CNS*2025 Florence

Single-cell optogenetic perturbations reveal stimulus-dependent network interactions

Deyue Kong*1, Joe Barreto2,Greg Bond2, Matthias Kaschube1, Benjamin Scholl2

1Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
2University of Colorado Anschutz Medical Campus, Department of Physiology and Biophysics, Aurora, Colorado, USA

*Email: kong@fias.uni-frankfurt.de


Introduction
Cortical computations arise through neuronal interactions and their dynamic reconfiguration in response to changing sensory contexts. Cortical interactions are proposed to engage distinct operational regimes that either amplify or suppress particular neuronal networks. A recent study in mouse primary visual cortex (V1) found competitive, suppressive interactions between nearby, similarly-tuned neurons, with exception of highly-correlated neuronal pairs showing facilitatory coupling [1]. It remains unclear whether such feature competition generalizes to cortical circuits with topographic organization, where neighboring neurons within columns exhibit similar tuning to visual features, and distal excitatory axons preferentially target similarly-tuned columns.
Methods
We investigated interactions between excitatory neurons in the ferret V1 and how network interactions depend on stimulus strength (contrast). We recorded the responses of layer 2/3 neurons to drifting gratings of eight directions at two contrast levels using 2-photon calcium imaging, while activating individual excitatory neurons with precise 2-photon optogenetics. We statistically quantified the effect of target photostimulation on neural activity (inferred spike rate) during visual stimulation using a Poisson generalized linear model (GLM). We then used our model to estimate a target’s influence on the surrounding neurons’ activity and their stimulus coding properties.
Results
Our analyses revealed interactions that depended on cortical distance, stimulus properties, and functional similarity between neuron pairs. Influence of photostimulated neurons strongly depended on cortical distance, but overall exhibited net suppression. Suppression was weakest between nearby neurons (<100µm), but was found across large cortical distances. Distant-dependent suppression was reduced when visual stimuli were low contrast. Examining functional-similar neurons, we found that noise correlations between neuron pairs were most predictive of measured interactions, showing a strong shift from amplification to competition: at low contrast, we observed local amplification between noise-correlated excitatory neurons, but increasing contrast led to a predominantly suppressive influence across all distances.
Discussion
Our data support predictions from theoretical models, such as stabilized supralinear networks (SSN), in which networks amplify weak feed-forward input, but sublinearly integrate strong inputs [2,3]. Furthermore, decoding analyses suggest that the contrast-dependent shift from facilitation to suppression correlates with improved decoding accuracy of direction. These findings demonstrate that stimulus contrast dynamically modulates recurrent interactions between excitatory neurons in ferret V1, likely by differentially engaging inhibitory neurons. Such dynamic modulation supports optimal encoding of sensory information within columnar cortices.




Acknowledgements

References
[1] Chettih, SN, Harvey, CD. Single-neuron perturbations reveal feature-specific competition in V1. Nature (2019).doi:10.1038/s41586-019-0997-6
[2] Rubin DB, Van Hooser SD, Miller KD. The stabilized supralinear network: a unifying circuit motif underlying multi-input integration in sensory cortex. Neuron(2015) doi: 10.1016/j.neuron.2014.12.026. PMID: 25611511; PMCID: PMC4344127.
[3] Heeger DJ, Zemlianova KO. A recurrent circuit implements normalization, simulating the dynamics of V1 activity. PNAS(2020). doi: 10.1073/pnas.2005417117. . PMID: 32843341; PMCID: PMC7486719.

Predicting neural responses to intra- and extracranial electric brain stimulation by means of the reciprocity theorem

Torbjørn V. Ness*¹, Christof Koch²,Gaute T. Einevoll¹,³
¹ Department of Physics, Norwegian University of Life Sciences, Ås, Norway
² Allen Institute, Seattle, WA, USA
³ Department of Physics, University of Oslo, Oslo, Norway

*Email: gaute.einevoll@nmbu.no



Introduction
Neural activity can be modulated through electric stimulation (ES), which is extensively used in both science and the clinic, including deep brain stimulation and temporal interference stimulation. While ES is grounded in well-established biophysics, it has proven difficult to gain a solid understanding of ES and its sensitivity to features like location, orientation, different cell types, and the ES frequency-content. This represents a major obstacle to the applications of ES.
Here, we show that the reciprocity theorem (RT) can be applied more broadly than previously recognized [1], offering a whole new perspective on ES which reproduces known features, explains surprising observations, and makes new predictions.



