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Attention is critical to our daily lives, from simple acts of reading or listening to a conversation to the more demanding situations of trying to concentrate in a noisy environment or driving on a busy roadway. This book offers a concise introduction to the science of attention, featuring real-world examples and fascinating studies of clinical disorders and brain injuries. It introduces cognitive neuroscience methods and covers the different types and core processes of attention. The links between attention, perception, and action are explained, along with exciting new insights into the brain mechanisms of attention revealed by cutting-edge research. Learning tools – including an extensive glossary, chapter reviews, and suggestions for further reading – highlight key points and provide a scaffolding for use in courses. This book is ideally suited for graduate or advanced undergraduate students as well as for anyone interested in the role attention plays in our lives.
Compulsive cleaning is a characteristic symptom of a particular subtype of obsessive–compulsive disorder (OCD) and is often accompanied by intense disgust. While overgeneralization of threat is a key factor in the development of obsessive–compulsive symptoms, previous studies have primarily focused on fear generalization and have rarely examined disgust generalization. A systematic determination of the behavioral and neural mechanisms underlying disgust generalization in individuals with contamination concern is crucial for enhancing our understanding of OCD.
Method
In this study, we recruited 27 individuals with high contamination concerns and 30 individuals with low contamination concerns. Both groups performed a disgust generalization task while undergoing functional magnetic resonance imaging (fMRI).
Results
The results revealed that individuals with high contamination concern had higher disgust expectancy scores for the generalization stimulus GS4 (the stimulus most similar to CS+) and exhibited higher levels of activation in the left insula and left putamen. Moreover, the activation of the left insula and putamen were positively correlated with a questionnaire core of the ratings of disgust and also positively correlated with the expectancy rating of CS+ during the generalization stage.
Conclusion
Hyperactivation of the insula and putamen during disgust generalization neutrally mediates the higher degree of disgust generalization in subclinical OCD individuals. This study indicates that altered disgust generalization plays an important role in individuals with high contamination concerns and provides evidence of the neural mechanisms involved. These insights may serve as a basis for further exploration of the pathogenesis of OCD in the future.
Parent factors impact adolescent’s emotion regulation, which has key implications for the development of internalizing psychopathology. A key transdiagnostic factor which may contribute to the development of youth internalizing pathology is parent anxiety sensitivity (fear of anxiety-related physiological sensations). In a sample of 146 adolescents (M/SDage = 12.08/.90 years old) and their parents (98% mothers) we tested whether parent anxiety sensitivity was related to their adolescent’s brain activation, over and above the child’s anxiety sensitivity. Adolescents completed an emotion regulation task in the scanner that required them to either regulate vs. react to negative vs. neutral stimuli. Parent anxiety sensitivity was associated with adolescent neural responses in bilateral orbitofrontal cortex (OFC), anterior cingulate, and paracingulate, and left dorsolateral prefrontal cortex, such that higher parent anxiety sensitivity was associated with greater activation when adolescents were allowed to embrace their emotional reaction(s) to stimuli. In the right OFC region only, higher parent anxiety sensitivity was also associated with decreased activation when adolescents were asked to regulate their emotional responses. The findings are consistent with the idea that at-risk adolescents may be modeling the heightened attention and responsivity to environmental stimuli that they observe in their parents.
This chapter introduces the methods used in cognitive neuroscience to study language processing in the human brain. It begins by explaining the basics of neural signaling (such as the action potential) and then delves into various brain imaging techniques. Structural imaging methods like MRI and diffusion tensor imaging are covered, which reveal the brain’s anatomy. The chapter then explores functional imaging approaches that measure brain activity, including EEG, MEG, and fMRI. Each method’s spatial and temporal resolution are discussed. The text also touches on non-invasive brain stimulation techniques like TMS and tES. Throughout, the chapter emphasizes the importance of converging evidence from multiple methods to draw robust conclusions about brain function. Methodological considerations such as the need for proper statistical comparisons are highlighted. The chapter concludes with a discussion of how neurodegenerative diseases have informed our understanding of language in the brain. Overall, this comprehensive overview equips readers with the foundational knowledge needed to critically evaluate neuroscience research on language processing.
