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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2025

The cognitive and neural bases of creative thought: a cross-domain meta-analysis of transcranial direct current stimulation studies

Creative thought enables humans to flexibly generate, evaluate and select novel and adaptive ideas according to different contexts. Decades of creativity research indicates that it involves at least two aspects: retrieval of previously acquired knowledge and manipulation of that knowledge. However, the cognitive processes underpinning these two aspects of creative thought remain underspecified. The broader clinical-cognitive neuroscience literature suggests that retrieval and manipulation of knowledge is underpinned by general purpose cognitive mechanisms supporting semantic cognition, controlled episodic memory retrieval, and executive mechanisms. To identify commonalities from converging evidence that points towards a unifying theory for the neurocognitive bases of creative thought, we reviewed and meta-analysed 152 studies from creativity and the relevant parallel cognitive neuroscience literature using transcranial direct current stimulation (tDCS). The results revealed three things: 1) current tDCS studies are heavily biased towards the frontal cortex (459/591 effect sizes; 77.7%); 2) only anodal tDCS over the left lateral frontal cortex promotes creativity (p <.01); and 3) anodal tDCS stimulation over the same region also promotes improvement in many other cognitive processes. The latter includes more efficient processing of semantic knowledge (p <.05), more accurate episodic memory retrieval (p <.05), better and more efficient manipulation of buffered knowledge (all p <.001), and more efficient response selection amongst competing options (i.e., task-setting; p <.01). By merging these previously separate literatures, tDCS studies support the notion that creative thought arises from general purpose cognitive mechanisms including controlled retrieval and temporary storage of semantic and episodic information, as well as executive mechanisms.

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bioRxiv2025

MEEGNet: An open source python library for the application of convolutional neural networks to MEG

Artificial Neural Networks (ANNs) are rapidly gaining traction in neuroscience, proving invaluable for decoding and modeling brain signals from techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Although these networks are beginning to find applications in magnetoencephalography (MEG), their use in this domain is still in the early stages. Here, we introduces MEEGNet, a novel Python library paired with an intuitive convolutional neural network (CNN) architecture designed primarily for MEG data, yet adaptable to EEG signals. The MEEGNet model was trained and cross-validated using MEG data from 643 participants across four classification tasks, including auditory and visual stimulus classification and age prediction. Our model achieves competitive performance across all tasks, with a notable balance of accuracy and efficiency--for instance, reaching 92.70% test accuracy in an auditory vs. visual classification task while maintaining shorter training times than other architectures. The MEEGNet pipeline also integrates latent space visualization tools, adapted for MEG and EEG data. These include saliency maps and Grad-CAM methods, which enhance the interpretability of ANN-based classification and help address the black-box critique of such models. Importantly, the MEEGNet library is designed for extensibility, allowing the neuroscience and machine learning communities to add functionalities and ANN models. By prioritizing usability, transparency, and interpretability, MEEGNet empowers MEG- and EEG-based research with a user-friendly and modular framework.

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bioRxiv2025

Trial-Level Representational Similarity Analysis

Neural representation refers to the brain activity that stands in for ones cognitive experience, and in cognitive neuroscience, a prominent method of studying neural representations is representational similarity analysis (RSA). While there are several recent advances in RSA, the classic RSA (cRSA) approach examines the structure of representations across numerous items by assessing the correspondence between two representational similarity matrices (RSMs): usually one based on a theoretical model of stimulus similarity and the other based on similarity in measured neural data. However, because cRSA cannot weigh the contributions of individual trials (RSM rows/columns), it is fundamentally limited in its ability to assess subject-, stimulus-, and trial-level variances that all influence representation. Here, we formally introduce trial-level RSA (tRSA), an analytical framework that estimates the strength of neural representation for singular experimental trials and evaluates hypotheses using multi-level models. First, we verified the correspondence between tRSA and cRSA in quantifying the overall representation strength across all trials. Second, we compared the statistical inferences drawn from both approaches using simulated data that reflected a wide range of scenarios. Compared to cRSA, the multi-level framework of tRSA was both more theoretically appropriate and significantly sensitive to true effects. Third, using real fMRI datasets, we further demonstrated several issues with cRSA, to which tRSA was more robust. Finally, we presented some novel findings of neural representations that could only be assessed with tRSA and not cRSA. In summary, tRSA proves to be a robust and versatile analytical approach for cognitive neuroscience and beyond.

