Biology subjects

Mortazavi, L.

Publications and source records attributed to Mortazavi, L..

3 recordsLinked to original sources

bioRxiv2026

Diffusion MRI Tractography Predicts Electrophysiological Connectivity and Explains Spectral Signatures of Evoked Potentials in the Human Brain

White matter fiber bundles are the structural conduits of information flow in the human brain and thereby mediate the spatial trajectories and timings of the electrophysiological signaling. Here, we combined diffusion magnetic resonance imaging (dMRI) and stereoelectroencephalography (SEEG) recordings in neurosurgical patients to develop an integrated framework for predicting and interpreting causal electrophysiological connectivity patterns between pairs of brain regions. We used repeated single-pulse electrical stimulation in 40 participants implanted with a total of 5794 intracranial electrodes throughout the human brain, encompassing both cortical and multiple thalamic nuclei. A nonlinear, time-frequency manifold learning approach was used to define electrophysiological connectivity, which was then compared with subject-specific and atlas-based structural connectivity. Across 150,000 electrode pairs, we found that the presence of a structural connection predicted causal electrophysiological connectivity with a probability of ~0.95; and its absence predicted the lack of the direct electrophysiological connectivity with a probability of ~0.8. We also show evidence of indirect/polysynaptic pathways supported by both modalities and reported neural features from time-frequency decomposition that distinguished between direct and indirect signaling. We demonstrated that an early phase-locked broadband component (10-70 ms) marked direct structural pathways, whereas delayed and slower components reflected indirect propagation. Notably, we reported that thalamic involvement within an indirect pathway results in increased latency (> 200 ms) and enhanced late oscillatory behavior, despite increased conduction velocity measures along thalamo-cortical pathways. Therefore, our multimodal framework maps human brain connectivity, bridging structural architecture, causal electrophysiological dynamics, and network-level communication.

neuroscience↗

bioRxiv2026

Dopamine Agonists in Parkinson's Disease Decouple Risk-Taking from Reward-Paired Cues by Enhancing Risky Choice in Their Absence

Background: Common side effects of dopamine replacement therapy (DRT) for Parkinson's Disease (PD) are addiction-like impulse control disorders, including compulsive gambling. Gambling products such as slot machines prominently feature reward-paired audiovisual stimuli, which can promote riskier choices on laboratory gambling tasks in both humans and rodents. In rats, risky decision making in the presence of reward-paired cues is dependent on dopamine D3 receptor activity and is increased by ropinirole, a D2/3 receptor agonist with a higher affinity for D3 receptors. In humans, it remains uncertain whether cue-induced risky choice is amplified by dopamine agonism. Methods: We tested effects of DRT on cue-induced risky choice in 62 patients with PD (34 on levodopa monotherapy, 27 on levodopa and dopamine agonists). Patients performed two versions of a risky decision-making task: one with and one without reward-paired sensory cues, both ON and OFF DRT. A group of 36 age-matched controls completed the same task versions without DRT. Results: Across all groups, participants made riskier choices when the task included reward-paired sensory cues. Levodopa monotherapy did not affect risky decision-making. However, a combination of levodopa and dopamine agonists increased risky choices specifically in the uncued version of the task, making performance indistinguishable from that on the cued task. Conclusions: Dopamine agonists combined with levodopa promote risk-taking even in the absence of reward-paired cues. This could translate into risky reward-seeking behaviors that are decoupled from contextual factors.

neuroscience↗

bioRxiv2023

Insight into differing decision-making strategies that underlie cognitively effort-based decision making using computational modeling in rats

RationaleThe rat Cognitive Effort Task (rCET), a rodent model of cognitive rather than physical effort, requires animals to choose between an easy or hard visuospatial discrimination, with a correct hard choice more highly rewarded. Like in humans, there is stable individual variation in choice behavior. In previous reports, animals were divided into two groups - workers and slackers - based on their mean preference for the harder option. Although these groups differed in their response to pharmacological challenges, the rationale for using this criterion for grouping was not robust. MethodsWe collated experimental data from multiple cohorts of male and female rats performing the rCET, and used a model-based framework combining drift diffusion modeling with cluster analysis to identify the decision-making processes underlying variation in choice behavior. ResultsWe verified that workers and slackers are statistically different groups, but also found distinct intra-group profiles. These subgroups exhibited dissociable performance during the attentional phase, linked to distinct decision-making profiles during choice. Reanalysis of previous pharmacology data using this model-based framework showed that serotonergic drug effects were explained by changes in decision boundaries and non-decision times, whilst scopolamines effects were driven by changes in decision starting points and rates of evidence accumulation. ConclusionsModeling revealed the decision-making processes that are associated with cognitive effort costs, and how these differ across individuals. Reanalysis of drug data provided insight into the mechanisms through which different neurotransmitter systems impact cognitively effortful attention and decision-making processes, with relevance to multiple psychiatric disorders.

neuroscience↗