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Diminished motivation to pursue and obtain primary and secondary rewards has been demonstrated in anorexia nervosa (AN). However, the neurobehavioral mechanisms underlying the behavioral activation component of aberrant reward motivation remains incompletely understood. This work aims to explore this underexplored facet of reward motivation in AN. We recruited female adolescents with AN, restricting type (n = 32) and a healthy control group (n = 28). All participants underwent functional magnetic resonance imaging (fMRI) while performing a monetary reward task. Diffusion MRI data was also collected to examine the reward motivation circuit's structural connectivity. Behavioral results demonstrated slower speed of reward-seeking behavior in those with AN compared with controls. Accompanying this was lower functional connectivity and reduced white matter structural integrity of the connection between the ventral tegmental area/substantia nigra pars compacta and the nucleus accumbens within the mesolimbic circuit. Further, there was evidence of neurobehavioral decoupling in AN between reward-seeking behavior and mesolimbic regional activation and functional connectivity. Aberrant activity of the bed nucleus of the stria terminalis (BNST) and its connectivity with the mesolimbic system was also evident in AN during the reward motivation period. Our findings suggest functional and structural dysconnectivity within a mesolimbic reward circuit, neurofunctional decoupling from reward-seeking behavior, and abnormal BNST function and circuit interaction with the mesolimbic system. These results show behavioral indicators of aberrant reward motivation in AN, particularly in its activational component. This is mediated neuronally by mesolimbic reward circuit functional and structural dysconnectivity as well as neurobehavioral decoupling. Based on these findings, we suggest a novel circuit-based mechanism of impaired reward processing in AN, with the potential for translation to developing more targeted and effective treatments in this difficult-to-treat psychiatric condition.

Emerging scientific evidence indicates that frequent digital technology use has a significant impact-both negative and positive-on brain function and behavior. Potential harmful effects of extensive screen time and technology use include heightened attention-deficit symptoms, impaired emotional and social intelligence, technology addiction, social isolation, impaired brain development, and disrupted sleep. However, various apps, videogames, and other online tools may benefit brain health. Functional imaging scans show that internet-naive older adults who learn to search online show significant increases in brain neural activity during simulated internet searches. Certain computer programs and videogames may improve memory, multitasking skills, fluid intelligence, and other cognitive abilities. Some apps and digital tools offer mental health interventions providing self-management, monitoring, skills training, and other interventions that may improve mood and behavior. Additional research on the positive and negative brain health effects of technology is needed to elucidate mechanisms and underlying causal relationships.
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Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are potentially life-threatening conditions whose partially overlapping phenomenology-distorted perception of appearance, obsessions/compulsions, and limited insight-can make diagnostic distinction difficult in some cases. Accurate diagnosis is crucial, as the effective treatments for AN and BDD differ. To improve diagnostic accuracy and clarify the contributions of each of the multiple underlying factors, we developed a two-stage machine learning model that uses multimodal, neurobiology-based, and symptom-based quantitative data as features: task-based functional magnetic resonance imaging data using body visual stimuli, graph theory metrics of white matter connectivity from diffusor tensor imaging, and anxiety, depression, and insight psychometric scores. In a sample of unmedicated adults with BDD (n = 29), unmedicated adults with weight-restored AN (n = 24), and healthy controls (n = 31), the resulting model labeled individuals with an accuracy of 76%, significantly better than the chance accuracy of 35% ([Formula: see text]). In the multivariate model, reduced white matter global efficiency and better insight were associated more with AN than with BDD. These results improve our understanding of the relative contributions of the neurobiological characteristics and symptoms of these disorders. Moreover, this approach has the potential to aid clinicians in diagnosis, thereby leading to more tailored therapy.

Cognitive behavioral therapy (CBT) is an effective treatment for many with obsessive-compulsive disorder (OCD). However, response varies considerably among individuals. Attaining a means to predict an individual's potential response would permit clinicians to more prudently allocate resources for this often stressful and time-consuming treatment. We collected resting-state functional magnetic resonance imaging from adults with OCD before and after 4 weeks of intensive daily CBT. We leveraged machine learning with cross-validation to assess the power of functional connectivity (FC) patterns to predict individual posttreatment OCD symptom severity. Pretreatment FC patterns within the default mode network and visual network significantly predicted posttreatment OCD severity, explaining up to 67% of the variance. These networks were stronger predictors than pretreatment clinical scores. Results have clinical implications for developing personalized medicine approaches to identifying individual OCD patients who will maximally benefit from intensive CBT.

