Issue In Neuroscience Research Articles

Context free and context-dependent conceptual representation in the brain.

How concepts are coded in the brain is a core issue in cognitive neuroscience. Studies have focused on how individual concepts are processed, but the way in which conceptual representation changes to suit the context is unclear. We parametrically manipulated the association strength between words, presented in pairs one word at a time using a slow event-related fMRI design. We combined representational similarity analysis and computational linguistics to probe the neurocomputational content of these trials. Individual word meaning was maintained in supramarginal gyrus (associated with verbal short-term memory) when items were judged to be unrelated, but not when a linking context was retrieved. Context-dependent meaning was instead represented in left lateral prefrontal gyrus (associated with controlled retrieval), angular gyrus, and ventral temporal lobe (regions associated with integrative aspects of memory). Analyses of informational connectivity, examining the similarity of activation patterns across trials between sites, showed that control network regions had more similar multivariate responses across trials when association strength was weak, reflecting a common controlled retrieval state when the task required more unusual associations. These findings indicate that semantic control and representational sites amplify contextually relevant meanings in trials judged to be related.

Non-human contributions to personality neuroscience - from fish through primates. An introduction to the special issue.

The most fundamental emotional systems that show trait control are evolutionarily old and extensively conserved. Psychology in general has benefited from non-human neuroscience and from the analytical simplicity of behaviour in those with simpler nervous systems. It has been argued that integration between personality, psychopathology, and neuroscience is particularly promising if we are to understand the neurobiology of human experience. Here, we provide some general arguments for a non-human approach being at least as productive in relation to personality, psychopathology, and their interface. Some early personality theories were directly linked to psychopathology (e.g., Eysenck, Panksepp, and Cloninger). They shared a common interest in brain systems that naturally led to the use of non-human data; behavioural, neural, and pharmacological. In Eysenck's case, this also led to the selective breeding, at the Maudsley Institute, of emotionally reactive and non-reactive strains of rat as models of trait neuroticism or trait emotionality. Dimensional personality research and categorical approaches to clinical disorder then drifted apart from each other, from neuropsychology, and from non-human data. Recently, the conceptualizations of both healthy personality and psychopathology have moved towards a common hierarchical trait perspective. Indeed, the proposed two sets of trait dimensions appear similar and may even be eventually the same. We provide, here, an introduction to this special issue of Personality Neuroscience, where the authors provide overviews of detailed areas where non-human data inform human personality and its psychopathology or provide explicit models for translation to human neuroscience. Once all the papers in the issue have appeared, we will also provide a concluding summary of them.

It’s time for sex in cognitive neuroscience

ABSTRACT In a discussion paper published in the special issue of Cognitive Neuroscience, Sex Differences in the Brain, we investigated whether certain experimental parameters contributed to findings in functional magnetic resonance imaging studies of sex differences during long-term memory. Experimental parameters included: the number of participants, stimulus type(s), whether or not performance was matched, whether or not sex differences were reported, the type of between-subject statistical test used, and the contrast(s) employed. None of these parameters determined whether or not differences were observed, as all included studies reported sex differences. We also conducted a meta-analysis to determine if there were any brain regions consistently activated to a greater degree in either sex. The meta-analysis identified sex differences (male > female) in the lateral prefrontal cortex, visual processing regions, parahippocampal cortex, and the cerebellum. We received eight commentaries in response to that paper. Commentaries called for an expanded discussion on various topics including the influence of sex hormones, the role of gender (and other social factors), the pros and cons of equating behavioral performance between the sexes, and interpreting group differences in patterns of brain activity. There were some common statistical assumptions discussed in the commentaries regarding the ‘file drawer’ issue (i.e., the lack of reporting of null results) and effect size. The current paper provides further discussion of the various topics brought up in the commentaries and addresses some statistical misconceptions in the field. Overall, the commentaries echoed a resounding call to include sex as a factor in cognitive neuroscience studies.

Black-box model reduction of the C. Elegans nervous system.

In recent years, modeling neurons and neuronal collections with high accuracy have become central issues of neuroscience. The development of efficient algorithms for their simulation as well as the increase in computational power and parallelization need to keep up with the quantity and complexity of novel recordings and reconstructions reported by the experimental neuroscientists. The extraction of low-order equivalents that capture the essential aspects of the high-accuracy models is an essential part of the simulation process. The complexity of these models require the use of black-box data-oriented reduction approaches. We create a detailed model of the nervous system of a very known organism, C. Elegans, and show that it can be reduced using a modified data-driven model reduction method up to the order of 4 with very little loss in accuracy. The reduced model is able to predict the behaviour of the original for time ranges beyond the data used for the reduction.

