Decomposing loss aversion from a single neural signal.

People often display stronger aversion to losses than appetite for equivalent gains, a widespread phenomenon known as loss aversion. The prevailing theory attributes loss aversion to a valuation bias that amplifies losses relative to gains. An alternative account attributes loss aversion to a response bias that avoids choices that might result in loss. By modeling the temporal dynamics of scalp electrical activity during decisions to accept or reject gambles within a sequential sampling framework, we decomposed valuation bias and response bias from a single event-related neural signal, the P3. Specifically, we found valuation bias manifested as larger sensitivity of P3 to losses than gains, which was localizable to reward-related brain regions. By contrast, response bias manifested as larger P3 preceding gamble acceptance than rejection and was localizable to motor cortex. Our study reveals the dissociable neural biomarkers of response bias and valuation bias underpinning loss-averse decisions.

Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception.

Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways. Using lamina-resolved MRI at 7 T, we measured the neural activity during imagery and perception of faces and scenes in high-level ventral visual cortex at the mesoscale of laminar organization that distinguishes feedforward from feedback signals. We found distinctive laminar profiles for imagery and perception of scenes and faces in the parahippocampal place area and the fusiform face area, respectively. Our findings provide insight into the neural basis of the phenomenology of visual imagery versus perception and shed new light into the mesoscale organization of feedforward and feedback information flow in high-level ventral visual cortex.

Thalamocortical interactions shape hierarchical neural variability during stimulus perception.

The brain is organized hierarchically to process sensory signals. But, how do functional connections within and across areas contribute to this hierarchical order? We addressed this problem in the thalamocortical network, while monkeys detected vibrotactile stimulus. During this task, we quantified neural variability and directed functional connectivity in simultaneously recorded neurons sharing the cutaneous receptive field within and across VPL and areas 3b and 1. Before stimulus onset, VPL and area 3b exhibited similar fast dynamics while area 1 showed slower timescales. During the stimulus presence, inter-trial neural variability increased along the network VPL-3b-1 while VPL established two main feedforward pathways with areas 3b and 1 to process the stimulus. This lower variability of VPL and area 3b was found to regulate feedforward thalamocortical pathways. Instead, intra-cortical interactions were only anticipated by higher intrinsic timescales in area 1. Overall, our results provide evidence of hierarchical functional roles along the thalamocortical network.

Virtual, mixed, and augmented realities: A commentary on their significance in cognitive neuroscience and neuropsychology.

The integration of virtual, mixed, and augmented reality technologies in cognitive neuroscience and neuropsychology represents a transformative frontier. In this Commentary, we conducted a meta-analysis of studies that explored the impact of Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR) on cognitive neuroscience and neuropsychology. Our review highlights the versatile applications of VR, ranging from spatial cognition assessments to rehabilitation for Traumatic Brain Injury. We found that MR and AR offer innovative avenues for cognitive training, particularly in memory-related disorders. The applications extend to addressing social cognition disorders and serving as therapeutic interventions for mental health issues. Collaborative efforts between neuroscientists and technology developers are crucial, with reinforcement learning and neuroimaging studies enhancing the potential for improved outcomes. Ethical considerations, including informed consent, privacy, and accessibility, demand careful attention. Our review identified common aspects of the meta-analysis, including the potential of VR technologies in cognitive neuroscience and neuropsychology, the use of MR and AR in memory research, and the role of VR in neurorehabilitation and therapy.

Combining patient-lesion and big data approaches to reveal hippocampal contributions to spatial memory and navigation.

Classic findings of impaired allocentric spatial learning and memory following hippocampal lesions indicate that the hippocampus supports cognitive maps of one's environment. Many studies assess navigation in vista space virtual reality environments and compare hippocampal-lesioned individuals' performance to that of small control samples, potentially stifling detection of preserved and impaired performance. Using the mobile app Sea Hero Quest, we examined navigation in diverse complex environments in two individuals with hippocampal lesions relative to demographically matched controls (N = 17,734). We found surprisingly accurate navigation in several environments, particularly those containing a constrained set of sub-goals, paths, and/or turns. Areas of impaired performance may reflect a role for the hippocampus in anterograde memory and more flexible and/or precise spatial representations, even when the need for allocentric processing is minimal. The results emphasize the value of combining single cases with big data and illustrate navigation performance profiles in individuals with hippocampal compromise.

Decomposing the multiple encoding benefit in visual long-term memory: Primary contributions by the number of encoding opportunities.