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bioRxiv2025

Wavelet analysis of dual-fMRI-hyperscanning reveals cooperation and communication dependent effects on inter-brain neuronal coherence.

Hyperscanning has allowed neuroscience to expand investigations into neuronal activation during social interactions. Rather than analyzing how a single brain responds, we can compare interactions and even synchronization between multiple actors in varying situations. This technique is commonly employed using functional near infrared spectroscopy (fNIRS). Specifically, social cooperation and competition have been thoroughly investigated using this approach. While functional magnetic resonance imaging (fMRI)-based hyperscanning is becoming more prevalent, a link to this fNIRS-based foundation is missing. We here use a dual-fMRI-hyperscanning setup and an established task to investigate neuronal coherence during social cooperative and competitive tasks. Wavelet transform coherence (WTC) allows us to explore task-specific frequency bands of interest of non-stationary neuronal activation signals of paired participants (n=60). We show that cooperation, compared to a control task, increases inter-brain neuronal coherence in regions associated with social interaction and the theory of mind (ToM) network. Verbal communication prior to the task expands this coherence to different regions of this network, including middle and superior temporal gyrus. This spatial shift suggests additional implementations of the ToM network depending on the cooperation approach taken by the participants. Our findings both support and expand on results by previous fNIRS-based studies and show that WTC is an effective way to model fMRI-based neuronal synchronization; thereby closing the gap between two popular hyperscanning methodologies. Significance StatementWithin social neuroscience a strong basis exists for fNIRS-based hyperscanning. In the past years fMRI-based hyperscanning has increased in popularity, yet the basis for these projects seems to develop independent of the existing background. The current study aims to connect these previous findings to the possibilities offered by fMRI, allowing both fields to benefit from the others advantages when determining optimal research paradigms or analyses.

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bioRxiv2025

A language network in the individualized functional connectomes of over 1,000 human brains doing arbitrary tasks

A century and a half of neuroscience has yielded many divergent theories of the neurobiology of language. Two factors that likely contribute to this situation include (a) conceptual disagreement about language and its component processes, and (b) intrinsic inter-individual variability in the topography of language areas. Recent functional magnetic resonance imaging (fMRI) studies of small numbers of intensively scanned individuals have argued that a language-selective brain network emerges bottom-up from correlations (individualized functional connectomics, iFC) in task-free (e.g., rest) or task-regressed activation timecourses. Here we test this hypothesis at scale and evaluate its practical utility for task-agnostic language localization: we apply iFC separately to each of 1,957 (fMRI) scanning sessions (1,199 unique brains), each consisting of diverse tasks. We find that iFC indeed reveals a largely left-hemisphere-dominant frontotemporal network that is more stable within individuals than between them, robust to the granularity of the parcellation, and selective for language. These results support the hypothesis that this network is a key structure in the functional organization of the adult brain and show that it can be recovered retrospectively from arbitrary imaging data, with implications for neuroscience, neurosurgery, and neural engineering.

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bioRxiv2025

Neural Networks and Foundation Models: Two Strategies for EEG-to-fMRI Prediction

Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two widely used neuroimaging techniques, with complementary strengths and weaknesses. Predicting fMRI activity from EEG activity could give us the best of both worlds, and open new horizons for neuroscience research and neurotechnology applications. Here, we formulate this prediction objective both as a classification task (predicting whether the fMRI signal increases or de- creases) and a regression task (predicting the value of this signal). We follow two distinct strategies: training classical machine learning and deep learning mod- els (including MLP, CNN, RNN, and transformer) on an EEG-fMRI dataset, or leveraging the capabilities of pre-trained large language models (LLMs) and large multimodal models. We show that predicting fMRI activity from EEG activity is possible for the brain regions defined by the Harvard-Oxford cortical atlas, in the context of subjects performing a neurofeedback task. Interestingly, both strategies yield promising results, possibly highlighting two complementary paths for our prediction objective. Furthermore, a Chain-of-Thought approach demonstrates that LLMs can infer the cognitive functions associated with EEG data, and subsequently predict the fMRI data from these cognitive functions. The natural combination of the two strategies, i.e., fine-tuning an LLM on an EEG-fMRI dataset, is not straightforward and would certainly require further study. These findings could provide important insights for enhancing neural interfaces and advancing toward a multimodal foundation model for neuroscience, integrating EEG, fMRI, and possibly other neuroimaging modalities.

