Regional Homogeneity

Abstract

Much effort has been made to understand the organizational principles of human brain function using functional magnetic resonance imaging (fMRI) methods, among which resting-state fMRI (rfMRI) is an increasingly recognized technique for measuring the intrinsic dynamics of the human brain. Functional connectivity (FC) with rfMRI is the most widely used method to describe remote or long-distance relationships in studies of cerebral cortex parcellation, interindividual variability, and brain disorders. In contrast, local or short-distance functional interactions, especially at a scale of millimeters, have rarely been investigated or systematically reviewed like remote FC, although some local FC algorithms have been developed and applied to the discovery of brain-based changes under neuropsychiatric conditions. To fill this gap between remote and local FC studies, this review will (1) briefly survey the history of studies on organizational principles of human brain function; (2) propose local functional homogeneity as a network centrality to characterize multimodal local features of the brain connectome; (3) render a neurobiological perspective on local functional homogeneity by linking its temporal, spatial, and individual variability to information processing, anatomical morphology, and brain development; and (4) discuss its role in performing connectome-wide association studies and identify relevant challenges, and recommend its use in future brain connectomics studies.

Characterizing dynamic local functional connectivity in the human brain

“…Other studies have also reported relationships between ReHo and many factors including age24, gender2425, and intelligence26. These findings indicated that the localized brain connectivity is associated with the physiological, pathological, and psychological changes in human brain27.…”

“…Another methodological concern is the 3-dimensional algorithm we used to compute ReHo. As cortical surface has complex folding patterns (sulcus and gyrus), the neighbouring voxels defined in 3d ReHo algorithm may not be the real neighbours in geodesic distance27, introducing some confounding effects. Therefore, in future studies, it could be beneficial to explore ReHo and its dynamics using surface-based 2d-ReHo algorithm2733.…”

Alternation of regional homogeneity in trigeminal neuralgia after percutaneous radiofrequency thermocoagulation

“…To optimize the trade-off between mitigation of partial volume effects and generation of Gaussian random fields, we chose 27 voxels to calculate the ReHo, as it is more appropriate for covering all directions in 3D space. [13] We implemented the computation of ReHo in individual native spaces to avoid multiple confounding sources introduced by head motion, imperfect image registration, and non-neural physiological processes. [13] For standardization purposes, individual ReHo maps were divided by their global average within the whole-brain mask.…”

“…[13] We implemented the computation of ReHo in individual native spaces to avoid multiple confounding sources introduced by head motion, imperfect image registration, and non-neural physiological processes. [13] For standardization purposes, individual ReHo maps were divided by their global average within the whole-brain mask. Then, spatial smoothing was performed using a 4 mm full-width at half-maximum (FWHM) Gaussian kernel.…”

“…ReHo measures the local synchronization of spontaneous fMRI signals by calculating similarity of dynamic fluctuations of voxels within a given cluster, which has been recognized as a highly sensitive, reproducible, and reliable neuroimaging marker in diagnosis and assessment of numerous nervous system diseases. [12,13] However, little is known about the alteration of ReHo in TN patients after effective neurosurgical treatment.…”