bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.09.23.753938

SILICA-SNC: FROM STREAMLINES TO NETWORKS

Abstract

Whole brain tractography contains rich trajectory-level information about white matter organization, yet conventional structural connectomes capture only part of it. We introduce Structural Network Coupling (SNC), a network representation enabled by SILICA (Streamline Independent Component Analysis). SILICA decomposes tractography into structural source components and their streamline-specific loading profiles, while SNC characterizes relationships among these components through their shared trajectory expression. The resulting component-level network retains direct correspondence to the underlying white-matter pathways and supports network visualization and graph-based analysis. SNC produced anatomically interpretable coupling among trajectory-resolved white-matter components and revealed a highly organized group architecture that independently recapitulated commissural, projection, and association systems. It also showed strong hemispheric segregation and structured cross-system coupling, and was highly stable across split-half samples. SNC extends SILICA from trajectory decomposition to network-level organization and provides a flexible framework for future structural and multimodal network analysis.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wu, L., Calhoun, V.. 2026-09-29. SILICA-SNC: FROM STREAMLINES TO NETWORKS. https://doi.org/10.64898/2026.09.23.753938

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

An atlas of the aging glycoproteome reveals how glycosylation modulates autoantibody binding in the brain

N-glycosylation generates substantial molecular diversity, yet how glycan composition varies throughout aging remains poorly understood. We mapped the N-glycoproteome of brain, liver, spleen, kidney, and plasma across nine ages spanning the mouse lifespan, identifying 80,618 unique N-glycopeptides at 9,211 glycosites on 4,391 glycoproteins, together with matched proteomic trajectories (glycoproteinatlas.org). Organ identity was the dominant determinant of glycan composition, but numerous glycosites nevertheless underwent remodeling with age. The brain showed a prominent age-associated increase in short paucimannose glycans, driven in part by the age-associated increase in the glycan-processing enzyme HEXB. In white matter, a hotspot for this change, aging increased the paucimannosylation of Myelin oligodendrocyte glycoprotein (MOG), a major target of central nervous system autoimmunity. We identified MOG-reactive autoantibodies in aged individuals and patients with MOG antibody-associated disease (MOGAD) and showed that the glycan at MOG N60 modulates autoantibody binding. Experimental autoimmune encephalomyelitis (EAE) recapitulated a subset of the age-associated glycosite changes, revealing shared glycosylation signatures between aging and autoimmune targeting of MOG. Together, these findings reveal site-specific N-glycosylation as both a marker of age and a regulator of molecular interactions.

neuroscience↗

Beyond rumination: an ecological momentary assessment of daily thinking patterns in maladaptive daydreaming

Maladaptive daydreaming is a form of maladaptive repetitive thought characterized by excessive, dysfunctional fantasy engagement, with notable links to psychological distress and poor well-being. Although symptoms can be reliably measured along a continuum, it remains unclear how they manifest in the phenomenological characteristics of everyday thinking, as well as which thought characteristics are unique to maladaptive daydreaming versus shared with trait rumination, a transdiagnostic manifestation of maladaptive repetitive thinking that frequently co-occurs with maladaptive daydreaming. To advance these questions, we conducted a pre-registered study using ecological momentary assessment to track the everyday thoughts of 446 young adults (aged 18 to 34) spanning a range of maladaptive daydreaming symptom severity as assessed with the 16-item Maladaptive Daydreaming Scale. Higher maladaptive daydreaming scores were associated with several poor mental health indicators, including higher trait rumination. Positive associations between maladaptive daydreaming scores and both negatively-valenced and task-unrelated thinking emerged, yet these relationships were accounted for by variance shared with rumination. Beyond rumination, higher maladaptive daydreaming scores were uniquely associated with worse mood, as well as greater internally-oriented attention, more socially-oriented thought, and greater overall thought persistence. Maladaptive daydreaming also uniquely moderated the relationship between internal orientation and social orientation, such that the positive association between internal and social thought content became stronger at higher levels of maladaptive daydreaming. Overall, these findings provide novel evidence that maladaptive daydreaming levels are linked to unique socioemotional features and restricted dynamics, beyond its overlap with general ruminative distress in everyday life. These insights suggest that clinical interventions should target immersive fantasy engagement alongside transdiagnostic maladaptive repetitive thinking.

neuroscience↗

Structural Evolution of Neural Networks during Zebrafish Early Development

The spatial architecture of neural networks underpins nervous system function, yet dynamic in vivo evidence for their early establishment process remains scarce. Here, using long-term confocal live-cell imaging of transgenic zebrafish, we dissected neural network development across the brain, yolk sac, and spinal cord at single-cell resolution. We reveal that repeated growth cone bifurcation is the fundamental cellular mechanism for network node formation. Despite distinct developmental origins, networks in all three regions exhibit highly conserved dynamics, strictly following Strahler hierarchical ordering and Hack's power-law scaling. Quantitative topological analysis shows that as development progresses, node numbers and clustering coefficients increase, while network density decreases. This reflects a structural optimization strategy balancing high functional efficiency with low wiring cost. These findings suggest that physical and geometric constraints, combined with simple local rules, drive the self-organization of complex neural networks, offering valuable principles for artificial network design.

neuroscience↗