nyström low-rank approximation could achieve competitive estimation compared with exact global filter while reducing the sampling rate by four orders of magnitude. In addition, the corresponding global filter improved patches-based method in both spatial and transform domain. We propose a global denoising framework for 3D MRI which extracts information from the entire dataset to restore each voxel. This large weighting matrix of the global filter is approximated using Nyström low-rank approximation with an adaptive k-means clustering sampling scheme, which significantly reduce the sampling rate as well as the running time. The proposed method is capable of denoising in multimodal MRI dataset and can be used to improve currently used patch-based methods.

Objective: In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics.Methods: Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male–female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis.Results: The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients.Conclusion: The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.

Background: Resting-state functional magnetic resonance imaging (RS-fMRI) has been proved to be a useful tool to study the brain mechanism in the quest to probe the distinct pattern of inter-region interactions in the brain. As an important application of RS-fMRI, the graph-based approach characterizes the brain as a complex network. However, the network is susceptible to its scale that determines the trade-off between sensitivity and anatomical variability. Objective: To balance sensitivity and anatomical variability, a pyramid representation of the functional network is proposed, which is composed of five individual networks reconstructed at multiple scales. Methods: The pyramid representation of the functional network was applied to two groups of participants, including patients with Alzheimer's disease (AD) and normal elderly (NC) individuals, as a demonstration. Features were extracted from the multi-scale networks and were evaluated with their inter-group differences between AD and NC, as well as the discriminative power in recognizing AD. Moreover, the proposed method was also validated by another dataset from people with autism. Results: The different features reflect the highest sensitivity to distinguish AD at different scales. In addition, the combined features have higher accuracy than any single scale-based feature. These findings highlight the potential use of multi-scale features as markers of the disrupted topological organization in AD networks. Conclusion: We believe that multi-scale metrics could provide a more comprehensive characterization of the functional network and thus provide a promising solution for representing the underlying functional mechanism in the human brain on a multi-scale basis.