Methods
The effect of ES on different biophysically detailed cell models is simulated with NEURON [2] and LFPy [3]. The ES is treated as a current point source which sets up an extracellular potential that is used as a boundary condition at each cellular compartment. The somatic membrane potential-responseVmis calculated. In the RT-based approach the current is inserted intracellularly in the soma, and the resulting extracellular potentialVecalculated. According to the RT the two approaches should give identical results for passive cell models (Vm=Ve, Fig. 1). For transcranial electric stimulation (tES), we used a detailed head model to estimate membrane potential responses to tES deep in the brain.
Results
In all tested cases the RT-based approach to simulating ES introduces zero error for passive cell models, and below a few percent error for subthreshold active cell models [1].
By leveraging the RT, we show that the effect of ES has a 1/rdecay for nearby neurons and 1/r² for distant neurons. Furthermore, for nearby neurons the ES response is approximately cell-type and frequency-independent, while for distant neurons (e.g., tES), pyramidal neurons are most strongly targeted at low frequencies, and interneurons at high frequencies, but with a less synchronous effect [1]. Finally, tES at conventional safety limits (<4 mA) induces subthreshold potential changes of ~40-200 µV, far below the threshold for direct neuronal firing [1].


Discussion
By applying RT, we provide a framework for understanding neural responses to ES, by leveraging our good understanding of extracellular potentials [4]. Our results indicate that conventional tES primarily affects neural activity via subtle subthreshold effects, suggesting indirect network-level mechanisms such as synchronization or stochastic resonance. The weak frequency dependence of subthreshold responses explains recent experimental findings [5], reinforcing RT as a powerful tool for modeling ES. Future work should incorporate network-level dynamics to assess the broader implications of these findings for neuromodulation and brain stimulation therapies.




Figure 1. Reciprocity theorem in the context of electrical brain stimulation: The somatic membrane potential response to an extracellular current injection at position r (panel A) corresponds to the extracellular potential at location r from the same current source injected into the soma .
Acknowledgements
T.V.N. and G.T.E. received funding from the European Union Horizon 2020 Research and Innovation Programme under Grant Agreement No. 101147319 [EBRAINS 2.0]. C.K. thanks the Allen Institute founder, Paul G. Allen, for his vision, encouragement, and support.
References
[1] Ness et al. (2025) bioRxivhttps://doi.org/10.1101/2024.08.04.603691
[2] The NEURON bookhttps://doi.org/10.1017/CBO9780511541612
[3] Hagen et al. (2018)https://doi.org/10.3389/fninf.2018.00092
[4] Halnes et al. (2024)https://doi.org/10.1017/9781009039826
[5] Lee et al. (2024) Neuronhttps://doi.org/10.1016/j.neuron.2024.05.009

Time precise closed-loop protocols for non-invasive infrared laser neural stimulation

Alicia Garrido-Peña¹,Pablo Sanchez-Martin¹, Irene Elices¹, Rafael. Levi¹, Francisco B. Rodriguez¹, Javier Castilla², Jesus Tornero², Pablo Varona¹
1. Grupo de Neurocomputación Biológica, Departamento de Ingeniería Informática, Escuela Politecnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
2. Center for Clinical Neuroscience, Hospital los Madroños, Brunete, Spain
*Email: alicia.garrido@uam.es

Introduction

In the context of the increasing interest in noninvasive neural stimulation techniques, infrared (IR) laser has shown its potential to achieve an effective and spatially localized stimulation. In our recent publication [1], we demonstrated that it is possible to modulate neural dynamics with a Continuous-Wave (CW) Near IR laser in terms of firing rate and spike waveform. We analyzed the biophysical cause of this effect in a computational model, observing a combined alteration of ionic channels and significant action of temperature change. We also assessed the illumination effect at different stages of the action potential generation with a closed-loop protocol. Here we extend these results and stimulation protocols.

Methods

We used a CW-IR laser focused with a micromanipulator in the ganglia of Lymnaea stagnalis and Carcius maenas, while recording membrane potential intracellularly. For the closed-loop protocols, we employed the RTXI software [2], designing algorithms in modules for spike, and burst prediction, target-driven stimulation and neuronal digital-twin experiments built with conductance-based models. The laser was triggered based on the ongoing activity with a precise electro-optical shutter (range of µs). Our software is open-source and available atgithub.com/GNB-UAM.

Results

We show the effectiveness of laser stimulation with high spatial resolution (by the nature of this technique) and temporal precision (by our real-time closed-loop protocols and an electro-optical shutter) on two different neural systems. For both cases, sustained CW laser illumination accelerates neural activity. We also report on the efficacy of activity-dependent illumination with a time-precise shutter. We extend our spike prediction algorithms to neural bursting activity and a target-driven stimulation. To leverage this closed-loop neurotechnology, we present a biohybrid circuit where a model neuron acts as a digital twin of the recorded cell. This enables real-time automatic decision-making based on the model tested response.