Although access to the seemingly infinite capacity of our visual long-term memory (VLTM) can be restricted by visual working memory (VWM) capacity at encoding and retrieval, access can be improved with repeated encoding. This leads to the multiple encoding benefit (MEB), the finding that VLTM performance improves as the number of opportunities to encode the same information increases over time. However, as the number of encoding opportunities increases, so do other factors such as the number of identical encoded VWM representations and chances to engage in successful retrieval during each opportunity. Thus, across two experiments, we disentangled the contributions of each of these factors to the MEB by having participants encode a varying number of identical objects across multiple encoding opportunities. Along with behavioural data, we also examined two established EEG correlates that track the number of maintained VWM representations, namely the posterior alpha suppression and the negative slow wave. Here, we identified that the primary mechanism behind the MEB was the number of encoding opportunities. That is, recognition memory performance was higher following an increase in the number of encoding opportunities, and this could not be attributed solely to an increase in the number of encoded VWM representations or successful retrieval. Our results thus contribute to the understanding of the fundamental mechanisms behind the influence of VWM on VLTM encoding.

The Transdiagnostic Connectome Project: a richly phenotyped open dataset for advancing the study of brain-behavior relationships in psychiatry.

An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs. Additionally, participants completed over 50 psychological and cognitive assessments. Here, we detail available behavioral data as well as raw and processed MRI derivatives. Associations between data processing and quality metrics, such as head motion, are reported. Processed data exhibit classic task activation effects and canonical functional network organization. Overall, we provide a comprehensive and analysis-ready transdiagnostic dataset, which we hope will accelerate the identification of illness-relevant features of brain functioning, enabling future discoveries in basic and clinical neuroscience.

National Institute of Mental Health Support for Cognitive Treatment Development in Schizophrenia: A Narrative Review.

For several decades the National Institute of Mental Health (NIMH) has supported basic and translational research into cognitive impairment in schizophrenia. This article describes the Institute's ongoing commitment to cognitive assessment and intervention research, as reflected by three signature initiatives-Measurement and Treatment Research to Improve Cognition in Schizophrenia; Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia; and Research Domain Criteria-and related funding announcements that span basic experimental studies, efficacy and comparative effectiveness trials, and implementation research designed to promote cognitive healthcare in real-world treatment settings. We discuss how trends in science and public health policy since the early 2000s have influenced NIMH treatment development activities, resulting in greater attention to (1) inclusive teams that reflect end-user perspectives on the utility of proposed studies; (2) measurement of discrete neurocognitive processes to inform targeted interventions; (3) clinical trials that produce useful information about putative illness mechanisms, promising treatment targets, and downstream clinical effects; and (4) "productive urgency" in pursuing feasible and effective cognitive interventions for psychosis. Programs employing these principles have catalyzed cognitive measurement, drug development, and behavioral intervention approaches that aim to improve neurocognition and community functioning among persons with schizophrenia. NIMH will maintain support for innovative and impactful investigator-initiated research that advances patient-centered, clinically effective, and continuously improving cognitive health care for persons with psychotic disorders.

Mapping the emotional homunculus with fMRI.

Emotions are commonly associated with bodily sensations, e.g., boiling with anger when overwhelmed with rage. Studies have shown that emotions are related to specific body parts, suggesting that somatotopically organized cortical regions that commonly respond to somatosensory and motor experiences might be involved in the generation of emotions. We used functional magnetic resonance imaging to investigate whether the subjective feelings of emotion are accompanied by the activation of somatotopically defined sensorimotor brain regions, thus aiming to reconstruct an "emotional homunculus." By defining the convergence of the brain activation patterns evoked by self-generated emotions during scanning onto a sensorimotor map created on participants' tactile and motor brain activity, we showed that all the evoked emotions activated parts of this sensorimotor map, yet with considerable overlap among different emotions. Although we could not find a highly specific segmentation of discrete emotions over sensorimotor regions, our results support an embodied experience of emotions.

Neuronal response of the primate striatum tail to face of socially familiar persons.

Recent studies have suggested that the basal ganglia, the center of stimulus-reward associative learning, are involved in social behavior. However, the role of the basal ganglia in social information processing remains unclear. Here, we demonstrate that the striatum tail (STRt) in macaque monkeys, which is sensitive to visual objects with long-term reward history (i.e., stable object value), is also sensitive to socially familiar persons. Many STRt neurons responded to face images of persons, especially those who took daily care of the subject monkeys. These face-responsive neurons also encoded stable object value. The strength of the neuronal modulation of social familiarity and stable object value biases were positively correlated. These results suggest that both social familiarity and stable object value information are mediated by a common neuronal mechanism. Thus, the representation of social information is linked to reward information in the STRt, not in the dedicated social information circuit.

Sleep-dependent decorrelation of hippocampal spatial representations.

Neuronal ensembles are crucial for episodic memory and spatial mapping. Sleep, particularly non-REM (NREM), is vital for memory consolidation, as it triggers plasticity mechanisms through brain oscillations that reactivate neuronal ensembles. Here, we assessed their role in consolidating hippocampal spatial representations during sleep. We recorded hippocampus activity in rats performing a spatial object-place recognition (OPR) memory task, during encoding and retrieval periods, separated by intervening sleep. Successful OPR retrieval correlated with NREM duration, during which cortical oscillations decreased in power and density as well as neuronal spiking, suggesting global downregulation of network excitability. However, neurons encoding specific spatial locations (i.e., place cells) or objects during OPR showed stronger synchrony with brain oscillations compared to non-encoding neurons, and the stability of spatial representations decreased proportionally with NREM duration. Our findings suggest that NREM sleep may promote flexible remapping in hippocampal ensembles, potentially aiding memory consolidation and adaptation to novel spatial contexts.