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bioRxiv2025

Data Heterogeneity Limits the Scaling Effect of Pretraining Neural Data Transformers

A key challenge in analyzing neuroscience datasets is the profound variability they exhibit across sessions, animals, and data modalities-i.e., heterogeneity. Several recent studies have demonstrated performance gains from pretraining neural foundation models on multi-session datasets, seemingly overcoming this challenge. However, these studies typically lack finegrained data scaling analyses. It remains unclear how different sources of heterogeneity influence model performance as the amount of pretraining data increases, and whether all sessions contribute equally to downstream performance gains. In this work, we systematically investigate how data heterogeneity impacts the scaling behavior of neural data transformers (NDTs) in neural activity prediction. We found that explicit sources of heterogeneity, such as brain region mismatches among sessions, reduced scaling benefits of neuron-level and region-level activity prediction performances. For tasks that do exhibit consistent scaling, we identified implicit data heterogeneity arising from cross-session variability. Through our proposed session-selection procedure, models pretrained on as few as five selected sessions outperformed those pretrained on the entire dataset of 84 sessions. Our findings challenge the direct applicability of traditional scaling laws to neural data and suggest that prior reports of multi-session scaling benefits may need to be re-examined in the light of data heterogeneity. This work both highlights the importance of incremental data scaling analyses and suggests new avenues toward optimally selecting pretraining data when developing foundation models on large-scale neuroscience datasets.

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bioRxiv2025

Machine Learning Matches Human Performance at Segmenting the Human Visual Cortex

A major problem in human visual neuroscience research is the localization of visual areas on the cortical surface. Currently available methods are capable of making detailed predictions about many areas, but human raters do not agree as well with these methods as they do with each other. Although highly accurate, human raters require substantial time and expertise that many researchers do not have. Additionally, human raters require functional data for drawing visual area boundaries that requires additional scan time, budget, and expertise to collect. Here, we train convolutional neural network (CNN) models to predict the boundaries and iso-eccentric regions of V1, V2, and V3 in both the Human Connectome Project dataset and the NYU Retinotopy dataset. CNNs trained to use the same functional data available to human raters predicted these boundaries with an accuracy similar to humans, while CNNs trained to use only anatomical data had a lower accuracy that was nonetheless higher than that of any currently available method. A comparison of the model accuracies when predicting eccentricity-based boundaries and polar angle-based boundaries suggests that eccentricity is substantially less closely tied to anatomical structure than polar angle and that the cortical magnification function, at least in terms of eccentricity, varies substantially between subjects. We further find that the fraction of V1, V2, and V3 that can be accurately parcellated into function regions using gray matter structural data alone is [~]75% ([~]80% of the inter-rater reliability of human experts), implying a much tighter coupling between structure and function in these areas than previously estimated. We conclude that machine learning techniques such as CNNs provide a powerful tool for mapping the brain with human accuracy and predict that such tools will become integral to neuroscience research going forward.

