Topological techniques have become a popular tool for studying information flows in neural networks. In particular, simplicial homology theory is used to analyze how cognitive representations of space emerge from large conglomerates of independent neuronal contributions. Meanwhile, a growing number of studies suggest that many cognitive functions are sustained by serial patterns of activity. Here, we investigate stashes of such patterns using path homology theory-an impartial, universal approach that does not require a priori assumptions about the sequences' nature, functionality, underlying mechanisms, or other contexts. We focus on the hippocampus-a key enabler of learning and memory in mammalian brains-and quantify the ordinal arrangement of its activity similarly to how its topology has previously been studied in terms of simplicial homologies. The results reveal that the vast majority of sequences produced during spatial navigation are structurally equivalent to one another. Only a few classes of distinct sequences form an ordinal schema of serial activity that remains stable as the pool of sequences consolidates. Importantly, the structure of both maps is upheld by combinations of short sequences, suggesting that brief activity motifs dominate physiological computations. This ordinal organization emerges and stabilizes on timescales characteristic of spatial learning, displaying similar dynamics. Yet, the ordinal maps generally do not reflect topological affinities-spatial and sequential analyses address qualitatively different aspects of spike flows, representing two complementary formats of information processing. This study employs path homology theory to examine serial patterns of neuronal activity in the hippocampus, a critical region for learning and memory. While the traditional, simplicial homology approaches used to model cognitive maps, path homology provides a universal framework for analyzing the ordinal arrangement of neuronal sequences without presupposing their nature or function. The findings reveal that a limited number of distinct sequence classes, supported by combinations of short activity motifs, form a stable ordinal schema over timescales corresponding to periods of spatial learning. Notably, the ordinal maps derived from these sequences do not capture topological affinities, highlighting that spatial and sequential analyses address distinct but complementary dimensions of neural information processing.
Stress is one of the most pervasive causes of mental ill health across the lifespan. Subjective dimensions of stress perception, such as perceived control, are especially potent in shaping stress responses. While the impact of reduced or no control over stress is well understood, much less is known about whether heightened feelings of control buffer against the negative impact of later stress. We designed a novel paradigm with excellent psychometric properties to sensitively capture and induce different states of subjective control. Across two studies with a non-clinical sample of 768 adults, we show a robust association between sense of control and stress as well as symptoms of mental ill health. More importantly, in a subsample of 295 participants, we show that compared to a neutral control group, inducing a heightened state of subjective control buffers against the impact of later stress. These findings demonstrate a causal role for a heightened sense of control in mitigating the negative impact of stressful experiences and spell out important directions for future preventative interventions.
Depolarizations that occur after action potentials, known as afterdepolarization potentials or ADPs, are important for neuronal excitability and stimulus evoked transient bursting. Slow inward and fast outward currents underlie the generation of such ADPs with modulation of ADP amplitudes occurring as a result of neuronal morphology. However, the relative contribution and role of these slow inward and fast outward currents in ADP generation is poorly understood in the context of somatic and dendritic localization as well as with varied dendritic properties. Using a two-compartment Hodgkin-Huxley type model of cerebellar stellate cells, the role of somatic and dendritic compartmentalization of ADP associated currents is investigated, revealing that dendritic (rather than somatic) slow inward and fast outward currents are the main contributors to ADP and spike-adding during both brief step current and AMPA current input. Additionally, dendritic size and passive properties of the dendrites were found to be key modulators of ADP amplitude. However, increasing magnitudes of NMDA current input resulted in nonmonotonic spike-adding in a manner dependent on dendritic Ca2+ influx and Ca2+ activated K+ currents, which was found to arise from tight regulation of stimulus evoked transient bursting through positive feedback on action potential generation by dendritic Ca2+ and subsequent negative feedback through Ca2+ activated K+ currents. This novel mechanism of ADPs and spike-adding regulation highlights the role of currents with slow timescales in ADPs, stimulus evoked transient bursting and neuronal excitability with implications for Ca2+ dependent synaptic plasticity and neuromodulation.