Exploring\ufeff electroencephalography with a model inspired by quantum mechanics

An outstanding issue in cognitive neuroscience concerns how the brain is organized across different conditions. For instance, during the resting-state condition, the brain can be clustered into reliable and reproducible networks (e.g., sensory, default, executive networks). Interestingly, the same networks emerge during active conditions in response to various tasks. If similar patterns of neural activity have been found across diverse conditions, and therefore, different underlying processes and experiences of the environment, is the brain organized by a fundamental organizational principle? To test this, we applied mathematical formalisms borrowed from quantum mechanisms to model electroencephalogram (EEG) data. We uncovered a tendency for EEG signals to be localized in anterior regions of the brain during “rest”, and more uniformly distributed while engaged in a task (i.e., watching a movie). Moreover, we found analogous values to the Heisenberg uncertainty principle, suggesting a common underlying architecture of human brain activity in resting and task conditions. This underlying architecture manifests itself in the novel constant KBrain, which is extracted from the brain state with the least uncertainty. We would like to state that we are using the mathematics of quantum mechanics, but not claiming that the brain behaves as a quantum object.

Sex differences in the brain

ABSTRACT This special issue of Cognitive Neuroscience focuses on sex differences in the brain. Berchicci, Bianco, and Di Russo found ERP auditory cortex activity was larger in females than males during sound perception. Spets, Fritch, Thakral, & Slotnick reported greater fMRI activity during high- versus low-confidence spatial memory in males than females within the lateral prefrontal cortex and other brain regions. Using fMRI resting-state data, Murray, Maurer, Peechatka, Frederick, Kaiser, and Janes observed females spent more time in transient dorsal attention/occipital/sensory-motor network states and males spent more time in transient salience network states, and de Lacy, Kutz, and Calhoun found that brain dynamism (transitioning between brain states) was correlated with anxiety/depression in males and drive/novelty-seeking/self-control in females. Kurth, Gaser, and Luders predicted the sex of girls and boys with an 80.4% accuracy using a classifier based on anatomic (MRI) data. In a discussion paper, Spets and Slotnick conducted an fMRI meta-analysis that revealed greater male than female long-term memory activity in the lateral prefrontal cortex, visual processing regions, and the parahippocampal cortex, and argued against the claim there is reporting bias in sex-differences studies. In response to this discussion paper, commentaries were written by Cahill; de Lacy; Hausmann; McGlade, Rogowska, and Yurgelun-Todd; Sneider and Silveri; Tejavibulya and Scheinost; Wiersch and Weis; Young and Compère. It is hoped that these findings will help motivate a shift in the field to consider sex as a factor in cognitive neuroscience studies.

Clinical and Translational Neuroscience: Time for a Change: “What’s Past Is Prologue”

The first issue of Clinical and Translational Neuroscience (CTN) (ISSN: 2514-183X) appeared in 2017 [...]

Predicting Human Intrinsic Functional Connectivity From Structural Connectivity: An Artificial Neural Network Approach

How structural connectivity (SC) constrains and shapes functional connectivity (FC) in the human brain to support rich cognitive functions has long been a core issue in neuroscience. Although evidence accumulate to suggest that FC strength is correlated with multiple aspects of SC, few studies has analyzed the SC-to-FC relationship in a multivariate manner. This paper proposed a novel usage of the feedforward neural network to predict FC strength as a nonlinear combination of 115 features that described the geometric and topological aspects of SC. The resulting model outperformed four state-of-the-art models in both terms of predictive power and generalizability. Model interpretation analyses found that the geometric features were generally more predictive than the topological ones, providing novel evidence for the significant impact of geometric relationships on FC generation. Comparison of feature contributions to predicting FC with different structural properties further revealed the crucial role of indirect structural paths for inducing FC, particularly between disconnected and/or distanced regions. Together, our results suggested that the flexible FC is significantly but unevenly influenced by the combination of geometric and topological characteristics of the structural network. The proposed framework would also be used for link prediction on top of an underlying topology.

Inconsistent effects of sleep deprivation on memory function.

In this review article, we aimed to discuss the role of sleep deprivation (SD) in learning and memory processing in basic and clinical studies. There are numerous studies investigating the effect of SD on memory, while most of these studies have shown the impairment effect of SD. However, some of these studies have reported conflicting results, indicating that SD does not impair memory performance or even improves it. So far, no study has discussed or compared the conflicting results of SD on learning and memory. Thus, this important issue in the neuroscience of sleep remains unknown. The main goal of this review article is to compare the similar mechanisms between the impairment and the improvement effects of SD on learning and memory, probably leading to a scientific solution that justifies these conflicting results. We focused on the inconsistent effects of SD on some mechanisms involved in learning and memory, and tried to discuss the inconsistent effects of SD on learning and memory.

