Biology subjects

Lea-Carnall, C. A.

Publications and source records attributed to Lea-Carnall, C. A..

8 recordsLinked to original sources

bioRxiv2025

Frequency-Dependent Neurostimulation in Whole Brain Network models

BackgroundBrain stimulation at specific frequencies has been shown to have therapeutic effects for certain neurological disorders. However, it remains unclear how these oscillatory inputs interact with neuronal dynamics and what drives this frequency selectivity. Here, we propose that this is achieved by targeting specific brain circuits through frequency-selective network entrainment. MethodsTo demonstrate that this type of entrainment can occur in the brain connectome structure, we use a minimal physics-based model of coupled oscillators that preserves only the essential structural connectivity of real brain networks. Using this model, we test how periodic stimulation influences network oscillatory dynamics, identifying sub-networks that synchronize with specific stimulation frequencies. Furthermore, we validate the suitability of the model to reproduce well-known frequency-selective entrainment using visual and auditory periodic stimulation. ResultsOur model reproduces selective network entrainment, whereby distinct stimulation frequencies favor the synchrony of different sub-networks. Crucially, entrainment exhibits a physics-predicted inverse relationship with frequency: lower-frequency stimulation favors broader synchrony across the network, while higher-frequency inputs produce spatially confined effects. These patterns emerge purely from network structure and oscillatory physics, independent of biological details. ConclusionThis physics-based approach elucidates the fundamental mechanistic principles governing frequency-selective brain network stimulation. Our minimal model demonstrates that selective entrainment of specific subnetworks can be achieved through frequency selection alone, largely determined by network structure and oscillatory dynamics. These findings provide a theoretical foundation for understanding how network architecture determines stimulation selectivity, supporting the development of principled approaches to targeted neuromodulation. Author summaryIn this work, we explore how external rhythmic stimulation interacts with the brains own rhythms. We use a physics-based model that represents the brain as a network of interconnected oscillators, linked according to the human connectome. Through this model, we show that stimulation effects are not limited to the targeted region but spread across the network, depending on the stimulation frequency, its spatial location and the underlying network dynamics. Our results reveal that low-frequency rhythms promote large-scale synchrony, while high-frequency rhythms engage more localized areas, all emerging from the brains structural connections and communication delays. The simulated cortical activation exhibits a frequency-selective profile that aligns strongly with that estimated from sensory-evoked electroencephalographic activity. This agreement suggests that differences between individuals in how their brain networks operate may explain why stimulation affects each person differently. Our findings offer a mechanistic framework for designing personalized stimulation protocols, illustrating how computational modeling can guide the optimization of noninvasive brain stimulation in both research and clinical contexts.

bioengineering↗

bioRxiv2025

Fronto-parietal effective connectivity during working memory in Neurofibromatosis Type 1 adolescents and neurotypical controls

Neurofibromatosis Type 1 (NF1) is a rare, single-gene neurodevelopmental disorder. Atypical brain activation patterns have been linked to working memory difficulties in individuals with NF1. The present work investigates if NF1 has increased inhibitory activity in the frontoparietal network during working memory tasks compared to neurotypical controls. Forty-three adolescents with NF1 and twenty-six age-matched neurotypical controls completed functional magnetic resonance imaging scans during a verbal working memory task. Dynamic causal models (DCMs) were estimated for bilateral frontoparietal network (dorsolateral and ventrolateral prefrontal cortices (dlPFC and vlPFC), superior and inferior parietal gyri (SPG and IPG)). The parametric empirical Bayes approach with Bayesian model reduction was used to test the hypothesis that NF1 diagnosis would be characterised by greater inhibitory self-connections (intrinsic connectivity). Leave-one-out cross-validation (LOO-CV) was performed to test the generalisability of group differences. NF1 participants demonstrated greater average intrinsic connectivity of left dlPFC, IPG, SPG and bilateral vlPFC. The DCM that best explained effects of working memory showed that NF1 group has increased intrinsic connectivity of left vlPFC, but weaker intrinsic connectivity of right vlPFC and left dlPFC. The parameters of these connections showed a modest but positive predictive correlation of r = 0.19 (p = 0.055) with diagnosis status, suggesting a trend toward predictive value. Overall, increased average intrinsic connectivity of left dlPFC, IPG, SPG and bilateral vlPFC in NF1, suggests reduced overall sensitivity of these regions to inputs. Working memory evoked different patterns of input processing in NF1, that cannot be characterised by increased inhibition alone. Instead, modulatory connectivity related to working memory showed less inhibitory self-connectivity of left dlPFC and left vlPFC, and more inhibitory intrinsic connectivity of right vlPFC in NF1. This discrepancy between average and modulatory connectivity suggests that overall NF1 participants are responsive to cognitive task-related inputs but may show atypical adaptation to the task demands of working memory.

