Neuroscience Data Research Articles

Neural correlates of preschoolers' passive-viewing false belief: Insights into continuity and change and the function of right temporoparietal activity in theory of mind development.

Behavioral research demonstrates a critical transition in preschooler's mental-state understanding (i.e., theory of mind; ToM), revealed most starkly in performance on tasks about a character's false belief (e.g., about an object's location). Questions remain regarding the neural and cognitive processes differentiating children who pass versus fail behavioral false-belief tasks and the extent to which there is continuity versus change in the ToM neural network. To address these questions, we analyzed event-related spectral power in the electroencephalogram (EEG) to investigate how preschoolers' neural activity during passive viewing of false-belief scenarios related to their explicit behavioral ToM performance. We found that neural activity during passive viewing of false-belief events (6-9Hz EEG 'alpha' suppression in right temporoparietal [RTP] electrodes) strongly related to children's explicit ToM. However, children's RTP alpha suppression differed depending on their explicit behavioral ToM performance: Children who did better on a broad battery of standard ToM tasks and who passed explicit behavioral false-belief tasks showed greater RTP alpha suppression when the character's belief first became false (during the 'location-change' event); whereas children who did worse on the ToM battery and who failed explicit behavioral false-belief tasks showed greater RTP alpha suppression only later when they could evaluate the character's behavior in the context of prior events (during the 'active-search' event). Findings shed light on what differentiates preschoolers who pass versus fail explicit false-belief tasks and raise questions about how to interpret existing neuroscience data from ToM tasks across infancy to adulthood. RESEARCH HIGHLIGHTS: Preschool children's neural activity (EEG 6-9Hz suppression in right temporoparietal [RTP] electrodes) during passive-viewing of false-belief events was related to their explicit behavioral theory-of-mind performance. Children who did better on a theory-of-mind (ToM) battery and passed explicit false-belief tasks showed greater RTP alpha suppression when the character's belief first became false. Children who performed worse on the ToM battery and failed explicit false-belief tasks showed greater RTP alpha suppression later when observing the character's search behavior. Findings reveal change in preschoolers' ToM neural correlates and suggest that the presence of RTP activity does not necessarily indicate 'mature' theory of mind.

Beyond the lab: Feasibility of cognitive neuroscience data collection during a speleological expedition

In human cognitive neuroscience and neuropsychology studies, laboratory-based research tasks have been important to establish principles of brain function and its relationship to behaviour; however, they differ greatly from real-life experiences. Several elements of real-life situations that impact human performance, such as stressors, are difficult or impossible to replicate in the laboratory. Expeditions offer unique possibilities for studying human cognition in complex environments that can transfer to other situations with similar features. For example, as caves share several of the physical and psychological challenges of safety-critical environments such as spaceflight, underground expeditions have been developed as an analogue for astronaut training purposes, suggesting that they might also be suitable for studying aspects of behaviour and cognition that cannot be fully examined under laboratory conditions. While a large range of topics and tools have been proposed for application in such environments, few have been used in the field. Methods and procedures for maximizing the robustness and scientific value of expedition research designs must first be developed and validated. We tested the feasibility of collecting human physiological, cognitive, and subjective experience data concerning brain state, sleep, cognitive workload, and fatigue, during a speleological expedition in a remote region. We document our approaches and challenges experienced, and provide recommendations and suggestions to aid future work. The data support the idea that cave expeditions are relevant naturalistic paradigms that offer unique possibilities for cognitive neuroscience to complement laboratory work and help improve human performance and safety in operational environments.

Responsible Use of Population Neuroscience Data: Toward Standards of Accountability and Integrity

Responsible Use of Population Neuroscience Data: Toward Standards of Accountability and Integrity

A dynamic generative model can extract interpretable oscillatory components from multichannel neurophysiological recordings.

Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters - the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations - all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

Population neuroscience data sharing: Policy ecology, infrastructure, practices and challenges

Population neuroscience data sharing: Policy ecology, infrastructure, practices and challenges

Pain and temporality: a merleau-pontyian approach

Chronic pain is a common disorder with enormous sociomedical importance. A major part of primary and secondary costs of illness is caused by the various pain syndromes. Nociception – the sensory perception of a painful stimulus – is a complex process relying on an intricate system of anatomical, neurophysiological and biochemical networks. This applies even more so to pain – the state of experiencing a nociceptive event, of interpreting it in terms of meaning for the affected individual and of suffering a range of emotions it elicits. This intricacy renders it obvious, that the empirical medical sciences alone cannot explain all aspects of pain. Hence, it has also become a focus of phenomenological research. One aspect of these investigations is the interaction of pain and the perception of the lived body’s spatiality. The focus of this article will build on these concepts to develop a construct of the alteration of temporality caused by chronic pain and the effects this spells out for the affected subject. To this end, I will primarily draw on Merleau-Ponty’s ideas of the lived body as well as on theories of enactivism and embodiment. I will also point out parallels to neuroscientific data, thereby demonstrating the proximity of phenomenology and neuroscience. A possible partial solution to the pain dilemma may be derived from psychology: techniques relying on cognitive behavioural intervention, awareness training, and existential analysis may provide alleviation to patients suffering from chronic pain.

Heart-to-heart: infant heart rate at 3 months is linked to infant-directed speech, mother-infant interaction, and later language outcomes.

Previous studies underscore the importance of speech input, particularly infant-directed speech (IDS) during one-on-one (1:1) parent-infant interaction, for child language development. We hypothesize that infants' attention to speech input, specifically IDS, supports language acquisition. In infants, attention and orienting responses are associated with heart rate deceleration. We examined whether individual differences in infants' heart rate measured during 1:1 mother-infant interaction is related to speech input and later language development scores in a longitudinal study. Using a sample of 31 3-month-olds, we assessed infant heart rate during mother-infant face-to-face interaction in a laboratory setting. Multiple measures of speech input were gathered at 3 months of age during naturally occurring interactions at home using the Language ENvironment Analysis (LENA) system. Language outcome measures were assessed in the same children at 30 months of age using the MacArthur-Bates Communicative Development Inventory (CDI). Two novel findings emerged. First, we found that higher maternal IDS in a 1:1 context at home, as well as more mother-infant conversational turns at home, are associated with a lower heart rate measured during mother-infant social interaction in the laboratory. Second, we found significant associations between infant heart rate during mother-infant interaction in the laboratory at 3 months and prospective language development (CDI scores) at 30 months of age. Considering the current results in conjunction with other converging theoretical and neuroscientific data, we argue that high IDS input in the context of 1:1 social interaction increases infants' attention to speech and that infants' attention to speech in early development fosters their prospective language growth.

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases.

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

Neuroscience Contribution in Educational Leadership. Challenges and Perspectives

Education is founded upon the instruction process and its outcomes, commonly called learning outcomes. The underlying foundation of this entire process is the brain. Neuroscience encompasses the study of brain function, including the intricate processes involved in human learning. It also investigates the factors that contribute to variations in learning abilities among individuals. Therefore, it is unsurprising that individuals engaged in educational science are highly interested in integrating the discoveries of neurosciences with educational theories of learning. Neuroscientific research has been conducted for approximately three decades. However, there has been a growing interest in integrating neuroscience and education in recent years. This is evident from the numerous articles published in books and scientific journals that explore the relationship between these two fields. Additionally, the integration of neuroscience into educational leadership presents both opportunities and challenges. Educational neuroscience posits that insights from neuroscience can enhance teaching in the classroom. However, there are practical and principled issues associated with educational neuroscience, which need to be addressed. Neuroscience is revealing to educational leadership that it is a social process, with the human brain being a social organ interacting with others in social contexts. Incorporating neuroscience into educational leadership has the potential to increase its effectiveness, but contradictory studies address the critical nature of this integration. The application of advances in psychology, behavioral economics, and cognitive neuroscience to study school leaders’ decision-making processes is a tantalizing area. Finally, it is essential that neuroscientific data and interpretations are rooted in education to be embraced by the education profession.

Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia.

Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.

The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR.

Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail of neuroscience data, driven no doubt by journal requirements and funder mandates. Concomitant with this shift to open is the increasing support of the FAIR data principles by neuroscience practices and infrastructure. FAIR is particularly critical for neuroscience with its multiplicity of data types, scales and model systems and the infrastructure that serves them. As envisioned from the early days of neuroinformatics, neuroscience is currently served by a globally distributed ecosystem of neuroscience-centric data repositories, largely specialized around data types. To make neuroscience data findable, accessible, interoperable, and reusable requires the coordination across different stakeholders, including the researchers who produce the data, data repositories who make it available, the aggregators and indexers who field search engines across the data, and community organizations who help to coordinate efforts and develop the community standards critical to FAIR. The International Neuroinformatics Coordinating Facility has led efforts to move neuroscience toward FAIR, fielding several resources to help researchers and repositories achieve FAIR. In this perspective, I provide an overview of the components and practices required to achieve FAIR in neuroscience and provide thoughts on the past, present and future of FAIR infrastructure for neuroscience, from the laboratory to the search engine.

Epistemic Justice and Neurodiversity: Mentalization–Based Treatment with Children in the Autistic Spectrum from a Dimensional Perspective

ABSTRACT Neurodiverse individuals often suffer the consequences of epistemic injustice, that lead to isolation and helplessness. By attempting to apply a mentalization lens to ASD, we seek to highlight the concept of neurodiversity as an invitation to “mentalize differences” that arise primarily as a response to the marginalization of people with autism spectrum disorders. We seek to move away from the non-mentalizing impact of the categoric deficit model, providing an integration of both existing theoretical and empirical clinical data into an intervention to address the heterogeneity of neurodiversity. An adaptation of mentalization-based treatment for children (MBT-C) to the needs of children and adolescents functioning in the Autistic Spectrum is discussed. A clinical model that integrates findings of developmental research, dyadic clinical psychodynamic intervention models and neuroscientific data with the aim to develop an integrative therapeutic intervention both relational and developmental in nature. A mentalization informed dimensional approach is applied to the scaffolding process necessary to accommodate the heterogeneity and overall unique needs of this population. The article explores the need for further empirical research which substantiates the need for both systemic and individual interventions which address the subjective experience of ASD children and adolescents and that of their families.

Non-ictal, interictal and ictal déjà vu: a systematic review and meta-analysis.

Déjà vu, French for "already seen," is a phenomenon most people will experience at least once in their lifetime. Emerging evidence suggests that déjà vu occurs in healthy individuals (as "non-ictal déjà vu") and in epilepsy patients during seizures (as "ictal déjà vu") and between seizures (as "interictal déjà vu"). Although the ILAE has recognized déjà vu as a feature of epileptic seizures, it is notably absent from the ICD-11. A lack of evidence-based research may account for this omission. To our knowledge, this study represents the first systematic review and meta-analysis on déjà vu experiences. Through detailed examinations of non-ictal, interictal and ictal déjà vu, we seek to highlight possible clinical implications. Rethinking the status quo of ictal déjà vu could potentially lead to earlier interventions and improve outcomes for epilepsy patients. This study was registered in PROSPERO (ID: CRD42023394239) on 5 February 2023. Systematic searches were conducted across four databases: EMBASE, MEDLINE, PsycINFO, and PubMed, from inception to 1 February 2023, limited to English language and human participants. Studies were included/excluded based on predefined criteria. Data was extracted according to the PICO framework and synthesized through a thematic approach. Meta-analyses were performed to estimate prevalence's of the phenomena. Study quality, heterogeneity, and publication bias were assessed. Database searching identified 1,677 records, of which 46 studies were included. Meta-analyses of prevalence showed that non-ictal déjà vu was experienced by 0.74 (95% CI [0.67, 0.79], p < 0.001) of healthy individuals, whereas interictal déjà vu was experienced by 0.62 (95% CI [0.48, 0.75], p = 0.099) and ictal déjà vu by 0.22 (95% CI [0.15, 0.32], p = 0.001) of epilepsy patients. Examinations of phenomenological (sex, age, frequency, duration, emotional valence, and dissociative symptoms) and neuroscientific (brain structures and functions) data revealed significant variations between non-ictal, interictal and ictal déjà vu on several domains. This systematic review and meta-analysis do not support the notion that non-ictal, interictal and ictal déjà vu are homogenous experiences. Instead, it provides insight into ictal déjà vu as a symptom of epilepsy that should be considered included in future revisions of the ICD-11. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=394239, CRD42023394239.