The functioning of brain networks can be broadly categorized as an interplay of two main contributors, namely the neurological processes dictating the local dynamics and the patterns of anatomical connections that enable the interactions between the various local processes, resulting in a global emergent behavior. Using Brain Network Modeling (BNM) we describe the constraints of the anatomical structure upon the resting state dynamics of the human brain. We identify a low-dimensional representation of the brain states, the Resting State Manifold (RSM), and leveraging network degeneracy and its relationship to structural properties, we identify dynamic patterns supported by the RSM. Noise driven dynamics of the BNM are used to explore the manifold and validate the contours of degeneracy. We demonstrate that the patterns of degeneracy regulate the dynamics of the broader system in the presence of external and/or internal perturbations, revealing the productive relationship between emergent network processes and their constituent network entities.
Historically, the assessment of surgical outcomes in patients with glioma has been focused on technical outcomes, such as volumetric analysis of the residual tumour, progression-free survival, and overall survival. Other outcomes, such as neurological deficits, can be challenging to assess in an objective, quantifiable, and comprehensive manner. As a result, no consensus is available on methods to systematically evaluate perioperative neurological, language, cognitive, and functional outcomes in patients with glioma. This variability contributes to suboptimal outcomes, hinders uniformity across multicentre studies, and limits comparability of reported results. Therefore, standardising key aspects of perioperative outcome assessment is crucial for these patients. The Personalized Interventions and Outcomes in Neurosurgical Oncology Research (PIONEER) Consortium and the Response Assessment in Neuro-Oncology (RANO) resect group are collaborative, multidisciplinary efforts that aim to standardise and enhance research and clinical practices in surgical neuro-oncology. In this Policy Review, both working groups review the evidence and provide recommendations for the standardisation of perioperative assessment of neurological morbidity, language function, overall function, and quality of life in adult patients with glioma. The Policy Review offers, for the first time, a structured framework for perioperative outcome assessment in glioma surgery. It aims to reduce heterogeneity in practice, facilitate multicentre studies, and enhance the methodological quality of these studies through more consistent and reproducible methods. Furthermore, homogeneous data will facilitate advances in personalised surgical care by enabling advanced computational modelling techniques.
The detection and characterization of fluorescent puncta are critical tasks in image analysis pipelines for fluorescence imaging. Existing methods for quantitative characterization of such puncta often suffer from biases and limitations, compromising the reliability and reproducibility of results. Moreover, the widespread adoption of many available analysis scripts is often hampered by over-optimization for specific samples, requiring extensive coding knowledge to repurpose for other datasets. We present WormSNAP (Worm SyNapse Analysis Program), a license-free, stand-alone, no-code approach to automated unbiased detection and characterization of 2D fluorescent puncta, originally developed to characterize images of the synapses residing in C. elegans nerve cords but suitable for broader 2D fluorescence image analysis. WormSNAP incorporates a local means thresholding algorithm and a user-friendly Graphical User Interface (GUI) for efficient and accurate analysis of large datasets, with user control of thresholding and restriction parameters and visualization options for further refinement. WormSNAP also calculates three types of correlation metrics for 2-channel images, enabling users to select the ideal metric for their dataset. WormSNAP provides robust and accurate fluorescent puncta detection in a variety of conditions, accelerating the image analysis workflow from data acquisition to figure generation.
Access to reperfusion therapies and stroke unit (SU) admission remains heterogeneous across Europe. Mapping tools can reveal service gaps and guide implementation strategies. MAPSTROKE provides regional mapping of existing stroke centres and identifies potential new sites in underserved areas. To apply a computational strategy to the Italian stroke care system to estimate national coverage for reperfusion therapies and quantify SU bed capacity under current constraints. Using MAPSTROKE geospatial modelling, we assessed (1) 45-min access to a hospital providing reperfusion treatment and (2) SU bed coverage limited by capacity. Population and stroke incidence data for 2023 were mapped on a hexagonal grid combining sources from the Italian Ministry of Health and the Kontur Dataset. Hospitals were classified as Comprehensive (CSC), Primary (PSC), Acute Stroke-Ready (ASRH) or Potential Acute Stroke Centres (PASC). Isochrones of 45 min were generated for hospitals performing reperfusion. Regional coverage was estimated, and a Partial Set Covering identified the minimal number of PASCs required to achieve ≥ 90% coverage. Stroke unit capacity was estimated using bed counts and mean length of stay (LOS). Among 535 hospitals (80 CSCs, 132 PSCs, 22 ASRHs, 301 PASCs), 91.7% of strokes were within 45 min of a hospital providing reperfusion treatment. Seven regions were below 90%, 6 achieved this target after optimisation. National SU capacity covered 79.2% of annual incidence, with a gap of 255 beds (158 with ideal LOS). The MAPSTROKE project reveals adequate reperfusion access but critical SU capacity disparities, underscoring the need for coordinated national strategies.
