pwc-archive/papers-with-abstracts huggingface dataset:
Automatic target validation based on neuroscientific literature mining for tractography
Authors: Xavier Vasques, Renaud Richardet, Sean L Hill, David Slater, Jean-Cedric Chappelier, Etienne Pralong, Jocelyne Bloch, Bogdan Draganski, Laura Cif
Date: 2025-02-17 00:00:00
Tasks: Articles, Literature Mining

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com
Morphological Neuron Classification Using Machine Learning
Authors: Xavier Vasques, Laurent Vanel, Guillaume Villette, Laura Cif
Date: 2025-02-17 00:00:00
Tasks: Classification

Classification and quantitative characterization of neuronal morphologies from histological neuronal reconstruction is challenging since it is still unclear how to delineate a neuronal cell class and which are the best features to define them by. The morphological neuron characterization represents a primary source to address anatomical comparisons, morphometric analysis of cells, or brain modeling. The objectives of this paper are (i) to develop and integrate a pipeline that goes from morphological feature extraction to classification and (ii) to assess and compare the accuracy of machine learning algorithms to classify neuron morphologies. The algorithms were trained on 430 digitally reconstructed neurons subjectively classified into layers and/or m-types using young and/or adult development state population of the somatosensory cortex in rats. For supervised algorithms, linear discriminant analysis provided better classification results in comparison with others. For unsupervised algorithms, the affinity propagation and the Ward algorithms provided slightly better results.
Targeting C99 Mediated Metabolic Disruptions with Ketone Therapy in Alzheimer's Disease
Authors: Hao Huang, Kaijing Xu, Michael Lardelli
Date: 2025-02-17 00:00:00

The role of ketone bodies in Alzheimers disease (AD) remains incompletely understood, particularly regarding their influence on amyloid pathology. While beta}hydroxybutyrate (BHB) has been implicated in neuroprotection, direct evidence for its effects on amyloid beta(Abeta) deposition, aggregation, or clearance is lacking. Furthermore, whether BHB acts as a disease modifying factor or merely confers transient metabolic benefits remains unclear. Addressing this gap is crucial for evaluating the therapeutic potential of ketone metabolism in AD. Here, we investigated the impact of ketone bodies on amyloidogenic toxicity using a Drosophila melanogaster model with targeted expression of human amyloid precursor protein (APP), beta secretase 1 (BACE1), Abeta, and the C99 fragment, an essential intermediate in Abeta generation. Surprisingly, we found that Abeta alone elicited minimal neurotoxicity, whereas C99 expression induced pronounced pathological effects, suggesting a critical, underappreciated role of C99 in AD progression. Further analysis revealed that C99 driven toxicity was associated with autophagic and lysosomal dysfunction, leading to impaired protein clearance, oxidative stress, and mitochondrial abnormalities. Using confocal microscopy and lysosomal pH sensitive markers, we demonstrated that BHB treatment restored lysosomal function and alleviated these pathological changes. Protein protein interaction network analysis in C99 expressing Drosophila brains identified protein phosphatase methylesterase 1 (PPME1) activation as a key driver of autophagic impairment, further supported by machine learning predictions. Finally, mathematical similarity analysis of PPI networks suggested that BHB may exert its neuroprotective effects through mTOR inhibition, positioning it as a potential endogenous modulator of AD related pathology.
Graceful forgetting: Memory as a process
Authors: Alain de Cheveigné
Date: 2025-02-16 00:00:00

A rational theory of memory is proposed to explain how we can accommodate unbounded sensory input within bounded storage space. Memory is stored as statistics, organized into complex structures that are constantly summarized and compressed to make room for new input. This process, driven by space constraints, is guided by heuristics that optimize the memory for future needs. Sensory input is rapidly encoded as simple statistics that are more slowly elaborated into more abstract constructs. This theory differs from previous accounts of memory by (a) its reliance on statistics, (b) its use of heuristics to guide the choice of statistics, and (c) the emphasis on memory as a process that is intensive, complex, and expensive. The theory is intended as an aid to make sense of our extensive knowledge of memory, and bring us closer to an understanding of memory in functional and mechanistic terms.
A recurrent vision transformer shows signatures of primate visual attention
Authors: Jonathan Morgan, Badr Albanna, James P. Herman
Date: 2025-02-16 00:00:00
Tasks: Change Detection

