Biology subjects

Asadpour, A.

Publications and source records attributed to Asadpour, A..

6 recordsLinked to original sources

bioRxiv2026

Mapping brain networks underlying abstract perceptual decision formation and its EEG signatures

Perceptual decisions have been proposed to involve a common mechanism of evidence accumulation operating across sensory modalities and motor contexts. In human EEG, the centroparietal positivity (CPP) tracks evidence accumulation in such a supramodal motor-independent manner, but its cortical sources are uncertain. Here we combined fMRI and EEG, acquired in separate sessions of the same supramodal change-detection paradigm, with individualized finite element head modeling to identify decision-related networks. A conjunction analysis across visual and auditory modalities and across motor and non-motor required tasks revealed consistent activation of bilateral postcentral, supramarginal, and superior parietal lobules, precentral gyrus, central operculum, middle frontal gyrus, and cerebellum. Forward modeling of fMRI-defined sources indicated that parietal-opercular regions were the dominant contributors to the response-locked and non-motor-required peak-locked CPP signal. These results implicate a network of brain areas in supramodal, motor-independent target-detection decisions, whose specific functional roles can be interrogated in more complex decision tasks in future.

neuroscience↗

bioRxiv2026

Neural latent representation of implicit perceptual decision confidence signatures from brain-wide intracranial EEG

Human perceptual decision confidence has been associated with brain-wide signalling, albeit with inconsistencies across studies. It remains unclear whether more robust neural latent representations exist, particularly for implicit confidence. We address this by applying complementary machine learning approaches to comprehensive, brain-wide intracranial electroencephalography (iEEG) data acquired without explicit confidence reports. Using explainable feature selection methods, we elucidated key spatiotemporal iEEG components, especially in the frontal cortex, that readily distinguished confidence characteristics related to decision correctness, difficulty, and speed, and that were transferable across motor modalities. Despite inconsistencies in the features identified across methods and conditions, dimensionality reductions revealed robust low-dimensional neural latent representations. Furthermore, their neural trajectories within a shared subspace enhanced the distinguishability of confidence characteristics. Overall, our results uncover low-dimensional spatial and temporal neural latent representations underlying human implicit perceptual decision confidence that are robust to analytical methods and decision conditions.

neuroscience↗

bioRxiv2026

Dual Immunoliposome Targeting of PD-L1 and CSF1R affects T-Cell readouts in tumor-conditioned co-cultures: An In Vitro Study in Glioblastoma and Medulloblastoma

Glioblastoma and medulloblastoma are characterized by an immunosuppressive tumor microenvironment, in which tumor-associated macrophages may impair T-cell function, in part through the expression of PD-L1 and CSF1R. Single-target therapies have shown inconsistent results in brain tumors, likely due to the complex interplay involving tumor-associated macrophages and immunosuppression. We developed two immunoliposomes functionalized with antibodies against PD-L1 and CSF1R to co-target these receptors on M2-like tumor-associated macrophages. TAM2Ms were generated by polarizing THP-1 monocytes with glioblastoma (A172, U87MG) and medulloblastoma (DAOY, ONS-76) tumor-conditioned medium together with IL-4 and IL-13. In co-culture with activated Jurkat T cells under tumor-conditioned medium-influenced conditions, combined PD-L1 and CSF1R immunoliposomes enhanced T-cell proliferation, reduced apoptosis, and improved migration toward tumor spheroids compared to single-target liposomal treatments or free antibodies. These benefits varied between glioblastoma and medulloblastoma in vitro models, reflecting distinct TAM2M phenotypes and microenvironment contexts. Dual immunoliposome targeting of PD-L1 and CSF1R may offer a promising strategy for reprogramming tumor-associated macrophages in brain tumor immunotherapy and warrants further evaluation in primary human macrophages and in vivo models to clarify efficacy and mechanisms.

neuroscience↗

bioRxiv2026

Two-State Effective Connectivity Predicts the effect of Anterior Cingulate Cortex Excitatory / Inhibitory Balance on Posterior Cingulate Cortex

