Computational modelling of human electrophysiological data

In two complementary studies, computational tools address clinical challenges in neuropathic pain and the effects of neuroactive medications on brain activity. We leveraged in-silico modeling and open-access data analysis pipelines to uncover complex neural dynamics associated with small fiber neuropathy (SFN) and the effect of neuroactive drugs on electrical brain activity. Both approaches demonstrate the utility of computational tools in finding solutions to complex clinical problems and advancing precision in neurological research. In the first study, the focus is on patients with SFN, who frequently experience persistent neuropathic pain, which remains a significant therapeutic challenge. The study examines dysfunction in mechanoinsensitive peripheral nerve fibers (CMi) as a potential cause of SFN-related pain. CMi fibers are responsible for transmitting noxious stimuli to the central nervous system. Using an in-silico biophysical model adapted to simulate ion channel behaviors and conductances in CMi fibers, we aimed to pinpoint molecular mechanisms contributing to CMi dysfunction. Microneurography data from 97 healthy individuals and 34 SFN patients were analyzed, revealing activity-dependent conduction velocity changes as a distinguishing feature in SFN patients compared to healthy individuals. The study employed the NEURON simulation environment, adapting an existing computational CMi fiber model to investigate how different combinations of ion channel conductances, Na-K pump dynamics, and membrane potential contribute to these observed conduction changes. Importantly, it was discovered that a combination of increased potassium conductance, reduced Na-K-pump activity, and a depolarized membrane potential best replicated the SFN dysfunction profile. The findings underscore that considering the interaction of multiple neural mechanisms may be essential for effective therapeutic strategies in SFN-related neuropathic pain. The second study focuses on the efficient analysis of effects of neuroactive medication on brain activity using electroencephalographic (EEG) data. An open-source computational pipeline was developed to utilize extensive EEG datasets from the Temple University Hospital to facilitate the study of drug-specific neural patterns of electrical brain activity. This offers the benefit that large, already available data sets can be used without recruiting new patients. The pipeline, structured into modular components, enables users to define specific individual medications or groups of medications and control groups for analysis. Standardized data preprocessing and statistical analysis provide consistent and reproducible results. As an example, the drugs carbamazepine (anticonvulsant) and risperidone (antipsychotic) were compared with two different control groups. The first control group consisted of people whose EEG was labeled as “normal”, while the second group consisted of people who received other drugs from the same class (anticonvulsant or antipsychotic, respectively). The results obtained from the pipeline showed good agreement with current research findings. The pipeline supports efficient, reproducible EEG analysis by offering a streamlined approach for testing hypotheses on drug effects in EEG data, with implications for future clinical research. Together, these studies highlight the role of computational methods in addressing complex neurological questions. By modeling ion channel contributions to SFN pain in silico, the first study proposes a multi-target approach to treatment, suggesting that clinical strategies could be optimized by considering the integrated effects of various neural pathways. The second study, by establishing a pipeline for EEG analysis, introduces a scalable and adaptable tool for exploring how different neuroactive drugs modulate brain activity, a potential asset for study design and clinical trials. These studies illustrate how computational neuroscience can expand the precision and accessibility of neurological research, offering new ways to address the complex mechanisms of neuropathic pain and medication-induced neural changes.