Dominic Dwyer

Being Liked Activates Primary Reward and Midline Self-Related Brain Regions

The experience of being liked is a key social event and fundamental to motivating human behavior, though little is known about its neural underpinnings. In this study, we examined the experience of being liked in a group of 15‐ to 24‐year‐old: a cohort for whom forming friendships has a great degree of salience, and for whom the explicit representation of relationships is familiar from their frequent use of social networking technologies. Study participants (n = 19) were led to believe that other participants had formed an opinion on their likability based on their appearance in a photograph, and during fMRI scanning viewed the photographs of people who had purportedly responded favorably to them (alongside photographs of control participants). Results indicated that being liked activated primary reward‐ and self‐related regions, including the nucleus accumbens, midbrain (in an area corresponding to the ventral tegmentum), ventromedial prefrontal cortex, posterior cingulate cortex (including retrosplenial cortex), amygdala, and insula/opercular cortex. Participants showed greater activation of ventromedial prefrontal cortex and amygdala in response to being liked by people that they regarded highly compared to those they regarded less so. Finally, being liked by the opposite compared to the same gender activated the right caudal orbitofrontal cortex and right anterior insula: areas important for the representation of primary somatic rewards. This study demonstrates that neural response to being liked has features that are consistent with response to other rewarding events, but it has additional features that reflect its intrinsically interpersonal character. Hum Brain Mapp, 2010.

Large-Scale Brain Network Dynamics Supporting Adolescent Cognitive Control

Adolescence is a time when the ability to engage cognitive control is linked to crucial life outcomes. Despite a historical focus on prefrontal cortex functioning, recent evidence suggests that differences between individuals may relate to interactions between distributed brain regions that collectively form a cognitive control network (CCN). Other research points to a spatially distinct and functionally antagonistic system—the default-mode network (DMN)—which typically deactivates during performance of control tasks. This literature implies that individual differences in cognitive control are determined either by activation or functional connectivity of CCN regions, deactivation or functional connectivity of DMN regions, or some combination of both. We tested between these possibilities using a multilevel fMRI characterization of CCN and DMN dynamics, measured during performance of a cognitive control task and during a task-free resting state, in 73 human adolescents. Better cognitive control performance was associated with (1) reduced activation of CCN regions, but not deactivation of the DMN; (2) variations in task-related, but not resting-state, functional connectivity within a distributed network involving both the CCN and DMN; (3) functional segregation of core elements of these two systems; and (4) task-dependent functional integration of a set of peripheral nodes into either one network or the other in response to prevailing stimulus conditions. These results indicate that individual differences in adolescent cognitive control are not solely attributable to the functioning of any single region or network, but are instead dependent on a dynamic and context-dependent interplay between the CCN and DMN.

Divergent effects of first-generation and second-generation antipsychotics on cortical thickness in first-episode psychosis

Background Whether there are differential effects of first-generation antipsychotics (FGAs) and second-generation antipsychotics (SGAs) on the brain is currently debated. Although some studies report that FGAs reduce grey matter more than SGAs, others do not, and research to date is limited by a focus on schizophrenia spectrum disorders. To address this limitation, this study investigated the effects of medication in patients being treated for first-episode schizophrenia or affective psychoses. Method Cortical thickness was compared between 52 first-episode psychosis patients separated into diagnostic (i.e. schizophrenia or affective psychosis) and medication (i.e. FGA and SGA) subgroups. Patients in each group were also compared to age- and sex-matched healthy controls (n = 28). A whole-brain cortical thickness interaction analysis of medication and diagnosis was then performed. Correlations between cortical thickness with antipsychotic dose and psychotic symptoms were examined. Results The effects of medication and diagnosis did not interact, suggesting independent effects. Compared with controls, diagnostic differences were found in frontal, parietal and temporal regions. Decreased thickness in FGA-treated versus SGA-treated groups was found in a large frontoparietal region (p < 0.001, corrected). Comparisons with healthy controls revealed decreased cortical thickness in the FGA group whereas the SGA group showed increases in addition to decreases. In FGA-treated patients cortical thinning was associated with higher negative symptoms whereas increased cortical thickness in the SGA-treated group was associated with lower positive symptoms. Conclusions Our results suggest that FGA and SGA treatments have divergent effects on cortical thickness during the first episode of psychosis that are independent from changes due to illness.

