Abstract LB001: Dynamic circuit remodeling during glioblastoma progression: Depth-resolved electrophysiology and cortical imaging define network biomarkers

Abstract Glioblastoma (GBM) profoundly alters neural circuit dynamics, yet most studies rely on static endpoint measurements that overlook how network dysfunction evolves over time. We address this gap through a longitudinal, multimodal framework that combines large-scale imaging, electrophysiology, behavior, computational modeling, and circuit perturbation to define dynamic biomarkers of GBM progression and restoration. We hypothesize that glioblastoma induces progressive and sex-dependent disruption of inhibitory network balance that weakens long-range cortical communication, and that restoring inhibitory tone can recover both neural synchrony and behavior. Using mesoscale cortical calcium imaging, depth-resolved Neuropixel recordings, and single- and multiphoton microscopy with multiple calcium indicators, we captured cortical and subcortical activity in syngeneic (SB28, KR158) and genetically engineered (GEMM) GBM models. Progressive circuit remodeling emerged as accelerated and directionally biased traveling waves, disrupted inter- and intrahemispheric coupling, and layer-persistent hyperexcitability characterized by delta-band elevation near the tumor core and high-frequency suppression in surrounding regions. Recurrent spatiotemporal motifs revealed evolving network patterns in both hemispheres, paralleling tumor infiltration observed in histology and highlighting widespread reorganization of cortical communication networks. To link these neural changes with functional outcomes, we performed simultaneous pupil and orofacial tracking during spontaneous activity, virtual-reality navigation, and social-interaction paradigms. Our deep learning models (DeepVision and DeepFace) extracted high-resolution behavioral features, while generalized linear models (GLMs) predicted cortical activity from these signals. GBM progression reduced the behavioral predictability of brain dynamics, reflecting degraded sensorimotor coupling and impaired state-dependent coordination across cortical regions. These findings establish behavioral and physiological signatures that mirror neural instability and can serve as scalable, noninvasive biomarkers for longitudinal disease monitoring. Finally, optogenetic activation of inhibitory neurons restored cortical synchrony, normalized oscillatory patterns, and improved behavioral performance. Remarkably, this intervention doubled the survival rate of female mice, revealing a sex-dependent therapeutic benefit likely mediated by hormonal modulation of inhibitory tone and circuit resilience. Together, this integrative platform—spanning cellular to systems scales, spontaneous to social behaviors, and physiological to optogenetic domains—provides a comprehensive view of how glioblastoma disrupts, and how targeted circuit modulation can restore, brain function. By bridging optical, electrophysiological, behavioral, and computational modalities, this work identifies quantitative and translatable network biomarkers for early detection, mechanism-guided intervention, and personalized therapy development, establishing a foundation for precision cancer neuroscience. Citation Format: Murat Yildirim, Tenesha Connor, Maryam Faisal, Omer Dinc, Kemal Ozdemirli, Frederick Bell, Berfin Dinc, Miguel Maldonado, Daniel Silver, Anthony Sloan, Justin Lathia. Dynamic circuit remodeling during glioblastoma progression: Depth-resolved electrophysiology and cortical imaging define network biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB001.