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bioRxiv2025

Localization of Realistic Spatial Patches of Complex Source Activity in MEG

Accurate localization of neural sources in Magnetoencephalography (MEG) and Electroencephalography (EEG) is essential for advancing clinical and research applications in neuroscience. Traditional approaches like dipole fitting (e.g., MUSIC, RAP-MUSIC) are limited to discrete focal sources, while distributed source imaging methods (e.g., MNE, sLORETA) assume sources distributed across the cortical surface. These methods, however, often fail to capture sources with complex spatial extents, limiting their accuracy in realistic settings. To address these limitations, we introduce PATCH-AP, an enhanced version of the Alternating Projection (AP) method that effectively localizes both discrete and spatially extended sources. We evaluated PATCH-AP against leading source localization methods, including distributed source imaging techniques (MNE, sLORETA), traditional dipole fitting (AP), and recent extended source methods (Convexity-Champagne (CC), FLEX-AP). PATCH-AP consistently outperformed these methods in simulations, achieving lower Earth Movers Distance (EMD) scores--a metric indicating closer alignment with the true source distribution. In tests with real MEG data from a face perception task, PATCH-AP demonstrated high alignment with the fusiform face area, a region critical for face processing. These results highlight PATCH-APs potential to enhance source localization accuracy, promising significant advancements in neuroscience research and clinical diagnostics.

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bioRxiv2025

Functional Brain Imaging and Targeted Lesion Studies Using Manganese-Enhanced MRI and Focused Ultrasound in Non-Conventional ModelSpecies

Linking behavior to its neuroanatomical basis in non-conventional model species remains a significant challenge due to the scarcity of imaging and molecular tools. Commonly used approaches such as electrophysiological recordings rely on precise stereotaxic atlases or species-specific antibodies, while optogenetics requires transgenic lines which are largely unavailable beyond classical model organisms (e.g., mice, rats, zebrafish). Moreover, surgical lesion studies, a staple for verifying brain structure and behavior relationships, are logistically complex in species lacking atlases or living in aquatic environments. Here, we present a protocol integrating Manganese-Enhanced Magnetic Resonance Imaging (MEMRI) and MR-guided High-Intensity Focused Ultrasound (HIFU) to overcome these limitations, which we demonstrate in the convict cichlid (Amatitlania nigrofasciata), a teleost fish lacking conventional neuroscience tools. MEMRI enables non-invasive, sub-millimeter resolution mapping of brain activity during behavior, and HIFU facilitates precise, surgery-free lesioning of targeted regions, adaptable to species without stereotaxic atlases. This combined approach offers a versatile, broadly applicable framework for linking brain structure and behavior in non-model organisms, advancing evolutionary and comparative neuroscience.

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bioRxiv2025

Scanning for Representation: A Scoping Review of Racial and Ethnic Diversity in MRI Studies of the Maternal Brain

A growing number of magnetic resonance imaging (MRI) studies are examining brain changes across pregnancy and early motherhood, gaining fundamental insight into the neural adaptations of motherhood, with critical clinical and policy implications for supporting mother, child, and family unit. As the field takes off, now is the time to take stock of the current literature and neuroscience practices, to ensure that the field is based on studies that are robust, representative, and transparent. Here, we conducted a scoping review to understand the racial and ethnic diversity of participants reported in MRI studies of the maternal brain, guided by the Joanna Briggs Institute methodology. Our findings highlight three key issues in the 185 identified studies of the maternal brain using MRI: (1) the widespread underreporting of participant racial and ethnic data, with only 38.38% of studies reporting race and/or ethnicity demographics; (2) the overrepresentation of white participants, with 46.83% of the samples that report race and/or ethnicity identifying as white/Caucasian; and (3) the disproportionate geographical locations of studies, with 68.65% of studies from North America or Europe and Central Asia. These findings raise concerns about the generalizability of existing research beyond WEIRD (western, educated, industrialized, rich and democratic) populations, and underscore the urgent need for concerted structural change in neuroscience research practices. While identifying a lack of diversity is only the first step, this scoping review serves as a call to action for greater representation in future research, for our own research group as well as others.

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bioRxiv2025

MoMo - Combining Neuron Morphology and Connectivity forInteractive Motif Analysis in Connectomes

Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex spatial maps consist of tree-like neurons interconnected by synapses. Motif analysis is a widely used method for identifying recurring subgraph patterns in connectomes. These motifs, thus, potentially represent fundamental units of information processing. However, existing computational tools often oversimplify neurons as mere nodes in a graph, disregarding their intricate morphologies. In this paper, we introduce MoMo, a novel interactive visualization framework for analyzing neuron morphology-aware motifs in large connectome graphs. First, we propose an advanced graph data structure that integrates both neuronal morphology and synaptic connectivity. This enables highly efficient, parallel subgraph isomorphism searches, allowing for interactive morphological motif queries. Second, we develop a sketch-based interface that facilitates the intuitive exploration of morphology-based motifs within our new data structure. Users can conduct interactive motif searches on state-of-the-art connectomes and visualize results as interactive 3D renderings. We present a detailed goal and task analysis for motif exploration in connectomes, incorporating neuron morphology. Finally, we evaluate MoMo through case studies with four domain experts, who asses the tools usefulness and effectiveness in motif exploration, and relevance to real-world neuroscience research. The source code for MoMo is available here.