Discussion

CW-IR laser illumination is a novel noninvasive neurotechnology with high temporal and spatial resolution. CW-IR laser effectively modulated neural activity in two different neural systems with distinct closed-loop protocols. These protocols employ a library for time-precise stimulation adaptable to different neural dynamics (e.g. tonic spiking or bursting activity) and different target goals (a specific firing rate, burst duration...). The digital-twin model also enables online adjustment by the combined modification of the stimulation parameters and the simulated neuron. Exploiting the advantages of closed-loop stimulation and real-time tools, expands the possibilities of this neurotechnology to novel research and clinical applications.
AcknowledgementsWork funded by PID2024-155923NB-I00, CPP2023-010818, PID2023-149669NB-I00 and PID2021-122347NB-I00.
References[1] Garrido-Peña, A., Sanchez-Martin, P., Reyes-Sanchez, M., Levi, R., Rodriguez, F. B., Castilla, J., Tornero, J., & Varona, P. (2024). Modulation of neuronal dynamics by sustained and activity-dependent continuous-wave near-infrared laser stimulation. Neurophotonics, 11(2), 024308.doi.org/10.1117/1.NPh.11.2.024308

[2] Patel, Y. A., George, A., Dorval, A. D., White, J. A., Christini, D. J., & Butera, R. J. (2017). Hard real-time closed-loop electrophysiology with the Real-Time eXperiment Interface (RTXI). PLOS Computational Biology, 13(5), e1005430.doi.org/10.1371/journal.pcbi.1005430

LTP-induced changes in spine geometry and actin dynamics on the timescale of the synaptic tag

Mitha Thomas*1, Cristian Alexandru Bogaciu2, Silvio Rizzoli2, Michael Fauth1

1Third Physics Institute, Georg-August University, Goettingen, Germany
2Department of Neuro- and Sensory Physiology, University Medical Center, Goettingen, Germany
*Email: mitha.thomas@phys.uni-goettingen.de

Introduction

Long-term potentiation of synapses can occur in two phases: an early phase which constitutes a transient increase in synaptic strength, and a late phase which sustains this increase for a longer duration. According to the synaptic tagging and capture hypothesis [1,2], a necessary condition for the late phase is the formation of a transient memory of the stimulation event - the ‘synaptic tag’ - which enables the synapse to capture newly synthesized proteins later on. What implements this transient memory on the timescale of hours remains elusive [2,3]. We follow the hypothesis that it is implemented by actin dynamics in interaction with spine geometry and test this using computational modelling and FRAP experiments.

Methods
Actin forms filaments in the spine which belong to distinct pools (dynamic and stable) with different turnover rates. To study the relation of actin pools with synaptic tagging, we derived a computational model of the interactions between the spine membrane and the actin pools undergoing plasticity. Dynamic actin is modelled as a Markov chain that considers several processes related to actin binding proteins, e.g., branching, capping and severing, which are modulated upon LTP [4, 5]. Stable actin is modelled as a low-pass filter of the dynamic pool with filter coefficients following binding and unbinding of crosslinking proteins. The spine membrane deforms according to the balance between the actin-generated force and the forces resulting from the physical properties of the membrane (Fig 1A-C).
Results
We first test whether can support memory on a timescale of hours without stable actin. At the onset of LTP, there is a rapid increase in dynamic actin, which increases the outward-directed force and, consequently, also the spine volume. However, these changes only last as long as the actin dynamics is modulated. When we introduce the stable pool, it exhibits an overshoot that persists on the timescale of hours, and hence, the synaptic tag. As more stable actin significantly increases the actin-generated force, this also transfers to a long-lasting spine volume increases (Fig 1D-H). To validate these model predictions experimentally, we perform chemical LTP on hippocampal spines and use FRAP to assess stable actin content after LTP. Also here, stable actin shows a significant increase agreeing with the overshoot in the model (Fig 1H).
Discussion
Using a combination of experiments and simulations, we have demonstrated that the dynamics of the stable actin pool after LTP-inducing stimulation leads to a long lasting alteration of actin dynamics and spine geometry. These dynamics fulfil the fundamental criteria for the tag, that is synapse specificity, independence from protein synthesis, and decay within hours. Thus, we present evidence the biophysical implementation of the synaptic tag may be based on the complex interaction of actin with spine membrane.



