pwc-archive/papers-with-abstracts huggingface dataset:
Mapping the Human Brain from the Prenatal Period to Infancy Using 3D Magnetic Resonance Imaging
Authors: Arnaud Cachia, Jean-François Mangin, Jessica Dubois
Date: 2025-04-30 00:00:00

Human brain development is a complex and dynamic process that begins during the first weeks of pregnancy and lasts until early adulthood. This chapter focuses on the developmental window from prenatal period to infancy, probably the most dynamic period across the entire lifespan. The availability of non-invasive three-dimensional Magnetic Resonance Imaging (MRI) methodologies has changed the paradigm and allows investigations of the living human brain structure - e.g. micro- and macrostructural features of cortical and subcortical regions and their connections, including cortical sulcation/gyrification, area, and thickness, as well as white matter microstructure and connectivity - beginning in utero. Because of its relative safety, MRI is well-adapted to study individuals at multiple time points and to longitudinally follow the changes in brain structure and function that underlie the early stages of cognitive development.
Neuro-imagerie néonatale : quelle valeur prédictive ?
Authors: Jessica Dubois, Lucie Hertz-Pannier, Sara Neumane
Date: 2025-04-30 00:00:00
Tasks: Diagnostic, Management

Premature birth and various pre- and peri-natal stresses can lead to a variety of brain lesions and have clearly been identified as major risk factors for neurodevelopmental disorders, with variable but multiple consequences that can significantly alter the functional outcome of children in the short, medium and long term. The main aim of the various diagnostic and prognostic markers available, based in particular on clinical and neuroimaging data, is to facilitate early detection of the various disorders and to optimise the follow-up and management of these babies at risk of deficiencies.
DB-GNN: Dual-Branch Graph Neural Network with Multi-Level Contrastive Learning for Jointly Identifying Within- and Cross-Frequency Coupled Brain Networks
Authors: Xiang Wang, Hui Xu, Jing Cai, Ta Zhou, XiBei Yang, Wei Xue
Date: 2025-04-29 00:00:00
Tasks: Contrastive Learning, EEG, Emotion Recognition, Graph Neural Network

Within-frequency coupling (WFC) and cross-frequency coupling (CFC) in brain networks reflect neural synchronization within the same frequency band and cross-band oscillatory interactions, respectively. Their synergy provides a comprehensive understanding of neural mechanisms underlying cognitive states such as emotion. However, existing multi-channel EEG studies often analyze WFC or CFC separately, failing to fully leverage their complementary properties. This study proposes a dual-branch graph neural network (DB-GNN) to jointly identify within- and cross-frequency coupled brain networks. Firstly, DBGNN leverages its unique dual-branch learning architecture to efficiently mine global collaborative information and local cross-frequency and within-frequency coupling information. Secondly, to more fully perceive the global information of cross-frequency and within-frequency coupling, the global perception branch of DB-GNN adopts a Transformer architecture. To prevent overfitting of the Transformer architecture, this study integrates prior within- and cross-frequency coupling information into the Transformer inference process, thereby enhancing the generalization capability of DB-GNN. Finally, a multi-scale graph contrastive learning regularization term is introduced to constrain the global and local perception branches of DB-GNN at both graph-level and node-level, enhancing its joint perception ability and further improving its generalization performance. Experimental validation on the emotion recognition dataset shows that DB-GNN achieves a testing accuracy of 97.88% and an F1- score of 97.87%, reaching the state-of-the-art performance.
Small brains but big challenges: white matter tractography in early life samples
Authors: Jessica Dubois, Mareike Grotheer, Joseph Yuan-Mou Yang, Jacques-Donald Tournier, Christian Beaulieu, Catherine Lebel
Date: 2025-04-29 00:00:00

