Neuroscience Community Research Articles

Against "silent" retractions in neuroscience.

When an academic paper is published in a journal that assigns a digital object identifier (DOI) to papers, this is a de facto fait accompli. Corrections or retractions are supposed to follow a specific protocol, especially in journals that claim to follow the Committee on Publication Ethics (COPE) guidelines. In this paper, we highlight a case of a new, fully open access neuroscience journal that claims to be COPE-compliant, yet has silently retracted two papers since all records, bibliometrics, and PDF files related to their existence have been deleted from the journal's website. Although this phenomenon does not seem to be common in the neurosciences, we consider that any opaque corrective measures in journals whose papers could be cited may negatively impact the wider neuroscience literature and community. Instead, we encourage transparency in retraction to promote truthfulness and trustworthiness.

AfrAbia's silent struggle: Pesticides and Parkinson's disease unveiled

AfrAbia consists of members from both the Arab League and the African Union. The historical and geographical effects connect the neighbouring regions of Africa and the Arab world on a cross-cultural level. AfrAbia experiences a greater prevalence of autosomal recessive neurodegenerative disorders because of elevated rates of consanguinity. The AfrAbian neuroscience community has unique problems due to the widespread occurrence of mental, neurological, and substance-use disorders. The limited physician-to-patient ratio can lead to heavy workloads per clinician, fostering expertise in their field due to increased practice opportunities arising from the demanding workload. The heightened clinical exposure leads to the emergence of study topics relevant to the local context. It improves the feasibility of clinical research, benefiting both the African population and the Global North. To summarise, there are abundant prospects for clinically focused neuroscience research to enhance healthcare in AfrAbia.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Growing Confidence and Remaining Uncertainty About Animal Consciousness

While the study of consciousness has had a controversial history and, until recently, a pessimistic prognosis, recent views within the neuroscientific community suggest that a maturing if incomplete scientific understanding of consciousness is close at hand. The purpose of this essay is to specify the major points on which in my opinion there is widespread though not universal agreement, as proposed benchmarks for the current state of consciousness research across the phylogenetic spectrum. Most published definitions of consciousness boil down to a focus on it as a process arising in a nervous system engaged with a body and its environment, giving rise to subjective (personal) experience. A broad consensus on the phenomenology of consciousness sees it consisting at a minimum of (1) awarenessand focused attention, (2) unity of perception, (3) qualitative variations in content, (4) mental causation, and (5) a sense of self. There is also broad agreement that the substrate of consciousness requires sizeable, complex nervous systems organized into several hierarchical levels of processing. Further insight is gained by reconstructing the evolution of subjective phenomenological experience ― most likely from multiple origins, hosted by a diversity of body and brain architectures, and diverging into markedly different forms across the animal kingdom. However, three major mysteries about phenomenological experience that remain unresolved are (1) the neurological correlates of consciousness, (2) the apparent gap between phenomenology and mechanism, and (3) the process that monitors the brain activity admitted into consciousness.

A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites.

Investigating and modelling the functionality of human neurons remains challenging due to the technical limitations, resulting in scarce and incomplete 3D anatomical reconstructions. Here we used a morphological modelling approach based on optimal wiring to repair the parts of a dendritic morphology that were lost due to incomplete tissue samples. In Drosophila, where dendritic regrowth has been studied experimentally using laser ablation, we found that modelling the regrowth reproduced a bimodal distribution between regeneration of cut branches and invasion by neighbouring branches. Interestingly, our repair model followed growth rules similar to those for the generation of a new dendritic tree. To generalise the repair algorithm from Drosophila to mammalian neurons, we artificially sectioned reconstructed dendrites from mouse and human hippocampal pyramidal cell morphologies, and showed that the regrown dendrites were morphologically similar to the original ones. Furthermore, we were able to restore their electrophysiological functionality, as evidenced by the recovery of their firing behaviour. Importantly, we show that such repairs also apply to other neuron types including hippocampal granule cells and cerebellar Purkinje cells. We then extrapolated the repair to incomplete human CA1 pyramidal neurons, where the anatomical boundaries of the particular brain areas innervated by the neurons in question were known. Interestingly, the repair of incomplete human dendrites helped to simulate the recently observed increased synaptic thresholds for dendritic NMDA spikes in human versus mouse dendrites. To make the repair tool available to the neuroscience community, we have developed an intuitive and simple graphical user interface (GUI), which is available in the TREES toolbox (www.treestoolbox.org).