Figure 1. Actin-spine membrane interactions. A-B: membrane deformation from imbalance between actin-generated force and membrane counter force. C: time evolution of actin dynamics with several associated processes/proteins. D: simulated spine at different time instants. t0: time of stimulation. E: Control and LTP spine volumes. F: amount of dynamic actin. G: amount of stable actin. H: stable actin fraction
Acknowledgements
This work was funded by the German Science Foundation under CRC1286 ”Quantitative Synaptology”, projects C03 and A03. We would like to thank Simon Dannenberg, Stefan Klumpp, Jannik Luboeinski, Francesco Negri, Christian Tetzlaff and Florentin Woergoetter for fruitful discussions on the project.
References
1.https://doi.org/10.1038/385533a0
2.https://doi.org/10.1038/nrn2963
3.https://doi.org/10.1002/iub.2261
4.https://doi.org/10.1016/j.neuron.2014.03.021
5.https://doi.org/10.3389/fnsyn.2020.00009

Identifying Dynamic-based Closed-loop Targets for Speech Processing Cochlear Implants

Cynthia Steinhardt*1,Menoua Keshishian2, Kim Stachenfeld1,3, Larry Abbott1
1 Center for Theoretical Neuroscience, Zuckerman Brain Science Institute, Columbia University, New York, New York USA
2 Department of Electrical Engineering, Columbia University, New York, New York USA
3 DeepMind, Google, London, United Kingdom


*Email: cs4248@columbia.edu



Introduction
Since the development of the first cochlear implant (CI) in 1957, over one million people have used these devices to regain hearing. However, CIs have a number of deficits, such as low efficacy in noise, and these deficits remain poorly understood [1]. CI algorithm research has focused on optimizing single-neuron voltage-driven activations in the cochlea, based on low-level auditory modeling but little work has focused on capturing known features of hierarchical speech processing across the brain [2]. We create a model system to investigate how CI-encoded speech affects phoneme and word comprehension, uncovering a dynamics-based signature for potential closed-loop CI applications.
Methods
We trained a DeepSpeech2 [3] model to convert spectrograms to phonemes using CTC Loss. Speech inputs were sourced from the LibriSpeech dataset. Speech was processed via the AB Generic Toolbox [4] to generate electrodograms, creating CI-transformed inputs or directly given to the model to simulate natural hearing. The model, trained on natural spectrograms, was then tested on CI-transformed inputs. Behavioral experiments were performed and compared to human results. We analyzed phoneme processing dynamics, using a distance metric to determine convergence patterns and tested dynamic signatures for feedback control [5].
Results
Our model exhibited human-like increases in phoneme reaction time with CI-transformed inputs and noise; phoneme confusion and word errors mirrored human behavior, as well [5]. Analysis revealed a specific time window per layer where correct phoneme comprehension dynamics converged for all phonemes, with increasing delays deeper in the network. We create a representation distance metric, measured via a Wasserstein metric between dynamics during comprehension and found it correlated (up to 0.78) with behavioral confusion of the model while processing these phonemes in sentences. Using a linear closed-loop controller, we then successfully pushed dynamics toward correct phoneme perception using this converged representation at a target.
Discussion
This study presents a plausible model for speech perception with and without a CI, validated against human data. We identify a dynamic signature predicting comprehension or confusion within 100 ms—a feasible intervention window. We demonstrate its use for closed-loop feedback and find evidence of human EEG evoked responses with similar dynamics [6], suggesting a potential EEG-based CI parameter selection method. We show plausibility here for a new cochlear implant paradigm, instead of mimicking cochlear processing, we determine pulse parameters that drive desired population-level neural representations of speech. This approach may generalize to other neural implants, as we understand those systems better.



Acknowledgements
We thank the Simons Society of Fellows (965377), Gatsby Charitable Trust (GAT3708), Kavli Foundation, and NIH (R01NS110893) for support.
References
● Boisvert, I., et al. (2020). CI outcomes in adults.PLoS One,15(5), e0232421.
● Rødvik, A. K., et al. (2018). CI vowel/consonant ID.J Speech Lang Hear Res,61(4), 1023-1050.
● Amodei, D., et al. (2015). Deep Speech 2.arXiv:1512.02595.
● Jabeim, A. (2024). AB-Generic-Python-Toolbox.GitHub.
● Steinhardt, C. R., et al. (2024). DeepSpeech CI performance.arXiv:2407.20535.
● Finke, M., et al. (2017). Stimulus effects on CI users.Audiol Neurotol,21(5), 305-315.

Automated identification of disease mechanisms in hiPSC-derived neuronal networks using simulation-based inference

Nina Doorn*1, Michel van1,2, Monica Frega3

1Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands

2Department of Neurology and Clinical Neurophysiology, Medisch Spectrum Twente, The Netherlands

3Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy


*Email: n.doorn-1@utwente.nl


Introduction
Human induced pluripotent stem cells (hiPSCs)-derived neuronal networks on multi-electrode arrays (MEAs) are a powerful tool to study neurological disordersin vitro[1]. The electric activity patterns of these networks differ between healthy and patient-derived neurons, reflecting underlying pathology (Fig. 1A). However, elucidating the underlying molecular mechanisms is challenging and requiresextensive, costly, and hypothesis-driven additional experiments.Biophysical models can link observable network activity to underlying molecular mechanisms by estimating model parameters that simulate the experimental observations. However, parameter estimation in such models is difficult due to stochasticity, non-linearity, and parameter degeneracy.

