Biology subjects

Varkanitsa, M.

Publications and source records attributed to Varkanitsa, M..

3 recordsLinked to original sources

bioRxiv2026

Communication as a Two-Person Neural Process: fNIRS Hyperscanning in Adults with and without Post-Stroke Aphasia

Communication is inherently interactive, yet most neuroimaging studies examine individuals in isolation. Here, we used functional near-infrared spectroscopy (fNIRS) hyperscanning to investigate interbrain synchrony (IBS) during naturalistic communication. In Study 1, 13 healthy familiar dyads completed Joint Singing, Joint Reading, and Picture Guessing tasks. IBS was quantified using wavelet transform coherence across bilateral frontal and temporoparietal regions. Joint Singing produced higher IBS than the other tasks in several regions, including the dorsolateral prefrontal cortex and temporoparietal junction, and selected effects were greater in true than pseudo-dyads. In Study 2, we applied the same paradigm to five individuals with chronic post-stroke aphasia interacting with trained speech-language pathologists. IBS could be estimated in all dyads after lesion-informed channel exclusion, although regional coverage varied with lesion location. These findings demonstrate that fNIRS hyperscanning can capture task-dependent interpersonal neural coupling during spoken interaction and can be extended to communication involving individuals with aphasia. This approach provides a foundation for studying how neural coordination between communication partners relates to functional communication and partner-supported interaction.

neuroscience↗

bioRxiv2026

Network measures from the REWIRED simulation framework enhance prediction of post-stroke aphasia severity

Predicting post-stroke aphasia severity remains challenging, in part because language outcomes reflect not only focal cortical damage but also widespread disruption of structural and functional networks. Computational models of large-scale cortical dynamics offer a principled way to infer these network-level consequences from patient-specific lesions. Here, we present and evaluate REWIRED, a lesion-informed cortical dynamics framework designed to simulate individualized alterations in large-scale brain network organization after stroke. We first evaluated whether simulation-derived functional connectivity captured patient-specific variation in empirical functional connectivity beyond lesion burden and structural disconnection alone. We then developed a multiscale feature set combining lesion volume, lesion distribution patterns, probabilistic disconnectome metrics, and simulation-derived measures of functional connectivity and effective information flow (EIF). Finally, using a nested support vector regression (SVR) framework in a separate dataset, we tested whether simulation-derived features improve prediction of chronic aphasia severity, measured by the Western Aphasia Battery - Revised Aphasia Quotient (WAB-AQ), beyond lesion-distribution and structural-connectivity predictors. Simulation-derived functional connectivity significantly predicted empirical functional connectivity beyond local lesion burden and structural disconnection alone. With respect to WAB-AQ prediction, lesion-based (Set 1) and disconnectome-based (Set 2a) features alone yielded modest accuracy. Adding simulation-derived features (Set 2b) produced substantial gains, and the full feature set (Set 3) achieved the best performance (RMSE = 14.5; r = 0.83), reaching accuracy that is competitive with recent multimodal neuroimaging approaches, despite relying solely on lesion-distribution inputs. EIF measures were consistently selected as top predictors, indicating that disruptions in interregional communication patterns carry behaviorally relevant information not captured by structural features alone. These results support REWIRED as a framework for linking structural injury to distributed network dysfunction and behavioral outcomes. By integrating lesion information with large-scale cortical dynamics modeling, REWIRED provides a foundation for future individualized modeling of recovery and rehabilitation.

neuroscience↗

bioRxiv2024

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities vary in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved. The heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses (precision fMRI) to examine the language-selective network and--for control purposes--the Multiple Demand (MD) network, which supports executive functions, in older adults (n=64) relative to young controls (n=483). In line with past claims, relative to young adults, the MD network of older adults shows weaker, less spatially extensive, and more topographically variable activations during an executive function task and reduced within-network functional connectivity. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds during language comprehension as strongly and selectively as in younger adults, and shows a similar degree of left-hemispheric lateralization and within-network functional connectivity. Our findings suggest that the language network remains young-like--at least on standard measures of function and connectivity--and align with behavioral preservation of language comprehension in healthy aging.

neuroscience↗