neuroscience↗

bioRxiv2025

Functional connectivity reveals increased network segregation and sensorimotor processing during working memory in adolescents with Neurofibromatosis Type 1

Neurofibromatosis type 1 (NF1) is a rare, monogenic condition associated with impairments in working memory processing. However, it is unknown to what extent these impairments are explained by disrupted connectivity and state transitions of the functional brain networks. This study characterised task-state functional connectivity (FC) and network controllability during working memory in NF1 and investigated whether altered controllability distinguished NF1 from neurotypical controls and related to individual differences in working memory performance. We analysed functional MRI data from 53 NF1 adolescents and 36 age-matched neurotypical controls performing a verbal N-back task. Compared to controls, NF1 participants showed reduced FC within frontoparietal network (particularly between the left prefrontal cortex and left intraparietal sulcus), reduced FC between default and limbic networks, and increased FC between control and somatomotor regions. Network controllability analysis revealed increased average controllability but reduced modal controllability and activation energy in prefrontal, parietal, temporal and retrosplenial regions in NF1, indicating that in NF1 these regions have enhanced capacity to drive the network toward easy-to-reach states and are readily activated within the current network organisation. The opposite pattern of reduced average controllability but increased modal controllability and activation energy were observed in somatosensory, auditory, extrastriate and temporal-parietal regions, suggesting enhanced capacity of these regions to drive the network towards difficult-to-reach states. These changes accurately predicted NF1 status, with an area under the ROC curve exceeding 80% across all controllability models. Furthermore, average controllability accurately predicted working memory performance in the NF1 group, generalising to out-of-sample data. In particular, left and right lateral ventral prefrontal cortex, left superior parietal lobule, left intraparietal sulcus consistently emerged as predictors of working memory performance across all measures. By demonstrating that altered regional network controllability in NF1 predicts working memory performance, this study provides a novel mechanistic account of the functional network alterations underlying cognitive difficulties in NF1 and establishes a framework for evaluating future therapeutic interventions through their effects on functional brain network control.

neuroscience↗

bioRxiv2024

Effects of non-invasive brain stimulation on effective connectivity during working memory task in Neurofibromatosis Type 1 patients

This study examined the effects of anodal transcranial direct current stimulation (atDCS) on effective connectivity during a working memory task. Eighteen adolescents with Neurofibromatosis Type 1 (NF1) completed a single{square}blind sham{square}controlled cross{square}over randomised atDCS trial. Dynamic causal modelling was used to estimate the effective connectivity between regions that showed working memory effects from the fMRI. Group-level inferences for between sessions (pre- and post-stimulation) and stimulation type (atDCS and sham) effects were carried out using the parametric empirical Bayes approach. A correlation analysis was performed to relate the estimated effective connectivity parameters of left dlPFC pre-atDCS and post-atDCS to the concentration of gamma-aminobutyric acid (GABA) measured via magnetic resonance spectroscopy (MRS-GABA). Next, correlation analysis was repeated using all working memory performance and all pre-atDCS and post-atDCS connectivity parameters. It was found that atDCS decreased average excitatory connectivity from left dorsolateral prefrontal cortex (dlPFC) to left superior frontal gyrus and increased average excitatory connectivity to left globus pallidus. Further, reduced average intrinsic (inhibitory) connectivity of left dlPFC was associated with lower MRS-GABA. However, none of the connectivity parameters of dlPFC showed any association with performance on a working memory task. These findings suggest that atDCS reorganised connectivity from frontal to fronto-striatal connectivity. As atDCS-related changes were not specific to the effect of working memory, they may have impacted general cognitive control processes. In addition, by reducing MRS-GABA, atDCS might make dlPFC more sensitive and responsive to external stimulation, such as performance of cognitive tasks. Highlights- atDCS was applied to left dlPFC in NF1 patients during working memory - After atDCS, no effect on modulatory connectivity - Evidence for increased N-back average connectivity from dlPFC to globus pallidus - Less dlPFC MRS-GABA was associated with less dlPFC inhibition