The supernumerary rubber hand illusion revisited: Perceived duplication of limbs and visuotactile events.

A controversial and unresolved issue in cognitive neuroscience is whether humans can experience supernumerary limbs as part of their own body. Some previous experiments have claimed that it is possible to elicit supernumerary hand illusions based on modified versions of the rubber hand illusion. However, other studies have provided conflicting results that suggest that only one rubber hand can be perceived as one's own. To address this issue, we developed a supernumerary hand illusion paradigm that allowed us to disambiguate ownership of individual rubber hands from simultaneous ownership of two fake hands. In our setup, the participant's real right hand was hidden under a platform, while two identical right rubber hands were placed in parallel on top of the platform in direct view of the participant. We applied synchronous strokes to both rubber hands and the real hand (SS), synchronous strokes to one rubber hand and the real hand and asynchronous strokes to the other model hand (AS and SA), or asynchronous strokes to both fake hands in relation to the real hand (AA). Our results demonstrate that a genuine illusion of owning two rubber hands can be elicited and that such a supernumerary hand illusion can be isolated from the sense of ownership of a single rubber hand both in terms of questionnaire ratings and threat-evoked skin conductance responses (SCRs). These findings advance our knowledge about the dynamic flexibility and fundamental constraints of body representation and emphasize the importance of correlated afferent signals for causal inference in body ownership. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Connecting quantitatively derived personality-psychopathology models and neuroscience.

Traditionally, personality has been conceptualized in terms of dimensions of human experience - habitual ways of thinking, feeling, and behaving. By contrast, psychopathology has traditionally been conceptualized in terms of categories of disorder - disordered thinking, feeling, and behaving. The empirical literature, however, routinely shows that psychopathology does not coalesce into readily distinguishable categories. Indeed, psychopathology tends to delineate dimensions that are relatively similar to dimensions of personality. In this special issue of Personality Neuroscience, authors took up the challenge of reconceptualizing personality and psychopathology in terms of connected and interrelated dimensions, and they considered the utility of pursuing neuroscientific inquiry from this more integrative perspective. In this editorial article, we provide the relevant background to the interface between personality, psychopathology, and neuroscience; summarize contributions to the special issue; and point toward directions for continued research and refinement. All told, it is evident that quantitatively derived, integrative models of personality-psychopathology represent a particularly promising conduit for advancing our understanding of the neurobiological foundation of human experience, both functional and dysfunctional.

Anterior-Posterior Gradient in the Integrated Processing of Forelimb Movement Direction and Distance in Macaque Parietal Cortex

A major issue of modern Neuroscience is to understand how the activity of cells and populations represents multiple spatial and motor features during goal-directed movements. The direction and distance (depth) of arm movements often appear to be controlled independently at the behavioral level, but it is unknown whether they share neural resources or not. We addressed this issue in parietal cortex that is known to integrate multiple signals. Using information theory, singular value decomposition and dimensionality reduction methods we compared direction and depth effect magnitude and measured their convergence across three parietal areas during an instructed delay 3D arm movement task. All methods showed a stronger direction effect during early movement preparation, whereas depth signals prevailed during movement execution. Going from anterior to posterior sectors, we found an increase in the number of cells that processed both signals and a stronger influence of depth information. These findings suggest a serial direction and depth processing that supports behavioral evidence. In addition, they reveal a gradient of joint versus independent control of these features in parietal cortex that could be framed in the context of its functional heterogeneity and its role in sensorimotor transformations.

Floating ideas on theta waves.

This special issue on the theta rhythm highlights recent experiments aimed at understanding the relationship between this slow, large amplitude oscillation and plasticity, fast oscillations, cellular activity and disease in both animals and humans. The articles in this issue of Behavioral Neuroscience use a number of approaches across different model systems and behavioral paradigms to provide an up-to-date account of recent progress in understanding how the theta rhythm coordinates neural activity in the service of cognition. Prominent themes that emerge are how theta is tightly related to movement in humans and rodents and how this rhythm could be leveraged as a biomarker for understanding and testing therapeutic approaches to treat psychiatric and neurological diseases. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

La plasticité neuronale : fondements épistémologiques et enjeux éthiques d’un modèle de dialogue interdisciplinaire entre psychanalyse et neurosciences

