Examining brain maturation during adolescence using graph Laplacian learning based Fourier transform

Front and center: Maturational dysregulation of frontal lobe functional neuroanatomic connections in attention deficit hyperactivity disorder

Frontal lobe function may not universally explain all forms of attention deficit hyperactivity disorder (ADHD) but the frontal lobe hypothesis described supports an internally consistent model for integrating the numerous behaviors associated with ADHD. The paper examines the developmental trajectories of frontal and prefrontal lobe development, framing ADHD as maturational dysregulation concluding that the cognitive, motor, and behavioral abilities of the presumptive majority of ADHD children may not primarily be disordered or dysfunctional but reflect maturational dysregulation that is inconsistent with the psychomotor and cognitive expectations for the child’s chronological and mental age. ADHD children demonstrate decreased activation of the right and middle prefrontal cortex. Prefrontal and frontal lobe regions have an exuberant network of shared pathways with the diencephalic region, also having a regulatory function in arousal as well as with the ascending reticular formation which has a capacity for response suppression to task-irrelevant stimuli. Prefrontal lesions oftentimes are associated with the regulatory breakdown of goal-directed activity and impulsivity. In conclusion, a presumptive majority of childhood ADHD may result from maturational dysregulation of the frontal lobes with effects on the direct, indirect and/or, hyperdirect pathways.

Functional network estimation using multigraph learning with application to brain maturation study

Although most dramatic structural changes occur in the perinatal period, a growing body of evidences demonstrates that adolescence and early adulthood are also important for substantial neurodevelopment. We were thus motivated to explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood using multi-paradigm task-based functional magnetic resonance imaging (fMRI) measurements. Different from conventional multigraph based FCN construction methods where the graph network was built independently for each modality/paradigm, we proposed a multigraph learning model in this work. It promises a better fitting to FCN construction by jointly estimating brain network from multi-paradigm fMRI time series, which may share common graph structures. To investigate the hub regions of the brain, we further conducted graph Fourier transform (GFT) to divide the fMRI BOLD time series of a node within the brain network into a range of frequencies. Then we identified the hub regions characterizing brain maturity through eigen-analysis of the low frequency components, which were believed to represent the organized structures shared by a large population. The proposed method was evaluated using both synthetic and real data, which demonstrated its effectiveness in extracting informative brain connectivity patterns. We detected 14 hub regions from the child group and 12 hub regions from the young adult group. We show the significance of these findings with a discussion of their functions and activation patterns as a function of age. In summary, our proposed method can extract brain connectivity network more accurately by considering the latent common structures between different fMRI paradigms, which are significant for both understanding brain development and recognizing population groups of different ages.

Correlation Guided Graph Learning to Estimate Functional Connectivity Patterns From fMRI Data

OBJECTIVE

Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity (FC) patterns have been used as fingerprints to predict individual differences in phenotypic measures, and cognitive dysfunction associated with brain diseases. In these applications, how to accurately estimate FC patterns is crucial yet technically challenging.

METHODS

In this article, we propose a correlation guided graph learning (CGGL) method to estimate FC patterns for establishing brain-behavior relationships. Different from the existing graph learning methods which only consider the graph structure across brain regions-of-interest (ROIs), our proposed CGGL takes into account both the temporal correlation of ROIs across time points, and the graph structure across ROIs. The resulting FC patterns reflect substantial inter-individual variations related to the behavioral measure of interest.

RESULTS

We validate the effectiveness of our proposed CGGL on the Philadelphia Neurodevelopmental Cohort data for separately predicting three behavioral measures based on resting-state fMRI. Experimental results demonstrate that the proposed CGGL outperforms other competing FC pattern estimation methods.

CONCLUSION

Our method increases the predictive power of the constructed FC patterns when establishing brain-behavior relationships, and gains meaningful insights into relevant biological mechanisms.

SIGNIFICANCE

The proposed CGGL offers a more powerful, and reliable method to estimate FC patterns, which can be used as fingerprints in many brain network studies.