Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin-adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to the growth cone, and (4) orientation dynamics guided by the substrate anisotropy. Dynamical systems analysis reveals that the saddle-node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems.
Objective While Alzheimer's disease (AD) and frontotemporal dementia (FTD) show some common memory deficits, these two disorders show partially overlapping complex spatiotemporal patterns of neural dynamics. The objective of this study is to characterize these patterns to better understand the general principles of neurodynamics in these conditions. Methods A comprehensive array of methods to study brain rhythms and functional brain networks are used in the study, from spectral power measures to Lyapunov exponent, phase synchronization, temporal synchrony patterns, and measures of the functional brain connectivity. Furthermore, machine learning techniques for classification are used to augment the methodology. Results Multiple measures (spectral, synchrony, functional network organization) indicate an array of differences between neurodynamics between AD and FTD, and control subjects across different frequency bands. Conclusions These differences taken together in an integrative way suggest that AD neural activity may be less coordinated and less connected across areas, and more random, while FTD shows more coordinated neural activity (except slow frontal activity). Significance AD and FTD may represent opposite changes from normal brain function in terms of the spatiotemporal coordination of neural activity. Deviations from normal in both directions may lead to neurological deficits, which are specific to each of the disorders.
Moein Khajehnejad, Forough Habibollahi, Devon Stoliker
et al.
Foundation models are transforming neuroscience but are often prohibitively large, data-hungry, and difficult to deploy. Here, we introduce BrainSymphony, a lightweight and parameter-efficient foundation model with plug-and-play integration of fMRI time series and diffusion-derived structural connectivity, allowing unimodal or multimodal training and deployment without architectural changes while requiring substantially less data compared to the state-of-the-art. The model processes fMRI time series through parallel spatial and temporal transformer streams, distilled into compact embeddings by a Perceiver module, while a novel signed graph transformer encodes anatomical connectivity from diffusion MRI. These complementary representations are then combined through an adaptive fusion mechanism. Despite its compact design, BrainSymphony consistently outperforms larger models on benchmarks spanning prediction, classification, and unsupervised network discovery. Highlighting the model's generalizability and interpretability, attention maps reveal drug-induced context-dependent reorganization of cortical hierarchies in an independent psilocybin neuroimaging dataset. BrainSymphony delivers accessible, interpretable, and clinically meaningful results and demonstrates that architecturally informed, multimodal models can surpass much larger counterparts and advance applications of AI in neuroscience.
Alice Longhena, Martin Guillemaud, Fabrizio De Vico Fallani
et al.
Graph theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent studies suggest that representing brain networks in a suitable geometric space can better capture their connectivity structure. This study introduces a novel approach to characterize brain connectivity changes using low dimensional, informative representations of networks in a latent geometric space. Specifically, the networks are embedded in a polar representation of the hyperbolic plane, the hyperbolic disk. Here, we use a geometric score, entirely based on the computation of distances between nodes in the latent space, to measure the effect of a perturbation on the nodes. Precisely, the score is a local measure of distortion in the geometric neighborhood of a node following a perturbation. The method is applied to a brain network dataset of patients with AD and healthy participants, derived from diffusion weighted (DWI) and functional (fMRI) magnetic resonance imaging scans. We show that, compared with standard graph measures, our method more accurately identifies the brain regions most affected by neurodegeneration. Notably, the abnormalities detected in memory related and frontal areas are robust across multiple brain parcellation scales. Finally, our findings suggest that the geometric perturbation score could serve as a potential biomarker for characterizing the progression of the disease.
Self-Other Reorganization (SOR) is a theory of how interacting entities or individuals, each of which can be described as an autocatalytic network, collectively exhibit cumulative, adaptive, open-ended change, or evolution. Zachar et al.'s critique of SOR stems from misunderstandings; it does not weaken the arguments in (Gabora & Steel, 2021). The formal framework of Reflexively Autocatalytic and foodset-derived sets (RAFs) enables us to model the process whereby, through their interactions, a set of elements become a 'collective self.' SOR shows how the RAF setting provides a means of encompassing abiogenesis and cultural evolution under the same explanatory framework and provides a plausible explanation for the origins of both evolutionary processes. Although SOR allows for detrimental stimuli (and products), there is (naturally) limited opportunity for elements that do not contribute to or reinforce a RAF to become part of it. Replication and cumulative, adaptive change in RAFs is well-established in the literature. Contrary to Zachar et al., SOR is not a pure percolation model (such as SIR); it encompasses not only learning (modeled as assimilation of foodset elements) but also creative restructuring (modeled as generation of foodset-derived elements), as well as the emergence of new structures made possible by new foodset- and foodset-derived elements. Cultural SOR is robust to degradation, and imperfect replication. Zachar et al.'s simulation contains no RAFs, and does not model SOR.
Spatio-temporal receptive fields (STRF) of visual neurons are often estimated using spike-triggered averaging of binary pseudo-random stimulus sequences. The spike train of a visual neuron is recorded simultaneously with the stimulus presentation. The neuron's STRF is estimated by averaging the stimulus frames that coincide with spikes at fixed latencies. Although this is a widely used technique, an analytical method for determining the statistical significance of the estimated value of the STRF pixels seems to be lacking. Such a significance test would be useful for identifying the significant features of the STRF and investigating their relationship with experimental variables. Here, the distribution of the estimated STRF pixel values is derived for given spike trains, under the null hypothesis that spike occurrences and stimulus values are statistically independent. This distribution is then used for computing amplitude thresholds to determine the STRF pixels where the null hypothesis can be rejected at a desired two-tailed significance level. It is also proposed that the size of the receptive field may be inferred from the significant pixels. The application of the proposed method is illustrated on spike trains collected from individual mouse retinal ganglion cells.
