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Bahrami, F.

Publications and source records attributed to Bahrami, F..

9 recordsLinked to original sources

Inverse Neural-Kinematic Dynamics Revealed by RQA During Motor Skill Learning

Motor skill acquisition involves dynamic co-adaptation of brain and behavior, yet the temporal coupling between neural and kinematic processes during learning remains unclear. Traditional analyses often treat the brain and behavior independently, potentially overlooking their interactive dynamics. In this study, we applied Recurrence Quantification Analysis (RQA), a nonlinear method for assessing temporal structure in complex systems, to both EEG and foot trajectory data collected during a seven-session motor learning task. Twelve healthy adults practiced drawing five shapes with their dominant foot on a digital tablet while EEG was recorded from sensorimotor cortex electrodes. We found a fundamental inverse relationship between neural and behavioral dynamics: as motor performance improved, EEG determinism in the left hemisphere increased by 2.7% (p<0.001), while kinematic determinism decreased by 18.4% (p<0.001), indicating more organized brain activity supporting more flexible movement patterns. EEG laminarity also increased by 1.5% (p<0.001), while kinematic laminarity decreased by 15.2% (p<0.001). Shape-specific analysis revealed that patterns B and M elicited the most pronounced neural-behavioral adaptations. These findings suggest that increasingly structured neural dynamics facilitate adaptive motor execution. Our dual-domain RQA framework offers a powerful tool to quantify learning efficiency and uncover the dynamic coupling between neural reorganization and behavioral adaptation.

neuroscience↗

NMDA Receptor Kinetics Drive Distinct Routes to Chaotic Firing in Pyramidal Neurons

Neuronal firing patterns emerge from complex interactions between intrinsic membrane properties and synaptic receptor dynamics. N-methyl-D-aspartate (NMDA) receptors critically shape calcium influx and synaptic plasticity through their voltage-dependent Mg2+ block and prolonged activation kinetics. We developed a Hodgkin-Huxley-type computational model incorporating NMDA, AMPA, and GABA receptor kinetics to investigate how NMDA receptor closing rates ({beta}NMDA) and glutamatergic stimulation frequency control neuronal dynamics. Systematic analysis of 2,942,093 inter-spike intervals across 1,961 parameter combinations revealed two mechanistically distinct pathways to firing irregularity. Pathway 1 involves rapid NMDA deactivation ({beta}NMDA > 0.06 ms-1) at elevated stimulation frequencies, producing deterministic chaos with compromised information encoding (entropy: 1.441 bits, mutual information: 0.185 bits). Pathway 2 results from slow NMDA deactivation ({beta}NMDA < 0.02 ms-1) under weak drive, creating irregularity through prolonged receptor activation and sustained calcium influx (entropy: 1.347 bits). An optimal kinetic window emerged at {beta}NMDA = 0.028 ms-1, maximizing information transfer (0.275 bits) while maintaining stable dynamics. Entropy-Lyapunov correlation analysis confirmed deterministic chaos (r = 0.150, p {inverted exclamation} 0.001). Frequency-dependent chaos onset thresholds demonstrated systematic erosion from 0.000 ms-1 at low frequencies to 0.150 ms-1 at high frequencies. GABAergic inhibition provided frequencyselective stabilization, expanding stable parameter space by 34.2 These findings establish NMDA receptor kinetics as fundamental controllers of cortical excitability and information processing. The dual-pathway framework provides mechanistic insights into addiction-related memory formation, where prolonged NMDA activation enables pathological plasticity, and visual processing disorders, where altered kinetics disrupt retinal function and cortical oscillatory balance. The identification of optimal kinetic windows and frequency-selective GABA modulation suggests therapeutic strategies targeting kinetically-specific interventions for neuropsychiatric disorders involving NMDA dysfunction.

neuroscience↗

Dysconnection and cognition in schizophrenia: a spectral dynamic causal modeling study

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC between the subnetworks of seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20% and 11% aberrant couplings. Overall, the proportion of diagnostic connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for cognitive assessment.

neuroscience↗

The critical role of spiny stellate cells in seizure onset based on dynamic analysis of a neural mass model