Attention is fundamental to both biological and artificial intelligence, yet research on animal attention and AI self attention remains largely disconnected. We propose a Recurrent Vision Transformer (Recurrent ViT) that integrates self-attention with recurrent memory, allowing both current inputs and stored information to guide attention allocation. Trained solely via sparse reward feedback on a spatially cued orientation change detection task, a paradigm used in primate studies, our model exhibits primate like signatures of attention, including improved accuracy and faster responses for cued stimuli that scale with cue validity. Analysis of self-attention maps reveals dynamic spatial prioritization with reactivation prior to expected changes, and targeted perturbations produce performance shifts similar to those observed in primate frontal eye fields and superior colliculus. These findings demonstrate that incorporating recurrent feedback into self attention can capture key aspects of primate visual attention.
Hybrid Brain-Machine Interface: Integrating EEG and EMG for Reduced Physical Demand
Authors: Daniel Wang, Katie Hong, Zachary Sayyah, Malcolm Krolick, Emma Steinberg, Rohan Venkatdas, Sidharth Pavuluri, Yipeng Wang, Zihan Huang
Date: 2025-02-15 00:00:00
Tasks: EEG, SSVEP

We present a hybrid brain-machine interface (BMI) that integrates steady-state visually evoked potential (SSVEP)-based EEG and facial EMG to improve multimodal control and mitigate fatigue in assistive applications. Traditional BMIs relying solely on EEG or EMG suffer from inherent limitations; EEG-based control requires sustained visual focus, leading to cognitive fatigue, while EMG-based control induces muscular fatigue over time. Our system dynamically alternates between EEG and EMG inputs, using EEG to detect SSVEP signals at 9.75 Hz and 14.25 Hz and EMG from cheek and neck muscles to optimize control based on task demands. In a virtual turtle navigation task, the hybrid system achieved task completion times comparable to an EMG-only approach, while 90% of users reported reduced or equal physical demand. These findings demonstrate that multimodal BMI systems can enhance usability, reduce strain, and improve long-term adherence in assistive technologies.
NeuroXVocal: Detection and Explanation of Alzheimer's Disease through Non-invasive Analysis of Picture-prompted Speech
Authors: Nikolaos Ntampakis, Konstantinos Diamantaras, Ioanna Chouvarda, Magda Tsolaki, Vasileios Argyriou, Panagiotis Sarigianndis
Date: 2025-02-14 00:00:00
Tasks: RAG, Retrieval-augmented Generation

The early diagnosis of Alzheimer's Disease (AD) through non invasive methods remains a significant healthcare challenge. We present NeuroXVocal, a novel dual-component system that not only classifies but also explains potential AD cases through speech analysis. The classification component (Neuro) processes three distinct data streams: acoustic features capturing speech patterns and voice characteristics, textual features extracted from speech transcriptions, and precomputed embeddings representing linguistic patterns. These streams are fused through a custom transformer-based architecture that enables robust cross-modal interactions. The explainability component (XVocal) implements a Retrieval-Augmented Generation (RAG) approach, leveraging Large Language Models combined with a domain-specific knowledge base of AD research literature. This architecture enables XVocal to retrieve relevant clinical studies and research findings to generate evidence-based context-sensitive explanations of the acoustic and linguistic markers identified in patient speech. Using the IS2021 ADReSSo Challenge benchmark dataset, our system achieved state-of-the-art performance with 95.77% accuracy in AD classification, significantly outperforming previous approaches. The explainability component was qualitatively evaluated using a structured questionnaire completed by medical professionals, validating its clinical relevance. NeuroXVocal's unique combination of high-accuracy classification and interpretable, literature-grounded explanations demonstrates its potential as a practical tool for supporting clinical AD diagnosis.
Structuring the Environment Nudges Participants Toward Hierarchical Over Shortest Path Planning
Authors: Valeria Simonelli, Davide Nuzzi, Gian Luca Lancia, Giovanni Pezzulo
Date: 2025-02-14 00:00:00
Tasks: Decision Making

Effective planning is crucial for navigating complex environments and achieving goals efficiently. In this study, we investigated how environmental structure influences the selection of planning strategies. Forty-two participants navigated a space station to collect colored spheres, with environments either structured (spheres grouped by color) or unstructured (spheres scattered randomly). We tested three types of plans: hierarchical (grouping spheres by color), shortest path (minimizing travel distance), and neutral (none of the above). By manipulating environmental structure, we were able to nudge participants toward a preference for hierarchical planning in structured environments, while shortest path plans were favored in unstructured environments. A mismatch between self-reported preferences and actual choices indicated that participants often adopted implicit strategies, unaware of their decision-making processes. These findings highlight the powerful effect of environmental cues on planning and suggest that even subtle changes in structure can guide the selection of planning strategies.
Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning
Authors: Wei Wu, Can Liao, Zizhen Deng, Zhengrui Guo, Jinzhuo Wang
Date: 2025-02-06 00:00:00
Tasks: Contrastive Learning