Prevalence of neuromyths among students and pre-service teachers

Recent studies have shown that the knowledge of society about the functioning of the brain does not correspond to neuroscience data. The high prevalence of neuromyths can become a problem in the development of educational technologies. The goal of this study is to identify the prevalence of neuromyths among pre-service teachers and students of other faculties. The prevalence of neuromyths among 958 university students was studied using an anonymous survey. It was found that the specialty influenced the results: chemistry and biology students, as well as physics, mathematics, and computer science students, answered better. The effect of education is weak. Nevertheless, it has led to an alignment of results between the specialties in the older group. Self-education has a significant positive effect on students’ recognition of neurofacts. The study confirms that certain neuromyths are especially popular among students and allows us to draw conclusions about the prevalence of neuromyths among future teachers.

Phantom oscillations in principal component analysis.

Principal component analysis (PCA) is a dimensionality reduction method that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional space. However, this simple interpretation fails for timeseries, spatial maps, and other continuous data. In these cases, nonoscillatory data may have oscillatory principal components. Here, we show that two common properties of data cause oscillatory principal components: smoothness and shifts in time or space. These two properties implicate almost all neuroscience data. We show how the oscillations produced by PCA, which we call "phantom oscillations," impact data analysis. We also show that traditional cross-validation does not detect phantom oscillations, so we suggest procedures that do. Our findings are supported by a collection of mathematical proofs. Collectively, our work demonstrates that patterns which emerge from high-dimensional data analysis may not faithfully represent the underlying data.

The shared and unique neural correlates of personal semantic, general semantic, and episodic memory.

One of the most common distinctions in long-term memory is that between semantic (i.e., general world knowledge) and episodic (i.e., recollection of contextually specific events from one's past). However, emerging cognitive neuroscience data suggest a surprisingly large overlap between the neural correlates of semantic and episodic memory. Moreover, personal semantic memories (i.e., knowledge about the self and one's life) have been studied little and do not easily fit into the standard semantic-episodic dichotomy. Here, we used fMRI to record brain activity while 48 participants verified statements concerning general facts, autobiographical facts, repeated events, and unique events. In multivariate analysis, all four types of memory involved activity within a common network bilaterally (e.g., frontal pole, paracingulate gyrus, medial frontal cortex, middle/superior temporal gyrus, precuneus, posterior cingulate, angular gyrus) and some areas of the medial temporal lobe. Yet the four memory types differentially engaged this network, increasing in activity from general to autobiographical facts, from autobiographical facts to repeated events, and from repeated to unique events. Our data are compatible with a component process model, in which declarative memory types rely on different weightings of the same elementary processes, such as perceptual imagery, spatial features, and self-reflection.

Prediction of individual brain age using movie and resting-state fMRI.