In neuroimaging, functional magnetic resonance imaging (fMRI) measures the blood-oxygenation-level dependent (BOLD) signal in the brain. The degree of correlation of the BOLD signal in spatially independent regions of the brain defines the functional connectivity of those regions. During a cognitive fMRI task, a psychophysiological interaction (PPI) analysis can be used to examine changes in the functional connectivity during specific contexts defined by the cognitive task. An example of such a task is one that engages the memory system, asking participants to learn pairs of unrelated words (encoding) and recall the second word in a pair when presented with the first word (retrieval). In the present study, we used this type of associative memory task and a generalized PPI (gPPI) analysis to compare changes in hippocampal connectivity in older adults who are carriers of the Alzheimer's disease (AD) genetic risk factor apolipoprotein-E epsilon-4 (APOEε4). Specifically, we show that the functional connectivity of subregions of the hippocampus changes during encoding and retrieval, the two active phases of the associative memory task. Context-dependent changes in functional connectivity of the hippocampus were significantly different in carriers of APOEε4 compared to non-carriers. PPI analyses make it possible to examine changes in functional connectivity, distinct from univariate main effects, and to compare these changes across groups. Thus, a PPI analysis may reveal complex task effects in specific cohorts that traditional univariate methods do not capture. PPI analyses cannot, however, determine directionality or causality between functionally connected regions. Nevertheless, PPI analyses provide powerful means for generating specific hypotheses regarding functional relationships, which can be tested using causal models. As the brain is increasingly described in terms of connectivity and networks, PPI is an important method for analyzing fMRI task data that is in line with the current conception of the human brain.

Gender dysphoria (GD) is characterized by incongruence between one's identity and gender assigned at birth. The biological mechanisms of GD are unclear. We investigated brain network connectivity patterns involved in own body perception in the context of self in GD. Twenty-seven female-to-male (FtM) individuals with GD, 27 male controls, and 27 female controls underwent resting state fMRI. We compared functional connections within intrinsic connectivity networks involved in self-referential processes and own body perception -default mode network (DMN) and salience network - and visual networks, using independent components analyses. Behavioral correlates of network connectivity were also tested using self-perception ratings while viewing own body images morphed to their sex assigned at birth, and to the sex of their gender identity. FtM exhibited decreased connectivity of anterior and posterior cingulate and precuneus within the DMN compared with controls. In FtM, higher "self" ratings for bodies morphed towards the sex of their gender identity were associated with greater connectivity of the anterior cingulate within the DMN, during long viewing times. In controls, higher ratings for bodies morphed towards their gender assigned at birth were associated with right insula connectivity within the salience network, during short viewing times. Within visual networks FtM showed weaker connectivity in occipital and temporal regions. Results suggest disconnectivity within networks involved in own body perception in the context of self in GD. Moreover, perception of bodies in relation to self may be reflective rather than reflexive, as a function of mesial prefrontal processes. These may represent neurobiological correlates to the subjective disconnection between perception of body and self-identification.

ObjectiveIndividuals with anorexia nervosa (AN) and body dysmorphic disorder (BDD) exhibit distorted perception and negative evaluations of their own appearance; however, little is known about how they perceive others' appearance, and whether or not the conditions share perceptual distortions.MethodThirty participants with BDD, 22 with AN, now weight-restored, and 39 healthy comparison participants (HC) rated photographs of others' faces and bodies on attractiveness, how overweight or underweight they were, and how much photographs triggered thoughts of their own appearance. We compared responses among groups by stimulus type and by level-of-detail (spatial frequency).ResultsCompared to HCs, AN and BDD had lower attractiveness ratings for others' bodies and faces for high-detail and low-detail images, rated bodies as more overweight, and were more triggered to think of their own appearance for faces and bodies. In AN, symptom severity was associated with greater triggering of thoughts of own appearance and higher endorsement of overweight ratings for bodies. In BDD, symptom severity was associated with greater triggering of thoughts of own appearance for bodies and higher overweight ratings for low-detail images. BDD was more triggered to think of own facial appearance than AN.DiscussionAN and BDD show similar behavioral phenotypes of negative appearance evaluations for others' faces and bodies, and have thoughts of their own appearance triggered even for images outside of their primary appearance concerns, suggesting a more complex cross-disorder body-image phenotype than previously assumed. Future treatment strategies may benefit from addressing how these individuals evaluate others in addition to themselves. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:127-138).