neuroscience↗

bioRxiv2024

Rhythmic modulation of subthalamo-pallidal interactions depends on synaptic rewiring through inhibitory plasticity

Rhythmic stimulation offers a paradigm to modulate brain oscillations and, therefore, influence brain function. A growing body of evidence indicates that reciprocal interactions between the neurons of the subthalamic nucleus (STN) and globus pallidus externus (GPe) play a central role in the emergence of abnormal synchronous beta (15-30 Hz) oscillations in Parkinsons disease (PD). The proliferation of inhibitory GPe-to-STN synapses following dopamine loss exacerbates this pathological activity. Rhythmic modulation of the STN and/or GPe, for example, by deep brain stimulation (DBS), can restore physiological patterns of activity and connectivity. Here, we tested whether dual targeting of STN-GPe by rhythmic stimulation can modulate pathologically strong GPe-to-STN synapses through inhibitory spike-timing-dependent plasticity (iSTDP). More specifically, we examined how time-shifted paired stimuli delivered to the STN and GPe can lead to inter-population synaptic rewiring. To that end, we first theoretically analysed the optimal range of stimulation time shift and frequency for effective synaptic rewiring. Then, as a minimal model for generating subthalamo-pallidal oscillations in healthy and PD conditions, we considered a biologically inspired STN-GPe loop comprised of conductance-based spiking neurons. Consistent with the theoretical predictions, rhythmic stimulation with appropriate time shift and frequency modified GPe-to-STN interactions through iSTDP, i.e., by long-lasting rewiring of pathologically strong synaptic connectivity. This ultimately caused desynchronising after-effects within each population such that excessively synchronous beta activity in the PD state was suppressed, resulting in a decoupling of the STN-GPe network and restoration of healthy dynamics in the model. Decoupling effects of the dual STN-GPe stimulation can be realised by time-shifted continuous and intermittent stimuli, as well as monopolar and bipolar simulation waveforms. Our findings demonstrate the critical role of neuroplasticity in shaping long-lasting stimulation effects and may contribute to the optimisation of a variety of multi-site stimulation paradigms aimed at reshaping dysfunctional brain networks by targeting plasticity.

neuroscience↗

bioRxiv2024

Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model

Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics with multi-state metastability. Multi-state metastability reflects the capacity of the brain to transition between different network configurations and cognitive states in response to changing environments or tasks, thus relating to cognitive flexibility. To study this phenomenon, we used a Kuramoto network of oscillators corresponding to a human brain atlas of 90 nodes, each with an intrinsic frequency of 40 Hz. The networks nodes were interconnected based on the structural connectivity strengths and delays found in the human brain. We identified global coupling and delay scale parameters corresponding to maximum spectral entropy, a proxy for maximal multi-state metastability. At this point, we show that multiple coherent (functional) sub-networks spontaneously emerge across multiple oscillatory modes, and persist in time for periods between 140 and 4389 ms. Most nodes in the network exhibit broad frequency spectra away from their intrinsic frequency, and switch between modes, in a manner similar to that reported in empirical resting-state neuroimaging data. We suggest that the obtained dynamics at maximum metastability is a suitable model of the awake brain. Further, we show that global coupling and delay scale parameters away from maximum metastability yield dynamical features similar to other brain states such as sleep and anesthesia. Therefore, spectral entropy also correlates with wakefulness and the synchrony of functional networks.

neuroscience↗

bioRxiv2021

Modelling neural entrainment and its persistence: influence of frequency of stimulation and phase at the stimulus offset