Brain age is a promising biomarker for predicting chronological age based on brain imaging data. Although movie and resting-state functional MRI techniques have attracted much research interest for the investigation of brain function, whether the 2 different imaging paradigms show similarities and differences in terms of their capabilities and properties for predicting brain age remains largely unexplored. Here, we used movie and resting-state functional MRI data from 528 participants aged from 18 to 87years old in the Cambridge Centre for Ageing and Neuroscience data set for functional network construction and further used elastic net for age prediction model building. The connectivity properties of movie and resting-state functional MRI were evaluated based on the connections supporting predictive model building. We found comparable predictive abilities of movie and resting-state connectivity in estimating brain age of individuals, as evidenced by correlation coefficients of 0.868 and 0.862 between actual and predicted age, respectively. Despite some similarities, notable differences in connectivity properties were observed between the predictive models using movie and resting-state functional MRI data, primarily involving components of the default mode network. Our results highlight that both movie and resting-state functional MRI are effective and promising techniques for predicting brain age. Leveraging its data acquisition advantages, such as improved child and patient compliance resulting in reduced motion artifacts, movie functional MRI is emerging as an important paradigm for studying brain function in pediatric and clinical populations.

Pynapple, a toolbox for data analysis in neuroscience.

Datasets collected in neuroscientific studies are of ever-growing complexity, often combining high-dimensional time series data from multiple data acquisition modalities. Handling and manipulating these various data streams in an adequate programming environment is crucial to ensure reliable analysis, and to facilitate sharing of reproducible analysis pipelines. Here, we present Pynapple, the PYthon Neural Analysis Package, a lightweight python package designed to process a broad range of time-resolved data in systems neuroscience. The core feature of this package is a small number of versatile objects that support the manipulation of any data streams and task parameters. The package includes a set of methods to read common data formats and allows users to easily write their own. The resulting code is easy to read and write, avoids low-level data processing and other error-prone steps, and is open source. Libraries for higher-level analyses are developed within the Pynapple framework but are contained within a collaborative repository of specialized and continuously updated analysis routines. This provides flexibility while ensuring long-term stability of the core package. In conclusion, Pynapple provides a common framework for data analysis in neuroscience.

Moral Foundations of Legal Communication

The article is founded on the position that social communication as an evolutionary option for the development of communication of all living beings must also include legal communication. In this existential context, legal communication is not reduced only to the transfer of symbolic (textual) information determining the behavior of subjects of law. It is also considered as a vital option for adapting to the environment, which allows both individuals and society to survive, develop and self-realize. Legal communication involves not just cooperation and interaction between legal subjects, but also the observance of the necessary conditions for the implicit and explicit goals of legal communication to be achieved and realized. Implicit (universal, transcendental, evolutionarily necessary) goals are reflected at the sociobiological level in the reciprocal altruism (ego-altruism) of communicants, at the philosophical (rational) level — in the principle of mutual legal and moral recognition, at the religious level — in the commandment “love your neighbor as yourself.” The authors reveal the connection between these concepts and the concept of communication by J. Habermas and the principle of mutual recognition by A. Honneth, on the one hand, and the idea of intuitive law by L.I. Petrażycki and the ideal of “free all-unity” by P.I. Novgorodtsev, on the other hand. It is shown that the findings of these scholars lie at the heart of the communicative theory of law and are supported by neuroscience data. According to the position put forward in this research, the rejection of mutual recognition inevitably entails the assertion of parochial altruism, the ideology of tribalism, the ideological justification of authoritarianism, violence as a universal political method, the neglect of human rights and, as a result, the deformation and destruction of legal communication.

Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration

Volumetric brain atlases are increasingly used to integrate and analyze diverse experimental neuroscience data acquired from animal models, but until recently a publicly available digital atlas with complete coverage of the rat brain has been missing. Here we present an update of the Waxholm Space rat brain atlas, a comprehensive open-access volumetric atlas resource. This brain atlas features annotations of 222 structures, of which 112 are new and 57 revised compared to previous versions. It provides a detailed map of the cerebral cortex, hippocampal region, striatopallidal areas, midbrain dopaminergic system, thalamic cell groups, the auditory system and main fiber tracts. We document the criteria underlying the annotations and demonstrate how the atlas with related tools and workflows can be used to support interpretation, integration, analysis and dissemination of experimental rat brain data.