Biology subjects

Diaconescu, A. O.

Publications and source records attributed to Diaconescu, A. O..

7 recordsLinked to original sources

bioRxiv2026

A Connectome-Constrained Jansen-Rit Framework for Inferring Cortical Gain Control and Ensemble Stability

Understanding how local circuit dynamics give rise to large-scale stability and instability of brain activity is a central challenge in computational neuroscience, with direct relevance for disorders characterized by disrupted excitatory-inhibitory balance, including schizophrenia spectrum disorder (SSD). Here, we introduce a principled methodology for recovering local neural parameters and low-dimensional dynamical biomarkers from a connectome-constrained Jansen-Rit (JR) neural mass model using variational free-energy inversion and sliding-window analysis. Each cortical region is modeled as a canonical excitatory-inhibitory microcircuit embedded within a whole-brain network whose long-range interactions are factorized into pyramidal-pyramidal, pyramidal- excitatory, and pyramidal-inhibitory subnetworks. Across 80 independent simulations, the inversion framework reliably recovered both microcircuit parameters and emergent biomarkers derived from neural states, including the mean-variance slope ({beta}1), its spatial variability, and the lag-1 autocorrelation ({rho}1). These quantities capture complementary aspects of cortical ensemble dynamics--gain sensitivity, regional heterogeneity, and temporal persistence associated with proximity to criticality--and were consistently estimated with minimal bias and high reliability. The recovered slope hierarchy [Formula] revealed an interpretable gain-control architecture in which inhibitory channels regulate damping, excitatory channels gate resonance, and pyramidal populations integrate network drive into stable output. Together, these results demonstrate that the JR model provides a tractable and biophysically grounded framework for linking synaptic parameters, network structure, and ensemble-level stability. Although motivated by questions surrounding psychosis risk and SSD, the proposed approach is general and establishes a foundation for future applications in model-based inference, network control, and adaptive neuromodulation.

neuroscience↗

bioRxiv2026

A computational neuroimaging account of impulsive premature decision-making

Impulsivity is a multidimensional construct with distinct implications for the pathophysiology and treatment of various neuropsychiatric conditions, including substance use disorders, behavioural addictions, attention deficit hyperactivity disorder, psychosis, and personality disorders. One proposed behavioural subtype, premature responding impulsivity (PRI), appears to be influenced by neuromodulatory pathways frequently implicated in these disorders, in particular dopamine neurotransmission, known for its role in contingency learning. Still, the neurobiological basis of PRI in humans remains insufficiently understood. Here, we theorize that PRI reflects the brains capacity to adapt to environmental uncertainty. To test this hypothesis, twenty-four healthy adults (mean age 22.6 years; 12 females) completed a novel decision-making task featuring alternating stable and volatile probabilistic cue contingencies while undergoing functional magnetic resonance imaging (fMRI). A hierarchical Bayesian model estimated PRI as an urgency-to-respond process, whose parameters were dynamically modulated by volatility. These model-derived indices correlated with established trait impulsivity measures, supporting their construct validity. Model-based fMRI analyses identified a distributed cortico-subcortical network including anterior insula, dorsal anterior cingulate cortex, striatum, and monoaminergic midbrain regions, whose activity tracked within-trial PRI estimates as they evolved over time. Connectivity analyses further showed that high volatility enhanced interactions between subnetworks typically associated with promoting or inhibiting impulsive action. Together, these results outline a neurocomputational account in which environmental uncertainty modulates PRI through interacting brain circuits, offering a principled framework for further probing the transdiagnostic role of impulsivity across neuropsychiatric and neuropsychopharmacological contexts.

neuroscience↗

bioRxiv2025

Evaluating the quality of brainstem ROI registration using structural and diffusion MRI