The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.
A gap in developing novel preclinical treatment strategies for ischemic stroke is predicting long-term outcome in experimental stroke models early during ischemia to reduce heterogeneity and sample size. Besides saving costs through improved risk stratification, reducing the number of animals is a requirement of the 3Rs principle. In this secondary analysis, we analyzed 28 Sprague-Dawley rats of a prospective data base that underwent 90-minute filament-occlusion of the middle cerebral artery (MCAO) to assess the predictive power of early variables at 30, 60, and 90 min after occlusion. The animals were sacrificed after 72 h. Infarct sizes were determined by hematoxylin staining. In a minimally invasive fashion, we recorded cerebral blood flow (CBF) with laser-Doppler flowmetry and direct current (DC)/alternating current-electrocorticography (ECoG) with epidural Ag/AgCl electrodes. Both CBF and ECoG markers correlated with the cortical infarct volumes. Multiclass receiver operating characteristic analysis identified the best predictors of three risk classes, and Spearman's rank correlation was used to explore relationships between ECoG and CBF. The slope of the CBF transients in response to spreading depolarization (SD) was the best biomarker at all time points, while the DC integral was the best epidural biomarker. Both correlated negatively at all time points (ρ < -0.68). In summary, we have found that early risk stratification during MCAO in rats is possible using minimally invasive biomarkers. This would enable, in particular, the early sorting out of animals with a low risk of cortical infarction in neuroprotection studies, where these animals typically distort the statistical results.
Previous research suggests interindividual variability in the location of the genital representation field and use-associated structural variation of genital field thickness associated with normative sexual activity in adult women. Using a sensory-tactile fMRI paradigm, we individually mapped genital fields of 128 women with and without exposure to childhood sexual abuse. We assessed whether structural variation of the individual genital field is driven by exposure to childhood sexual abuse or sexual frequency in the past year. We show that exposure to childhood sexual abuse associated with reduced thickness of individually-mapped genital cortex. Earlier abuse onset predicted greater reductions of genital field thickness. There was no effect of sexual frequency in the past year on genital field thickness. Classic neuroplasticity research indicates amplifying effects of stimulation on sensory cortex. In contrast, our results show long-lasting damaging effects of inappropriate stimulation during early development, emphasizing the need to protect children from sexual adversity.
The paper addresses the problem of parameter estimation (or identification) in dynamical networks composed of an arbitrary number of FitzHugh-Nagumo neuron models with diffusive couplings between each other. It is assumed that only the membrane potential of each model is measured, while the other state variable and all derivatives remain unmeasured. Additionally, constant potential measurement errors in the membrane potential due to sensor imprecision are considered. To solve this problem, firstly, the original FitzHugh-Nagumo network is transformed into a linear regression model, where the regressors are obtained by applying a filter-differentiator to specific combinations of the measured variables. Secondly, the speed-gradient method is applied to this linear model, leading to the design of an identification algorithm for the FitzHugh-Nagumo neural network. Sufficient conditions for the asymptotic convergence of the parameter estimates to their true values are derived for the proposed algorithm. Parameter estimation for some networks is demonstrated through computer simulation. The results confirm that the sufficient conditions are satisfied in the numerical experiments conducted. Furthermore, the algorithm's capabilities for adjusting the identification accuracy and time are investigated. The proposed approach has potential applications in nervous system modeling, particularly in the context of human brain modeling. For instance, EEG signals could serve as the measured variables of the network, enabling the integration of mathematical neural models with empirical data collected by neurophysiologists.