Methods
Here, we address this challenge using simulation-based inference (SBI), a machine-learning approach that allows efficient statistical inference of biophysical model parameters using only simulations [2]. We apply SBI to our previously validated biophysical model of hiPSC-derived neuronal networks on MEA[3], which includesHodgkin-Huxley-type neurons and detailed synaptic models (Fig. 1B). To train SBI, we simulated 300,000 network configurations, varying key parameters governing synaptic and intrinsic neuronal properties (Fig. 1C). We used a neural density estimator to infer posterior distributions of these model parameters given experimental MEA recordings from healthy, pharmacologically treated, and patient-derived networks (Fig 1D).

Results
SBI accurately inferred ground-truth parameters in synthetic data and successfully identified known disease mechanisms in patient-derived neuronal networks. In networks from patients with the genetic epilepsies Dravet Syndrome and GEFS+, SBI predicted reduced sodium and potassium conductances and increased synaptic depression, which was experimentally verified. InCACNA1Ahaploinsufficient networks, SBI correctly identified impaired connectivity. Additionally, SBI detected drug-induced changes, such as prolonged synaptic depression following Dynasore treatment.
Discussion
SBI enables automated and probabilistic inference of biophysical parameters, offering advantages over traditional parameter estimation methods, which can be time-consuming, lack uncertainty quantification, or cannot deal with parameter degeneracy. Our results show how SBI can be used with biophysical models to identify possible disease mechanisms from patient-derived neuronal data. Ourproposed analysis pipeline enables researchers to extract crucial mechanistic information from MEA measurements in a systematic, cost-effective, and rapid manner, paving the way for targeted experiments and novel insights into disease.






Figure 1. Figure 1. A) The activity of in vitro neuronal networks cultured from hiPSCs of healthy controls and patients is measured using MEAs. B) The computational model with biophysical parameters in blue. C) A Neural density estimator is trained on model simulations. Afterward, experimental data is passed through the estimator to approximate the D) posterior distributions. Adapted from [4].
Acknowledgements
This work was supported by the Netherlands Organisation for Health Research and Development ZonMW; BRAINMODEL PSIDER program 10250022110003 (to M.F.). We thank Eline van Hugte, Marina Hommersom, and Nael Nadif Kasri for providing MEA recordings from patient-derived and genome-editedin vitroneuronal networks.
References
● 1.https://doi.org/10.1016/J.STEMCR.2021.07.001
● 2.https://doi.org/10.7554/ELIFE.56261
● 3.https://doi.org/10.1016/J.STEMCR.2024.09.001
● 4.https://doi.org/10.1101/2024.05.23.595522

Linking hubness, embryonic neurogenesis, transcriptomics and diseases in human brain networks

Ibai Diez*1,2, Fernando Garcia-Moreno*3,4,5, Nayara Carral-Sainz6, Sebastiano Stramaglia7, Alicia Nieto-Reyes8, Mauro D’Amato5,9,10, Jesús Maria Cortes5,11,12,Paolo Bonifazi5,11,13


1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
3Achucarro Basque Center for Neuroscience, Scientific Park of the University of the Basque Country (UPV/EHU), Leioa, Spain.
4Department of Neuroscience, Faculty of Medicine and Odontology, UPV/EHU, Barrio Sarriena s/n, Leioa, Bizkaia, Spain.
5IKERBASQUE: The Basque Foundation for Science, Bilbao, Spain.
6Departamento de Ciencias de la Tierra y Física de la Materia Condensada, Facultad de Ciencias, Universidad de Cantabria, Santander, Spain.
7Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, and INFN, Sezione di Bari, Italy.
8Departamento de Matemáticas, Estadística y Computación, Facultad de Ciencias, Universidad de Cantabria, Santander, Spain.
9Department of Medicine and Surgery, LUM University, Casamassima, Italy.
10Gastrointestinal Genetics Lab, CIC bioGUNE - BRTA, Derio, Spain.
11Computational Neuroimaging Lab, Biocruces-Bizkaia Health Research Institute, Barakaldo, Spain.
12Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
13Department of Physics, University of Bologna, Italy.


