A Novel Directed Seed-Based Connectivity Analysis Toolbox Applied to Human and Marmoset Resting-State FMRI.

Estimating the direction of functional connectivity (FC) can help further elucidate complex brain function. However, the estimation of directed FC at the voxel level in fMRI data, and evaluating its performance, has yet to be done. We therefore developed a novel directed seed-based connectivity analysis (SCA) method based on normalized pairwise Granger causality that provides greater detail and accuracy over ROI-based methods.

A reappraisal of the default mode and frontoparietal networks in the common marmoset brain.

In recent years the common marmoset homolog of the human default mode network (DMN) has been a hot topic of discussion in the marmoset research field. Previously, the posterior cingulate cortex regions (PGM, A19M) and posterior parietal cortex regions (LIP, MIP) were defined as the DMN, but some studies claim that these form the frontoparietal network (FPN). We restarted from a neuroanatomical point of view and identified two DMN candidates: Comp-A (which has been called both the DMN and FPN) and Comp-B.

Group Surrogate Data Generating Models and similarity quantification of multivariate time-series: A resting-state fMRI study.

Advancements in non-invasive brain analysis through novel approaches such as big data analytics and in silico simulation are essential for explaining brain function and associated pathologies. In this study, we extend the vector auto-regressive surrogate technique from a single multivariate time-series to group data using a novel Group Surrogate Data Generating Model (GSDGM). This methodology allowed us to generate biologically plausible human brain dynamics representative of a large human resting-state (rs-fMRI) dataset obtained from the Human Connectome Project.

Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox.

An important goal in neuroscience is to elucidate the causal relationships between the brain's different regions. This can help reveal the brain's functional circuitry and diagnose lesions. Currently there are a lack of approaches to functional connectome estimation that leverage the state-of-the-art in deep learning architectures and training methodologies.