Functional and Structural Connectomics

Human brain is a complex wiring system in which diverse behaviors are supported by ubiquitous functional fluctuations. Although striking efforts have been made to investigate the association between brain structure connectivity (SC) and function connectivity (FC), the SC-FC coupling mechanism is largely elusive. Recently, we have developed a novel analytic approach, called HoloBrain (Liu, Dan et al. 2023), to characterize the interference patterns formed by the BOLD signals modulated by a collection of harmonic wavelets (stemming from wirings of white matter fibers) across a widespread graph spectrum. In this work, we have applied our HoloBrain technique to task-fMRI data from HCP-YA. Compared to conventional network analysis methods, HoloBrain offers a new window to investigate the task-specific footprint of SC-FC coupling through the lens of cross-frequency coupling (CFC), demonstrating great potential in discovering novel neurobiological biomarkers for resting-state fMRI studies. 

The wiring of the brain connects micro-architecturally diverse neuronal populations. These neuronal populations have distinct anatomical and cellular makeups and thanks to modern technological advances, this heterogeneity in the brain's micro-architecture can be imaged with unprecedented detail and depth. The conventional graph model, however, encodes brain connectivity as a network of nodes and edges, and abstracts away the rich biological detail of each node [1]. 

The brain is a complex system with dynamic network changes. Functional connectivity – the measurement of correlated brain activity – is a commonly-used approach to characterizing brain network changes. Despite the wealth of neuroscience studies that have reported reliable state-dependent changes to macroscale brain network organization (Cole et al., 2014), there is no widely accepted theory or understanding of what these functional connectivity changes are for. However, theoretical work in systems neuroscience (at the level of local spiking neurons) has demonstrated that state-dependent neural correlations can be understood from a neural coding framework (Panzeri et al., 2022). Prior theory posits that noise correlations (NC) -- idiosyncratic with functional connectivity -- can be interpreted only if the underlying signal correlation (SC) -- similarity of task tuning (or task co-activations) between pairs of neural units -- is known. Here we investigate whether the theoretical framework used to study neural coding in neuronal spikes can account for macroscale brain network changes (Ito and Murray, 2022). 

How brain extraordinary activity coordinates its complex clearance system is a fundamental question in systems neuroscience (Yeo BT et al., 2011; Mollon JD et al., 2022). Serving a role in waste clearance, glymphatic system is critical to brain health and cognitive performance (Hablitz LM et al., 2021). But how it relates with functional network within cortical regions remains elusive. Moreover, non-invasive regional assessment for glymphatic system is lacking (Kamagata K et al., 2021). Therefore, we aimed to unravel the characteristics of network-level glymphatic function and validate a potential regional assessment (i.e., free-water) for glymphatic clearance. We further explore whether network-level glymphatic clearance was integrated with network connectome. 

Despite a relatively fixed anatomical structure, the human brain can support rich cognitive functions, triggering particular interest in investigating structure-function relationships (Honey, Sporns et al. 2009). Myelin is a vital brain microstructure marker; however, most myelin studies have constructed a structural covariance network at the population level, making individual cognitive or behavioral predictions impossible. Therefore, examining the myelin microstructural and functional relationship at the individual level is urgently needed but is still elusive. Recently, higher-order representations (beyond the node or edge level) emerged, including simplicial complexes (Giusti, Pastalkova et al. 2015), persistent homology (Liang and Wang 2017), neural network (Suárez, Richards et al. 2021), subgraphs (Przulj 2007), and motifs (Benson, Gleich et al. 2016), which have proven to be extremely useful in understanding and comparing complex networks. Nevertheless, few studies have examined individual myelin microstructure-function relationships using higher-order representations. Here, we quantify the individual-level microstructure-function relationship using a higher-order framework and explore the microstructure-function higher-order relationship across individual cognitive scores, development and network scale. 

How brain architecture shapes function is a deep question which has occupied many researchers, from the perspective of network neuroscience (Bullmore and Sporns 2009), brain modelling (Breakspear 2017) and spectral graph theory (Atasoy, Donnelly, and Pearson 2016). Some have even suggested that geometry plays a particularly relevant role in shaping functional activity (Pang et al. 2023a), although see this ongoing discussion (Faskowitz et al. 2023; Pang et al. 2023b). Here we focus on probing the importance of the rare long-range exceptions to the exponential distance rule of brain wiring (Markov et al. 2013). New evidence using turbulence has demonstrated the fundamental role of long-range connectivity in shaping optimal brain information processing (Deco et al. 2021). Here we used Laplacian decomposition of four different graph representations of the underlying anatomy to derive anatomical brain modes: exponential-distance rule (EDR) (Ercsey-Ravasz et al. 2013) and long-range exceptions (EDR+LR), geometry-based modes (geometry) and EDR modes (EDR binary and EDR continuous) (Figure 1 A). Our results show that EDR+LR achieves significantly better reconstruction of long-range functional connectivity (FC) compared to the other mode representations. Furthermore, pertinent to time-critical information processing, we show that a small subset of modes achieves a disproportionately high reconstruction of task MRI activity. When this subset of modes is considered, EDR+LR achieves better reconstruction for the 47 HCP tasks compared to the other mode representations, suggesting that less is more for information processing in the brain.