ObjectivesThe aim of this article is to describe a new paradigm for an interdisciplinary dialogue between psychoanalysis and neuroscience, which respects the particularities of each discipline, while at the same time allowing for the conception of an intersection that can lay the foundations of a real meeting between both disciplines. We shall examine the epistemological grounds that can lead to this interdisciplinary dialogue, in order to avoid the possible pitfalls or methodological misunderstandings that could arise from such a meeting. In particular, the article will explain the possibilities of a paradigm that could avoid a monodisciplinary reductionism, with one of the two disciplines legitimating or subsuming the other. MethodAt first, we shall try to deal with pre-existing difficulties, which can appear in the creation of a new interdisciplinary dialogue between psychoanalysis and biology. We can show that, from the beginning, the creation of the psychoanalysis as an autonomous discipline, with self-epistemological foundations, forced Freud to think about the future conditions of an interdisciplinary dialogue with biology. In a second step, we shall study how the latest significant scientific advances have induced an important reconfiguration of the relationship between psychoanalysis and biology, mainly through the emergence of neuroscience and through the improvements introduced by experimental research on the neuronal plasticity. Finally, after having described the molecular mechanisms that explain the plasticity of the nervous system, we shall observe that neurobiological findings can show points of intersection with Freudian theory. ResultsThe latest neurobiological research on neuronal plasticity, with the experimental discoveries about the inscription of experience in the nervous system through synaptic mechanisms, can be used as the foundation for a paradigm of an interdisciplinary dialogue with psychoanalysis. This paradigm could be built on a mutual observation that one's subjective experience leaves a lasting impression, either a synaptic trace from the neurobiological point of view, or a memory trace from the psychic point of view. Pierre Magistretti, neurobiologist, and François Ansermet, psychoanalyst, have proposed a new epistemological paradigm based upon the possibility of an intersection between both disciplines, separated from theoretical ideas concerning a meeting between psychoanalysis and neurosciences. The paradigm of the intersection allows for the possibility of an interdisciplinary dialogue, respecting the epistemological particularity and the heterogeneity of both disciplines. DiscussionThe latest neurobiological research of Cristina Alberini and Yanin Dudai on synaptic mechanisms of consolidation – reconsolidation leads to remarkable findings: the molecular mechanisms that are the source of the stabilization of long-term memory could also be the source of a discontinuity between experience and its inscription as traces. The interdisciplinary dialogue, introduced through the discovery of a discontinuity in the re-association of the traces linked to Freud's theory on memory traces and their re-associations, could clear the way for a debate on major ethical issues in psychoanalysis and neurosciences around plasticity. Taking into account the concept of plasticity leads us to emphasize the concept of the person, beyond genetic and social determinisms. From this perspective, psychoanalysis and neuroscience could meet by transcending linear determinisms, and by relinquishing any predictions when it comes to studying the person in its idiosyncrasy. ConclusionNeurobiological research on neuronal plasticity leads to a new epistemological paradigm for an interdisciplinary dialogue between psychoanalysis and neurosciences, from the creation of an intersection involving the observation that experience records and leaves traces. This paradigm of intersection has the benefits of transcending any opposition between both disciplines, and at the same time of guaranteeing the specificities and autonomy of each. Findings about the synaptic mechanisms of plasticity allow for a reconfiguration of an interdisciplinary dialogue, while clearing the way for debates on major ethical issues. These issues are put into the current context of the “time of the living”, which traverses every scientific discipline. Specifically, this paradigm allows us to imagine a meeting between psychoanalysis and neuroscience, through the same transcendence of linear and determinist points of view concerning causal relations, and through a new identification of the singularity of the person in her/his self-determination.

Spontaneously emerging patterns in human visual cortex and their functional connectivity are linked to the patterns evoked by visual stimuli.

The function of spontaneous brain activity is an important issue in neuroscience. Here we test the hypothesis that patterns of spontaneous activity code representational patterns evoked by stimuli. We compared in human visual cortex multivertex patterns of spontaneous activity to patterns evoked by ecological visual stimuli (faces, bodies, scenes) and low-level visual features (e.g., phase-scrambled faces). Specifically, we identified regions that preferred particular stimulus categories during localizer scans (e.g., extrastriate body area for bodies), measured multivertex patterns for each category during event-related task scans, and then correlated over vertices these stimulus-evoked patterns to the pattern measured on each frame of resting-state scans. The mean correlation coefficient was essentially zero for all regions/stimulus categories, indicating that resting multivertex patterns were not biased toward particular stimulus-evoked patterns. However, the spread of correlation coefficients between stimulus-evoked and resting patterns, positive and negative, was significantly greater for the preferred stimulus category of an ROI. The relationship between spontaneous and stimulus-evoked multivertex patterns also governed the temporal correlation or functional connectivity of patterns of spontaneous activity between individual regions (pattern-based functional connectivity). Resting multivertex patterns related to an object category fluctuated preferentially between ROIs preferring the same category, and fluctuations of the pattern for a category (e.g., body) within its preferred ROIs were largely uncorrelated with fluctuations of the pattern for a disparate category (e.g., scene) within its preferred ROIs. These results support the proposal that spontaneous multivertex activity patterns are linked to stimulus-evoked patterns, consistent with a representational function for spontaneous activity.NEW & NOTEWORTHY Spontaneous brain activity was once thought to reflect only noise, but evidence of strong spatiotemporal regularities has motivated a search for functional explanations. Here we show that the spatial pattern of spontaneous activity in human high-level and early visual cortex is related to the spatial patterns evoked by stimuli. Moreover, these patterns partly govern spontaneous spatiotemporal interactions between regions, so-called functional connectivity. These results support the hypothesis that spontaneous activity serves a representational function.

EL PROCESO DECISORIO EN LA ESTIMACIÓN DE METAS PRESUPUESTARIAS: UN ESTUDIO COMPARATIVO ENTRE BRASIL Y PERÚ

Este estudio tuvo como objetivo comprobar si hay diferencias en el comportamiento decisorio de funcionarios que actúan en empresas situadas en Brasil o en Perú, cuando estos funcionarios realizan estimaciones de metas presupuestales. Como base teórica para el análisis del proceso de toma de decisión se presentó un modelo que considera temas de economía, psicología y neurociencia cognitiva. El estudio de naturaleza cuantitativa descriptiva, se caracteriza como un Survey. Para la recolección de datos en esa etapa se desarrolló un cuestionario electrónico con 30 preguntas cerradas, usando una escala ordinal de 10 puntos, con el cual se obtuvieron los siguientes totales: en Brasil (303 encuestados) y en Perú (73 encuestados). Los datos fueron analizados empleándose ecuaciones estructurales (método PLS-SEM). Los resultados muestran que hay semejanzas y diferencias en el comportamiento de los gestores en Brasil y Perú. Tanto en Brasil como en Perú los mecanismos para selección de la información afectan la decisión racional como la que se basa en expertos; en Brasil los aspectos socio afectivos afectan la decisión racional, pero no la decisión por expertos, mientras que en Perú ella afecta a los dos tipos de decisión; el peso de las relaciones entre búsqueda de la información y factores socio afectivos sobre la decisión racional o sobre el expertos varían dentro de los diferentes países, pero el modelo estructural propuesto para análisis del proceso de decisión se mantiene.

Study of functional connectivity during anesthesia based on sparse partial least squares

Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant ( P<0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.

Neural magnetic field dependent fMRI toward direct functional connectivity measurements: A phantom study

Recently, the main issue in neuroscience has been the imaging of the functional connectivity in the brain. No modality that can measure functional connectivity directly, however, has been developed yet. Here, we show the novel MRI sequence, called the partial spinlock sequence toward direct measurements of functional connectivity. This study investigates a probable measurement of phase differences directly associated with functional connectivity. By employing partial spinlock imaging, the neural magnetic field might influence the magnetic resonance signals. Using simulation and phantom studies to model the neural magnetic fields, we showed that magnetic resonance signals vary depending on the phase of an externally applied oscillating magnetic field with non-right flip angles. These results suggest that the partial spinlock sequence is a promising modality for functional connectivity measurements.

Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction.

Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-loop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb's mechanical impedance. The proposed stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.

Study of sensory information processing depending on visual stimulus complexity based on multichannel EEG signals

Introduction: Analysis of electrical activity in the cortical neural network during the processing of visual information is one ofthe most interesting issues in modern neuroscience. The particular attention of the researchers is attracted by the study of neuralactivity during complex visual stimuli processing. Purpose: Studying the process of sensory information processing in the corticalneural network based on recorded electrical activity signals (EEG). Results: We have studied neural activity during visual informationprocessing based on the stimulus-related change in the spectral EEG energy in the 15–30 Hz frequency band. Using the developedapproach, we analyzed the influence of the visual stimulus complexity on the features of spatio-temporal neural activity. It has beenfound that at low complexity the spectral amplitude of the EEG in the range of 15–30 Hz increases mainly in the parietal zone. Withincreasing complexity, the spectral amplitude of the EEG increases simultaneously in different parts of the cortex, mainly in the frontalregion. Practical relevance: The identified features of neural dynamics can be used in the development of passive brain-computerinterfaces to monitor a person’s cognitive state and evaluate the cognitive load in real time.