Growing evidence suggests that excitatory neurons in the brain play a significant role in seizure generation. Nonetheless, spiny stellate cells are cortical excitatory non-pyramidal neurons in the brain, which their basic role in seizure occurrence is not well understood. In the present research, we study the critical role of spiny stellate cells or the excitatory interneurons (EI), for the first time, in epileptic seizure generation using an extended neural mass model inspired by Liu and Wang model in 2017. Applying bifurcation analysis on this modified model, we investigated the rich dynamics corresponding to the epileptic seizure onset and transition between interictal and ictal states caused by EI connectivity to other cell types. Our results indicate that the transition between interictal and ictal states (preictal signal) corresponds to a supercritical Hopf bifurcation, and thus, the extended model suggests that before seizure onset, the amplitude and frequency of neural activities gradually increase. Moreover, we showed that 1) the altered function of GABAergic and glutamatergic receptors of EI can cause seizure, and 2) the pathway between the thalamic relay nucleus and EI facilitates the transition from interictal to the ictal activity by decreasing the preictal period. Thereafter, we considered both sensory and cortical periodic inputs to study model responses to various harmonic stimulations. Bifurcation analysis of the model, in this case, suggests that the initial state of the model might be the main cause for the transition between interictal and ictal states as the stimulus frequency changes. The extended thalamocortical model shows also that the amplitude jump phenomenon and nonlinear resonance behavior result from the preictal state of the modified model. These results can be considered as a step forward to a deeper understanding of the mechanisms underlying the transition from normal activities to epileptic activities.

neuroscience↗

Predicting transdermal fentanyl delivery using physics-based simulations for tailored therapy

Transdermal fentanyl patches are an effective alternative to the sustained-release of oral morphine for chronic pain treatment. Due to the narrow therapeutic range of fentanyl, the fentanyl concentration in the blood needs to be controlled carefully. Only then, effective pain relief can be reached while avoiding adverse effects such as respiratory depression. In this study, a physics-based digital twin of the patient was developed by implementing mechanistic models for transdermal drug uptake and the patients pharmacokinetic and pharmacodynamics response. A digital twin is a virtual representation of the patient and the transdermal drug delivery system, which is linked to the real-world patient by patient feedback, sensor data of specific biomarkers, or customizing the twin to a particular patient characteristic, for example, based on the age. This digital twin can predict the transdermal drug delivery processes in-silico. Our twin is used first to predict conventional therapys effect for using fentanyl patches on a virtual patient at different ages. The results show that by aging, the maximum transdermal fentanyl flux and maximum concentration of fentanyl in the blood decrease by 11.4% and 7.0%, respectively. Nonetheless, by aging, the pain relief increases by 45.2% despite the lower concentration of fentanyl in the blood for older patients. As a next step, the digital twin was used to propose a tailored therapy, based on the patients age, to deliver fentanyl based on the patients needs to alleviate pain. This predesigned therapy consisted of customizing the duration of applying and changing the commercialized fentanyl patches based on the calculated pain intensity. According to this therapy, a patient of 20 years old needs to change the patch 2.1 times more frequently compared to conventional therapy, which led to 30% more pain relief and 315% more time without pain. In addition, the digital twin was updated by the patients pain intensity feedback. Such therapy led to an increase in the patients breathing rate while having effective pain relief, therefore providing a safer and more comfortable treatment for the patient. We quantified the added value of a patients physics-based digital twin and sketched the future roadmap for implementing such twin-assisted treatment into the clinics. NomenclatureO_ST_ABSSymbolsC_ST_ABSci The concentration of fentanyl in layer i (in the drug uptake model) [ng ml-1] cp The concentration of fentanyl in the central compartment [ng ml-1] cr The concentration of fentanyl in the rapid equilibrated compartment [ng ml-1] cs The concentration of fentanyl in the slow equilibrated compartment [ng ml-1] cg The concentration of fentanyl in the gastrointestinal compartment [ng ml-1] cl The concentration of fentanyl in the hepatic compartment [ng ml-1] ce The concentration of fentanyl in the effect compartment [ng ml-1] Di Diffusion coefficient of fentanyl in layer i (in the mechanistic model) [m2 s-1] D0 Base diffusion coefficient of fentanyl [m2 s-1] DT Diffusion coefficient of fentanyl at temperature T [m2 s-1] D306 Diffusion coefficient of fentanyl at 306[K] [m2 s-1] dpt The thickness of the transdermal patch [{micro}m] dsc The thickness of the stratum corneum [{micro}m] dvep The thickness of the viable epidermis [{micro}m] dEdm The thickness of the equivalent dermis [{micro}m] Ei The intensity of effect i [Formula]The baseline of effect i [Formula]The maximum effect i EC50,i The concentration related to half-maximum effect i [ng ml-1] fu The fraction of unbound fentanyl in plasma ji Fentanyl flux in layer i (in the mechanistic model) Ki/j The partition coefficient of fentanyl between layer i to j (in the mechanistic model) Ki The drug capacity in layer i (in the mechanistic model) kcs Inter-compartmental first-order equilibrium rate constant (central to slow equilibrated) [min-1] kcr Inter-compartmental first-order equilibrium rate constant (central to rapid equilibrated) [min-1] kcg Inter-compartmental first-order equilibrium rate constant (central to gastrointestinal) [min-1] kch Inter-compartmental first-order equilibrium rate constant (central to hepatic) [min-1] ksc Inter-compartmental first-order equilibrium rate constant (slow equilibrated to central) [min-1] krc Inter-compartmental first-order equilibrium rate constant (rapid equilibrated to central) [min-1] khc Inter-compartmental first-order equilibrium rate constant (hepatic to central) [min-1] kgh Inter-compartmental first-order equilibrium rate constant (gastrointestinal to hepatic) [min-1] kmet Metabolization rate constant [min-1] kre Renal clearance rate constant [min-1] ke Inter-compartmental first-order equilibrium rate constant (for effect compartment) [min-1] SI Sensitivity index t Time [h] tD Time lag [h] [Formula]Dependent variable related to xi for sensitivity analysis Vc The apparent volume of the central compartment [L] Vs The apparent volume of the slow equilibrated compartment [L] Vr The apparent volume of the rapid equilibrated compartment [L] Vg The apparent volume of the gastrointestinal compartment [L] Vh The apparent volume of the hepatic compartment [L] xi The independent variable which sensitivity analysis is done based on it {gamma}Hill coefficient {psi}i Drug potential in domain i [ng ml-1]

