@article{21378, abstract = {From insects to mammals, essential brain functions, such as forming long-term memories (LTMs), increase metabolic activity in stimulated neurons to meet the energetic demand associated with brain activation. However, while impairing neuronal metabolism limits brain performance, whether expanding the metabolic capacity of neurons boosts brain function remains poorly understood. Here, we show that LTM formation of flies and mice can be enhanced by increasing mitochondrial metabolism in central memory circuits. By knocking down the mitochondrial Ca2+ exporter Letm1, we favour Ca2+ retention in the mitochondrial matrix of neurons due to reduction of mitochondrial H+/Ca2+ exchange. The resulting increase in mitochondrial Ca2+ over-activates mitochondrial metabolism in neurons of central memory circuits, leading to improved LTM storage in training paradigms in which wild-type counterparts of both species fail to remember. Our findings unveil an evolutionarily conserved mechanism that controls mitochondrial metabolism in neurons and indicate its involvement in shaping higher brain functions, such as LTM.}, author = {Amrapali Vishwanath, Anjali and Comyn, Typhaine and Mira, Rodrigo G. and Brossier, Claire and Pascual-Caro, Carlos and Faour, Maya and Boumendil, Kahina and Chintaluri, Chaitanya and Ramon-Duaso, Carla and Fan, Ruolin and Ghosh, Kishalay and Farrants, Helen and Berwick, Jean-Paul and Sivakumar, Riya and Lopez-Manzaneda, Mario and Schreiter, Eric R. and Preat, Thomas and Vogels, Tim P and Rangaraju, Vidhya and Busquets-Garcia, Arnau and Plaçais, Pierre-Yves and Pavlowsky, Alice and de Juan-Sanz, Jaime}, issn = {2522-5812}, journal = {Nature Metabolism}, number = {2}, pages = {467--488}, publisher = {Springer Nature}, title = {{Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species}}, doi = {10.1038/s42255-026-01451-w}, volume = {8}, year = {2026}, } @article{22295, abstract = {Despite the functional diversity of over 100 causal genes1,2,3, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.}, author = {Schwarz, Lena A and Dotter, Christoph and Isaev, Sergey and Lisi, Michela and Malzl, Daniel and Büschl, Christoph and Ladstätter, Sabrina and Oliveira, Bárbara and Barel, Matteo and Basilico, Bernadette and Chintaluri, Chaitanya and Gorkiewicz, Sarah and Goudarzi, Mohammad and Belinova, Tereza and Reichl, Stephan and Sendžikaitė, Gintarė and Arcot Jayaram, Satish and Koppensteiner, Peter and Sommer, Christoph M and Vogels, Tim P and Menche, Jörg and Adameyko, Igor and Kharchenko, Peter Vasili and Bock, Christoph and Novarino, Gaia}, issn = {1476-4687}, journal = {Nature}, publisher = {Springer Nature}, title = {{Cortical development dynamics across autism spectrum disorder mouse models}}, doi = {10.1038/s41586-026-10679-1}, year = {2026}, } @article{15169, abstract = {Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.}, author = {Chintaluri, Chaitanya and Bejtka, Marta and Sredniawa, Wladyslaw and Czerwinski, Michal and Dzik, Jakub M. and Jedrzejewska-Szmek, Joanna and Wojciki, Daniel K.}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {3}, publisher = {Public Library of Science}, title = {{kCSD-python, reliable current source density estimation with quality control}}, doi = {10.1371/journal.pcbi.1011941}, volume = {20}, year = {2024}, } @article{14666, abstract = {So-called spontaneous activity is a central hallmark of most nervous systems. Such non-causal firing is contrary to the tenet of spikes as a means of communication, and its purpose remains unclear. We propose that self-initiated firing can serve as a release valve to protect neurons from the toxic conditions arising in mitochondria from lower-than-baseline energy consumption. To demonstrate the viability of our hypothesis, we built a set of models that incorporate recent experimental results indicating homeostatic control of metabolic products—Adenosine triphosphate (ATP), adenosine diphosphate (ADP), and reactive oxygen species (ROS)—by changes in firing. We explore the relationship of metabolic cost of spiking with its effect on the temporal patterning of spikes and reproduce experimentally observed changes in intrinsic firing in the fruitfly dorsal fan-shaped body neuron in a model with ROS-modulated potassium channels. We also show that metabolic spiking homeostasis can produce indefinitely sustained avalanche dynamics in cortical circuits. Our theory can account for key features of neuronal activity observed in many studies ranging from ion channel function all the way to resting state dynamics. We finish with a set of experimental predictions that would confirm an integrated, crucial role for metabolically regulated spiking and firmly link metabolic homeostasis and neuronal function.}, author = {Chintaluri, Chaitanya and Vogels, Tim P}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {48}, publisher = {National Academy of Sciences}, title = {{Metabolically regulated spiking could serve neuronal energy homeostasis and protect from reactive oxygen species}}, doi = {10.1073/pnas.2306525120}, volume = {120}, year = {2023}, } @article{17132, abstract = {Extracellular recording is an accessible technique used in animals and humans to study the brain physiology and pathology. As the number of recording channels and their density grows it is natural to ask how much improvement the additional channels bring in and how we can optimally use the new capabilities for monitoring the brain. Here we show that for any given distribution of electrodes we can establish exactly what information about current sources in the brain can be recovered and what information is strictly unobservable. We demonstrate this in the general setting of previously proposed kernel Current Source Density method and illustrate it with simplified examples as well as using evoked potentials from the barrel cortex obtained with a Neuropixels probe and with compatible model data. We show that with conceptual separation of the estimation space from experimental setup one can recover sources not accessible to standard methods.}, author = {Chintaluri, Chaitanya and Bejtka, Marta and Średniawa, Władysław and Czerwiński, Michał and Dzik, Jakub M. and Jędrzejewska-Szmek, Joanna and Kondrakiewicz, Kacper and Kublik, Ewa and Wójcik, Daniel K.}, issn = {1553-7358}, journal = {PLOS Computational Biology}, number = {5}, publisher = {Public Library of Science}, title = {{What we can and what we cannot see with extracellular multielectrodes}}, doi = {10.1371/journal.pcbi.1008615}, volume = {17}, year = {2021}, } @article{8127, abstract = {Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.}, author = {Gonçalves, Pedro J. and Lueckmann, Jan-Matthis and Deistler, Michael and Nonnenmacher, Marcel and Öcal, Kaan and Bassetto, Giacomo and Chintaluri, Chaitanya and Podlaski, William F. and Haddad, Sara A. and Vogels, Tim P and Greenberg, David S. and Macke, Jakob H.}, issn = {2050-084X}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{Training deep neural density estimators to identify mechanistic models of neural dynamics}}, doi = {10.7554/eLife.56261}, volume = {9}, year = {2020}, }