The Platonic Representation Hypothesis suggests a universal, modality-independent reality representation behind different data modalities. Inspired by this, we view each neuron as a system and detect its multi-segment activity data under various peripheral conditions. We assume there's a time-invariant representation for the same neuron, reflecting its intrinsic properties like molecular profiles, location, and morphology. The goal of obtaining these intrinsic neuronal representations has two criteria: (I) segments from the same neuron should have more similar representations than those from different neurons; (II) the representations must generalize well to out-of-domain data. To meet these, we propose the NeurPIR (Neuron Platonic Intrinsic Representation) framework. It uses contrastive learning, with segments from the same neuron as positive pairs and those from different neurons as negative pairs. In implementation, we use VICReg, which focuses on positive pairs and separates dissimilar samples via regularization. We tested our method on Izhikevich model-simulated neuronal population dynamics data. The results accurately identified neuron types based on preset hyperparameters. We also applied it to two real-world neuron dynamics datasets with neuron type annotations from spatial transcriptomics and neuron locations. Our model's learned representations accurately predicted neuron types and locations and were robust on out-of-domain data (from unseen animals). This shows the potential of our approach for understanding neuronal systems and future neuroscience research.
Coarse-Graining Cascades Within Food Webs
Authors: Justin D. Yeakel
Date: 2025-02-16 00:00:00

Quantifying population dynamics is a fundamental challenge in ecology and evolutionary biology, particularly for species that are cryptic, microscopic, or extinct. Traditional approaches rely on continuous representations of population size, but in many cases, the precise number of individuals is unknowable. Here, we present a coarse-grained population model that simplifies population dynamics to binary states - high or low - determined by the balance of bottom-up resource availability and top-down predation pressure. This Boolean framework provides a minimal yet analytically tractable alternative to traditional Lotka-Volterra-based models, enabling direct insights into the role of food web structure in shaping community stability. Using this approach, we investigate how trophic interactions influence population persistence, cyclic dynamics, and extinction risk across model food webs. We find that top-down effects are a primary driver of cycling, aligning with theoretical expectations from traditional population models, and that trophic position strongly influences extinction risk, with higher-trophic species more prone to persistent low-population states. Additionally, we explore the role of trophic short-circuits -- direct interactions between apex predators and low-trophic prey -- and find that they can buffer cascades and alter extinction patterns in ways that are often overlooked in classical models. By simplifying population dynamics to a two-state system, this framework provides a powerful tool for disentangling the structural drivers of community stability. These results highlight the potential of coarse-grained approaches to complement existing models, offering new insights into trophic interactions, extinction risks, and the susceptibility of species to trophic cascades.
Optimal Control of Lantana camara: An Entropy-Based Sustainable Strategy
Authors: Shyam Kumar, Preet Mishra, R K Brojen Singh
Date: 2025-02-09 00:00:00

Framing control policies to mitigate the impact of invasive plants on indigenous biodiversity within the Sustainable Development Goals (SDG) framework is the primary objective of this work. Using reported ecological dynamics of the invasive species \textit{Lantana camara}, we develop a minimal three-species network model, where each node follows generalized Lotka-Volterra (GLV) dynamical equations. Employing Lie algebra and network control theory, we establish the model's controllability and accessibility criteria. Through nonlinear optimization programming, we derive sustainable policies for controlling abundances of \textit{Lantana camara}. We also have used Shannon entropy as an indicator to assess the sustainability of these optimal policies. The analysis of the sensitivity measured using this technique reveals that the control strategy is critically dependent on the ratio of the intrinsic growth rates of the \textit{Lantana camara} and the control plant. Thus, we get a modular algorithmic decision support mechanism for designing control policies to manage \textit{Lantana camara} abundances.
Beyond Cortisol! Physiological Indicators of Welfare for Dogs: Deficits, Misunderstandings and Opportunities
Authors: ML Cobb, AG Jimenez, NA Dreschel
Date: 2025-02-17 00:00:00
Tasks: Heart Rate Variability