AbstractA new algorithm for the estimation of the robustness of a dynamical system’s equilibrium is presented. Unlike standard approaches, the algorithm does not aim to identify the entire basin of attraction of the solution. Instead, it iteratively estimates the so-called local integrity measure, that is, the radius of the largest hypersphere entirely included in the basin of attraction of a solution and centred in the solution. The procedure completely overlooks intermingled and fractal regions of the basin of attraction, enabling it to provide a significant engineering quantity in a very short time. The algorithm is tested on four different mechanical systems of increasing dimension, from 2 to 8. For each system, the variation of the integrity measure with respect to a system parameter is evaluated, proving the engineering relevance of the results provided. Despite some limitations, the algorithm proved to be a viable alternative to more complex and computationally demanding methods, making it a potentially appealing tool for industrial applications.
AbstractIt is of great significance to develop non‐precious metal catalysts with excellent catalytic activity, stability, and acid resistance for biomass valorization. Herein, catalytic amination of biomass carbonyl compounds was achieved via a Leuckart‐type reaction over Co nanoparticles (NPs) embedded N‐doped carbon catalyst, which was prepared by thermolysis of ZIF‐67 precursor at different temperatures in the N2 atmosphere. The Co/NC‐800 catalyst exhibited excellent catalytic activity and recyclability in furfural reductive amination to mono‐substituted formamide, which was attributed to the synergistic catalytic action of Co NPs and nitrogen base sites of the catalyst. The reductive amination mechanisms were elucidated by theoretical calculations, and showed that the initial formation of C−N bond was derived from the condensation of furfural and formamide, followed by dehydration to form C=N double bond, which was then reduced by hydrogen species Co−H− and NH+. The developed catalytic system was applicable to different carbonyls for the synthesis of corresponding N‐formyl compounds with up to 99 % yield.
Md Navid Akbar, Marianna La Rocca, Rachael Garner
et al.
Post-traumatic epilepsy (PTE) is a life-long complication of traumatic brain injury (TBI) and is a major public health problem that has an estimated incidence that ranges from 2%-50%, depending on the severity of the TBI. Currently, the pathomechanism that in-duces epileptogenesis in TBI patients is unclear, and one of the most challenging goals in the epilepsy community is to predict which TBI patients will develop epilepsy. In this work, we used diffusion-weighted imaging (DWI) of 14 TBI patients recruited in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx)to measure and analyze fractional anisotropy (FA), obtained from tract-based spatial statistic (TBSS) analysis. Then we used these measurements to train two support vector machine (SVM) models to predict which TBI patients have developed epilepsy. Our approach, tested on these 14 patients with a leave-two-out cross-validation, allowed us to obtain an accuracy of 0.857 $\pm$ 0.18 (with a 95% level of confidence), demonstrating it to be potentially promising for the early characterization of PTE.
System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron's receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron's receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.
Bjørn Erik Juel, Renzo Comolatti, Giulio Tononi
et al.
An agent's actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent's actions are "caused from within", as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents ("animats"), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents' sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences ("what caused what?") in complex networks, we evaluate the direct causes of the animats' actions. In addition, we extend this framework to trace the causal chain ("causes of causes") leading to an animat's actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat's actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.
PierGianLuca Porta Mana, Claudia Bachmann, Abigail Morrison
Automated classification methods for disease diagnosis are currently in the limelight, especially for imaging data. Classification does not fully meet a clinician's needs, however: in order to combine the results of multiple tests and decide on a course of treatment, a clinician needs the likelihood of a given health condition rather than binary classification yielded by such methods. We illustrate how likelihoods can be derived step by step from first principles and approximations, and how they can be assessed and selected, illustrating our approach using fMRI data from a publicly available data set containing schizophrenic and healthy control subjects. We start from the basic assumption of partial exchangeability, and then the notion of sufficient statistics and the "method of translation" (Edgeworth, 1898) combined with conjugate priors. This method can be used to construct a likelihood that can be used to compare different data-reduction algorithms. Despite the simplifications and possibly unrealistic assumptions used to illustrate the method, we obtain classification results comparable to previous, more realistic studies about schizophrenia, whilst yielding likelihoods that can naturally be combined with the results of other diagnostic tests.
We prove that the HOMFLYPT polynomial of a link, colored by partitions with a xed number of rows is a q-holonomic function. Specializing to the case of knots colored by a partition with a single row, it proves the existence of an (a;q) super-polynomial of knots in 3-space, as was conjectured by string theorists. Our proof uses skew Howe duality that reduces the evaluation of web diagrams and their ladders to a Poincare-Birkho- Witt computation of an auxiliary quantum group of rank the number of strings of the ladder diagram.
The design of high quality factor (Q) optical cavities in two dimensional photonic crystal (PC) slab waveguides based upon a momentum space picture is presented. The results of a symmetry analysis of defect modes in hexagonal and square host photonic lattices are used to determine cavity geometries that produce modes which by their very symmetry reduce the vertical radiation loss from the PC slab. Further improvements in the Q are achieved through tailoring of the defect geometry in Fourier space to limit coupling between the dominant momentum components of a given defect mode and those momentum components which are either not reflected by the PC mirror or which lie within the radiation cone of the cladding surrounding the PC slab. Numerical investigations using the finite-difference time-domain (FDTD) method predict that radiation losses can be significantly suppressed through these methods, culminating with a graded square lattice design whose total Q approaches 10;5 with a mode volume of approximately 0.25 cubic half-wavelengths in vacuum.