Adaptation of Magnified Analysis of the Proteome for Excitatory Synaptic Proteins in Varied Samples and Evaluation of Cell Type-Specific Distributions.

Growing evidence suggests a remarkable diversity and complexity in the molecular composition of synapses, forming the basis for the brain to execute complex behaviors. Hence, there is considerable interest in visualizing the spatial distribution of such molecular diversity at individual synapses within intact brain circuits. Yet this task presents significant technical challenges. Expansion microscopy approaches have revolutionized our view of molecular anatomy. However, their use to study synapse-related questions outside of the labs developing them has been limited. Here we independently adapted a version of Magnified Analysis of the Proteome (MAP) and present a step-by-step protocol for visualizing over 40 synaptic proteins in brain circuits. Surprisingly, our findings show that the advantage of MAP over conventional immunolabeling was primarily due to improved antigen recognition and secondarily physical expansion. Furthermore, we demonstrated the versatile use of MAP in brains perfused with paraformaldehyde or fresh-fixed with formalin and in formalin-fixed paraffin-embedded tissue. These tests expand the potential applications of MAP to combinations with slice electrophysiology or clinical pathology specimens. Using male and female mice expressing YFP-ChR2 exclusively in interneurons, we revealed a distinct composition of AMPA and NMDA receptors and Shank family members at synapses on hippocampal interneurons versus on pyramidal neurons. Quantitative single synapse analyses yielded comprehensive cell type distributions of synaptic proteins and their relationships. These findings exemplify the value of the versatile adapted MAP procedure presented here as an accessible tool for the broad neuroscience community to unravel the complexity of the "synaptome" across brain circuits and disease states.

Proust and involuntary retrieval.

Proust was undoubtedly a pioneer in exploring cognitive processes engaged in memory. The analysis of the episode of the madeleine, as well as the study of Proust's statements on the goals of his work, clearly reveal the visionary side of this author. Long before several concepts entered into mainstream scientific thought, Proust proposed, among other things, that recall was a reconstruction, that a sensory cue could provoke a memory recall, and that we should distinguish between voluntary and involuntary memory. Through numerous episodes of "involuntary reminiscence" scattered throughout his work, Proust illustrates a particular form of autobiographic memory recall: a recall that does not involve consciousness and whose starting point is an emotion provoked by a specific cue. This recall, which leads, according to Proust, to a more intense revival of the memory than voluntary recall, has only reached prominence in cognitive science more than 80 years later. Additionaly, Proust underlined the determinant role that emotion may have in this particular form of recall. On the other hand, studies on animals have shown that the presentation of a retrieval cue could induce emotional reactions followed by a facilitation of the memory retrieval associated with the cue. The existence of these data, which support Proust's proposals, should encourage the neuroscience community to further explore, in humans and animals, this form of cue elicited emotion that initiated involuntary recall of autobiographical memory.

Global Versus Local Theories of Consciousness and the Consciousness Assessment Issue in Brain Organoids

Any attempt at consciousness assessment in organoids requires careful consideration of the theory of consciousness that researchers will rely on when performing this task. In cognitive neuroscience and the clinic, there are tools and theories used to detect and measure consciousness, typically in human beings, but none of them is neither fully consensual nor fit for the biological characteristics of organoids. I discuss the existing attempt relying on the Integrated Information Theory and its models and tools. Then, I revive the distinction between global theories of consciousness and local theories of consciousness as a thought-provoking one for those engaged in the difficult task of adapting models of consciousness to the biological reality of brain organoids. The “microconsciousness theory” of Semir Zeki is taken as an exploratory path and illustration of a theory defending that minimal networks can support a form of consciousness. I suggest that the skepticism prevailing in the neuroscience community regarding the possibility of organoid consciousness relies on some assumptions related to a globalist account of consciousness and that other accounts are worth exploring at this stage.

Growing Confidence and Remaining Uncertainty About Animal Consciousness

While the study of consciousness has had a controversial history and, until recently, a pessimistic prognosis, recent views within the neuroscientific community suggest that a maturing if incomplete scientific understanding of consciousness is close at hand. Most published definitions of consciousness boil down to a focus on it as a process arising in a nervous system engaged with a body and its environment, giving rise to subjective (personal) experience. A broad consensus on the phenomenology of consciousness sees it consisting at a minimum of (1) awarenessand focused attention, (2) unity of perception, (3) qualitative variations in content, (4) mental causation, and (5) a sense of self. There is also broad agreement that the substrate of consciousness requires sizeable, complex nervous systems organized into several hierarchical levels of processing. Further insight is gained by reconstructing the evolution of subjective phenomenological experience ― most likely from multiple origins, hosted by a diversity of body and brain architectures, and diverging into markedly different forms across the animal kingdom. The three major mysteries that remain about phenomenological experience are (1) the neurological correlates of consciousness, (2) the apparent gap between phenomenology and mechanism, and (3) the agent that monitors the brain activity admitted into consciousness.