We measured brain activity using a functional magnetic resonance imaging (fMRI) paradigm and conducted a whole-brain analysis while healthy adult Democrats and Republicans made non-hypothetical food choices. While the food purchase decisions were not significantly different, we found that brain activation during decision-making differs according to the participant’s party affiliation. Models of partisanship based on left insula, ventromedial prefrontal cortex, precuneus, superior frontal gyrus, or premotor/supplementary motor area activations achieve better than expected accuracy. Understanding the differential function of neural systems that lead to indistinguishable choices may provide leverage in explaining the broader mechanisms of partisanship.
Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have a major impact. This study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms.
Methods
Two datasets were studied: (A) one consisted of n = 54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n = 15 subjects treated for 8 weeks. Connectome predictive modeling (CPM) was used to predict treatment response, as assessed with the PARS. The main analysis included network edges positively correlated with treatment outcome and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses are also presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r, and mean absolute error (MAE).
Results
The main model showed a MAE of approximately 3.5 (95% CI: [3.1–3.8]) points, an R2 of 0.08 [−0.14–0.26], and an r of 0.38 [0.24–0.511]. When testing this model in the left-out sample (B), the results were similar, with an MAE of 3.4 [2.8–4.7], R2−0.65 [−2.29–0.16], and r of 0.4 [0.24–0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2.
Conclusions
The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, this study does not support the extensive use of CPM to predict outcomes in pediatric anxiety.
Neurobiological theories draw on neurobiological evidence from fMRI but also plenty of other neuroscientific methods for theory development: On a fundamental level, neurobiological theories are neurobiological explanations about the nature of the brain-behavior link.
Depression has been linked to disruptions in resting-state networks (RSNs). However, inconsistent findings on RSN disruptions, with variations in reported connectivity within and between RSNs, complicate the understanding of the neurobiological mechanisms underlying depression.
Methods
A systematic literature search of PubMed and Web of Science identified studies that employed resting-state functional magnetic resonance imaging (fMRI) to explore RSN changes in depression. Studies using seed-based functional connectivity analysis or independent component analysis were included, and coordinate-based meta-analyses were performed to evaluate alterations in RSN connectivity both within and between networks.
Results
A total of 58 studies were included, comprising 2321 patients with depression and 2197 healthy controls. The meta-analysis revealed significant alterations in RSN connectivity, both within and between networks, in patients with depression compared with healthy controls. Specifically, within-network changes included both increased and decreased connectivity in the default mode network (DMN) and increased connectivity in the frontoparietal network (FPN). Between-network findings showed increased DMN–FPN and limbic network (LN)–DMN connectivity, decreased DMN–somatomotor network and LN–FPN connectivity, and varied ventral attention network (VAN)–dorsal attentional network (DAN) connectivity. Additionally, a positive correlation was found between illness duration and increased connectivity between the VAN and DAN.
Conclusions
These findings not only provide a comprehensive characterization of RSN disruptions in depression but also enhance our understanding of the neurobiological mechanisms underlying depression.
According to the aberrant salience proposal, reward processing abnormality, specifically erroneous reward prediction error (RPE) signaling due to stimulus-independent release of dopamine, underlies delusions in schizophrenia. However, no studies to date have examined RPE-associated brain activations in relation to this symptom.
Methods
Seventy-eight patients with a DSM-5 diagnosis of schizophrenia/schizoaffective disorder and 43 healthy individuals underwent fMRI while they performed a probabilistic monetary reward task designed to generate a measure of RPE. Ratings of delusions and referentiality were made in the patients.
Results
Using whole-brain, voxel-based analysis, schizophrenia patients showed only minor differences in RPE-associated activation compared to healthy controls. Within the patient group, however, severity of delusions was inversely associated with RPE-associated activation in areas including the caudate nucleus, the thalamus and the left pallidum, as well as the lateral frontal cortex bilaterally, the pre- and postcentral gyrus and supplementary motor area, the middle cingulate gyrus, and parts of the temporal and parietal cortex. A broadly similar pattern of association was seen for referentiality.