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bioRxiv2025

Developmental, time-of-day, and stimulus-specific Golgi-Cox staining patterns detected in the mouse brain

Well-coordinated brain activity is a crucial driver of bodily functions and is refined by environmental input. Understanding the structure of brain areas that regulate various functions and how the environment affects the neural responses in the brain has been fundamental in advancing the field of neuroscience and medicine. The Golgi-Cox method is a histological approach that has allowed researchers to view neuronal structures with unmatched and detailed resolution, allowing for the comparison between non-diseased and diseased models, for instance. However, this method is known to stain neurons sparsely, which is useful for distinguishing structural components, but unpredictably, which is difficult for reproducibility and targeted studies. Here, we use three approaches to demonstrate a predictable pattern of cell staining using the Golgi-Cox method. We show that neuronal maturity, time of day, and response to environmental stimuli affect the number of cells stained by the Golgi-Cox method. Furthermore, we found low variability within each experimental group, which indicates staining reproducibility under controlled environments. Our study highlights important parameters for using the Golgi-Cox method and demonstrates its feasibility for broader application in answering neuroscience-based questions. SummaryOur study provides previously unknown insights demonstrating that the historical Golgi-Cox staining pattern of neurons is specifically linked to age, time-of-day, and light-responsiveness at night in the mouse brain.

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bioRxiv2025

Rethinking Kalman Filters for Motor Brain-Machine Interface: The Fundamental Limitations and A Perspective Shift

The Kalman filter has been introduced to the motor brain-machine interface (BMI) field for over 20 years and remains one of the most widely used models due to its simple and intuitive nature. However, this paper demonstrates that the application of Kalman filters in the BMI field violates the models own assumptions and numerous neuroscience principles, resulting in six critical limitations: (1) the observation matrix mapping from kinematics to neural activity causes individual neurons to be modeled independently, ignoring neural population activity; aligned kinematic and neural activity sequences fail to capture preparatory information; (3) the presence of behaviorally irrelevant neural activity causes observation weights to be excessively underestimated; (4) noise in the observation matrix renders posterior covariance estimates biased; (5) Kalman gain computation in observation space leads to repeated noise accumulation and (6) computational inefficiency. All these problems can be resolved through a simple perspective shift: separating the decoder from the Kalman filter and treating its predicted kinematics as the Kalman filters observation rather than neural activity. Experiments conducted on CRCNS-recorded monkey dorsal premotor area and primary motor signals performing computer cursor control tasks validate the existence of the six limitations and their violations of neuroscience principles, while demonstrating the superiority of the improved Kalman filter across all evaluated metrics.

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bioRxiv2025

Simulated Language Acquisition in a Biologically Realistic Model of the Brain

Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. NO_SCPLOWEMOC_SCPLOW is a simple mathematical formulation of six basic and broadly accepted principles of neuroscience: excitatory neurons, brain areas, random synapses, Hebbian plasticity, local inhibition, and inter-area inhibition. We implement with NO_SCPLOWEMOC_SCPLOW a simulated neuromorphic system, which is capable of basic language acquisition: starting from a tabula rasa, the system learns, in any language, the semantics of words, their syntactic role (verb versus noun), the word order of the language, and the ability to generate novel sentences, all through the exposure to a number of grounded sentences in the same language that is similar to language acquisition by humans. We discuss several possible extensions and implications of this result.