* These authors contributed equally to this work; Corresponding author:paol.bonifazi@gmail.com

Intro. The human brain is organized across multiple spatial scales, where micro-scale circuits integrate into macro-scale networks via long-range connections. Understanding the connectivity rules shaping networks is key to deciphering brain function and the effects of neurological damage. Previous studies have explored brain network maturation, but a link between adult connectivity and the sequential evolutionarily preserved neurogenesis remains unestablished. Inspired by the preferential attachment model in network science shaped by the “rich gets richer” principle 1, this study2hypothesizes that brain network topology follows an "older gets richer" principle, where earlier-developed circuits play central roles in adult connectivity. Our hypothesis extrapolates on the macro-scale level previous evidence that hippocampal hubs are early born GABAergic neurons3-5.
Methods. Brain circuits were categorized by their First neurogenic Time (FirsT), determined from developmental neuromeres. Eighteen macro-circuits (MACs) were identified based on available neurodevelopmental data. Structural and functional brain networks were reconstructed using 7-Tesla dMRI and resting-state fMRI from 184 subjects. Connectivity metrics were assessed at high (2,566 ROIs) and low (18 MACs) resolutions. Eigenvector centrality was calculated for each ROI and MAC, with correlations between FirsT and connectivity patterns. Brain transcriptomic data were mapped to connectivity metrics, and enrichment analysis identified associated biological processes and disease relevance.
Results. Significant correlations between structural connectivity and FirsT supported the "older gets richer" principle, with early-born circuits exhibiting higher structural hubness. In contrast, functional centrality was positively correlated with FirsT, highlighting late-maturing circuits' functional prominence. Connectivity strength was stronger among circuits with similar neurogenic timing, supporting a "preferential age attachment" mechanism. Gene expression analysis revealed correlations with FirsT and connectivity metrics, with enriched pathways linked to neurodevelopment, synaptic function, and neuropsychiatric disorders. Disease-associated genes (e.g., APOE for Alzheimer’s, SCN1A for epilepsy) showed significant enrichment at correlation extremes, suggesting differential genetic influences on brain network organization and pathology susceptibility.

Discussion. The study examines adult brain networks reconstructed from MRI to analyze how early neurogenesis affects structural and functional connectivity. Structural findings confirm that older brain regions act as stronger hubs ("older gets richer"). Functional and structural networks follow a "preferential age attachment" rule, linking neurogenesis timing to network topology. Genetic analysis ties neurodevelopmental disorders to network centrality, highlighting disease-linked transcriptional alterations.



Acknowledgements
We thank M De Pittá, D Papo, D Marinazzo, A Mazzoni, Y Ben-Ari for comments. Funds: ANR by MCIN/AEI/10.13039/501100011033 and “ERDF”; PB by Ikerbasque, the Ministerio Economia, Industria y Competitividad (MICINN, Spain) and Maratoia EITB (grant PID2021-127163NB-I00 and BIO22/ALZ/010/BCB);FGMby Ikerbasque, MICINN(grant PID2021-125156NB-I00) andBasque Gov (grant PIBA_2022_1_0027).
References
1.Barabási, A.-L. & Albert, R. Emergence of Scaling in Random Networks.Science286, 509–512 (1999).
2.Diez I et al, https://doi.org/10.1101/2022.04.01.486541
3.Bonifazi, P.et al.GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks.Science326, 1419–1424 (2009).
4.Picardo, M. A.et al.Pioneer GABA Cells Comprise a Subpopulation of Hub Neurons in the Developing Hippocampus.Neuron71, 695–709 (2011).
5.Bocchio, M.et al.Hippocampal hub neurons maintain distinct connectivity throughout their lifetime.Nat Commun11, 4559 (2020).

Back to the Future: Integrating Event-Based and Network Diffusion Models to Predict Individual Tau Progression in Alzheimer's Disease

Robin Sandell*1, Justin Torok1, Kamalini Ranasinghe1, Srikantan Nagarajan1, Ashish Raj1

1Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States

*Email: robin.sandell@ucsf.edu


Introduction

This paper presents a novel method combining an Event-Based Model (EBM) and a Network Diffusion Model (NDM) to predict individual tau protein progression in Alzheimer's disease. Statistical EBMs can infer longitudinal progression from cross-sectional data but lack mechanistic understanding, while biophysical NDMs provide mechanistic clarity but require data on a longitudinal timescale. Our hybrid approach overcomes these limitations. Using only single-visit data, our model can go back in time to infer initial seeding patterns and predict future progression. Analysis reveals high initial heterogeneity in seeding patterns that converges over time with two main seed archetypes correlating with distinct clinical presentations.



Methods


We analyzed data from 650 patients from the Alzheimer’s Neuroimaging Disease Initiative, including tau-PET, MRI, and cognitive scores. EBM assigned a disease stage to each patient based on their biomarker values, enabling a common timescale across subjects [1,2,]. NDM simulated tau progression on the brain’s structural connectivity with two rate parameters: τ accumulation rate and spread rate[3,4]. We optimized NDM parameters and tau seed pattern to accurately predict each subject’s empirical tau map at their EBM assigned stage. Applications include prediction of individuals’ future tau patterns, analysis of inter-subject heterogeneity over time, and identification of tau seed archetypes through clustering analysis.