In the human brain, white matter development is a complex and long-lasting process involving intermingling micro-and macrostructural mechanisms, such as fiber growth, pruning and myelination. Did you know that all these neurodevelopmental changes strongly affect MRI signals, with consequences on tractography performances and reliability? This communication aims to elaborate on these aspects, highlighting the importance of tracking and studying the developing connections with dedicated approaches.
Exploring internal representation of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects
Authors: Asaki Kataoka, Yoshihiro Nagano, Masafumi Oizumi
Date: 2025-04-29 00:00:00
Tasks: Contrastive Learning, Few-Shot Learning, Language Acquisition, Novel Concepts, Self-Supervised Learning

Recent advances in self-supervised learning have attracted significant attention from both machine learning and neuroscience. This is primarily because self-supervised methods do not require annotated supervisory information, making them applicable to training artificial networks without relying on large amounts of curated data, and potentially offering insights into how the brain adapts to its environment in an unsupervised manner. Although several previous studies have elucidated the correspondence between neural representations in deep convolutional neural networks (DCNNs) and biological systems, the extent to which unsupervised or self-supervised learning can explain the human-like acquisition of categorically structured information remains less explored. In this study, we investigate the correspondence between the internal representations of DCNNs trained using a self-supervised contrastive learning algorithm and human semantics and recognition. To this end, we employ a few-shot learning evaluation procedure, which measures the ability of DCNNs to recognize novel concepts from limited exposure, to examine the inter-categorical structure of the learned representations. Two comparative approaches are used to relate the few-shot learning outcomes to human semantics and recognition, with results suggesting that the representations acquired through contrastive learning are well aligned with human cognition. These findings underscore the potential of self-supervised contrastive learning frameworks to model learning mechanisms similar to those of the human brain, particularly in scenarios where explicit supervision is unavailable, such as in human infants prior to language acquisition.
Better artificial intelligence does not mean better models of biology
Authors: Drew Linsley, Pinyuan Feng, Thomas Serre
Date: 2025-04-08 00:00:00

Deep neural networks (DNNs) once showed increasing alignment with primate perception and neural responses as they improved on vision benchmarks, raising hopes that advances in AI would yield better models of biological vision. However, we show across three benchmarks that this alignment is now plateauing - and in some cases worsening - as DNNs scale to human or superhuman accuracy. This divergence may reflect the adoption of visual strategies that differ from those used by primates. These findings challenge the view that progress in artificial intelligence will naturally translate to neuroscience. We argue that vision science must chart its own course, developing algorithms grounded in biological visual systems rather than optimizing for benchmarks based on internet-scale datasets.
Uncovering potential effects of spontaneous waves on synaptic development: the visual system as a model
Authors: Jennifer Crodelle, Wei P. Dai
Date: 2025-04-26 00:00:00
Tasks: Triplet

Spontaneous waves are ubiquitous during early brain development and are hypothesized to drive the development of receptive fields (RFs). Different stages of spontaneous waves in the retina have been observed to coincide with the development of the retinotopic map, ON-OFF segregation, and orientation selectivity in the early visual pathway of mammals, and can be characterized by different activity patterns in the retina and downstream areas. Stage II waves, which occur in rodents right after birth, have been implicated in a possible synaptic pruning process, relating these stage II retinal waves to the refinement of the retinotopic map. However, the mechanisms underlying the activity-dependent effects of retinal waves on synapses into the primary visual cortex (V1) are poorly understood. In this work, we build a biologically-constrained model of the development of thalamocortical synapses of V1 cells driven by stage II retinal waves using a spike-timing dependent triplet learning rule. Using this model, together with a reduced rate-based model, we propose mechanisms underlying such a pruning process and predict how characteristics of the retinal waves may lead to different RF structures, including periodic RFs. We introduce gap junctions into the V1 network and show that such a coupling can serve to promote precise local retinotopy. Finally, we discuss how the spatial distribution of synaptic weights at the end of stage II may affect the emergence of orientation selectivity of V1 neurons during stage III waves. The mechanisms uncovered in this work may be useful in understanding synaptic structures that emerge across cortical regions during development.
Modular integration of neural connectomics, dynamics and biomechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans
Authors: Jimin Kim, Jeremy T. Florman, Julia A. Santos, Mark J. Alkema, Eli Shlizerman
Date: 2025-04-25 00:00:00

Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting model variations. Here we develop modWorm: a modular modeling framework for the nematode C. elegans. modWorm allows for construction of a model as an integrated series of configurable, exchangeable modules each describing specific biophysical processes across different modalities. Utilizing modWorm, we propose a base neuro-mechanical model for C. elegans built upon the complete connectome. The model integrates a series of 7 modules: i) intra-cellular dynamics, ii) electrical and iii) chemical extra-cellular neural dynamics, iv) translation of neural activity to muscle calcium dynamics, v) muscle calcium dynamics to muscle forces, vi) muscle forces to body postures and vii) proprioceptive feedback. We validate the base model by in-silico injection of constant currents into neurons known to be associated with locomotion behaviors and by applying external forces to the body. Applications of in-silico neural stimuli experimentally known to modulate locomotion show that the model can recapitulate natural behavioral responses such as forward and backward locomotion as well as mid-locomotion responses such as avoidance and turns. Furthermore, through in-silico ablation surveys, the model can infer novel neural circuits involved in sensorimotor behaviors. To further dissect mechanisms of locomotion, we utilize modWorm to introduce empirical based model variations and model optimizations to elucidate their effects on simulated locomotion. Our results show that modWorm can be utilized to identify neural circuits which control, mediate and generate natural behavior.
A computational model of infant sensorimotor exploration in the mobile paradigm
Authors: Josua Spisak, Sergiu Tcaci Popescu, Stefan Wermter, Matej Hoffmann, J. Kevin O'Regan
Date: 2025-04-24 00:00:00

We present a computational model of the mechanisms that may determine infants' behavior in the "mobile paradigm". This paradigm has been used in developmental psychology to explore how infants learn the sensory effects of their actions. In this paradigm, a mobile (an articulated and movable object hanging above an infant's crib) is connected to one of the infant's limbs, prompting the infant to preferentially move that "connected" limb. This ability to detect a "sensorimotor contingency" is considered to be a foundational cognitive ability in development. To understand how infants learn sensorimotor contingencies, we built a model that attempts to replicate infant behavior. Our model incorporates a neural network, action-outcome prediction, exploration, motor noise, preferred activity level, and biologically-inspired motor control. We find that simulations with our model replicate the classic findings in the literature showing preferential movement of the connected limb. An interesting observation is that the model sometimes exhibits a burst of movement after the mobile is disconnected, casting light on a similar occasional finding in infants. In addition to these general findings, the simulations also replicate data from two recent more detailed studies using a connection with the mobile that was either gradual or all-or-none. A series of ablation studies further shows that the inclusion of mechanisms of action-outcome prediction, exploration, motor noise, and biologically-inspired motor control was essential for the model to correctly replicate infant behavior. This suggests that these components are also involved in infants' sensorimotor learning.
Seizure duration is associated with multiple timescales in interictal iEEG band power
Authors: Mariella Panagiotopoulou, Gabrielle M. Schroeder, Jess Blickwedel, Fahmida A Chowdhury, Beate Diehl, Jane de Tisi, John S. Duncan, Alison Cronie, Jennifer Falconer, Ryan Faulder, Veronica Leach, Shona Livingstone, Rhys H. Thomas, Peter N. Taylor, Yujiang Wang
Date: 2025-04-24 00:00:00
Tasks: EEG