Accurate registration of regions of interest (ROIs) from standard atlases to participants native spaces is a critical step in fMRI studies, as it directly affects the reliability of sampled BOLD signals. While T1-weighted (T1w) image-based ROI registration is well validated and widely adopted in cortical fMRI, its performance degrades in brainstem studies due to the small size, dense packing, and poor visibility of brainstem nuclei on T1w contrast. We hypothesized that incorporating diffusion MR images, containing more information about internal brainstem architecture, should improve ROI registration accuracy. To test this, we developed four registration pipelines that either included or excluded diffusion-based alignment components and evaluated their performance using data from n=20 healthy participants. Registration accuracy was assessed using Dice coefficient for the red nucleus (RN) and the substantia nigra (SN), and mis-registration fraction--a metric developed for nuclei that cannot be manually delineated--for the dorsal raphe nucleus (DRN). The results showed that diffusion-based pipelines, using fractional anisotropy (FA) images, non-diffusion-weighted (b0) images, and multivariate combination, outperformed the T1w-only baseline. Probabilistic maps derived from inverse-transformed native ROIs further supported improved sensitivity to inter-individual anatomical variability in the diffusion-augmented pipelines. In addition, analysis of gradient magnitude maps from the Jacobian determinants revealed associations between localized deformation and image modality-specific landmarks. These findings demonstrate the potential of diffusion-augmented pipelines for improving brainstem ROI registration, which could enhance the robustness of fMRI studies on brainstem disorders characterized by functional dysregulation.

neuroscience↗

bioRxiv2022

The effect of lysergic acid diethylamide (LSD) on whole-brain functional and effective connectivity

Psychedelics have emerged as promising candidate treatments for various psychiatric conditions, and given their clinical potential, there is a need to identify biomarkers that underlie their effects. Here, we investigate the neural mechanisms of lysergic acid diethylamide (LSD) using regression dynamic causal modelling (rDCM), a novel technique that assesses whole-brain effective connectivity (EC) during resting-state functional magnetic resonance imaging (fMRI). We modelled data from two randomized, placebo-controlled, double-blind, cross-over trials, in which 45 participants were administered 100g LSD and placebo in two resting-state fMRI sessions. We compared EC against whole-brain functional connectivity (FC) using classical statistics and machine learning methods. Multivariate analyses of EC parameters revealed widespread increases in interregional connectivity and reduced self-inhibition under LSD compared to placebo, with the notable exception of primarily decreased interregional connectivity and increased self-inhibition in occipital brain regions. This finding suggests that LSD perturbs the Excitation/Inhibition balance of the brain. Moreover, random forests classified LSD vs. placebo states based on FC and EC with comparably high accuracy (FC: 85.56%, EC: 91.11%) suggesting that both EC and FC are promising candidates for clinically-relevant biomarkers of LSD effects.

neuroscience↗

bioRxiv2019

Neural Arbitration between Social and Individual Learning Systems

Decision making often requires integrating self-gathered information with information acquired from observing others. Depending on the situation, it may be beneficial to rely more on one than the other source, taking into account that either information may be imprecise or deceiving. The process by which one source is selected over the other based on perceived reliability, here defined as arbitration, has not been fully elucidated. In this study, we formalised arbitration as the relative reliability (precision) of predictions afforded by each learning system using hierarchical Bayesian models. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and self-sampled outcomes. The number of points participants wagered on their predictions reflected arbitration: The higher the relative precision of one learning system over the other and the lower the intention volatility, the more points participants wagered on a given trial. Functional neuroimaging demonstrated that the arbitration signal was independent of decision confidence and involved modalityspecific brain regions. Arbitrating in favour of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain whereas arbitrating in favour of social information engaged ventromedial prefrontal cortex and the temporoparietal junction. These findings are in line with domain specificity and indicate that relative precision captures arbitration between social and individual learning systems at both the behavioural and neural level.

neuroscience↗

bioRxiv2019

Atypical processing of uncertainty in individuals at risk for psychosis

BackgroundCurrent theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in at-risk mental state (ARMS) individuals. MethodsNon-medicated ARMS individuals (n=13) and control participants (n=13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour - with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental volatility - and used these computational quantities for analyses of fMRI data. ResultsComputational modelling of ARMS individuals behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of ARMS individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in ARMS was negatively associated with clinical measures of global functioning. ConclusionsOur results suggest a multi-faceted learning abnormality in ARMS individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation.

neuroscience↗

bioRxiv2019

Ketamine Affects Prediction Errors About Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities. Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the worlds statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

neuroscience↗