pharmacology and toxicology↗

Inverse mechanistic modeling of transdermal drug delivery for fast identification of optimal model parameters

Transdermal drug delivery systems are a key technology to administer drugs with a high first-pass effect in a non-invasive and controlled way. Physics-based modeling and simulation are on their way to become a cornerstone in the engineering of these healthcare devices since it provides a unique complementarity to experimental data and insights. Simulations enable to virtually probe the drug transport inside the skin at each point in time and space. However, the tedious experimental or numerical determination of material properties currently forms a bottleneck in the modeling workflow. We show that multiparameter inverse modeling to determine the drug diffusion and partition coefficients is a fast and reliable alternative. We demonstrate this strategy for transdermal delivery of fentanyl. We found that inverse modeling reduced the normalized root mean square deviation of the measured drug uptake flux from 26 to 9%, when compared to the experimental measurement of all skin properties. We found that this improved agreement with experiments was only possible if the diffusion in the reservoir holding the drug was smaller than the experimentally-measured diffusion coefficients suggested. For indirect inverse modeling, which systematically explores the entire parametric space, 30 000 simulations were required. By relying on direct inverse modeling, we reduced the number of simulations to be performed to only 300, so a factor 100 difference. The modeling approachs added value is that it can be calibrated once in-silico for all model parameters simultaneously by solely relying on a single measurement of the drug uptake flux evolution over time. We showed that this calibrated model could accurately be used to simulate transdermal patches with other drug doses. We showed that inverse modeling is a fast way to build up an accurate mechanistic model for drug delivery. This strategy opens the door to clinically-ready therapy that is tailored to patients.

pharmacology and toxicology↗

Predicting transdermal fentanyl delivery using mechanistic simulations for tailored therapy