Background Seizure severity can change from one seizure to the next within individual people with epilepsy. It is unclear if and how seizure severity is modulated over longer timescales. Characterising seizure severity variability over time could lead to tailored treatments. In this study, we test if continuously-recorded interictal intracranial EEG (iEEG) features encapsulate signatures of such modulations. Methods We analysed 20 subjects with iEEG recordings of at least one day. We identified cycles on timescales of hours to days embedded in long-term iEEG band power and associated them with seizure severity, which we approximated using seizure duration. In order to quantify these associations, we created linear-circular statistical models of seizure duration that incorporated different band power cycles within each subject. Findings In most subjects, seizure duration was weakly to moderately correlated with individual band power cycles. Combinations of multiple band power cycles significantly explained most of the variability in seizure duration. Specifically, we found 70% of the models had a higher than 60% adjusted $R^2$ across all subjects. From these models, around 80% were deemed to be above chance-level (p-value < 0.05) based on permutation tests. Models included cycles of ultradian, circadian and slower timescales in a subject-specific manner. Interpretation These results suggest that seizure severity, as measured by seizure duration, may be modulated over timescales of minutes to days by subject-specific cycles in interictal iEEG signal properties. These cycles likely serve as markers of seizure modulating processes. Future work can investigate biological drivers of these detected fluctuations and may inform novel treatment strategies that minimise seizure severity.
Summary statistics of learning link changing neural representations to behavior
Authors: Jacob A. Zavatone-Veth, Blake Bordelon, Cengiz Pehlevan
Date: 2025-04-23 00:00:00

How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical physics offer a potential answer: for a given task, there are often a small set of summary statistics that are sufficient to predict performance as the network learns. Here, we review recent advances in how summary statistics can be used to build theoretical understanding of neural network learning. We then argue for how this perspective can inform the analysis of neural data, enabling better understanding of learning in biological and artificial neural networks.
Application of an attention-based CNN-BiLSTM framework for in vivo two-photon calcium imaging of neuronal ensembles: decoding complex bilateral forelimb movements from unilateral M1
Authors: Ghazal Mirzaee, Jonathan Chang, Shahrzad Latifi
Date: 2025-04-23 00:00:00
Tasks: Deep Learning

Decoding behavior, such as movement, from multiscale brain networks remains a central objective in neuroscience. Over the past decades, artificial intelligence and machine learning have played an increasingly significant role in elucidating the neural mechanisms underlying motor function. The advancement of brain-monitoring technologies, capable of capturing complex neuronal signals with high spatial and temporal resolution, necessitates the development and application of more sophisticated machine learning models for behavioral decoding. In this study, we employ a hybrid deep learning framework, an attention-based CNN-BiLSTM model, to decode skilled and complex forelimb movements using signals obtained from in vivo two-photon calcium imaging. Our findings demonstrate that the intricate movements of both ipsilateral and contralateral forelimbs can be accurately decoded from unilateral M1 neuronal ensembles. These results highlight the efficacy of advanced hybrid deep learning models in capturing the spatiotemporal dependencies of neuronal networks activity linked to complex movement execution.
A Few-Shot Metric Learning Method with Dual-Channel Attention for Cross-Modal Same-Neuron Identification
Authors: Wenwei Li, Liyi Cai, Wu Chen, Anan Li
Date: 2025-04-23 00:00:00
Tasks: Metric Learning

In neuroscience research, achieving single-neuron matching across different imaging modalities is critical for understanding the relationship between neuronal structure and function. However, modality gaps and limited annotations present significant challenges. We propose a few-shot metric learning method with a dual-channel attention mechanism and a pretrained vision transformer to enable robust cross-modal neuron identification. The local and global channels extract soma morphology and fiber context, respectively, and a gating mechanism fuses their outputs. To enhance the model's fine-grained discrimination capability, we introduce a hard sample mining strategy based on the MultiSimilarityMiner algorithm, along with the Circle Loss function. Experiments on two-photon and fMOST datasets demonstrate superior Top-K accuracy and recall compared to existing methods. Ablation studies and t-SNE visualizations validate the effectiveness of each module. The method also achieves a favorable trade-off between accuracy and training efficiency under different fine-tuning strategies. These results suggest that the proposed approach offers a promising technical solution for accurate single-cell level matching and multimodal neuroimaging integration.
Restricted Repetitive Behaviors in Adolescent Males with Autism: Volatility in Brain Functional Connectivities
Authors: Gerardo Noriega
Date: 2025-04-22 00:00:00