Protocol to study dam-pup social transmission using a modified paradigm for transmission of food preference.

The social transmission of food preference, a rudimentary form of social learning, has primarily been studied in pairs of adult rodents. Here, we present a protocol to explore the parent-offspring context in social learning using an adaptation of this classic paradigm for rodent dam-pup dyads. We describe steps for studying weanling mice from the same mother and present a worked example using weight-based (food consumption) and time-based (exploration) indices of social learning.

Event segmentation in ADHD: neglect of social information and deviant theta activity point to a mechanism underlying ADHD.

Background: Attention-deficit/hyperactivity disorder (ADHD) is one of the most frequently diagnosed psychiatric conditions in children and adolescents. Although the symptoms appear to be well described, no coherent conceptual mechanistic framework integrates their occurrence and variance and the associated problems that people with ADHD face. Aims: The current study proposes that altered event segmentation processes provide a novel mechanistic framework for understanding deficits in ADHD. Methods: Adolescents with ADHD and neurotypically developing (NT) peers watched a short movie and were then asked to indicate the boundaries between meaningful segments of the movie. Concomitantly recorded electroencephalography (EEG) data were analysed for differences in frequency band activity and effective connectivity between brain areas. Results: Compared with their NT peers, the ADHD group showed less dependence of their segmentation behaviour on social information, indicating that they did not consider social information to the same extent as their unaffected peers. This divergence was accompanied by differences in EEG theta band activity and a different effective connectivity network architecture at the source level. Specifically, NT adolescents primarily showed error signalling in and between the left and right fusiform gyri related to social information processing, which was not the case in the ADHD group. For the ADHD group, the inferior frontal cortex associated with attentional sampling served as a hub instead, indicating problems in the deployment of attentional control. Conclusions: This study shows that adolescents with ADHD perceive events differently from their NT peers, in association with a different brain network architecture that reflects less adaptation to the situation and problems in attentional sampling of environmental information. The results call for a novel conceptual view of ADHD, based on event segmentation theory.

Open-source platform for kinematic analysis of mouse forelimb movement.

Here we present an open-source behavioral platform and software solution for studying fine motor skills in mice performing reach-to-grasp task. We describe steps for assembling the box, training mice to perform the task, and processing the video with the custom software pipeline to analyze forepaw kinematics. The behavioral platform uses readily available and 3D-printed components and was designed to be affordable and universally reproducible. We provide the schematics, 3D models, code, and assembly instructions in the open GitHub repository.

When Maturation is Not Linear: Brain Oscillatory Activity in the Process of Aging as Measured by Electrophysiology.

Changes in brain oscillatory activity are commonly used as biomarkers both in cognitive neuroscience and in neuropsychiatric conditions. However, little is known about how its profile changes across maturation. Here we use regression models to characterize magnetoencephalography power changes within classical frequency bands in a sample of 792 healthy participants, covering the range 13 to 80 years old. Our findings unveil complex, non-linear power trajectories that defy the traditional linear paradigm, with notable cortical region variations. Interestingly, slow wave activity increases correlate with improved cognitive performance throughout life and larger gray matter volume in the elderly. Conversely, fast wave activity diminishes in adulthood. Elevated low-frequency activity during aging, traditionally seen as compensatory, may also signify neural deterioration. This dual interpretation, highlighted by our study, reveals the intricate dynamics between brain oscillations, cognitive performance, and aging. It advances our understanding of neurodevelopment and aging by emphasizing the regional specificity and complexity of brain rhythm changes, with implications for cognitive and structural integrity.

Cognition of Time and Thinking Beyond.

A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience.

Cognitive neuroscience has considerable untapped potential to translate our understanding of brain function into applications that maintain, restore, or enhance human cognition. Complex, real-world phenomena encountered in daily life, professional contexts, and in the arts, can also be a rich source of information for better understanding cognition, which in turn can lead to advances in knowledge and health outcomes. Interdisciplinary work is needed for these bi-directional benefits to be realized. Our cognitive neuroscience team has been collaborating on several interdisciplinary projects: hardware and software development for brain stimulation, measuring human operator state in safety-critical robotics environments, and exploring emotional regulation in actors who perform traumatic narratives. Our approach is to study research questions of mutual interest in the contexts of domain-specific applications, using (and sometimes improving) the experimental tools and techniques of cognitive neuroscience. These interdisciplinary attempts are described as case studies in the present work to illustrate non-trivial challenges that come from working across traditional disciplinary boundaries. We reflect on how obstacles to interdisciplinary work can be overcome, with the goals of enriching our understanding of human cognition and amplifying the positive effects cognitive neuroscientists have on society and innovation.