Unpacking unstructured data: A pilot study on extracting insights from neuropathological reports of Parkinson's Disease patients using large language models.

The aim of this study was to make unstructured neuropathological data, located in the NeuroBioBank (NBB), follow Findability, Accessibility, Interoperability, and Reusability principles and investigate the potential of large language models (LLMs) in wrangling unstructured neuropathological reports. By making the currently inconsistent and disparate data findable, our overarching goal was to enhance research output and speed. The NBB catalog currently includes information from medical records, interview results, and neuropathological reports. These reports contain crucial information necessary for conducting an in-depth analysis of NBB data but have multiple formats that vary across different NBB biorepositories and change over time. In this study, we focused on a subset of 822 donors with Parkinson's disease (PD) from seven NBB biorepositories. We developed a data model with combined Brain Region and Pathological Findings data at its core. This approach made it easier to build an extraction pipeline and was flexible enough to convert resulting data to Common Data Elements, a standardized data collection tool used by the neuroscience community to improve consistency and facilitate data sharing across studies. This pilot study demonstrated the potential of LLMs in structuring unstructured neuropathological reports of PD patients available in the NBB. The pipeline enabled successful extraction of detailed tissue-level (microscopic) and gross anatomical (macroscopic) observations, along with staging information from pathology reports, with extraction quality comparable to manual curation results. To our knowledge, this is the first attempt to automatically standardize neuropathological information at this scale. The collected data have the potential to serve as a valuable resource for PD researchers, facilitating integration with clinical information and genetic data (such as genome-wide genotyping and whole-genome sequencing) available through the NBB, thereby enabling a more comprehensive understanding of the disease.

The Role of Progranulin (PGRN) in the Pathogenesis of Glioblastoma Multiforme.

Glioblastoma multiforme (GBM) represents the most common and aggressive malignant form of brain tumour in adults and is characterized by an extremely poor prognosis with dismal survival rates. Currently, expanding concepts concerning the pathophysiology of GBM are inextricably linked with neuroinflammatory phenomena. On account of this fact, the identification of novel pathomechanisms targeting neuroinflammation seems to be crucial in terms of yielding successful individual therapeutic strategies. In recent years, the pleiotropic growth factor progranulin (PGRN) has attracted significant attention in the neuroscience and oncological community regarding its neuroimmunomodulatory and oncogenic functions. This review of the literature summarizes and updates contemporary knowledge about PGRN, its associated receptors and signalling pathway involvement in GBM pathogenesis, indicating possible cellular and molecular mechanisms with potential diagnostic, prognostic and therapeutic targets in order to yield successful individual therapeutic strategies. After a review of the literature, we found that there are possible PGRN-targeted therapeutic approaches for implementation in GBM treatment algorithms both in preclinical and future clinical studies. Furthermore, PGRN-targeted therapies exerted their highest efficacy in combination with other established chemotherapeutic agents, such as temozolomide. The results of the analysis suggested that the possible implementation of routine determinations of PGRN and its associated receptors in tumour tissue and biofluids could serve as a diagnostic and prognostic biomarker of GBM. Furthermore, promising preclinical applications of PGRN-related findings should be investigated in clinical studies in order to create new diagnostic and therapeutic algorithms for GBM treatment.

CAT: a computational anatomy toolbox for the analysis of structural MRI data.

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Open Data governance at the Canadian Open Neuroscience Platform (CONP): From the Walled Garden to the Arboretum.

Scientific research communities pursue dual imperatives in implementing strategies to share their data. These communities attempt to maximize the accessibility of biomedical data for downstream research use, in furtherance of open science objectives. Simultaneously, such communities safeguard the interests of research participants through data stewardship measures and the integration of suitable risk disclosures to the informed consent process. The Canadian Open Neuroscience Platform (CONP) convened an Ethics and Governance Committee composed of experts in bioethics, neuroethics, and law to develop holistic policy tools, organizational approaches, and technological supports to align the open governance of data with ethical and legal norms. The CONP has adopted novel platform governance methods that favor full data openness, legitimated through the use of robust deidentification processes and informed consent practices. The experience of the CONP is articulated as a potential template for other open science efforts to further build upon. This experience highlights informed consent guidance, deidentification practices, ethicolegal metadata, platform-level norms, and commercialization and publication policies as the principal pillars of a practicable approach to the governance of open data. The governance approach adopted by the CONP stands as a viable model for the broader neuroscience and open science communities to adopt for sharing data in full open access.