Conclusions
According to this study, while patients with schizophrenia as a group do not show marked alterations in RPE signaling, delusions and referentiality are associated with reduced activation in parts of the prefrontal cortex and the basal ganglia, though not specifically the ventral striatum. The direction of the changes is on the face of it contrary to that predicted by aberrant salience theory.
The success of deep brain stimulation (DBS) relies on applying carefully titrated therapeutic stimulation at specific targets. Once implanted, the electrical stimulation parameters at each electrode contact can be modified. Iteratively adjusting the stimulation parameters enables testing for the optimal stimulation settings. Due to the large parameter space, the currently employed empirical testing of individual parameters based on acute clinical response is not sustainable. Within the constraints of short clinical visits, optimization is particularly challenging when clinical features lack immediate feedback, as seen in DBS for dystonia and depression and with the cognitive and axial side effects of DBS for Parkinson’s disease. A personalized approach to stimulation parameter selection is desirable as the increasing complexity of modern DBS devices also expands the number of available parameters. This review describes three emerging imaging and electrophysiological methods of personalizing DBS programming. Normative connectome-base stimulation utilizes large datasets of normal or disease-matched connectivity imaging. The stimulation location for an individual patient can then be varied to engage regions associated with optimal connectivity. Electrophysiology-guided open- and closed-loop stimulation capitalizes on the electrophysiological recording capabilities of modern implanted devices to individualize stimulation parameters based on biomarkers of success or symptom onset. Finally, individual functional MRI (fMRI)-based approaches use fMRI during active stimulation to identify parameters resulting in characteristic patterns of functional engagement associated with long-term treatment response. Each method provides different but complementary information, and maximizing treatment efficacy likely requires a combined approach.
This study explored the relationship between multifaceted multilingualism and cognitive shifting through a task-switching paradigm using fMRI. Multilingualism was modeled from both convergent (i.e., integrated multilingual index) and divergent (i.e., L2 proficiency, interpreting training, language entropy) perspectives. Participants identified letters or numbers based on task cues, with Repeat trials maintaining the same task and Switch trials requiring a different task. Switch cost (Switch–Repeat) was used to reflect shifting demands. Better task-switching performance was associated with a higher integrated multilingual index and interpreting training. Neuroimaging indicated that multilinguals predominantly engaged left-hemisphere regions for switching, with extensive multilingual experience requiring fewer neural resources for switch cost (i.e., more efficient processing for cognitive control). During task switching, brain connectivity was regulated locally by L2 proficiency, and globally by interpreting training. These findings underscore the importance of considering multifaceted multilingual experience to understand its impact on cognitive function and brain activity.
Fully updated and revised, Cognitive and Social Neuroscience of Aging, 2nd Edition provides an accessible introduction to aging and the brain. Now with full color throughout, it includes over fifty figures illustrating key research findings and anatomical diagrams. Adopting an integrative perspective across domains of psychological function, this edition features expanded coverage of multivariate methods, moral judgments, cognitive reserve, prospective memory, event boundaries, and individual differences related to aging, including sex, race, and culture. Although many declines occur with age, cognitive neuroscience research reveals plasticity and adaptation in the brain as a normal function of aging. With this perspective in mind, the book emphasizes the ways in which neuroscience methods have enriched and changed thinking about aging.
This chapter reviews theories of cognitive aging, considering how those classic theories intersect with those informed by cognitive neuroscience methods. The chapter also reviews cognitive neuroscience methods, reviewing methods to study the structural integrity of the brain as well as those used to investigate brain function or the ways in which multiple measures can be combined. The chapter ends with discussion of recent methodological advances, including multivariate analysis methods and the study of beta-amyloid and tau.
Maternal perinatal mental health is essential for optimal brain development and mental health of the offspring. We evaluated whether maternal depression during the perinatal period and early life of the offspring might be selectively associated with altered brain function during emotion regulation and whether those may further correlate with physiological responses and the typical use of emotion regulation strategies.