Inhibitory Control in Young Adolescents: The Role of Sex, Intelligence, and Temperament

OBJECTIVE Inhibitory control is associated with temperament and intelligence, which together form an essential component of the ability to adaptively regulate behavior. Impairments in inhibitory control have been linked with a host of common and debilitating conditions, often in a sex-dependent manner. However, sex differences in inhibitory control are often not expressed experimentally during task performance. Here, we sought to examine how sex, temperament, and intelligence are related to different aspects of inhibitory control. METHOD We recruited a large sample of early adolescents (n = 153; mean age 12.6 years) to comprehensively investigate the relationship between sex, self-reported and parent-reported temperamental effortful control, and intelligence with different aspects of inhibitory control--namely, strategic (or proactive) control and evaluative (or reactive) control, assessed using a modified Stroop task. RESULTS Compared with males, females were more efficient in their use of strategic control to reduce the magnitude of response conflict. There was no sex difference in evaluative control. Further, whereas high intelligence was associated with fewer errors for both males and females, effortful control was associated with performance accuracy only in females. CONCLUSIONS These findings highlight sex differences in the relationship of inhibitory control to individual differences in temperamental effortful control in early adolescents and reinforce the generalized positive effects of intelligence.

Classifying Schizophrenia Using Multimodal Multivariate Pattern Recognition Analysis: Evaluating the Impact of Individual Clinical Profiles on the Neurodiagnostic Performance

Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.

Aberrant Functional Whole-Brain Network Architecture in Patients With Schizophrenia: A Meta-analysis

Findings from multiple lines of research provide evidence of aberrant functional brain connectivity in schizophrenia. By using graph-analytical measures, recent studies indicate that patients with schizophrenia exhibit changes in the organizational principles of whole-brain networks and that these changes relate to cognitive symptoms. However, there has not been a systematic investigation of functional brain network changes in schizophrenia to test the consistency of these changes across multiple studies. A comprehensive literature search was conducted to identify all available functional graph-analytical studies in patients with schizophrenia. Effect size measures were derived from each study and entered in a random-effects meta-analytical model. All models were tested for effects of potential moderator variables as well as for the presence of publication bias. The results of a total of n = 13 functional neuroimaging studies indicated that brain networks in patients with schizophrenia exhibit significant decreases in measures of local organization (g = -0.56, P = .02) and significant decreases in small-worldness (g = -0.65, P = .01) whereas global short communication paths seemed to be preserved (g = 0.26, P = .32). There was no evidence for a publication bias or moderator effects. The present meta- analysis demonstrates significant changes in whole brain network architecture associated with schizophrenia across studies.

Cortico-limbic network abnormalities in individuals with current and past major depressive disorder.

BACKGROUND Brain abnormalities in fronto-temporal structures have been implicated in major depressive disorder (MDD). This study aims to identify their anatomical distribution and their relation to the time course of the disease. METHODS A whole-brain voxel based morphometry analysis was conducted to assess gray and white matter alterations in 56 participants with a lifetime history of MDD, including currently depressed (cMDD) and remitted patients (rMDD), and 33 matched healthy controls (HC). RESULTS Compared to HC, MDD participants showed increased white matter volume (WMV) in the uncinate fasciculus (UF) and decreased gray matter density (GMD) on the ventromedial prefrontal cortex (vmPFC). The increased WMV in UF was driven by both cMDD and rMDD groups and positively correlated with depression scores. The GMD decrease in the vmPFC resulted mainly from abnormalities in rMDD and was not correlated with depression scores. Finally, temporal UF and vmPFC white matter showed strong structural covariance suggesting functional interactions between these two brain regions. LIMITATIONS The retrospective and cross-sectional design of the study limits the generalizability of the results. Information concerning ongoing treatment did not allow the exploration of interactions between medication and observed abnormalities. The duration of the remission period could have influenced abnormalities in the subgroup of remitted patients. CONCLUSIONS Fronto-temporal alterations in MDD consist of alterations in a cortico-limbic network involving the ventromedial prefrontal cortex and temporal white matter tracts. State-like abnormalities in the UF survive remission and persist as trait-like abnormalities together with alteration in the vmPFC.