The entrainment (synchronization) of brain oscillations to the frequency of sensory stimuli is a key mechanism that shapes perceptual and cognitive processes, such that atypical neural entrainment leads to neuro-psychological deficits. ObjectiveWe investigated the dynamic of neural entrainment. Particular attention was paid to the oscillatory behavior that succeed the end of the stimulation, since the persistence (reverberation) of neural entrainment may condition future sensory representations based on predictions about stimulus rhythmicity. ApproachA modified Jansen-Rit neural mass model of coupled cortical columns generated a time series whose frequency spectrum resembled that of the electroencephalogram. We evaluated spectro-temporal features of entrainment, during and after rhythmic stimulation of different frequencies, as a function of the resonance frequency of the neural population and the coupling strength between cortical columns. We tested if the duration of the entrainment persistence depended on the state of the neural network at the time the stimulus ends. Main ResultsThe entrainment of the column that received the stimulation was maximum when the frequency of the entrainer was within a narrow range around the resonance frequency of the column. When this occurred, entrainment persisted for several cycles after the stimulus terminated, and the propagation of the entrainment to other columns was facilitated. Propagation depended on the resonance frequency of the second column, and the coupling strength between columns. The duration of the persistence of the entrainment depended on the phase of the neural oscillation at the time the entrainer terminated, such that falling phases (from{pi} /2 to 3{pi}/2 in a sine function) led to longer persistence than rising phases (from 0 to{pi} /2 and 3{pi}/2 to 2{pi}). SignificanceThe study bridges between models of neural oscillations and empirical electrophysiology, and provides insights to the use of rhythmic sensory stimulation for neuroenhancement.

bioengineering↗

bioRxiv2021

From synaptic activity to human in vivo quantification of neurotransmitter dynamics: a neural modelling approach

Understanding the role of neurotransmitters glutamate and GABA during normal and abnormal brain function and under external stimulation in humans are critical neuroscientific and clinical goals. The recent development of functional 1H-Magnetic resonance spectroscopy (fMRS) has allowed us to study neuro-transmitter activity in vivo for the first time. However, the physiological basis of the observed fMRS signal remains unclear. It has been proposed that fMRS detects shifts in metabolite concentrations as they move from presynaptic vesicles, where they are largely invisible, to extracellular and cytosolic pools, where they are visible. Here we bridge the gap between neural dynamics and fMRS by developing a mean-field model to link the neurotransmitter dynamics at the microscopic-level to the macroscopic-level imaging measurements. GABA and glutamate are described as cycling between three metabolic pools: in the vesicles; active in the cleft; or undergoing recycling in the astrocytic or neuronal cytosol. We interrogate the model by applying a current to manipulate the mean membrane potential and firing rate of the neural populations. We find that by disregarding the contribution from the vesicular pool, our model predicts activity-dependent changes in the MRS signal, which are consistent with reported empirical findings. Further, we show that current magnitude and direction has a selective effect on the GABA/glutamate-MRS signal: inhibitory stimulation leads to reduction of both metabolites, whereas excitatory stimulation leads to increased glutamate and decreased GABA. In doing so, we link neural dynamics and fMRS and provide a mechanistic account for the activity-dependent change in the observed MRS signal. Key Points SummaryO_LIThe recent development of functional 1H-Magnetic resonance spectroscopy (fMRS) has allowed us to study neurotransmitter activity in vivo for the first time in humans. However, the physiological basis of the observed fMRS signal is unclear. C_LIO_LIIt has been proposed that fMRS detects shifts in metabolite concentrations as they move from presynaptic vesicles, where they are largely invisible to MRS, to extracellular and cytosolic pools, where they are visible to MRS. C_LIO_LIWe test this hypothesis using a mean field model which links the neural dynamics of neurotransmitters at the microscopic-level to the macroscopic-level imaging measurements obtained in experimental studies. C_LIO_LIBy disregarding activity in the vesicular pool, our model can generate activity-dependent changes in the MRS signal in response to stimulation which are consistent with experimental findings in the literature. C_LIO_LIWe provide a mechanistic account for the activity-dependent change in observed neurotransmitter concentrations using MRS. C_LI

neuroscience↗