Methods
Participants included 163 young adults (49% female, 28–30 years) from the ELSPAC prenatal birth cohort who took part in its neuroimaging follow-up and had complete mental health data from the perinatal period and early life. Maternal depressive symptoms were measured mid-pregnancy, 2 weeks, 6 months, and 18 months after birth. Regulation of negative affect was studied using functional magnetic resonance imaging, concurrent skin conductance response (SCR) and heart rate variability (HRV), and assessment of typical emotion regulation strategy.
Results
Maternal depression 2 weeks after birth interacted with sex and showed a relationship with greater brain response during emotion regulation in a right frontal cluster in women. Moreover, this brain response mediated the relationship between greater maternal depression 2 weeks after birth and greater suppression of emotions in young adult women (ab = 0.11, SE = 0.05, 95% CI [0.016; 0.226]). The altered brain response during emotion regulation and the typical emotion regulation strategy were also as sociated with SCR and HRV.
Conclusions
These findings suggest that maternal depression 2 weeks after birth predisposes female offspring to maladaptive emotion regulation skills and particularly to emotion suppression in young adulthood.
Impaired emotion regulation has been proposed as a putative endophenotype in bipolar disorder (BD). Functional magnetic resonance imaging (fMRI) studies investigating this in unaffected first-degree relatives (UR) have thus far yielded incongruent findings. Hence, the current paper examines neural subgroups among UR during emotion regulation.
Methods
71 UR of patients with BD and 66 healthy controls (HC) underwent fMRI scanning while performing an emotion regulation task. Hierarchical cluster analysis was performed on extracted signal change during emotion down-regulation in pre-defined regions of interest (ROIs). Identified subgroups were compared on neural activation, demographic, clinical, and cognitive variables.
Results
Two subgroups of UR were identified: subgroup 1 (39 UR; 55%) was characterized by hypo-activity in the dorsolateral, dorsomedial, and ventrolateral prefrontal cortex and the bilateral amygdalae, but comparable activation to HC in the other ROIs; subgroup 2 (32 UR; 45%) was characterized by hyperactivity in all ROIs. Subgroup 1 had lower success in emotion regulation compared to HC and reported more childhood trauma compared to subgroup 2 and HC. Subgroup 2 reported more anxiety, lower functioning, and greater attentional vigilance toward fearful faces compared to HC. Relatives from both subgroups were poorer in recognizing positive faces compared to HC.
Conclusions
These findings may explain the discrepancy in earlier fMRI studies on emotion regulation in UR, showing two different subgroups of UR that both exhibited aberrant neural activity during emotion regulation, but in opposite directions. Furthermore, the results suggest that impaired recognition of positive facial expressions is a broad endophenotype of BD.
Maternal depression is associated with difficulties in understanding and adequately responding to children’s emotional signals. Consequently, the interaction between mother and child is often disturbed. However, little is known about the neural correlates of these parenting difficulties. Motivated by increasing evidence of the amygdala’s important role in mediating maternal behavior, we investigated amygdala responses to child sad and happy faces in mothers with remitted major depression disorder (rMDD) relative to healthy controls.
Methods
We used the sensitivity subscale of the emotional availability scales and functional magnetic resonance imaging in 61 rMDD and 27 healthy mothers to examine the effect of maternal sensitivity on mothers’ amygdala responses to their children’s affective facial expressions.
Results
For mothers with rMDD relative to controls, we observed decreased maternal sensitivity when interacting with their child. They also showed reduced amygdala responses to child affective faces that were associated with lower maternal sensitivity. Connectivity analysis revealed that this blunted amygdala response in rMDD mothers was functionally correlated with reduced activation in higher-order medial prefrontal areas.
Conclusions
Our results contribute toward a better understanding of the detrimental effects of lifetime depression on maternal sensitivity and associated brain responses. By targeting region-specific neural activation patterns, these results are a first step toward improving the prediction, prevention, and treatment of depression-related negative effects on mother–child interaction.
Anxiety symptoms are elevated among people with joint hypermobility. The underlying neural mechanisms are attributed theoretically to effects of variant connective tissue on the precision of interoceptive representations contributing to emotions.
Aim
To investigate the neural correlates of anxiety and hypermobility using functional neuroimaging.
