What contributes to individual differences in brain structure?

Individual differences in adult human brain structure have been found to reveal a great deal of information about variability in behaviors, cognitive abilities and mental and physical health. Driven by such evidence, what contributes to individual variation in brain structure has gained accelerated attention as a research question. Findings thus far appear to support the notion that an individual’s brain architecture is determined largely by genetic and environmental influences. This review aims to evaluate the empirical literature on whether and how genes and the environment contribute to individual differences in brain structure. It first considers how genetic and environmental effects may separately contribute to brain morphology, by examining evidence from twin, genome-wide association, cross-sectional and longitudinal studies. Next, evidence for the influence of the complex interplay between genetic and environmental factors, characterized as gene-environment interactions and correlations, is reviewed. In evaluating the extant literature, this review will conclude that both genetic and environmental factors play critical roles in contributing to individual variability in brain structure

Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model

Typical fMRI studies have focused on either the mean trend in the blood-oxygen-level-dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed. We introduce the Variance Design General Linear Model (VDGLM), a novel framework that facilitates the detection of variance effects. We designed the framework for general use in any fMRI study by modeling both mean and variance in BOLD activation as a function of experimental design. The flexibility of this approach allows the VDGLM to i) simultaneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrate the use of the VDGLM in a working memory application and show that engagement in a working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure

I expect, therefore I see: individual differences in visual awareness

Predictive processing theories posit that awareness of the visual world emerges as the brain engages in predictive inference about the causes of its sensory input. At each level of the processing hierarchy top-down predictions are corrected by bottom-up sensory prediction error to form behaviourally optimal inferences about the state of the visual world. Research suggests there may be individual differences in predictive processing mechanisms such that some individuals are more reliant on prior knowledge, whereas others assign more weight to sensory evidence. Predictive processing biases are thought to manifest in a range of typical and atypical perceptual experiences including proneness to perceptual illusions, sensory sensitivity in autism, and hallucinations in psychosis. The overarching aim of this thesis was to investigate whether in the general population predictive processing biases predict individual differences in visual awareness. Change blindness was selected as the central paradigm of investigation, as it can be conceptualised as a failure to incorporate a novel change into the current prediction about the state of the visual world. The empirical work in Chapter 2 aimed to characterise individual differences in visual change detection using naturalistic scenes and to identify the perceptual and cognitive measures that predict noticing ability. There were reliable individual differences in change detection that generalised to ecologically valid displays. The ability to notice visual changes was predicted by the strength and stability of perceptual predictions, as measured by the accuracy of visual short-term memory and attentional control in the face of distractors. In Chapter 3 I used voxel-based-morphometry to investigate whether inter-individual variability in brain structure predicts individual differences in visual awareness. The latter was assessed by the change blindness task as well as its strongest predictor measures (visual short-term memory, attentional capture, and perceptual rivalry). Regions of interest (ROIs) were selected in the parietal and visual cortices based on previous evidence that these areas are causally involved in the awareness of visual stimuli. This study aimed to discover whether the average grey matter density in the ROIs predict susceptibility to CB. The ROI-based analyses revealed the average grey matter density in left posterior parietal cortex predicted visual short-term memory accuracy but none of the other hypothesised relationships were significant. Chapter 4 aimed to measure individual differences in the reliance on prior knowledge by employing the Mooney face detection task. In this task participants disambiguated faces in two-tone degraded images before and after the presentation of the original versions of the images. Better change detection was predicted by Mooney face detection without any prior knowledge of the images, a measure of ‘perceptual closure’ or an ability to generate a gestalt of a scene. The attention to detail subscale of the autism spectrum also predicted superior change detection. Reliance on prior knowledge in visual perception (assessed by improvement in Mooney face detection after seeing original images) did not consistently predict atypical perceptual experiences associated with the autism spectrum or schizotypy. Chapter 5 was an investigation into, firstly, whether there is a general predictive processing bias, which manifests across different methods of inducing prior knowledge, or whether such a bias is paradigm-specific and, secondly, whether reliance on priors predicts perceptual experiences and traits. All prior manipulations in this study lead to an increased tendency to see the expected stimulus in a binocular rivalry display, except adaptation, which lead to a suppression of visual awareness. Attentional control, perceptual priming, expectancy, and imagery loaded onto a common factor, suggesting that the strength of selective attention is closely linked with the facilitatory effect of expectation. The strength of adaptation predicted superior change detection and perceptual priming predicted the propensity to experience perceptual illusions. Taken together, these findings suggest that there are reliable individual differences in visual change detection, and these are predicted by the strength of visual short-term memory representations, attentional control, perceptual closure ability, as well as the strength of low-level adaptation. Possessing expectations facilitates the entry of the corresponding percept into awareness, irrespective of the method of prior induction. The facilitatory effect that priors exert on visual awareness across different methods is closely linked with the ability to exert attentional control. This suggests that the effects of expectations on awareness may be attentional. However, predictive processing biases were method-specific in that a facilitatory effect using one prior induction method will not necessarily predict the magnitude of the effect using a different method. Some prior effects (e.g., perceptual priming, imagery, and adaptation) yielded correlations with perceptual experiences and traits in the general population. As the research in this thesis is correlational, future studies will need to delineate the effects of expectation, attention, and adaptation on visual awareness and explore the neural representations of these mechanisms