BackgroundfMRI graph theory reveals resting-state brain networks, but has never been used in pediatric OCD.MethodsWhole-brain resting-state fMRI was acquired at 3T from 21 children with OCD and 20 age-matched healthy controls. BOLD connectivity was analyzed yielding global and local graph-theory metrics across 100 child-based functional nodes. We also compared local metrics between groups in frontopolar, supplementary motor, and sensorimotor cortices, regions implicated in recent neuroimaging and/or brain stimulation treatment studies in OCD.ResultsAs in adults, the global metric small-worldness was significantly (P

Individuals with body dysmorphic disorder (BDD) suffer from preoccupations with perceived defects in physical appearance, causing severe distress and disability. Although BDD affects 1-2% of the population, the neurobiology is not understood. Discrepant results in previous volumetric studies may be due to small sample sizes, and no study has investigated cortical thickness in BDD. The current study is the largest neuroimaging analysis of BDD. Participants included 49 medication-free, right-handed individuals with DSM-IV BDD and 44 healthy controls matched by age, sex, and education. Using high-resolution T1-weighted magnetic resonance imaging, we computed vertex-wise gray matter (GM) thickness on the cortical surface and GM volume using voxel-based morphometry. We also computed volumes in cortical and subcortical regions of interest. In addition to group comparisons, we investigated associations with symptom severity, insight, and anxiety within the BDD group. In BDD, greater anxiety was significantly associated with thinner GM in the left superior temporal cortex and greater GM volume in the right caudate nucleus. There were no significant differences in cortical thickness, GM volume, or volumes in regions of interest between BDD and control subjects. Subtle associations with clinical symptoms may characterize brain morphometric patterns in BDD, rather than large group differences in brain structure.

Cranial electrotherapy stimulation (CES) is a U.S. Food and Drug Administration (FDA)-approved treatment for insomnia, depression, and anxiety consisting of pulsed, low-intensity current applied to the earlobes or scalp. Despite empirical evidence of clinical efficacy, its mechanism of action is largely unknown. The goal was to characterize the acute effects of CES on resting state brain activity. Our primary hypothesis was that CES would result in deactivation in cortical and subcortical regions. Eleven healthy controls were administered CES applied to the earlobes at subsensory thresholds while being scanned with functional magnetic resonance imaging in the resting state. We tested 0.5- and 100-Hz stimulation, using blocks of 22 sec "on" alternating with 22 sec of baseline (device was "off"). The primary outcome measure was differences in blood oxygen level dependent data associated with the device being on versus baseline. The secondary outcome measures were the effects of stimulation on connectivity within the default mode, sensorimotor, and fronto-parietal networks. Both 0.5- and 100-Hz stimulation resulted in significant deactivation in midline frontal and parietal regions. 100-Hz stimulation was associated with both increases and decreases in connectivity within the default mode network (DMN). Results suggest that CES causes cortical brain deactivation, with a similar pattern for high- and low-frequency stimulation, and alters connectivity in the DMN. These effects may result from interference from high- or low-frequency noise. Small perturbations of brain oscillations may therefore have significant effects on normal resting state brain activity. These results provide insight into the mechanism of action of CES, and may assist in the future development of optimal parameters for effective treatment.

Through protein interactions mediated by their cytoplasmic C termini the GluN2A and GluN2B subunits of NMDA receptors (NMDARs) have a key role in the formation of NMDAR signaling complexes at excitatory synapses. Although these signaling complexes are thought to have a crucial role in NMDAR-dependent forms of synaptic plasticity such as long-term potentiation (LTP), the role of the C terminus of GluN2A in coupling NMDARs to LTP enhancing and/or suppressing signaling pathways is unclear. To address this issue we examined the induction of LTP in the hippocampal CA1 region in mice lacking the C terminus of endogenous GluN2A subunits (GluN2A[superscript [delta]C/[delta]C]. Our results show that truncation of GluN2A subunits produces robust, but highly frequency-dependent, deficits in LTP and a reduction in basal levels of extracellular signal regulated kinase 2 (ERK2) activation and phosphorylation of AMPA receptor GluA1 subunits at a protein kinase A site (serine 845). Consistent with the notion that these signaling deficits contribute to the deficits in LTP in GluN2A[superscript [delta]C/[delta]C] mice, activating ERK2 and increasing GluA1 S845 phosphorylation through activation of [Beta]-adrenergic receptors rescued the induction of LTP in these mutants. Together, our results indicate that the capacity of excitatory synapses to undergo plasticity in response to different patterns of activity is dependent on the coupling of specific signaling pathways to the intracellular domains of the NMDARs and that abnormal plasticity resulting from mutations in NMDARs can be reduced by activation of key neuromodulatory transmitter receptors that engage converging signaling pathways.