MDGA2 encodes a membrane-associated protein that is critical for regulating glutamatergic synapse development, modulating neuroligins (Nlgns), and maintaining excitatory-inhibitory synaptic balance. While MDGA2 functions have been extensively studied in murine and cellular models, its association with human developmental disorders has yet to be established. Through exome sequencing, we identified seven distinct homozygous loss-of-function variants in MDGA2 in nine individuals from seven consanguineous families, all presenting with developmental and epileptic encephalopathy (DEE). Clinically, these individuals exhibited a consistent phenotype including infantile hypotonia, severe neurodevelopmental delay, intractable seizures, along with distinct dysmorphic features. Neuroimaging findings included delayed/incomplete myelination, early-onset brain atrophy, white-matter thinning, basal ganglia volume loss, and small hippocampi. Functional studies of three representative nonsense variants revealed impaired MDGA2 membrane trafficking, disrupted Nlgn1 interaction, and perturbed MDGA2-mediated excitatory synaptic functions in mammalian expression systems and cultured hippocampal neurons. Our findings support the involvement of MDGA2 in a subtype of autosomal-recessive DEE. This not only underscores a loss-of-function pathogenic mechanism but also highlights the previously unrecognized role of MDGA2 in human synaptic development and regulation, significantly expanding our understanding of the genetic architecture of DEEs.
This study aims to evaluate the global burden of adverse effects of medical treatment (AEMT) using data from the Global Burden of Disease Study (GBD) 2021. Data were extracted from the GBD 2021, covering 204 countries/territories from 1990 to 2021. AEMT was defined using ICD-9 and ICD-10 codes, encompassing complications from medical procedures, treatments, or healthcare exposures. Estimates were categorized into fatal and non-fatal outcomes and stratified by age, sex, year, and covariates, including the Socio-demographic Index (SDI). Mortality-incidence ratios (MIRs), defined as the ratio of mortality calculated by dividing the number of deaths by the total incident cases, were analyzed. In 2021, the global age-standardized prevalence, incidence, disability-adjusted life years (DALYs), and mortality rates of AEMT were 11.48 (95% uncertainty interval [UI], 8.86-14.13), 150.44 (131.19-171.81), 64.19 (51.06-73.11), and 1.53 (1.29-1.68) per 100,000 population, respectively. DALY rates were highest in the early neonatal group (4,789.47 per 100,000 population [95% UI, 3,682.00-5,963.30]), while mortality rates followed a U-shaped pattern across age groups. In 2021, MIRs were highest at both ends of the age range: the early neonatal group (0.58 [95% UI, 0.55-0.58]) and the 95+ age group (0.05 [0.04-0.06]). This pattern was consistent across all SDI quintiles, with higher MIRs observed in lower SDI quintiles. The significantly higher prevalence and incidence rates of AEMT among the older population in high SDI quintiles, compared to lower SDI quintiles, could be attributed to the healthcare overutilization, highlighting the need for policy adjustments.
Gluatamatergic synaptic transmission, critical for learning and memory, relies on precise regulation of extracellular glutamate levels by astrocytic transporters, particularly EAAT2. While existing models of AMPA and NMDA receptor kinetics often oversimplify glutamate dynamics or become computationally intractable, this study develops a balanced, biophysically grounded model that integrates glutamate transport, receptor sensitivity, and electrotonic effects. Using rat hippocampal slices, we recorded postsynaptic currents in CA1 pyramidal neurons under control conditions and during glutamate transporter blockade. The proposed mathematical model, formulated as a system of seven ordinary differential equations, distinguishes somatic and dendritic compartments, synaptic plasticity, and differential glutamate sensitivity of AMPA and NMDA receptors. Key findings reveal that the glutamate transporter blockade prolongs NMDA receptor-mediated currents without altering AMPA receptor kinetics, consistent with the higher glutamate sensitivity of NMDA receptors. The model also predicts glutamate concentrations in synaptic and extrasynaptic spaces, offering insights into spatial neurotransmitter dynamics. Furthermore, it accounts for voltage-dependent NMDA responses and short-term plasticity observed experimentally. By bridging the gap between oversimplified and overly complex approaches, this work provides a versatile tool for studying synaptic transmission in normal and pathological conditions, such as epilepsy or neurodegenerative diseases, where glutamate dysregulation plays a central role.
The human brain is a complex adaptive system characterized by dynamic processes operating across multiple spatio-temporal scales. Capturing these dynamics requires computational models that can integrate different levels of resolution. In this work we present a multiscale co-simulation framework that couples whole-brain modeling with a detailed point-neuron model of the human hippocampal CA1 region. We used a high-resolution implementation of the "The Virtual Brain" (TVB), in which cortical surface mesh vertices are embedded with the Spatial Epileptor Model (SEM). At the microscale, the CA1 model captures neuronal activity at micrometer spatial and sub-millisecond temporal resolution. This integration enables the simulation of macroscale epileptic dynamics with microscale neuronal precision within anatomically grounded brain regions, facilitating cross-scale communication. These results demonstrate the potential of this approach to advance mechanism-driven, personalized medicine in clinical neuroscience.
