Enhancing precision in human neuroscience

Improving mentalizing deficits in older age with region-specific transcranial direct current stimulation

Older adults have difficulties to detect the intentions, thoughts, and feelings of others, indicating an age-associated decline of socio-cognitive abilities that are known as "mentalizing". These deficits in mental state recognition are driven by neurofunctional alterations in brain regions that are implicated in mentalizing, such as the right temporo-parietal junction (rTPJ) and the dorso-medial prefrontal cortex (dmPFC). We tested whether focal transcranial current stimulation (tDCS) of the rTPJ and dmPFC has the potential to eliminate mentalizing deficits in older adults. Mentalizing deficits were assessed with a novel mindreading task that required the recognition of mental states in child faces. Older adults (n = 60) performed worse than younger adults (n = 30) on the mindreading task, indicating age-dependent deficits in mental state recognition. These mentalizing deficits were ameliorated in older adults who received sham-controlled andodal tDCS over the rTPJ (n = 30) but remained unchanged in older adults who received sham-controlled andodal tDCS over the dmPFC (n = 30). We, thus, showed for the first time that anodal tDCS over the rTPJ has the potential to remediate age-dependent mentalizing deficits in a region-specific way. This provides a rationale for exploring stimulation-based interventions targeting mentalizing deficits in older age.

Reliability of variability and complexity measures for task and task-free BOLD fMRI

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.

Cognition and saccadic eye movement performance are impaired in chronic rhinosinusitis

BACKGROUND

Patients with chronic rhinosinusitis (CRS) can experience cognitive dysfunction. The literature on this topic mostly reflects patient-reported measurements. Our goal was to assess cognitive function in patients with CRS using objective measures, including saccadic eye movements-a behavioral response reflecting cognitive and sensory information integration that is often compromised in conditions with impaired cognition.

METHODS

Participants (N = 24 with CRS, N = 23 non-CRS healthy controls) enrolled from rhinology clinic underwent sinonasal evaluation, quality of life assessment (Sino-nasal Outcome Test 22 [SNOT-22]), and cognitive assessment with the Neuro-QOL Cognitive Function-Short Form, the Montreal Cognitive Assessment (MoCA), and recording of eye movements using video-oculography.

RESULTS

Participants with CRS were more likely to report cognitive dysfunction (Neuro-QOL; 45.8% vs. 8.7%; p = 0.005) and demonstrate mild or greater cognitive impairment (MoCA; 41.7% vs. 8.7%; p = 0.005) than controls. Additionally, participants with CRS performed worse on the MoCA overall and within the executive functioning and memory domains (all p < 0.05) and on the anti-saccade (p = 0.014) and delay saccade (p = 0.044) eye movement tasks. Poorer performance on the MoCA (r = -0.422; p = 0.003) and the anti-saccade (r = -0.347; p = 0.017) and delay saccade (r = -0.419; p = 0.004) eye movement tasks correlated with worse CRS severity according to SNOT-22 scores.

CONCLUSION

This study is the first to utilize objective eye movement assessments in addition to researcher-administered cognitive testing in patients with CRS. These patients demonstrated a high prevalence of cognitive dysfunction, most notably within executive functioning and memory domains, with the degree of dysfunction correlating with the severity of CRS.

Neuropsychobiology of fear-induced bradycardia in humans: progress and pitfalls

In the last century, the paradigm of fear conditioning has greatly evolved in a variety of scientific fields. The techniques, protocols, and analysis methods now most used have undergone a progressive development, theoretical and technological, improving the quality of scientific productions. Fear-induced bradycardia is among these techniques and represents the temporary deceleration of heart beats in response to negative outcomes. However, it has often been used as a secondary measure to assess defensive responding to threat, along other more popular techniques. In this review, we aim at paving the road for its employment as an additional tool in fear conditioning experiments in humans. After an overview of the studies carried out throughout the last century, we describe more recent evidence up to the most contemporary research insights. Lastly, we provide some guidelines concerning the best practices to adopt in human fear conditioning studies which aim to investigate fear-induced bradycardia.

The future of psychophysiology, then and now

Since its founding in 1973, Biological Psychology has showcased and provided invaluable support to psychophysiology, a field that has grown and changed enormously. This article discusses some constancies that have remained fundamental to the journal and to the field as well as some important trends. Some aspects of our science have not received due consideration, affecting not only the generalizability of our findings but the way we develop and evaluate our research questions and the potential of our field to contribute to the common good. The article offers a number of predictions and recommendations for the next period of growth of psychophysiology.

Variation in the mu-opioid receptor gene (OPRM1) moderates the influence of maternal sensitivity on child attachment

The endogenous opioid system is thought to play an important role in mother-infant attachment. In infant rhesus macaques, variation in the μ-opioid receptor gene (OPRM1) is related to differences in attachment behavior that emerges following repeated separation from the mother; specifically, infants carrying at least one copy of the minor G allele of the OPRM1 C77G polymorphism show heightened and more persistent separation distress, as well as a pattern of increased contact-seeking behavior directed towards the mother during reunions (at the expense of affiliation with other group members). Research in adult humans has also linked the minor G allele of the analogous OPRM1 A118G polymorphism with greater interpersonal sensitivity. Adopting an interactionist approach, we examined whether OPRM1 A118G genotype and maternal (in)sensitivity are associated with child attachment style, predicting that children carrying the G allele may be more likely to develop an ambivalent attachment pattern in response to less sensitive maternal care. The sample consisted of 191 mothers participating with their children (n = 223) in the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) project, a community-based, birth cohort study of Canadian mothers and their children assessed longitudinally across the child's development. Maternal sensitivity was coded from at-home mother-child interactions videotaped when the child was 18 months of age. Child attachment was assessed at 36 months using the Strange Situation paradigm. As predicted, G allele carriers, but not AA homozygotes, showed increasing odds of being classified as ambivalently attached with decreasing levels of maternal sensitivity. Paralleling earlier non-human animal research, this work provides support for the theory that endogenous opioids contribute to the expression of attachment behaviors in humans.

Coregistration of EEG and eye-tracking in infants and developing populations

Infants cannot be instructed where to look; therefore, infant researchers rely on observation of their participant's gaze to make inferences about their cognitive processes. They therefore started studying infant attention in the real world from early on. Developmental researchers were early adopters of methods combining observations of gaze and behaviour with electroencephalography (EEG) to study attention and other cognitive functions. However, the direct combination of eye-tracking methods and EEG to test infants is still rare, as it includes specific challenges. The current article reviews the development of co-registration research in infancy. It points out specific challenges of co-registration in infant research and suggests ways to overcome them. It ends with recommendations for implementing the co-registration of EEG and eye-tracking in infant research to maximise the benefits of the two measures and their combination and to orient on Open Science principles while doing so. In summary, this work shows that the co-registration of EEG and eye-tracking in infant research can be beneficial to studying natural and real-world behaviour despite its challenges.

Unraveling neural pathways of political engagement: bridging neuromarketing and political science for understanding voter behavior and political leader perception

Introduction

Political neuromarketing is an emerging interdisciplinary field integrating marketing, neuroscience, and psychology to decipher voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding.

Methods

This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders.

Results

This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former.

Discussion

The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science, and machine learning, in turn enabling predictive insights into voter preferences and behavior.