This paper aims to initiate new conversations about the use of physiological indicators when assessing the welfare of dogs. There are significant concerns about construct validity - whether the measures used accurately reflect welfare. The goal is to provide recommendations for future inquiry and encourage debate. We acknowledge that the scientific understanding of animal welfare has evolved and bring attention to the shortcomings of commonly used biomarkers like cortisol. These indicators are frequently used in isolation and with limited salient dog descriptors, so fail to reflect the canine experience adequately. Using a systems approach, we explore various physiological systems and alternative indicators, such as heart rate variability and oxidative stress, to address this limitation. It is essential to consider factors like age, body weight, breed, and sex when interpreting these biomarkers correctly, and researchers should report on these in their studies. This discussion identifies possible indicators for both positive and negative experiences. In conclusion, we advocate for a practical, evidence-based approach to assessing indicators of canine welfare, including non-invasive collection methods. We acknowledge the complexity of evaluating experiential responses in dogs across different situations and the need for continued work to improve practices and refine terminology. This will enhance our ability to accurately understand welfare and improve the wellbeing of dogs, serving to inform standards of animal welfare assessment. We hope this will promote more fundamental research in canine physiology to improve construct validity, leading to better practices, ultimately improving the lives of dogs.
Towards Precision Oncology: Predicting Mortality and Relapse-Free Survival in Head and Neck Cancer Using Clinical Data
Authors: Naman Dhariwal, Abeyankar Giridharan
Date: 2025-02-16 00:00:00

Head and neck squamous cell carcinoma (HNSCC) presents significant challenges in clinical oncology due to its heterogeneity and high mortality rates. This study aims to leverage clinical data and machine learning (ML) principles to predict key outcomes for HNSCC patients: mortality, and relapse-free survival. Utilizing data sourced from the Cancer Imaging Archive, an extensive pipeline was implemented to ensure robust model training and evaluation. Ensemble and individual classifiers, including XGBoost, Random Forest, and Support Vectors, were employed to develop predictive models. The study identified key clinical features influencing HNSCC mortality outcomes and achieved predictive accuracy and ROC-AUC values exceeding 90\% across tasks. Support Vector Machine strongly excelled in relapse-free survival, with an recall value of 0.99 and precision of 0.97. Key clinical features including loco-regional control, smoking and treatment type, were identified as critical predictors of patient outcomes. This study underscores the medical impact of using ML-driven insights to refine prognostic accuracy and optimize personalized treatment strategies in HNSCC.
Cognitive Neural Architecture Search Reveals Hierarchical Entailment
Authors: Lukas Kuhn, Sari Saba-Sadiya, Gemma Roig
Date: 2025-02-16 00:00:00
Tasks: Neural Architecture Search

Recent research has suggested that the brain is more shallow than previously thought, challenging the traditionally assumed hierarchical structure of the ventral visual pathway. Here, we demonstrate that optimizing convolutional network architectures for brain-alignment via evolutionary neural architecture search results in models with clear representational hierarchies. Despite having random weights, the identified models achieve brain-alignment scores surpassing even those of pretrained classification models - as measured by both regression and representational similarity analysis. Furthermore, through traditional supervised training, architectures optimized for alignment with late ventral regions become competitive classification models. These findings suggest that hierarchical structure is a fundamental mechanism of primate visual processing. Finally, this work demonstrates the potential of neural architecture search as a framework for computational cognitive neuroscience research that could reduce the field's reliance on manually designed convolutional networks.
Implicit Neural Representations of Molecular Vector-Valued Functions
Authors: Jirka Lhotka, Daniel Probst
Date: 2025-02-15 00:00:00
Tasks: Decoder

Molecules have various computational representations, including numerical descriptors, strings, graphs, point clouds, and surfaces. Each representation method enables the application of various machine learning methodologies from linear regression to graph neural networks paired with large language models. To complement existing representations, we introduce the representation of molecules through vector-valued functions, or $n$-dimensional vector fields, that are parameterized by neural networks, which we denote molecular neural fields. Unlike surface representations, molecular neural fields capture external features and the hydrophobic core of macromolecules such as proteins. Compared to discrete graph or point representations, molecular neural fields are compact, resolution independent and inherently suited for interpolation in spatial and temporal dimensions. These properties inherited by molecular neural fields lend themselves to tasks including the generation of molecules based on their desired shape, structure, and composition, and the resolution-independent interpolation between molecular conformations in space and time. Here, we provide a framework and proofs-of-concept for molecular neural fields, namely, the parametrization and superresolution reconstruction of a protein-ligand complex using an auto-decoder architecture and the embedding of molecular volumes in latent space using an auto-encoder architecture.