Noninvasive measurement of exchange is paramount in different fields, ranging from material to biological sciences. Unaccounted exchange may even blur microstructural or other characteristics of multicompartmental systems studied by MR methods. Despite the growing interest in diffusion-exchange studies of complex systems, comparative studies remain scarce. Most existing investigations have applied different diffusion MR methods to different biological samples under varying experimental conditions, making direct comparisons difficult. Moreover, the lack of a gold standard for exchange rate measurements further complicates efforts to validate and interpret results. To address these challenges, we employed two diffusion NMR-based methods-the constant-gradient pulsed-field gradient (CG-PFG) and the recently introduced filter-exchange NMR spectroscopy (FEXSY)-to investigate apparent water exchange in yeast cells and optic nerves, both before and after fixation. We first evaluated the effect of the q $$ q $$ values on the extracted indices and then evaluated the repeatability and reproducibility of the measurements. The CG-PFG and FEXSY experiments were collected on the same sample to allow comparison of the results. The intracellular mean residence times (MRTs) ( τ i $$ {\tau}_i $$ ) extracted from the log-linear fit of the CG-PFG NMR experiments were found to be 554 ± 6 $$ 554\pm 6 $$ and 337 ± 10 ms $$ 337\pm 10\kern0.3em \mathrm{ms} $$ for yeast cells before and after fixation, respectively. The respective τ i $$ {\tau}_i $$ values extracted from the FEXSY experiments before and after fixation were found to be 368 ± 14 $$ 368\pm 14 $$ and 146 ± 24 ms $$ 146\pm 24\kern0.3em \mathrm{ms} $$ . Despite the difference in absolute values of MRTs, the same qualitative behavior is observed in the two methodologies, and both could be analyzed using the bicompartmental Kärger model. The same methodologies were then used to study exchange in the more complex porcine optic nerves. There, the bicompartmental Kärger model analysis is shown to be inadequate. Extensive Monte Carlo simulations are used to narrow down on the most possible explanation, suggesting that optic nerves are multicompartmental systems where not all spins are free to undergo exchange. Supporting theoretical calculations point to the existence of at least one additional nonexchanging restricted compartment. Thus, a tricompartmental model is derived and used to analyze the data. The new model fits the data significantly better and results in dramatically different exchange rates when used on white matter (WM) data: CG-PFG experiments were found to be 730 ± 40 $$ 730\pm 40 $$ and 803 ± 16 ms $$ 803\pm 16\kern0.3em \mathrm{ms} $$ for optic nerves before and after fixation, respectively. The respective τ i $$ {\tau}_i $$ values extracted from the FEXSY experiments before and after fixation were found to be 530 ± 125 $$ 530\pm 125 $$ and 387 ± 104 ms $$ 387\pm 104\kern0.3em \mathrm{ms} $$ . These values are considerably lower than the values previously reported. Finally, we use simulations to show that the quantitative discrepancy between CG-PFG and FEXSY can be attributed, at least partially, to the difference in T 2 $$ {T}_2 $$ values between the intracellular and extracellular compartments. We thus encourage the pairing of exchange and spin-spin relaxation measurements in future studies. We end with a discussion on the current state of the diffusion-exchange field, where we attempt to put a spotlight on essential corner stones that are still missing despite the great advance of recent years: experimental standardization, method comparison, and adequate modeling.
In Alzheimer's disease (AD), pathological tau protein shows a progressive accumulation of post-translational modifications (PTMs), reflecting disease severity, progression, and prion-like activity. Although many neurodegenerative diseases with dementia display tau aggregates, the pathological proteoforms of tau protein from each disease type remain unknown. Here, using a quantitative mass spectrometry-based proteomics platform, FLEXITau, deep characterization of pathological tau protein isolated from the brains of 203 human subjects with AD, familial AD (fAD), chronic traumatic encephalopathy (CTE), corticobasal degeneration (CBD), Pick's disease (PiD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB)-a non-tauopathy symptomatic control-and healthy controls (CTR) is performed. Unsupervised data analyses and supervised machine learning identify distinct molecular features of pathological tau for each disease, enabling molecular disease stratification. This study identifies potential disease-specific biomarkers and therapeutic targets for tauopathies and provides critical quantitative information for pharmacokinetic modeling required for therapeutic and disease mechanism studies.