Method
We used functional magnetic resonance neuroimaging to quantify regional brain responses to emotional stimuli (facial expressions) in people with generalised anxiety disorder (GAD) (N = 30) and a non-anxious comparison group (N = 33). All participants were assessed for joint laxity and were classified (using Brighton Criteria) for the presence and absence of hypermobility syndrome (HMS: now considered hypermobility spectrum disorder).
Results
Participants with HMS showed attenuated neural reactivity to emotional faces in specific frontal (inferior frontal gyrus, pre-supplementary motor area), midline (anterior mid and posterior cingulate cortices) and parietal (precuneus and supramarginal gyrus) regions. Notably, interaction between HMS and anxiety was expressed in reactivity of the left amygdala (a region implicated in threat processing) and mid insula (primary interoceptive cortex) where activity was amplified in people with HMS with GAD. Severity of hypermobility in anxious, compared with non-anxious, individuals correlated with activity within the anterior insula (implicated as the neural substrate linking anxious feelings to physiological state). Amygdala-precuneus functional connectivity was stronger in participants with HMS, compared with non-HMS participants.
Conclusions
The predisposition to anxiety in people with variant connective tissue reflects dynamic interactions between neural centres processing threat (amygdala) and representing bodily state (insular and parietal cortices). Correspondingly, interventions to regulate amygdala reactivity while enhancing interoceptive precision may have therapeutic benefit for symptomatic hypermobile individuals.
Psychostimulants and nonstimulants have partially overlapping pharmacological targets on attention-deficit/hyperactivity disorder (ADHD), but whether their neuroimaging underpinnings differ is elusive. We aimed to identify overlapping and medication-specific brain functional mechanisms of psychostimulants and nonstimulants on ADHD.
Methods
After a systematic literature search and database construction, the imputed maps of separate and pooled neuropharmacological mechanisms were meta-analyzed by Seed-based d Mapping toolbox, followed by large-scale network analysis to uncover potential coactivation patterns and meta-regression analysis to examine the modulatory effects of age and sex.
Results
Twenty-eight whole-brain task-based functional MRI studies (396 cases in the medication group and 459 cases in the control group) were included. Possible normalization effects of stimulant and nonstimulant administration converged on increased activation patterns of the left supplementary motor area (Z = 1.21, p < 0.0001, central executive network). Stimulants, relative to nonstimulants, increased brain activations in the left amygdala (Z = 1.30, p = 0.0006), middle cingulate gyrus (Z = 1.22, p = 0.0008), and superior frontal gyrus (Z = 1.27, p = 0.0006), which are within the ventral attention network. Neurodevelopmental trajectories emerged in activation patterns of the right supplementary motor area and left amygdala, with the left amygdala also presenting a sex-related difference.
Conclusions
Convergence in the left supplementary motor area may delineate novel therapeutic targets for effective interventions, and distinct neural substrates could account for different therapeutic responses to stimulants and nonstimulants.
Decision making usually involves uncertainty and risk. Understanding which parts of the human brain are activated during decisions under risk and which neural processes underly (risky) investment decisions are important goals in neuroeconomics. Here, we analyze functional magnetic resonance imaging (fMRI) data on 17 subjects who were exposed to an investment decision task from Mohr, Biele, Krugel, Li, and Heekeren (in NeuroImage 49, 2556–2563, 2010b). We obtain a time series of three-dimensional images of the blood-oxygen-level dependent (BOLD) fMRI signals. We apply a panel version of the dynamic semiparametric factor model (DSFM) presented in Park, Mammen, Wolfgang, and Borak (in Journal of the American Statistical Association 104(485), 284–298, 2009) and identify task-related activations in space and dynamics in time. With the panel DSFM (PDSFM) we can capture the dynamic behavior of the specific brain regions common for all subjects and represent the high-dimensional time-series data in easily interpretable low-dimensional dynamic factors without large loss of variability. Further, we classify the risk attitudes of all subjects based on the estimated low-dimensional time series. Our classification analysis successfully confirms the estimated risk attitudes derived directly from subjects’ decision behavior.
Significant heterogeneity in network structures reflecting individuals’ dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME’s ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.