Exploring dysconnectivity of the large-scale neurocognitive network across psychiatric disorders using spatiotemporal constrained nonnegative matrix factorization method.

Psychiatric disorders usually have similar clinical and neurobiological features. Nevertheless, previous research on functional dysconnectivity has mainly focused on a single disorder and the transdiagnostic alterations in brain networks remain poorly understood. Hence, this study proposed a spatiotemporal constrained nonnegative matrix factorization (STCNMF) method based on real reference signals to extract large-scale brain networks to identify transdiagnostic changes in neurocognitive networks associated with multiple diseases. Available temporal prior information and spatial prior information were first mined from the functional magnetic resonance imaging (fMRI) data of group participants, and then these prior constraints were incorporated into the nonnegative matrix factorization objective functions to improve their efficiency. The algorithm successfully obtained 10 resting-state functional brain networks in fMRI data of schizophrenia, bipolar disorder, attention deficit/hyperactivity disorder, and healthy controls, and further found transdiagnostic changes in these large-scale networks, including enhanced connectivity between right frontoparietal network and default mode network, reduced connectivity between medial visual network and default mode network, and the presence of a few hyper-integrated network nodes. Besides, each type of psychiatric disorder had its specific connectivity characteristics. These findings provide new insights into transdiagnostic and diagnosis-specific neurobiological mechanisms for understanding multiple psychiatric disorders from the perspective of brain networks.

A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI.

At present, fMRI studies mainly focus on the entire low-frequency band (0. 01-0.08 Hz). However, the neuronal activity is dynamic, and different frequency bands may contain different information. Therefore, a novel multi-frequency-based dynamic functional connectivity (dFC) analysis method was proposed in this study, which was then applied to a schizophrenia study. First, three frequency bands (Conventional: 0.01-0.08 Hz, Slow-5: 0.0111-0.0302 Hz, and Slow-4: 0.0302-0.0820 Hz) were obtained using Fast Fourier Transform. Next, the fractional amplitude of low-frequency fluctuations was used to identify abnormal regions of interest (ROIs) of schizophrenia, and dFC among these abnormal ROIs was implemented by the sliding time window method at four window-widths. Finally, recursive feature elimination was employed to select features, and the support vector machine was applied for the classification of patients with schizophrenia and healthy controls. The experimental results showed that the proposed multi-frequency method (Combined: Slow-5 and Slow-4) had a better classification performance compared with the conventional method at shorter sliding window-widths. In conclusion, our results revealed that the dFCs among the abnormal ROIs varied at different frequency bands and the efficiency of combining multiple features from different frequency bands can improve classification performance. Therefore, it would be a promising approach for identifying brain alterations in schizophrenia.

A Novel Constrained Non-negative Matrix Factorization Method for Group Functional Magnetic Resonance Imaging Data Analysis of Adult Attention-Deficit/Hyperactivity Disorder.

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder that often persists into adulthood. Extracting brain networks from functional magnetic resonance imaging (fMRI) data can help explore neurocognitive disorders in adult ADHD. However, there is still a lack of effective methods to extract large-scale brain networks to identify disease-related brain network changes. Hence, this study proposed a spatial constrained non-negative matrix factorization (SCNMF) method based on the fMRI real reference signal. First, non-negative matrix factorization analysis was carried out on each subject to select the brain network components of interest. Subsequently, the available spatial prior information was mined by integrating the interested components of all subjects. This prior constraint was then incorporated into the NMF objective function to improve its efficiency. For the sake of verifying the effectiveness and feasibility of the proposed method, we quantitatively compared the SCNMF method with other classical algorithms and applied it to the dynamic functional connectivity analysis framework. The algorithm successfully extracted ten resting-state brain functional networks from fMRI data of adult ADHD and healthy controls and found large-scale brain network changes in adult ADHD patients, such as enhanced connectivity between executive control network and right frontoparietal network. In addition, we found that older ADHD spent more time in the pattern of relatively weak connectivity. These findings indicate that the method can effectively extract large-scale functional networks and provide new insights into understanding the neurobiological mechanisms of adult ADHD from the perspective of brain networks.