Gamma oscillation ([Formula: see text]) is an important neuronal rhythmic activity closely related to various brain functions, such as Parkinson's disease (PD). By identifying several direct glutamatergic projections from the cortical-thalamic system (CTS) to the subthalamic nucleus (STN)-external globus pallidus (GPe) circuit, we have established a novel cortical-basal ganglia-thalamus (CBGT) model. Within the CBGT model, we systematically investigate the dynamical mechanisms underlying the origin and control of [Formula: see text]. We find that significant narrowband [Formula: see text] in the 30-100 Hz range can emerge in the BG by adjusting the coupling weights and delays within the BG. Supercritical and subcritical Hopf bifurcations (SPHB and SBHB) can be used to explain the mechanisms underlying the origin of [Formula: see text]. We observe that all four direct glutamatergic projections from the CTS to the STN-GPe circuit can effectively inhibit [Formula: see text]. Interestingly, adjusting the activation levels of the thalamus and cortex can exert significant bidirectional Hopf bifurcation control over [Formula: see text] in the BG, through interaction among these four projections. This bidirectional Hopf bifurcation regulatory phenomenon exhibits good robustness with respect to parameters in the CBGT model, and parameters in the BG have a significant impact on the control patterns. Furthermore, we find that all pathways in the CTS actively participate in controlling [Formula: see text] in the BG, by adjusting the activation levels of cortical and thalamic nuclei. We observe the existence of significant high and low critical mean discharge rates (CMDR) in BG nuclei, as well as complex triggered mean discharge rates (TMDR) in cortical and thalamic nuclei, at the critical boundaries between [Formula: see text] and the stable state. These key dynamical indicators might provide testable foundations for experimental research. Notably, our computational findings suggest that cortical and thalamic nuclei could theoretically serve as potential targets for regulating oscillatory activity in the BG, providing a hypothesis for future experimental investigation into supplementary therapeutic approaches for PD.
The evolution from disturbed brain activity to physiological brain rhythms can precede recovery in patients with disorders of consciousness (DoC). Accordingly, intriguing questions arise: What are the pathophysiological factors associated to disrupted brain rhythms in patients with DoC, and are there potential pathways for individual patients with DoC to return to normal brain rhythms? We addressed these questions at the individual subject level using biophysical simulations based on electroencephalography (EEG). The main findings are that unconscious patients exhibit a loss of excitatory corticothalamic synaptic strength. Synaptic plasticity in this excitatory corticothalamic circuitry facilitates the return of physiological brain rhythms, characterized by the reappearance of spectral peaks and flattening of the aperiodic (1/f) component of the power spectrum, in the selection of patients with DoC, particularly in those who are minimally conscious. The extent to which this occurred was correlated with cerebral glucose uptake. The current findings emphasize the importance of excitatory thalamocortical activity in reestablishing normal brain rhythms after brain injury and show that biophysical modelling of the corticothalamic circuitry could help select patients who might be potentially receptive to treatment and undergo plasticity.
Many neuronal processes are temperature-sensitive. Cooling by 10 [Formula: see text]C typically slows ion channel dynamics by more than a factor of two (Q[Formula: see text] [Formula: see text]). Nevertheless, behaviors can remain robust despite variations in brain temperature. For instance, cooling the premotor nucleus HVC in zebra finches by 10 [Formula: see text]C slows song production by only a factor of Q[Formula: see text] [Formula: see text]. Here we examine the temperature robustness of the synaptic chain network within HVC. Burst spike propagation along such a chain network is postulated to control the tempo of the song. We show that the dynamics of this network are resilient to cooling and that the slowing of burst propagation exhibits a Q[Formula: see text] similar to that observed for the song. We identify two key factors underlying this robustness: the reliance on axonal delays, which are more resistant to temperature changes than ion channels, and enhanced synaptic efficacy at lower temperatures. We propose that these mechanisms represent general principles by which neural circuits maintain functional stability despite temperature fluctuations in the brain. SIGNIFICANCE STATEMENT: Many animal behaviors remain robust despite temperature fluctuations in the brain. By studying timing circuits in songbirds, we identify key circuit elements that contribute to this resilience, including axonal delays and synaptic integration. Our work highlights how these mechanisms interact to maintain stable neuronal dynamics in response to temperature changes.