Learning Motor Cues in Brain-Muscle Modulation.

Current studies for brain-muscle modulation often analyze selected properties in electrophysiological signals, leading to a partial understanding. This article proposes a cross-modal generative model that converts brain activities measured by electroencephalography (EEG) to corresponding muscular responses recorded by electromyography (EMG). Examining the generation process in the model highlights how the motor cue, representing implicit motor information hidden within brain activities, modulates the interaction between brain and muscle systems. The proposed model employs a two-stage generation process to bridge the semantic gap in cross-modal signals. Initially, the shared movement-related information between EEG and EMG signals is extracted using a contrastive learning framework. These shared representations act as conditional vectors in the subsequent EMG generation stage based on generative adversarial networks (GANs). Experiments on a self-collected multimodal electrophysiological signal data set show the algorithm's superiority over existing time series generative methods in cross-modal EMG generation. Further insights derived from the model's inference process underscore the brain's strategy for muscle control during movements. This research provides a data-driven approach for the neuroscience community, offering a comprehensive perspective of brain-muscular modulation.

Bibliometric analysis of neuroscience publications quantifies the impact of data sharing.

Neural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. The rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions. The application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.

Preserving Derivative Information while Transforming Neuronal Curves

The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.

From grouping and cooperation to menstruation: Spiny mice (Acomys cahirinus) are an emerging mammalian model for sociality and beyond

While spiny mice are primarily used as a model for Type II diabetes and for studying complex tissue regeneration, they are also an emerging model for a variety of studies examining hormones, behavior, and the brain. We began studying the spiny mouse to take advantage of their highly gregarious phenotype to examine how the brain facilitates large group-living. However, this unique rodent can be readily bred and maintained in the lab and can be used to ask a wide variety of scientific questions. In this brief communication we provide an overview of studies that have used spiny mice for exploring physiology and behavior. Additionally, we describe how the spiny mouse can serve as a useful model for researchers interested in studying precocial development, menstruation, cooperation, and various grouping behaviors. With increasingly available technological advancements for non-traditional organisms, spiny mice are well-positioned to become a valuable organism in the behavioral neuroscience community.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process.We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM’s ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM.Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Identification of State-Specific Proteomic and Transcriptomic Signatures of Microglia-Derived Extracellular Vesicles

Microglia are resident immune cells of the brain that play important roles in mediating inflammatory responses in several neurological diseases via direct and indirect mechanisms. One indirect mechanism may involve extracellular vesicle (EV) release, so that the molecular cargo transported by microglia-derived EVs can have functional effects by facilitating intercellular communication. The molecular composition of microglia-derived EVs, and how microglial activation states impact EV composition and EV-mediated effects in neuroinflammation, remain poorly understood. We hypothesize that microglia-derived EVs have unique molecular profiles that are determined by microglial activation state. Using size-exclusion chromatography to purify EVs from BV2 microglia, combined with proteomic (label-free quantitative mass spectrometry or LFQ-MS) and transcriptomic (mRNA and noncoding RNA seq) methods, we obtained comprehensive molecular profiles of microglia-derived EVs. LFQ-MS identified several classic EV proteins (tetraspanins, ESCRT machinery, and heat shock proteins), in addition to over 200 proteins not previously reported in the literature. Unique mRNA and microRNA signatures of microglia-derived EVs were also identified. After treating BV2 microglia with lipopolysaccharide (LPS), interleukin-10, or transforming growth factor beta, to mimic pro-inflammatory, anti-inflammatory, or homeostatic states, respectively, LFQ-MS and RNA seq revealed novel state-specific proteomic and transcriptomic signatures of microglia-derived EVs. Particularly, LPS treatment had the most profound impact on proteomic and transcriptomic compositions of microglia-derived EVs. Furthermore, we found that EVs derived from LPS-activated microglia were able to induce pro-inflammatory transcriptomic changes in resting responder microglia, confirming the ability of microglia-derived EVs to relay functionally relevant inflammatory signals. These comprehensive microglia-EV molecular datasets represent important resources for the neuroscience and omics communities and provide novel insights into the role of microglia-derived EVs in neuroinflammation.

A comparison of neuroelectrophysiology databases

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.