Results

Our hybrid model successfully predicted empirical tau using individual tau seed patterns (mean R=0.85) (Fig. 1b). Longitudinal validation confirmed the model's predictive ability (mean R=0.81)(Fig. 1b). Analysis of tau patterns revealed decreasing heterogeneity over disease progression (Fig. 1c). Two primary seed archetypes emerged: focal entorhinal (typical AD) and diffuse temporal (Fig. 1d). The diffuse temporal pattern correlated with earlier disease onset, higher APOE4 carrier frequency, younger age, and faster tau accumulation rates, suggesting a more aggressive disease variant despite similar cognitive impairment levels at diagnosis (Fig. 1e,f).


Discussion

This paper presents a novel hybrid approach combining an Event-Based Model and a Network Diffusion Model to predict individual tau progression in Alzheimer's disease. The method infers initial seeding patterns from single-visit data to forecast future progression. Surprisingly, heterogeneity across subjects was highest at disease onset and decreased over time, suggesting convergence rather than divergence of pathology. Two primary seed archetypes emerged: focal entorhinal (typical AD) and diffuse temporal (associated with earlier onset, higher APOE4 frequency, and faster progression). The hybrid model outperformed prior work while providing mechanistic insights into tau progression that could inform personalized therapeutic strategies[2].








Figure 1. a. Project flow chart. b. Illustration of NDM model fitting and validation for a single patient. c. Distribution of pairwise R correlations between subjects model predicted tau at each stage indicating a process of convergence in tau patterning as disease progresses. d. Emergent tau seed archetypes. e. Demographic variables for each archetypes. f. Total tau over time for each archetypes.
Acknowledgements
We thank ADNI for making their data available to us.

References


● Aksman, L.M., et al. (2021). pySuStaIn: Python implementation of SuStaIn. SoftwareX, 16.https://doi.org/10.1016/j.softx.2021.100811
● Vogel, J.W., et al. (2021). Four trajectories of tau deposition in AD. Nature Medicine, 27(5).https://doi.org/10.1038/s41591-021-01309-6
● Raj, A., et al. (2012). Network diffusion model of disease progression. Neuron, 73(6).https://doi.org/10.1016/j.neuron.2011.12.040
● Anand, C., et al. (2022). Microglia effects on tauopathy using nexopathy models. Scientific Reports, 12(1).https://doi.org/10.1038/s41598-022-24687-4

The Virtual Parkinsonian Patient: the effects of L-dopa and Deep brain Stimulation on whole-brain dynamics

Marianna Angiolelli*1,2, Gabriele Casagrande1, Letizia Chiodo2, Damien Depannemaecker1, Viktor Jirsa1, Pierpaolo Sorrentino1,3


1Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
2Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
3Department of Biomedical Sciences, University of Sassari, Sassari, Italy


*Email: marianna.angiolelli@unicampus.it
IntroductionParkinson’s disease is a progressive neurodegenerative disease characterized by the loss of dopaminergic neurons in the substantia nigra. The primary treatment for PD involves the administration of levodopa, but long-term use of it is associated with complications, necessitating alternative therapeutic strategies. One approach is deep brain stimulation (DBS), a neuromodulatory treatment that delivers electrical stimulation to specific brain regions (most often,the subthalamic nucleus). While DBS can be an effective therapy, optimal stimulation parameters are specific to each patient, and finding them can be challenging. Today, parameter tuning is based on a trial-and-error process, which is time-consuming, exhausting for the patient, requires a highly skilled dedicated team, and has very high chances of missing the optimal setting.
MethodsTo predict the effects of DBS on large-scale brain dynamics, we employed a mean-field neural mass model based on the adaptive quadratic integrate-and-fire (aQIF) framework [1], where Dopamine is included. The model was extended to incorporate an external current simulating a biphasic stimulation, mimicking DBS effects. Each brain region was modeled as a neural mass, with connectivity based on individual structural connectomes. The model includes excitatory, inhibitory, and neuromodulatory connections. EEG and deep electrode recordings in the STN validated the predictions. A Bayesian inversion with DNN inferred the neural state in ON/OFF conditions, quantifying parameter uncertainty.
ResultsFirst, we investigate different conditions varying simulations of L-Dopa administration and then changing DBS parameters analyzing large-scale brain activity and its impact on neural avalanche (spontaneous bursts of activations) topological properties. For all patients, we correctly infer that the dopaminergic tone is higher given the dynamics observed after administration of L-Dopa, and lower before the administration of L-Dopa [2]. The same approach enables us to tell apart pre- and post-DBS states across multiple patients, quantifying the effects of stimulation on large-scale brain dynamics.
DiscussionThis work provides a framework to understand how L-Dopa and DBS influence large-scale neural activity, offering insights into mechanisms and optimization for PD treatment. Unlike most PD models focusing on beta-range activity [3], we emphasize aperiodic activities instead, which have only received limited attention in Parkinson’s disease thus far. Furthermore, we focus on large-scale dynamics and efficient parameter estimation with uncertainty, rather than fitting a cost function. This approach explicitly accounts for Dopamine levels and stimulation amplitude, bridging pathophysiology, and personalized clinical predictions of clinical effectiveness.



