Biology subjects

Knapic, S.

Publications and source records attributed to Knapic, S..

3 recordsLinked to original sources

bioRxiv2026

Mitigating activity mixing with personalized whole-brain modeling

Mechanistic neuroimaging-based biomarkers based on localized brain activities or interactions are a central tenet in precision and personalized psychiatry. However, their accuracy may be limited by Activity Mixing that is a novel construct indicating the entanglement of any local neuronal activity with activities elsewhere in the network through long-range spatiotemporal correlations, which degrades the localization of brain-symptom associations. Here, we posit that the ill-posed inverse problem of activity mixing can be mitigated by fitting of generative whole-brain models. We developed a multi-objective fitting approach to estimate subject-specific local- and inter-areal brain-dynamics control parameters from neuroimaging observables. By integrating both synchronization and criticality metrics, this approach yields personalized parameters capture individual brain network dynamics more accurately than raw observables. In silico validation demonstrated that fitted model parameters improved brain-symptom correlation estimates by 30-85% and reduced false-negative rates by approximately [~]67% relative to conventional observables-based analyses. As in vivo proof-of-concept, resting-state magnetoencephalography (MEG) data from 230 patients with major depressive disorder (MDD) showed that aberrant brain criticality in the alpha-frequency band (11 Hz) was a significant predictor of disability with a correlation coefficient of 0.236 (95% confidence interval (CI) = [0.206, 0.266]) in 27 (CI = [23, 31]) significant cortical parcels. Model fitting both improved this correlation estimate by [~]56% up to 0.368 (CI = [0.341, 0.405]) and localized it [~]25% more narrowly to 20 (CI = [18, 22]) parcels. These findings suggest that model fitting can mitigate the effects of activity mixing and provide control-parameter estimates that delineate mechanistic biomarkers for brain disorders more accurately than the raw brain imaging observables.

neuroscience↗

bioRxiv2025

Low-dimensional brain-symptom associations delineate depression phenotypes with distinct connectivity biomarkers and symptom profiles

Depression is neurobiologically and clinically heterogeneous. New approaches using resting-state functional MRI (rs-fMRI) functional connectivity (FC) data have modeled the neural basis of depression heterogeneity and revealed unique neural phenotypes. Yet, no studies have identified depression phenotypes from electrophysiological magnetoencephalography (MEG) data although MEG measures human brain dynamics at millisecond precision. We demonstrate here unique depression phenotypes based on MEG-oscillation FC. We collected resting-state MEG, MRI, and clinical symptom data from 263 patients with unipolar depression and 75 healthy controls. We assessed MEG-FC with two oscillatory coupling-mode measures that are fundamental for information processing. To define normative phenotypes, we computed their latent-space low-dimensional brain-symptom associations, and used these components to identify phenotypes using unsupervised machine learning. We identified five stable depression phenotypes that were characterized by unique symptom profiles and distinct spectral patterns. Our results demonstrate new neural underpinnings of depression heterogeneity and reveal unique neural phenotypes with potential personalized diagnostic value.

neuroscience↗

bioRxiv2024

Hierarchical model of human brain oscillations

The brain operates at the critical transition between order and disorder which supports optimal information processing. Whole-brain computational modeling is a powerful tool for uncovering the system-level mechanisms behind large-scale brain activity in both healthy and pathological states. However, most previous approaches have focused on either functional connectivity or criticality, making it difficult to capture both aspects simultaneously. Here, we introduce a new method based on a Hierarchical Kuramoto model that incorporates two levels of hierarchy. In our model, each node contains a large number of coupled oscillators, which allows us to examine both local synchronization and long-distance interactions between brain regions. The model produces critical-like dynamics marked by emergent long-range temporal correlations (LRTCs) and both inter-areal phase synchronization and amplitude correlations during the transition from asynchronous to synchronous states. Notably, structure-function coupling shows distinct patterns: correlations with structural connectivity peak at criticality for LRTCs and amplitude correlations, but decay for local and inter-areal phase synchronization. Comparisons with human resting-state magnetoencephalography (MEG) data reveal that the models behavior most closely resembles MEG phase synchronization and multi-peak power spectra on the subcritical side of an extended critical regime, supporting the hypothesis that the human brain operates in this state.

neuroscience↗