How ageing changes the mnemonic bias of visual behaviour

Published online: 02 Jun 2017.Ageing is associated with deficits in cognitive control, including attention and working memory processes. However, how ageing influences the interactions between these cognitive systems is not well understood. The present study compared the oculomotor behaviour and search performance of two different age groups in a well-established memory-guided visual selection paradigm. The results show that ageing can lead to impairments in the way memory representations bias the control of attention, which notably dissociate in the early stages of oculomotor orientation in search and the later process of attentional disengagement from memory distracters. The implications of these findings for theories of cognitive ageing are discussed.DS acknowledges support from the Severo Ochoa Programme for Centres/Units of Excellence in R&D (SEV-2015-490) and project grant PSI2016-76443-P (METAAWARE) funded by the Spanish Ministry of Economy and Competitiveness, Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER)

Impulsivity and Compulsivity in Anorexia Nervosa: Cognitive Systems Underlying Variation in Appetite Restraint from an RDoC Perspective

Contemporary nomenclature for anorexia nervosa (AN) describes the eating disorder as transdiagnostic, with overlapping facets of impulsivity and compulsivity contributing to variations in binge-purge, restrictive eating and maladaptive cognitions. It is important to understand how these facets interact, given that those diagnosed with AN often fluctuate and relapse–as opposed to maintaining a stable diagnosis—between Diagnostic and Statistical Manual version 5 (DSM-5) categories, over the life course. The National Institute of Health’s Research Domain Criteria (NIH RDoC) subscribes to the transdiagnostic view of mental disorders and provides progressive guidelines for neuroscience research. As such, using the RDoC guidelines may help to pinpoint how impulsivity and compulsivity contribute to the cognitive mechanisms underlying variations in appetite restraint in eating disorders and common psychiatric comorbidities such as anxiety and obsessive-compulsive disorder. Exploring impulsivity and compulsivity in AN from the perspective of the RDoC cognitive systems domain is aided by measures of genetic, molecular, cellular, neural, physiological, behavioural and cognitive task paradigms. Thus, from the standpoint of the RDoC measures, this chapter will describe some of the ways in which impulsivity and compulsivity contribute to the cognitive systems associated with appetite restraint in AN, with the aim of further clarifying a model of appetite restraint to improve treatment interventions