Transdermal drug delivery is a key technology for administering drugs. However, most devices are "one-size-fits-all", even though drug diffusion through the skin varies significantly from person-to-person. For next-generation devices, personalization for optimal drug release would benefit from an augmented insight into the drug release and percutaneous uptake kinetics. Our objective was to quantify the changes in transdermal fentanyl uptake with regards to the patients age and the anatomical location where the patch was placed. We also explored to which extent the drug flux from the patch could be altered by miniaturizing the contact surface area of the patch reservoir with the skin. To this end, we used validated mechanistic modeling of fentanyl diffusion, storage, and partitioning in the epidermis to quantify drug release from the patch and the uptake within the skin. A superior spatiotemporal resolution compared to experimental methods enabled in-silico identification of peak concentrations and fluxes, and the amount of stored drug and bioavailability. The patients drug uptake showed a 36% difference between different anatomical locations after 72 h, but there was a strong interpatient variability. With aging, the drug uptake from the transdermal patch became slower and less potent. A 70-year-old patient received 26% less drug over the 72-h application period, compared to an 18-year-old patient. Additionally, a novel concept of using micron-sized drug reservoirs was explored in silico. These reservoirs induced a much higher local flux ({micro}g cm-2 h-1) than conventional patches. Up to a 200-fold increase in the drug flux was obtained from these small reservoirs. This effect was mainly caused by transverse diffusion in the stratum corneum, which is not relevant for much larger conventional patches. These micron-sized drug reservoirs open new ways to individualize reservoir design and thus transdermal therapy. Such computer-aided engineering tools also have great potential for in-silico design and precise control of drug delivery systems. Here, the validated mechanistic models can serve as a key building block for developing digital twins for transdermal drug delivery systems.

bioengineering↗

A novel tolerance index to identify heat tolerance in cultivated and wild barley genotypes

Thermal stress at the reproductive stage poses a substantial constraint on cereal production worldwide. This study was conducted to assess tolerance to terminal high-temperature stress in 45 wild (Hordeum vulgare ssp. spontaneum) genotypes, 4 cultivars (H. vulgare ssp. vulgare), 98 F3 and 79 BC1F2 families derived from hybridization of the most tolerant wild genotype and a susceptible cultivar Mona. Results of analysis of variance showed the significant genotypic and high-temperature stress effects on all the traits studied. In contrast to the cultivated genotypes, the wild ones were found less affected by high-temperature stress. The multivariate analysis highlighted the additional high-temperature tolerance components in the tolerant families and wild genotypes. Grain yield strongly correlated (p < 0.01) with stress tolerance, yield stability, and heat tolerance indices. The reduction in the reproduction period caused by high-temperature was much higher in cultivated genotypes than in wild ones. In conclusion, the ingenuous-focused strategies like escape/avoidance are being used primarily to cope with heat stress by cultivars, while adaptive-focused coping strategies such as tolerance are being implemented by wild barley.

plant biology↗

From a biological template model to gait assistance with an exosuit

By invention of soft wearable assistive devices, known as exosuits, a new aspect in assisting unimpaired subjects is introduced. In this study, we designed and developed an exosuit with compliant biarticular thigh actuators, called BAExo. Unlike common method of using rigid actuators in exosuits, the BAExo is made of serial elastic actuators (SEA) resembling artificial muscles (AM). This bioinsipred design is complemented by the novel control concept of using the ground reaction force to adjust these AMs stiffness in the stance phase. By locking the motors in the swing phase the SEAs will be simplified to passive biarticular springs, which is sufficient for leg swinging. The key concept in our design and control approach is synthesizing human locomotion to develop assistive device, instead of copying the outputs of human motor control. Analysing human walking assistance using an experiment-based OpenSim model demonstrates the advantages of the proposed design and control of BAExo, regarding metabolic cost reduction and efficiency of the system. In addition, pilot experiments with the recently developed BAExo hardware support the applicability of the introduced method. Author summaryAging and mobility of elderly people are of crucial concern in developed countries. The U.S. Census Bureau reports that by the middle of the 21st century, about 80 million Americans will be 65 or older. According to the groups research, medical costs resulting from falls by the elderly are expected to approach $32.4 billion by 2020. Therefore, assistance of elderly people and making the assistive devices more intelligent is a need in near future. However, this is not the only application of assistive devices. Exosuits, as soft wearable robots, introduced a new aspect in assisting a large range of population, even healthy young people. We introduce a novel design and control method for a new exosuit. As the research in the field of wearable assistive devices is growing in recent years and its application in daily life becomes more evident for the society, such studies with a unique view in design and control could have a significant impact. Our proposed biologically inspired approach could be potentially applied to other exosuits.

bioengineering↗