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bioRxiv2025

NeuroSuite for Long-term Functional and Structural Studies of Air-Liquid Interface Cerebral Organoids

Over the past decade, air-liquid interface cerebral organoids (ALI-COs) have emerged as powerful in vitro models that capture essential structural and functional traits of the human brain, offering an exciting alternative to traditional animal models in neuroscience. Yet, the full potential of these systems has remained untapped due to the lack of non-invasive, long-term electrophysiological tools capable of preserving organoid integrity. Existing techniques, ranging from patch clamping to rigid and 3D microelectrode arrays, often compromise organoid growth and disrupt delicate cytoarchitecture. Here, we present NeuroSuite, an innovative bioelectronic platform designed to overcome these challenges. At its core is Neuroweb, a perforated, ultra-thin, and conformable organic microelectrode array engineered for minimal disruption of nutrient and oxygen exchange. Neuroweb is reusable and supports stable recordings for over six months, making it uniquely suited for longitudinal studies. Coated with poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), a high-performance mixed ionic-electronic conductor, Neuroweb delivers exceptional signal-to-noise ratio recordings with high spatial precision. By pairing Neuroweb with NeuroMaps, an intuitive software for interactive analysis and visualisation, NeuroSuite enables long-term, non-invasive tracking and spatial mapping of electrical activity from brain organoids and ex vivo brain slices at the air-liquid interface. Following rigorous validation, we demonstrate that NeuroSuite can capture both high- and low-frequency throughout maturation. Our pipeline reveals evolving network connectivity, including the development of GABA-ergic interneurons, and concurrent shifts in high-frequency spiking and low-frequency oscillations indicative of a refinement in the excitatory-inhibitory balance. Finally, automated data acquisition and spatial spike mapping highlight local activity changes in response to media composition, a factor often overlooked in conventional recordings. NeuroSuite thus opens a new frontier in organoid neuroscience, enabling precise, long-term monitoring essential for modelling neurological diseases, understanding human brain development, and accelerating drug discovery. TeaserConformal organic bioelectronic arrays, combined with an open-access toolbox for analysis and visualisation, reveal real-time and long-term neural dynamics in brain organoid slices at the air-liquid interface.

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bioRxiv2025

Structural Eigenmodes of the Brain to Improve the Source Localisation of EEG: Application to Epileptiform Activity

A fundamental view of neuroscience is that, in addition to neuronal activity, the structure of the brain constrains and explains brain function. An alluring formalism in computational neuroscience has been the generation of structural eigenmodes of neural activity from a matrix representing the anatomy of the brain. Traditionally, brain connectomics has been the gold standard for the coupling between structure and function. However, it has recently been suggested that simpler brain geometry can provide more explanatory power in fMRI. An adjacent modality is the source localisation problem of EEG, which aims to identify the underlying generators of EEG recordings. The underdetermined nature of the problem requires sufficient constraints to produce realistic and unique solutions of source activity. In this work, we presented a simple framework for incorporating different forms of structural brain eigenmodes to constrain the source localisation problem in epilepsy. We found that geometric eigenmodes were able to reconstruct the spread of a seizure through the brain slightly better than connectome eigenmodes, and both types of structural modes significantly outperformed commonly used approaches.

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bioRxiv2025

Generalized brain-state modeling with KenazLBM

The large-scale functional state of a human brain remains difficult to characterize, much less predict. Regardless, techniques have been engineered to electrically neuromodulate the brain to treat a subset of neurologic and psychiatric disorders with moderate efficacy. Accurate characterization of a brains instantaneous functional state has stymied the development of more effective neuromodulation paradigms. Advanced computational methods are required to address this gap and enable large-scale neuroscience. Here we define the concept of generalized brain-state modeling across humans as Large Brain-State Modeling (LBM) and present KenazLBM as the worlds first example. KenazLBM can instantaneously characterize the functional state of a persons brain with raw iEEG data, and predict future brain-states. KenazLBM was trained on over 17.9 billion unique multichannel tokens from people undergoing intracranial electroencephalography (iEEG) recordings, and has learned to interrelate brain-states between people into a common interpretable topology. Most importantly, the model generalizes to unseen subject data with significant recording channel heterogeneity from the training set. We offer KenazLBM as a first generalized brain-state model to serve as a new paradigm of basic neuroscience inquiry and potential translation into neuromodulation therapeutics.

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