Aims Excitatory/inhibitory imbalance - the equilibrium between glutamatergic excitation and {gamma}-Aminobutyric acid (GABA)ergic inhibition - is implicated in psychosis and may precede psychosis onset. Methods To investigate whether excitatory/inhibitory balance relates to subclinical psychotic experiences, anxiety and depression, we developed a network-level measure integrating Magnetic Resonance Spectroscopy Glutamate/GABA ratios with effective connectivity during resting-state fMRI. In 20 healthy young adults (aged between 18-32; 50% female), we first measured excitatory/inhibitory balance, operationalised as Glutamate/GABA and Glutamate+Glutamine(Glx)/GABA ratio, in the dorsal anterior cingulate cortex. Second, we applied Two-state Dynamic Causal Modelling to assess effective connectivity of the default-mode network, using these ratios as empirical priors to determine how they translate into network connectivity. Third, we ran Bayesian Model Selection at the model and family levels to identify the optimal models. Results The two-state model with Glx/GABA ratio as direct input into the posterior cingulate cortex best explained the data. This best-fitting model incorporated the neurometabolite ratio with Glutamate and GABA and used two-state connectivity. Conclusions Excitatory/inhibitory balance underlies resting-state effective connectivity, with the Glx/GABA ratio shaping connectivity networks. This pathway highlights excitatory/inhibitory balance as a predictor of two-state brain networks, potentially aiding identification of high-risk individuals and informing new interventions.

neuroscience↗

bioRxiv2025

Limitations of Dynamic Causal Modelling for Multistable Cortical Circuits

Dynamic causal modelling (DCM) is widely used to infer effective connectivity from neuroimaging data. However, its applicability to neural systems with complex, multistable dynamics remains uncertain--particularly when using the standard estimation approach based on variational Bayesian inference under the Laplace approximation. To investigate this limitation, we constructed biologically grounded cortical columnar neural mass models exhibiting three distinct multistable regimes: bistable fixed points associated with decision-making, coexisting oscillatory states through period-doubling bifurcations, and deterministic chaotic dynamics. These models were used to simulate local field potentials, which served as inputs to DCM. Bayesian model selection successfully identified the correct model architecture in all cases. However, Bayesian model averaging of the winning models failed to accurately estimate extrinsic connectivity parameters, leading to substantial discrepancies between the dynamics of the reconstructed and ground-truth systems. These results suggest that even when model selection is accurate, parameter estimation can break down under complex dynamics. Compared to previous applications of DCM to simpler neural systems, our study highlights significant limitations in its ability to capture the structure of multistable and globally nonlinear dynamics. We conclude that caution is warranted when applying variational Laplace-based DCM procedures to experimental paradigms involving bifurcations, chaotic trajectories, or other forms of dynamical complexity.

neuroscience↗

bioRxiv2024

Effective connectivity predicts distributed neural coding of perceptual decision confidence, uncertainty and speed

Decision-making is often accompanied by a level of confidence regarding the accuracy of ones decision. Previous studies have indicated neural activity associated with perceptual decision confidence during sensory stimulus presentation. Choice-based reaction time (RT) has been suggested as an indirect but more objective measure of decision confidence - generally faster RT for higher confidence. However, it is unclear whether choice confidence and RT are mediated by distinct neural pathways, and whether their neural correlates are encoded nonlinearly. Within a perceptual decision-making task, we applied fMRI-informed EEG-based effective connectivity analysis via dynamic causal modelling (DCM) on event-related potentials and found the frontoparietal network for fast-vs-slow RT condition to be different from that of high-vs-low confidence rating condition. Furthermore, trial-by-trial DCM analysis predicted cortical layer-based, distributed and nonlinear coding of RT, confidence or uncertainty. Collectively, our study suggests that decision confidence and speed are instantiated by different dynamical networks distributed across cortical layers.

neuroscience↗