This paper studies subtypes of restricted, repetitive and stereotypical behaviors (RRBs) in adolescent males with autism spectrum disorder (ASD) from the viewpoint of the dynamics of brain functional connectivities (FCs). Data from the ABIDE-II repository and Repetitive Behavior Scale-Revised (RBS-R) metrics are used to form two ASD groups with tightly controlled demographics; one comprises subjects with scores above threshold for the self-injurious behaviors (SIBs) subscale, and the other subjects with scores below threshold for SIBs, but above threshold for at least one of the other subscales (stereotyped, compulsive, ritualistic, insistence on sameness, restricted interests). The dynamics of the coherence for FCs across distinct frequency bands are compared against matched controls, using a novel volatility measure computed in time-frequency space. We find statistically significant differences, on average, in the volatility of a relatively small set of FCs, most mapping to either the default mode network or the cerebellum, in the mid- and high-frequency bands, and yielding higher volatility in subjects with high levels of SIBs. Results suggest a distinct underlying profile for SIBs involving multiple brain regions associated with rewards and emotions processing. The work contributes to the identification of neural substrates potentially underlying behavioral subtypes, and may help target interventions.
Geometry of color perception. Part 1: Structures and metrics of a homogeneous color space
Authors: Edoardo Provenzi
Date: 2025-04-21 00:00:00

This is the first half of a two-part paper dealing with the geometry of color perception. Here we analyze in detail the seminal 1974 work by H.L. Resnikoff, who showed that there are only two possible geometric structures and Riemannian metrics on the perceived color space P compatible with the set of Schr\"odinger's axioms completed with the hypothesis of homogeneity. We recast Resnikoff's model into a more modern colorimetric setting, provide a much simpler proof of the main result of the original paper and motivate the need of psychophysical experiments to confute or confirm the linearity of background transformations, which act transitively on P. Finally, we show that the Riemannian metrics singled out by Resnikoff through an axiom on invariance under background transformations are not compatibles with the crispening effect, thus motivating the need of further research about perceptual color metrics.
Mapping Emotional Feeling in the Body: A Tripartite Framework for Understanding the Embodied Mind
Authors: Tatsuya Daikoku, Maiko Minatoya, Masaki Tanaka
Date: 2025-04-21 00:00:00

People often speak of being "moved beyond words" when witnessing awe-inspiring natural beauty, experiencing profound moral elevation, or encountering powerful works of art and music. Such moments evoke intensely embodied sensations - a swelling in the chest, a quickened heartbeat, or a sudden stillness that floods the entire body. These experiences are difficult to articulate, yet they are vividly real and spatially anchored in bodily experience. This review examines the emerging method of body mapping, which captures how individuals subjectively localize emotional sensations across the body. Drawing on over two dozen empirical studies, we propose a tripartite framework for understanding the origins of emotional bodily maps: (1) bottom-up physiological signals, (2) top-down behavioral engagement (including motor actions), and (3) conceptual and metaphorical constructions. Crucially, we argue that bodily maps are not mere reflections of physiological monitoring, but expressive interfaces between brain, body, and conceptualization. Because they rely on introspective sensation rather than verbal articulation, bodily maps offer a promising language-independent tool for assessing emotional experience - particularly in cross-cultural research, developmental studies, and clinical contexts where verbal reporting may be limited or unreliable. Ultimately, bodily maps reveal the hidden geography of emotion-those feelings that elude precise definition or verbalization, like being moved beyond words, yet are undeniably real and embodied - offering a compelling window into how emotions are formed, shared, and lived through the body.