Joint Analysis of Functional and Structural Connectomes Between Preterm and Term Infant Brains via Canonical Correlation Analysis With Locality Preserving Projection

Brain functional gradients are related to cortical folding gradient

Cortical folding is closely linked to brain functions, with gyri acting more like local functional "hubs" to integrate information than sulci do. However, understanding how anatomical constraints relate to complex functions remains fragmented. One possible reason is that the relationship is estimated on brain mosaics divided by brain functions and cortical folding patterns. The boundaries of these hypothetical hard-segmented mosaics could be subject to the selection of functional/morphological features and as well as the thresholds. In contrast, functional gradient and folding gradient could provide a more feasible and unitless platform to mitigate the uncertainty introduced by boundary definition. Based on the MRI datasets, we used cortical surface curvature as the folding gradient and related it to the functional connectivity transition gradient. We found that, at the local scale, the functional gradient exhibits different function transition patterns between convex/concave cortices, with positive/negative curvatures, respectively. At the global scale, a cortex with more positive curvature could provide more function transition efficiency and play a more dominant role in more abstractive functional networks. These results reveal a novel relation between cortical morphology and brain functions, providing new clues to how anatomical constraint is related to the rise of an efficient brain function architecture.

A 1-Tesla MRI system for dedicated brain imaging in the neonatal intensive care unit

Objective

To assess the feasibility of a point-of-care 1-Tesla MRI for identification of intracranial pathologies within neonatal intensive care units (NICUs).

Methods

Clinical findings and point-of-care 1-Tesla MRI imaging findings of NICU patients (1/2021 to 6/2022) were evaluated and compared with other imaging modalities when available.

Results

A total of 60 infants had point-of-care 1-Tesla MRI; one scan was incompletely terminated due to motion. The average gestational age at scan time was 38.5 ± 2.3 weeks. Transcranial ultrasound (n = 46), 3-Tesla MRI (n = 3), or both (n = 4) were available for comparison in 53 (88%) infants. The most common indications for point-of-care 1-Tesla MRI were term corrected age scan for extremely preterm neonates (born at greater than 28 weeks gestation age, 42%), intraventricular hemorrhage (IVH) follow-up (33%), and suspected hypoxic injury (18%). The point-of-care 1-Tesla scan could identify ischemic lesions in two infants with suspected hypoxic injury, confirmed by follow-up 3-Tesla MRI. Using 3-Tesla MRI, two lesions were identified that were not visualized on point-of-care 1-Tesla scan: (1) punctate parenchymal injury versus microhemorrhage; and (2) small layering IVH in an incomplete point-of-care 1-Tesla MRI with only DWI/ADC series, but detectable on the follow-up 3-Tesla ADC series. However, point-of-care 1-Tesla MRI could identify parenchymal microhemorrhages, which were not visualized on ultrasound.

Conclusion

Although limited by field strength, pulse sequences, and patient weight (4.5 kg)/head circumference (38 cm) restrictions, the Embrace^® point-of-care 1-Tesla MRI can identify clinically relevant intracranial pathologies in infants within a NICU setting.

Interpretive JIVE: Connections with CCA and an application to brain connectivity

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure.