Neuroscience Paradigms Research Articles

The puzzle of mood rationality

AbstractMoods, orthodoxy holds, exist outside the space of reasons. A depressed subject may change their thoughts and behaviors as a result of their depression. But, according to this view, their mood gives them no genuine reason to do so. Instead, moods are mere causal influences on cognition. The issue is that moods, with their diffuse phenomenology, appear to lack intentionality (Directionlessness). But intentionality appears to be a necessary condition on rationality (The Content Constraint). Together, these principles conflict with the idea that moods are rational states of mind (Mood Rationality). The inconsistency of these three principles is the Puzzle of Mood Rationality. Now to many, this puzzle is hardly vexing: we should reject Mood Rationality. But, I argue, Mood Rationality is true despite its unpopularity. Thus, we need another way of resolving the puzzle. To do so, I distinguish intentionality as a first‐personal, phenomenological notion from representation as a third‐personal, cognitive scientific notion. I then argue that moods satisfy a revised version of the Content Constraint and sketch an account of moods as representational but non‐intentional mental states, drawing on the Valuationist paradigm in affective neuroscience. I end by showing how this account enables us to explain moods’ rationality.

A study on the effects of transcranial direct current stimulation combined with motor imagery on brain function based on electroencephalogram and near infrared spectrum

Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.

Frequency-tagging of spatial attention using periliminal flickers

Abstract Steady-State Visually Evoked Potentials (SSVEP) manifest as a sustained rhythmic activity that can be observed in surface electroencephalography (EEG) in response to periodic visual stimuli, commonly referred to as flickers. SSVEPs are widely used in fundamental cognitive neuroscience paradigms and Brain-Computer Interfaces (BCI) due to their robust and rapid onset. However, they have drawbacks related to the intrusive saliency of flickering visual stimuli, which may induce eye strain, cognitive fatigue, and biases in visual exploration. Previous findings highlighted the potential of altering features of flicker stimuli to improve user experience. In this study, we propose to reduce the amplitude modulation depth of flickering stimuli down to the individuals’ perceptual visibility threshold (periliminal) and below (subliminal). The stimulus amplitude modulation depth represents the contrast difference between the two alternating states of a flicker. A simple visual attention task where participants responded to the presentation of spatially-cued target stimuli (left and right) was used to assess the validity of such periliminal and subliminal frequency-tagging probes to capture spatial attention. The left and right sides of the screen, where target stimuli were presented, were covered by large flickers (13 and 15 Hz respectively). The amplitude modulation depth of these flickers was manipulated across three conditions: control, periliminal, and subliminal. The latter two levels of flickers amplitude modulation depth were defined through a perceptual visibility threshold protocol on a single- subject basis. Subjective feedback indicated that the use of periliminal and subliminal flickers substantially improved user experience. The present study demonstrates that periliminal and subliminal flickers evoked SSVEP responses that can be used to derive spatial attention in frequency-tagging paradigms. The single-trial classification of attended space (left versus right) based on SSVEP response reached an average accuracy of 81.1% for the periliminal and 58% for the subliminal conditions. These findings reveal the promises held by the application of inconspicuous flickers to both cognitive neuroscience research and BCI development.

Strict certainty preference in the predictive brain: a new perspective on financial innovations and their role in the real economy

The dominant paradigm in neuroscience considers the brain to be a prediction engine. The brain generates predictions first, which are then contrasted with information to generate error signals. Finite brain resources are subsequently spent in selectively processing the error signals based on their relative value with higher value signals getting a priority. In this way, the brain can be thought of as optimizing on its own internal resources before seeking to optimize on the resources available in the external world. We show that such considerations change the cost–benefit calculations of certain vs uncertain outcomes in the brain, giving rise to, what can be termed as, a strict certainty preference. A new perspective on prominent financial innovations (such as securitization, interest rate swaps, and credit default swaps) emerges, with a dark side that potentially leads to a misallocation of resources towards low NPV projects.

Attachment and borderline personality disorder as the dance unfolds: A quantitative analysis of a novel paradigm

Current research on personality disorders strives to identify key behavioural and cognitive facets of patient functioning, to unravel the underlying root causes and maintenance mechanisms. This process often involves the application of social paradigms — however, these often only include momentary affective depictions rather than unfolding interactions. This constitutes a limitation in our capacity to probe core symptoms, and leaves potential findings uncovered which could help those who are in close relationships with affected individuals. Here, we deployed a novel task in which subjects interact with four unknown virtual partners in a turn-taking paradigm akin to a dance, and report on their experience with each. The virtual partners embody four combinations of low/high expressivity of positive/negative mood. Higher scores on our symptomatic measures of attachment anxiety, avoidance, and borderline personality disorder (BPD) were all linked to a general negative appraisal of all the interpersonal experiences. Moreover, the negative appraisal of the partner who displayed a high negative/low positive mood was tied with attachment anxiety and BPD symptoms. The extent to which subjects felt responsible for causing partners’ distress was most strongly linked to attachment anxiety. Finally, we provide a fully-fledged exploration of move-by-move action latencies and click distances from partners. This analysis underscored slower movement initiation from anxiously attached individuals throughout all virtual interactions. In summary, we describe a novel paradigm for second-person neuroscience, which allowed both the replication of established results and the capture of new behavioural signatures associated with attachment anxiety, and discuss its limitations.

Эмоция и аффект в мировой политике

In the world (but not yet in Russia specifically) there is already an extensive and rapidly growing amount of literature on the role and place of emotions both in international relations (IR) and in the foreign policy of states. This gives grounds to talk about the “emotional turn” within the framework of IR as a research discipline. But it is also well known that emotions are studied not only by IR, but in psychology, in various areas of neuroscience and in sociology as well. This article deals with some ontological and epistemological issues, arising from the results of multidirectional efforts of many academics to conceptualize the international political role of emotions on the basis of the leading international relations theories. The immediate goal is the exploration in the chosen subject area of the methodological problem of the integration of social science knowledge with achievements from the field of natural sciences. It is proposed to pay particularly close attention in this sense to the paradigm of affective neuroscience. The phenomenon of emotion, in principle, does not fit well with categories, which until now the IR has been focused on. Methodological imperatives of objective research push for the exclusion of subjective emotional experience from the scope of consideration. Constructivists, in their turn, treat emotion as a cognitive belief rather than as a bodily state. But in the article, the emotion is represented as a phenomenon which is both mental and physical. This is a monist view, going back to Spinoza. The research leads to the conclusion that, although social constructivism has good potential for studying the emotions in IR, when intentionally ignoring the biological nature of emotions, social constructivists can weaken their own position in comparison with those academic opponents, who are committed to instrumental rationalism in their approach. Constructivists thus limit with no good reason their own agenda when studying emotions and hinder the development of a transdisciplinary approach to the study of emotions in IR in particular.

Brain energetics and the connectionist concept in cognitive neuroscience.

One of the central paradigms of modern neuroscience is the connectionist concept suggesting that the brain's cognitive functions are carried out at the level of neural networks through complex interactions among neurons. This concept considers neurons as simple network elements whose function is limited to generating electrical potentials and transmitting signals to other neurons. Here, I focus on the neuroenergetic aspect of cognitive functions and argue that many findings from this field challenge the concept that cognitive functions are performed exclusively at the level of neural circuits. Two of these findings are particularly foretelling. First, activation of the cerebral cortex in humans (sensory stimulation or solving cognitive problems) is not associated with a significant increase in energy demand. Second, the energetic cost of the brain per unit mass in primates, including Homo sapiens, is approximately proportional to the number of cerebral neurons but not to the number of synapses, the complexity of neural networks, or the level of brain's intellectual abilities. These findings contradict the predictions of the connectionist concept. Rather, they suggest that cognitive functions are generated by intraneuronal mechanisms that do not require much energy. In this context, interactions among neurons would serve to coordinate activities of neurons performing elementary cognitive functions. This function of the network mechanisms also does not require much energy.

Geometric constraints on human brain function

The anatomy of the brain necessarily constrains its function, but precisely how remains unclear. The classical and dominant paradigm in neuroscience is that neuronal dynamics are driven by interactions between discrete, functionally specialized cell populations connected by a complex array of axonal fibres1–3. However, predictions from neural field theory, an established mathematical framework for modelling large-scale brain activity4–6, suggest that the geometry of the brain may represent a more fundamental constraint on dynamics than complex interregional connectivity7,8. Here, we confirm these theoretical predictions by analysing human magnetic resonance imaging data acquired under spontaneous and diverse task-evoked conditions. Specifically, we show that cortical and subcortical activity can be parsimoniously understood as resulting from excitations of fundamental, resonant modes of the brain’s geometry (that is, its shape) rather than from modes of complex interregional connectivity, as classically assumed. We then use these geometric modes to show that task-evoked activations across over 10,000 brain maps are not confined to focal areas, as widely believed, but instead excite brain-wide modes with wavelengths spanning over 60 mm. Finally, we confirm predictions that the close link between geometry and function is explained by a dominant role for wave-like activity, showing that wave dynamics can reproduce numerous canonical spatiotemporal properties of spontaneous and evoked recordings. Our findings challenge prevailing views and identify a previously underappreciated role of geometry in shaping function, as predicted by a unifying and physically principled model of brain-wide dynamics.

The contribution of coherence field theory to a model of consciousness: electric currents, EM fields, and EM radiation in the brain.

A paradigm in neuroscience is developing which views resonance as the phenomenon responsible for consciousness. Much progress is being made in the investigation of how resonance as oscillating flows within the brain's electric field might result in production of mind from matter. But it's mostly unknown how vibrations among features of matter such as nanoscale atomic structures and photonic waves may participate in forming the basic substance of first-person consciousness, meaning percepts such as colors, textures, sounds, thoughts, feelings et cetera. Initial evidence at the leading edge of quantum biology suggests that light and atoms combine to form synchronously resonating structures of contiguous energy which I have termed coherence fields. My hypothesis is that coherence fields as atomic nodes within expanses of integrating photonic waves are the fundamental unit of first-person percepts insofar as they arise from electromagnetic matter. A concept of quantum coherence is formulated based on a new phenomenology of matter's nanoscale properties, and this is shown to tie what we have thus far discovered of neural anatomy into a comprehensive model of how electrical impulses travel through neurons as electron currents driven by coherence at the quantum scale. Transmembrane electric fields generated by ionic currents, synaptic phase regulation, and perhaps further mechanisms have been hypothesized as responsible for local field potentials (LFP) oscillations. Some insights into how emergent, macroscopic waves in the brain's electric field may reciprocally impact LFP propagation to control arousal, attention, and volition are briefly discussed. Activation of neural tissue is closely linked to temperature variation, and it is hypothesized that this is not merely a waste byproduct but constitutes a signature of coherence field modulation, with photonic waves of a primarily infrared spectral range functioning as an interstitial medium of the basic percept field. A variety of possible routes to coherence field modulation are outlined that derive from the mechanisms of electric currents, EM fields, EM radiation, and entanglement. If future experimental designs continue to validate coherence field theory, this could set science on course to resolve the mind/body problem.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems.

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.SIGNIFICANCE STATEMENT The brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

The role of neurotransmitter systems in mediating deep brain stimulation effects in Parkinson's disease.

Deep brain stimulation (DBS) is among the most successful paradigms in both translational and reverse translational neuroscience. DBS has developed into a standard treatment for movement disorders such as Parkinson’s disease (PD) in recent decades, however, specific mechanisms behind DBS’s efficacy and side effects remain unrevealed. Several hypotheses have been proposed, including neuronal firing rate and pattern theories that emphasize the impact of DBS on local circuitry but detail distant electrophysiological readouts to a lesser extent. Furthermore, ample preclinical and clinical evidence indicates that DBS influences neurotransmitter dynamics in PD, particularly the effects of subthalamic nucleus (STN) DBS on striatal dopaminergic and glutamatergic systems; pallidum DBS on striatal dopaminergic and GABAergic systems; pedunculopontine nucleus DBS on cholinergic systems; and STN-DBS on locus coeruleus (LC) noradrenergic system. DBS has additionally been associated with mood-related side effects within brainstem serotoninergic systems in response to STN-DBS. Still, addressing the mechanisms of DBS on neurotransmitters’ dynamics is commonly overlooked due to its practical difficulties in monitoring real-time changes in remote areas. Given that electrical stimulation alters neurotransmitter release in local and remote regions, it eventually exhibits changes in specific neuronal functions. Consequently, such changes lead to further modulation, synthesis, and release of neurotransmitters. This narrative review discusses the main neurotransmitter dynamics in PD and their role in mediating DBS effects from preclinical and clinical data.

Translational neuronal ensembles: Neuronal microcircuits in psychology, physiology, pharmacology and pathology.

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

‘L’hallucination que la réalité provoque’: A Neuroscientific Reading of Louis-Ferdinand Céline’s Féerie pour une autre fois I and Normance

Abstract This article offers a radically new reading of Louis-Ferdinand Céline’s Féerie pour une autre fois i (1952) and Normance (1954). The article draws on an increasingly influential paradigm in contemporary neuroscience known as ‘predictive processing’. As a theoretical framework, predictive processing has played a critical role in our understanding of perceptual hallucinations. The article reads Céline’s accounts of his and his narrators’ perceptual troubles through predictive processing’s core tenets, in particular the role played by prior experience in structuring perception, whether standard or aberrant. The salient ‘prior’ for Céline in these works is his traumatic experience of the First World War. The approach adopted in this article allows for a reading of these works that attends to Céline’s various psychological afflictions while nevertheless eschewing any laundering of his reputation as a notorious antisemite by the invocation of ‘madness’. The article concludes with a critique of Allen Thiher’s 1972 book, Céline: The Novel as Delirium, and point to the works of Isabelle Blondiaux as representing a more rigorous way of approaching mental suffering in Céline’s works.

An Approach to Neuroimaging Interpersonal Interactions in Mental Health Interventions

BackgroundConventional paradigms in clinical neuroscience tend to be constrained in terms of ecological validity, raising several challenges to studying the mechanisms mediating treatments and outcomes in clinical settings. Addressing these issues requires real-world neuroimaging techniques that are capable of continuously collecting data during free-flowing interpersonal interactions and that allow for experimental designs that are representative of the clinical situations in which they occur. MethodsIn this work, we developed a paradigm that fractionates the major components of human-to-human verbal interactions occurring in clinical situations and used functional near-infrared spectroscopy to assess the brain systems underlying clinician-client discourse (N = 30). ResultsCross-brain neural coupling between people was significantly greater during clinical interactions compared with everyday life verbal communication, particularly between the prefrontal cortex (e.g., inferior frontal gyrus) and inferior parietal lobule (e.g., supramarginal gyrus). The clinical tasks revealed extensive increases in activity across the prefrontal cortex, especially in the rostral prefrontal cortex (area 10), during periods in which participants were required to silently reason about the dysfunctional cognitions of the other person. ConclusionsThis work demonstrates a novel experimental approach to investigating the neural underpinnings of interpersonal interactions that typically occur in clinical settings, and its findings support the idea that particular prefrontal systems might be critical to cultivating mental health.

Collective and individual cognition in the activity of a law enforcer

It is noted that legal and cognitive sciences underestimate the importance of the factor of individual analysis of materials by the judge-rapporteur and the degree of its influence on the adoption of the final procedural decision in the collegial consideration of the case. At the same time, the thesis has not been formulated in relation to the field of law, according to which the sole consideration of cases by the courts is within the scope of most of the laws of collective cognition of reality. The activity of the law enforcer related to decision-making is based not only on the analysis of the law and the circumstances of the incident, but also on their interpretations by the persons involved in the case. This means that the cognition of reality by the court is not only collective, but also multi-stage, multilevel, mediated. The connection of the outcome of the case with the subjective assessment of the events by its participants and the proposed versions of what happened, on the one hand, and the emergence of a new scientific paradigm in cognitive neuroscience and epistemology, in which knowledge of the world is considered as the fruit of collective activity, raises the question of the need to clarify the scientific and practical understanding of the legal principle of the immediacy of judicial proceedings, including in order to clarify the status of the judge-speaker, other speakers of cases within the framework of existing procedures.

Modeling Neurodegeneration in silico With Deep Learning.

Deep neural networks, inspired by information processing in the brain, can achieve human-like performance for various tasks. However, research efforts to use these networks as models of the brain have primarily focused on modeling healthy brain function so far. In this work, we propose a paradigm for modeling neural diseases in silico with deep learning and demonstrate its use in modeling posterior cortical atrophy (PCA), an atypical form of Alzheimer’s disease affecting the visual cortex. We simulated PCA in deep convolutional neural networks (DCNNs) trained for visual object recognition by randomly injuring connections between artificial neurons. Results showed that injured networks progressively lost their object recognition capability. Simulated PCA impacted learned representations hierarchically, as networks lost object-level representations before category-level representations. Incorporating this paradigm in computational neuroscience will be essential for developing in silico models of the brain and neurological diseases. The paradigm can be expanded to incorporate elements of neural plasticity and to other cognitive domains such as motor control, auditory cognition, language processing, and decision making.

Statistical analysis of periodic data in neuroscience

Many experimental paradigms in neuroscience involve driving the nervous system with periodic sensory stimuli. Neural signals recorded using a variety of techniques will then include phase-locked oscillations at the stimulation frequency. The analysis of such data often involves standard univariate statistics such as T-tests, conducted on the Fourier amplitude components (ignoring phase), either to test for the presence of a signal, or to compare signals across different conditions. However, the assumptions of these tests will sometimes be violated because amplitudes are not normally distributed, and furthermore weak signals might be missed if the phase information is discarded. An alternative approach is to conduct multivariate statistical tests using the real and imaginary Fourier components. Here the performance of two multivariate extensions of the T-test are compared: Hotelling’s T2 and a variant called Tcirc2. A novel test of the assumptions of Tcirc2 is developed, based on the condition index of the data (the square root of the ratio of eigenvalues of a bounding ellipse), and a heuristic for excluding outliers using the Mahalanobis distance is proposed. The Tcirc2 statistic is then extended to multi-level designs, resulting in a new statistical test termed ANOVAcirc2. This has identical assumptions to Tcirc2, and is shown to be more sensitive than MANOVA when these assumptions are met. The use of these tests is demonstrated for two publicly available empirical data sets, and practical guidance is suggested for choosing which test to run. Implementations of these novel tools are provided as an R package and a Matlab toolbox, in the hope that their wider adoption will improve the sensitivity of statistical inferences involving periodic data.

Prefrontal activation and pupil dilation during n-back task performance: A combined fNIRS and pupillometry study

The n-back task is one of the most commonly used working memory (WM) paradigms in cognitive neuroscience. Converging evidence suggests activation in the lateral prefrontal cortex (PFC) and pupil dilation [a proxy for locus coeruleus (LC) activation] during this task. However, it remains unclear whether the lateral PFC and the LC are functionally associated during n-back task performance. This study's aim was to examine the relationship between changes in lateral PFC activity and the pupil diameter and to evaluate the effect of WM load on such relationship during the n-back task. Thirty-nine healthy young adults (10 males, 29 females) underwent a number n-back paradigm with 0- and 3-back conditions. Their prefrontal hemodynamics and changes in pupil size during task performance were simultaneously measured using a 16-channel functional near-infrared spectroscopy (fNIRS) device and a wearable eye tracker. Young adults exhibited significant activation in the bilateral lateral PFC and significant increases in pupil size when the WM load was high (i.e., 3-back) but not low (i.e., 0-back) compared with the resting period. Interestingly, significant positive correlations were found between changes in lateral PFC activity and pupil size during the 0-back task only. These correlations tended to be stronger during the 0-back than the 3-back condition. Thus, the functional relationship between the lateral PFC and the LC may vary at different load levels during the n-back task. Our findings have important implications for neuropsychiatric research and support concurrent fNIRS and pupillometric measurements for a better understanding of the mechanisms underlying WM processing.

Young Domestic Pigs (Sus scrofa) Can Perform Pavlovian Eyeblink Conditioning.

Introduction: Pigs have been an increasingly popular preclinical model in nutritional neuroscience, as their anatomy, physiology, and nutrition requirements are highly comparable to those of humans. Eyeblink conditioning is one of the most well-validated behavioral paradigms in neuroscience to study underlying mechanisms of learning and memory formation in the cerebellum. Eyeblink conditioning has been performed in many species but has never been done on young pigs. Therefore, our aim here was to develop and validate an eyeblink conditioning paradigm in young pigs.Method: Eighteen intact male pigs were artificially reared from postnatal day 2–30. The eyeblink conditioning setup consisted of a sound-damping box with a hammock that pigs were placed in, which allowed the pig to remain comfortable yet maintain a typical range of head motion. In a delay conditioning paradigm, the conditional stimulus (CS) was a 550 ms blue light-emitting diode (LED), the unconditional stimulus (US) was a 50 ms eye air-puff, the CS-US interval was 500 ms. Starting at postnatal day 14, pigs were habituated for 5 days to the eyeblink conditioning setup, followed by 5 daily sessions of acquisition training (40 paired CS-US trials each day).Results: The group-averaged amplitude of conditioned eyelid responses gradually increased over the course of the 5 days of training, indicating that pigs learned to make the association between the LED light CS and the air-puff US. A similar increase was found for the conditioned response (CR) probability: the group-averaged CR probability on session 1 was about 12% and reached a CR probability of 55% on day 5. The latency to CR peak time lacked a temporal preference in the first session but clearly showed preference from the moment that animals started to show more CRs in session 2 and onwards whereby the eyelid was maximally closed exactly at the moment that the US would be delivered.Conclusion: We concluded that 3-week-old pigs have the capability of performing in a cerebellar classical conditioning task, demonstrating for the first time that eyeblink conditioning in young pigs has the potential to be a valuable behavioral tool to measure neurodevelopment.

Individualized cognitive neuroscience needs 7T: Comparing numerosity maps at 3T and 7T MRI

The field of cognitive neuroscience is weighing evidence about whether to move from the current standard field strength of 3 Tesla (3T) to ultra-high field (UHF) of 7T and above. The present study contributes to the evidence by comparing a computational cognitive neuroscience paradigm at 3T and 7T. The goal was to evaluate the practical effects, i.e. model predictive power, of field strength on a numerosity task using accessible pre-processing and analysis tools. Previously, using 7T functional magnetic resonance imaging and biologically-inspired analyses, i.e. population receptive field modelling, we discovered topographical organization of numerosity-selective neural populations in human parietal cortex. Here we show that these topographic maps are also detectable at 3T. However, averaging of many more functional runs was required at 3T to reliably reconstruct numerosity maps. On average, one 7T run had about four times the model predictive power of one 3T run. We believe that this amount of scanning would have made the initial discovery of the numerosity maps on 3T highly infeasible in practice. Therefore, we suggest that the higher signal-to-noise ratio and signal sensitivity of UHF MRI is necessary to build mechanistic models of the organization and function of our cognitive abilities in individual participants.