ABSTRACT In graph theory, “multilayer networks” represent systems involving several interconnected topological levels. One example in neuroscience is the stratification of connections between different cortical depths or “laminae,” which is becoming noninvasively accessible in humans using ultrahigh-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7-T fMRI (1-mm 3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We compared networks where the interregional connections were limited to a single cortical depth only (“layer-by-layer matrices”) with those considering all possible connections between areas and cortical depths (“multilayer matrix”). We utilized global and local graph theory features that quantitatively characterize network attributes including network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared with the layer-by-layer versions. Superficial depths of the cortex dominated information transfer, and deeper depths drove clustering. These differences were largest in frontotemporal and limbic regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information; thus, multilayer connectomics could provide a methodological framework for studies on how information flows across this stratification. AUTHOR SUMMARY With the advent of ultrahigh-resolution functional MRI (fMRI), increased noninvasive accessibility of the human cortical laminae has enabled more advanced study of the human cortex. One potential methodological approach to studying cortical laminae is through multilayer connectomics, whereby the human cortex is represented by several interconnected networks. We applied a multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7-T fMRI. We utilized global and local graph theory features that quantitatively characterize network attributes including network composition, nodal centrality, path-based measures, and hub segregation. We detected global and local functional differences between cortical depths that were augmented using multilayer connectomics compared with the layer-by-layer versions. Thus, multilayer connectomics could provide a methodological framework for studies on how the human cortex functions.

Alzheimer’s disease (AD) is a severe neurodegenerative disorder that affects a growing worldwide elderly population. Identification of brain functional biomarkers is expected to help determine preclinical stages for targeted mechanistic studies and development of therapeutic interventions to deter disease progression. Connectomic analysis, a graph theory–based methodology used in the analysis of brain-derived connectivity matrices was used in conjunction with percolation theory targeted attack model to investigate the network effects of AD-related amyloid deposition. We used matrices derived from resting-state functional magnetic resonance imaging collected on mice with extracellular amyloidosis (TgCRND8 mice, n = 17) and control littermates ( n = 17). Global, nodal, spatial, and percolation-based analysis was performed comparing AD and control mice. These data indicate a short-term compensatory response to neurodegeneration in the AD brain via a strongly connected core network with highly vulnerable or disconnected hubs. Targeted attacks demonstrated a greater vulnerability of AD brains to all types of attacks and identified progression models to mimic AD brain functional connectivity through betweenness centrality and collective influence metrics. Furthermore, both spatial analysis and percolation theory identified a key disconnect between the anterior brain of the AD mice to the rest of the brain network. Author Summary Accurate biomarkers of Alzheimer’s disease (AD) are needed for early diagnosis and treatments. Connectomic analysis, a graph theory approach, coupled with percolation theory, a network attack approach, were applied here to analyze neuroimaging through a quantitative lens. We report a marker of AD vulnerability, which highlighted a core network disconnected from key hubs, notably within the anterior portion of the brain disconnected. Additionally, preliminary models using targeted attacks provide potential pathways of neurodegeneration from the control state to the diseased state. These findings show key differences in brain connectivity due to AD and provide a potential methodology for identifying biomarkers.