Anatomical and Functional Organization of Domain-General Brain Regions

How does complex brain activity organize thought and behaviour? Theoretical proposals have long emphasized that intelligent behaviour must be supported by a flexible control system. Numerous brain imaging studies identified a domain-general or “multiple-demand” (MD) brain system co-activated accompanying many tasks and is hypothesised to play a central role in cognitive control. However, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding its anatomy and physiology. To address these limitations, the experiments in chapters 2 and 3 capitalize on novel multi-modal magnetic resonance imaging (MRI) methods developed by the Human Connectome Project. Chapter 2 delineated nine cortical MD patches per hemisphere and subdivided them into 10 regions forming a core of most strongly activated and functionally interconnected regions, surrounded by a penumbra of 17 additional regions. MD activations were also identified in specific subcortical and cerebellar regions. Chapter 3 investigated the relation between the newly defined MD regions and previously identified sensory-biased cortical regions. Contrasting auditory and visual low working memory demands revealed the strongest sensory-biases are localized just outside of MD regions. And additional working memory demands revealed MD activations showed no sensory biases. Chapter 4 used human electrophysiological recordings from the lateral frontal cortex to functionally map cognitive control regions during awake neurosurgeries. By contrasting a hard vs easy cognitive demand, spectral analysis revealed localized power increases in the gamma range (>30 Hz) that overlap with a canonical mask of the fronto-parietal control network. These findings contrast with spatially non-specific power decreases in the beta range (12-30 Hz). Thus, using similar task difficulty manipulations, electrophysiology and MRI functional signals converged on localizing lateral frontal regions related to cognitive control and support their clinical potential for intraoperative mapping of cognitive control. All together, the distributed anatomical organization, mosaic functional preferences, and strong functional interconnectivity of MD regions, suggest a skeleton for integrating and organizing the diverse components of cognitive operations. The precise anatomical delineation of MD regions provides the groundwork for refined analyses of their functions

The Role and Sources of Individual Differences in Critical-Analytic Thinking: a Capsule Overview

Critical-analytic thinking is typically conceived as a meta-construct that arises at the junction of a problem state (i.e., a situation that requires analysis that challenges previous assumptions) and an individual (i.e., an entity with the capacity to exercise critical-analytic thinking). With regard to the latter, there is a substantial body of research focusing on developmental and educational prerequisites for critical-analytic thinking. A less studied aspect of critical-analytic thinking pertains to individual differences, particularly in the set of foundational or componential cognitive skills that embody this construct. The bottom line here is whether, all else being equal (i.e., the same situation and the same developmental/educational stage), there is variation in whether, when, and how people think critically/analytically. We argue that there is unequivocal evidence for both the existence and importance of individual differences in critical-analytic thinking. This review focuses on theoretical and empirical evidence, identifying the cognitive processes that serve as the sources of these individual differences and capturing these processes’ differential contributions to both the critical and analytic components of this construct.National Institutes of Health (U.S.) (Grant HD079143

Functional Connectivity Between Superior Parietal Lobule and Primary Visual Cortex “at Rest” Predicts Visual Search Efficiency

Spatiotemporal activity that emerges spontaneously “at rest” has been proposed to reflect individual a priori biases in cognitive processing. This research focused on testing neurocognitive models of visual attention by studying the functional connectivity (FC) of the superior parietal lobule (SPL), given its central role in establishing priority maps during visual search tasks. Twenty-three human participants completed a functional magnetic resonance imaging session that featured a resting-state scan, followed by a visual search task based on the alphanumeric category effect. As expected, the behavioral results showed longer reaction times and more errors for the within-category (i.e., searching a target letter among letters) than the between-category search (i.e., searching a target letter among numbers). The within-category condition was related to greater activation of the superior and inferior parietal lobules, occipital cortex, inferior frontal cortex, dorsal anterior cingulate cortex, and the superior colliculus than the between-category search. The resting-state FC analysis of the SPL revealed a broad network that included connections with the inferotemporal cortex, dorsolateral prefrontal cortex, and dorsal frontal areas like the supplementary motor area and frontal eye field. Noteworthy, the regression analysis revealed that the more efficient participants in the visual search showed stronger FC between the SPL and areas of primary visual cortex (V1) related to the search task. We shed some light on how the SPL establishes a priority map of the environment during visual attention tasks and how FC is a valuable tool for assessing individual differences while performing cognitive tasks.This research was supported by grants from the Spanish Department of Economy and Competitiveness (PSI2013-47504-R); and Jaume I University (P1·1B2013-63). Authors E.B., MA.PG and A.MP. were supported by pre-doctoral graduate program grants (National FPU to E.B; National FPI to MA.PG; and Jaume I University FPI to A.MP