Acknowledgements
The project leading to this publication has received funding from the Excellence Initiative of Aix-Marseille Université - A*Midex, a French “Investissements d’Avenir programme” AMX-21-IET-017
References
[1] Depannemaecker, D., Duprat, C., Casagrande, G., Saggio, M., Athanasiadis, A. P., Angiolelli, M., ... & Jirsa, V. (2024). A next generation neural mass model with neuromodulation. bioRxiv, 2024-06.
[2] Angiolelli, M., Depannemaecker, D., ... & Sorrentino, P. (2024). The Virtual Parkinsonian Patient. medRxiv, 2024-07.
[3] Meier, J. M., Perdikis, D., Blickensdörfer, A., ... & Ritter, P. (2022). Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain. Experimental Neurology, 354, 114111.

Cortical Oscillatory Dynamics in Parkinsonian Networks: Biomarkers and the Potential of Theta Frequency Stimulation

June Jung1, Donald W Doherty1, Adam Newton1, Adriana Galvan5, Thomas Wichmann5, Salvador Dura-Bernal1, Hong-Yuan Chu4, Samuel Neymotin3, William W Lytton1,2

1Department of Physiology and Pharmacology, SUNY Downstate Medical Center, NY,2Kings County Hospital, Brooklyn, NY, USA3Nathan Kline Institute, Orangeburg, NY, USA,4Georgetown University, DC, USA,5Emory University, Atlanta, GA, USA
Introduction

Parkinson’s disease (PD) is marked by characteristic motor symptoms including tremors, stiffness, slowed movement, and balance issues, along with non-motor symptoms like cognitive challenges and difficulty making decisions. Symptom onset and severity vary among individuals. While dopaminergic neuron degeneration in the substantia nigra pars compacta (SNc) is a primary cause of motor dysfunction, recent studies highlight the critical role of disrupted oscillatory activity in the primary motor cortex (M1) in PD pathology. Mouse models of PD, including 6-OHDA mouse, and mitoPark mouse have shown reduced excitability in corticospinal pyramidal tract (PT) neurons.
Methods
We adapted an established mouse primary motor cortex (M1) framework, to make a Parkinsonian motor cortex (PD M1) computational model to investigate changes in neural oscillatory activity. The model incorporated experimental observations from the MitoPark mouse including decreased PT intrinsic excitability and reduced thalamocortical synapse strength to PT neurons (decreased 25% at 16–18 weeks; 50% at 25–28 weeks), correlating with disease progression. Multiple oscillation measures were analyzed as potential biomarkers for tracking disease severity.
Results
In vitro results were used to simulate in vivo Parkinsonian cortical activity, revealing progressively disrupted neuronal firing and increased beta oscillations (~20 Hz) with disease progression.Beta-gamma coupling and modulation index were two oscillatory measures that were significantly reduced under Parkinsonian conditions, with the modulation index progressively declining as the disease advanced. Theta-frequency stimulation suppressed beta bursts, enhanced beta-gamma coupling, and partially restored disrupted cortical network activity caused by PD pathophysiology.
Discussion
These findings suggest that the modulation index may serve as a biomarker for tracking Parkinsonian disease severity. Moreover, theta-frequency stimulation of inhibitory interneurons may help restore imbalanced cortical oscillations and could offer an alternative or complementary strategy to current high-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) in PD patients.





AcknowledgementsThis research was funded in part by Aligning Science Across Parkinson’s [ASAP-020572] through the Michael J. Fox Foundation for Parkinson’s Research (MJFF). For the purpose of open access, the author has applied a CC BY public copyright license to all Author Accepted Manuscripts arising from this submission.
References
1. Chen, L., Daniels, S., Kim, Y., & Chu, H.-Y. (2021). Decreased excitability of motor cortical neurons in parkinsonism. Journal of Neuroscience, 41(25), 5553–5565.https://doi.org/10.1523/JNEUROSCI.2694-20.2021

2. Dura-Bernal, S., et al. (2023). Multiscale model of M1 circuits. Cell Reports, 42(6), 112574.https://doi.org/10.1016/j.celrep.2023.112574