Mapping the relationship between subgenual cingulate cortex functional connectivity and depressive symptoms across adolescence

Changes in the functional connectivity of the subgenual anterior cingulate cortex (SGC) have been linked with depressive symptoms. The aim of this study was to map this relationship across mid to late adolescence. Employing a longitudinal functional magnetic resonance imaging (fMRI) design, associations between patterns of resting-state SGC functional connectivity and symptoms of depression were examined at two time points in an initial sample of 72 adolescents. Using a region-of-interest approach, these associations were evaluated cross-sectionally and longitudinally. Cross-sectionally, weaker SGC functional connectivity with the posterior cingulate cortex (PCC), angular gyrus and dorsal prefrontal cortex at baseline, and weaker SGC connectivity with the dorsomedial prefrontal cortex (DMPFC) and ventromedial prefrontal cortex at follow-up, were associated with higher depressive symptoms. Longitudinally, a decrease in SGC functional connectivity with DMPFC, PCC, angular gyrus and middle temporal gyrus was associated with higher depressive symptoms at follow-up. The observation of weaker SGC connectivity predicting increased symptoms contrasts with the majority of resting-state fMRI studies in clinically depressed populations. Taken together with these past studies, our findings suggest depression-related changes in SGC functional connectivity may differ across developmental and illness stages.

Structural brain changes are associated with response of negative symptoms to prefrontal repetitive transcranial magnetic stimulation in patients with schizophrenia

Impaired neural plasticity may be a core pathophysiological process underlying the symptomatology of schizophrenia. Plasticity-enhancing interventions, including repetitive transcranial magnetic stimulation (rTMS), may improve difficult-to-treat symptoms; however, efficacy in large clinical trials appears limited. The high variability of rTMS-related treatment response may be related to a comparably large variation in the ability to generate plastic neural changes. The aim of the present study was to determine whether negative symptom improvement in schizophrenia patients receiving rTMS to the left dorsolateral prefrontal cortex (DLPFC) was related to rTMS-related brain volume changes. A total of 73 schizophrenia patients with predominant negative symptoms were randomized to an active (n=34) or sham (n=39) 10-Hz rTMS intervention applied 5 days per week for 3 weeks to the left DLPFC. Local brain volume changes measured by deformation-based morphometry were correlated with changes in negative symptom severity using a repeated-measures analysis of covariance design. Volume gains in the left hippocampal, parahippocampal and precuneal cortices predicted negative symptom improvement in the active rTMS group (all r⩽−0.441, all P⩽0.009), but not the sham rTMS group (all r⩽0.211, all P⩾0.198). Further analyses comparing negative symptom responders (⩾20% improvement) and non-responders supported the primary analysis, again only in the active rTMS group (F(9, 207)=2.72, P=0.005, partial η 2=0.106). Heterogeneity in clinical response of negative symptoms in schizophrenia to prefrontal high-frequency rTMS may be related to variability in capacity for structural plasticity, particularly in the left hippocampal region and the precuneus.

Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia

Identifying distinctive subtypes of schizophrenia could ultimately enhance diagnostic and prognostic accuracy. We aimed to uncover neuroanatomical subtypes of chronic schizophrenia patients to test whether stratification can enhance computer-aided discrimination of patients from control subjects. Unsupervised, data-driven clustering of structural MRI (sMRI) data was used to identify 2 subtypes of schizophrenia patients drawn from a US-based open science repository (n = 71) and we quantified classification improvements compared to controls (n = 74) using supervised machine learning. We externally validated the unsupervised and supervised learning models in a heterogeneous German validation sample (n = 316), and characterized symptom, cognition, and longitudinal symptom change signatures. Stratification improved classification accuracies from 68.5% to 73% (subgroup 1) and 78.8% (subgroup 2), respectively. Increased accuracy was also found when models were externally validated, and an average gain of 9% was found in supplementary analyses. The first subgroup was associated with cortical and subcortical volume reductions coupled with substantially longer illness duration, whereas the second subgroup was mainly characterized by cortical reductions, reduced illness duration, and comparatively less negative symptoms. Individuals within each subgroup could be identified using just 10 clinical questions at an accuracy of 81.2%, and differential cognitive and symptom course signatures were suggested in multivariate analyses. Our findings suggest that sMRI-based subtyping enhances the neuroanatomical discrimination of schizophrenia by identifying generalizable brain patterns that align with a clinical staging model of the disorder. These findings could be used to improve illness stratification for biomarker-based computer-aided diagnoses.