Resting-state Functional Connectivity Data Research Articles

Nucleus Accumbens Functional Connectivity with the Default Mode Network: Black Children’s Diminished Returns of Household Income

Introduction: Although research has established a link between socioeconomic status (SES) and neuroimaging measures, weaker SES effects are shown for Blacks than Whites. This is, in part, due to processes such as stratification, racism, mineralization, and othering of Black people in the US. Purpose: This study had two aims: First to test the association between household income and the nucleus accumbens (NAcc) resting-state functional connectivity with the Default Mode Network (DMN) in children, and second, to investigate racial heterogeneity in this association. Methods: This cross-sectional study used data from the Adolescent Brain Cognitive Development (ABCD) study. We analyzed the resting-state functional connectivity data using Magnetic Resonance Imaging (rsfMRI) of 7903 US pre-adolescents who were between ages 9 and 10 years old. The main outcome was the NAcc resting-state functional connectivity with DMN. The independent variable was household income. Age, sex, and family structure were the study covariates. Race was the moderator. Mixed-effects regression models were used for data analysis with and without interaction terms between household income and race. Results: Higher household income was associated with higher NAcc resting-state functional connectivity with DMN. Race showed a statistically significant interaction with household income suggesting that the NAcc resting-state functional connectivity with DMN was significantly weaker for Black compared to White pre-adolescents. Conclusions: In line with Minorities’ Diminished Returns (MDRs), the association between household income and pre-adolescents NAcc resting-state functional connectivity with DMN is weaker in Black than in White children. This result is of interest because DMN’s functional connectivity with NAcc may have a role in cognitive flexibility and reward processing. The weaker links between SES indicators and neuroimaging findings for Blacks than for Whites may reflect the racialization of Black people in the US. Social stratification, racism, and discrimination may minimize the returns of SES for Black families, who have been oppressed for centuries.

The utility of maraviroc, an antiretroviral agent used to treat HIV, as treatment for opioid abuse? Data from MRI and behavioural testing in rats.

Background:Maraviroc is an antiretroviral agent and C-C chemokine coreceptor 5 (CCR5) antagonist that is currently used to treat human immunodeficiency virus. CCR5/μ-opioid receptor heterodimerization suggests that maraviroc could be a treatment for oxycodone abuse. We treated rats with maraviroc to explore its effect on oxycodone-seeking and its interference with the analgesic effects of oxycodone. We used resting-state blood-oxygen-level-dependent functional connectivity to assess the effect of maraviroc on oxycodone-enhanced coupling in the reward circuitry and performed behavioural tests to evaluate the effect of maraviroc on oxycodone rewarding properties and on oxycodone-seeking after prolonged abstinence.Methods:Two groups of rats were exposed to 8 consecutive days of oxycodone-conditioned place preference training and treatment with maraviroc or vehicle. Two additional groups were trained to self-administer oxycodone for 10 days and then tested for drug seeking after 14 days of abstinence with or without daily maraviroc treatment. We tested the effects of maraviroc on oxycodone analgesia using a tail-flick assay. We analyzed resting-state functional connectivity data using a rat 3-dimensional MRI atlas of 171 brain areas.Results:Maraviroc significantly decreased conditioned place preference and attenuated oxycodone-seeking behaviour after prolonged abstinence. The analgesic effect of oxycodone was maintained after maraviroc treatment. Oxycodone increased functional coupling with the accumbens, ventral pallidum and olfactory tubercles, but this was reduced with maraviroc treatment.Limitations:All experiments were performed in male rats only.Conclusion:Maraviroc treatment attenuated oxycodone-seeking in abstinent rats and reduced functional coupling in the reward circuitry. The analgesic effects of oxycodone were not affected by maraviroc.

Oxytocin, PTSD, and sexual abuse are associated with attention network intrinsic functional connectivity

Childhood maltreatment is linked to Posttraumatic Stress Disorder (PTSD) in adulthood. Neural attention network function contributes to resilience against PTSD following maltreatment; oxytocin administration alters functional connectivity differentially among resilient to PTSD groups. The present study examined intrinsic connectivity between ventral and dorsal neural attention networks (VAN and DAN) to clarify the nature of dysfunction versus resilience in the context of maltreatment-related PTSD, and to explore differential dysfunction related to varied aspects of maltreatment. Oxytocin administration was examined as a factor in these relationships. Resting-state functional connectivity data were collected from 39 adults with maltreatment histories, with and without PTSD, who were randomly assigned to receive oxytocin or placebo. We found that PTSD and sexual abuse (SA) were associated with reduced VAN-DAN connectivity. There were no significant effects with regard to physical abuse. Oxytocin was associated with greater VAN-DAN connectivity strength. These preliminary findings suggest dysfunction within attentional systems in PTSD, as well as following SA. Further, oxytocin may help ameliorate attentional neurocircuitry dysfunction in individuals with PTSD and those with maltreatment histories.

Multivariate Machine Learning Analyses in Identification of Major Depressive Disorder Using Resting-State Functional Connectivity: A Multicentral Study.

Diagnosis of major depressive disorder (MDD) using resting-state functional connectivity (rs-FC) data faces many challenges, such as the high dimensionality, small samples, and individual difference. To assess the clinical value of rs-FC in MDD and identify the potential rs-FC machine learning (ML) model for the individualized diagnosis of MDD, based on the rs-FC data, a progressive three-step ML analysis was performed, including six different ML algorithms and two dimension reduction methods, to investigate the classification performance of ML model in a multicentral, large sample dataset [1021 MDD patients and 1100 normal controls (NCs)]. Furthermore, the linear least-squares fitted regression model was used to assess the relationships between rs-FC features and the severity of clinical symptoms in MDD patients. Among used ML methods, the rs-FC model constructed by the eXtreme Gradient Boosting (XGBoost) method showed the optimal classification performance for distinguishing MDD patients from NCs at the individual level (accuracy = 0.728, sensitivity = 0.720, specificity = 0.739, area under the curve = 0.831). Meanwhile, identified rs-FCs by the XGBoost model were primarily distributed within and between the default mode network, limbic network, and visual network. More importantly, the 17 item individual Hamilton Depression Scale scores of MDD patients can be accurately predicted using rs-FC features identified by the XGBoost model (adjusted R2 = 0.180, root mean squared error = 0.946). The XGBoost model using rs-FCs showed the optimal classification performance between MDD patients and HCs, with the good generalization and neuroscientifical interpretability.

Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity.

The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into ‘young’ and ‘old’ groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into ‘young’ and ‘old’ based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between ‘old’ and ‘young’ cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30–31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns.

Developing functional network connectivity of the dorsal anterior cingulate cortex mediates externalizing psychopathology in adolescents with child neglect

Childhood adversity has been associated with elevated risk for psychopathology. We investigated whether development of functional brain networks important for executive function (EF) could serve as potential mediators of this association. We analyzed data of 475 adolescents, a subsample of the multisite longitudinal NCANDA (National Consortium on Alcohol and Neurodevelopment in Adolescence) cohort with completed measures of childhood trauma, resting-state functional brain connectivity data, and symptoms of internalizing and externalizing psychopathology at baseline and follow-up years 1–4. Using parallel process latent growth models, we found that childhood adversity was associated with increased risk for externalizing/internalizing behaviors. We specifically investigated whether functional connectivity of the dorsal anterior cingulate cortex (dACC) to brain regions within the cingulo-opercular (CO) network, a well-known EF network that underlies control of attention and self-regulation, mediates the association between adversity and symptoms of psychopathology. We found that childhood adversity, specifically child neglect was negatively associated with functional connectivity of the dACC within the CO network, and that this connectivity mediated the association between neglect and externalizing behaviors. Our study advances a mechanistic understanding of how childhood adversity may impact the development of psychopathology, highlighting the relevance of dACC functional networks particularly for externalizing psychopathology.

Altered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder.

Converging evidence from neuroimaging studies has revealed altered connectivity in cortical-subcortical networks in youth and adults with autism spectrum disorder (ASD). Comparatively little is known about the development of cortical-subcortical connectivity in infancy, before the emergence of overt ASD symptomatology. Here, we examined early functional and structural connectivity of thalamocortical networks in infants at high familial risk for ASD (HR) and low-risk controls (LR). Resting-state functional connectivity and diffusion tensor imaging data were acquired in 52 6-week-old infants. Functional connectivity was examined between 6 cortical seeds-prefrontal, motor, somatosensory, temporal, parietal, and occipital regions-and bilateral thalamus. We found significant thalamic-prefrontal underconnectivity, as well as thalamic-occipital and thalamic-motor overconnectivity in HR infants, relative to LR infants. Subsequent structural connectivity analyses also revealed atypical white matter integrity in thalamic-occipital tracts in HR infants, compared with LR infants. Notably, aberrant connectivity indices at 6weeks predicted atypical social development between 9 and 36months of age, as assessed with eye-tracking and diagnostic measures. These findings indicate that thalamocortical connectivity is disrupted at both the functional and structural level in HR infants as early as 6 weeks of age, providing a possible early marker of risk for ASD.

Detection of prenatal alcohol exposure using machine learning classification of resting-state functional network connectivity data.

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting-state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC), which revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data were subjected to binary classification using support vector machine (SVM) -based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n=48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males-only sample (n=24; 12 PAE, 12 SAC), and a females-only sample (n=24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicates an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

Connectome-Based Predictive Modeling of Individual Anxiety.

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational "lesion" approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.

Determining the impact of age and sex on the psychophysical and neurophysiological response to thermal pain across the adult lifespan.

AimsDetermine sex‐ and age‐associated psychophysical and neurophysiological differences in the processing of pain across the adult lifespan.DesignPreliminary, exploratory, cross‐sectional study.MethodsUsing psychophysics (to measure intensity and unpleasantness) and functional magnetic resonance imaging blood oxygenation level dependent methods (to measure stimulus‐evoked brain activation), we will examine sex‐ and age‐associated differences in thermal pain processing and their underlying neurophysiology in a broad range of healthy adults (ages 30–89). We will acquire resting state functional connectivity data for secondary analyses exploring whether resting state connectivity predicts psychophysical and neurophysiological responses to thermal pain. To examine the effects of altered blood flow, we will acquire resting‐state arterial spin labeling magnetic resonance imaging data to quantify resting cerebral blood flow. We will interpret findings in the context of a proposed neural model of pain, ageing, and sex. Study funding was received in June of 2014. Ethical approval was obtained from the Vanderbilt University IRB prior to study initiation.ConclusionExploring the biological reasons for age‐ and sex‐associated differences in pain processing will increase our understanding of pain in older adults. The paucity of neurobiological evidence to support best practice pain management in older adults places these individuals at risk for poor pain management.ImpactPoorly treated pain in older adults is a critical public health problem associated with a poor quality of life and increased healthcare costs. Understanding how age and sex have an impact on central processing of pain across the lifespan is a critical step toward improving personalized pain medicine.

The Cerebellum Links to Positive Symptoms of Psychosis: A Systematic Review and Meta-analysis

AbstractBackgroundPositive symptoms of psychosis may be the result of faulty coordination and automatization of motor and higher order cognitive functions, partly due to cerebellar dysfunction. Specifically, auditory verbal hallucinations (AVH) have been related to altered processing of sensory feedback to one’s own action. Such alterations highlight the role of dysfunctional cerebellar circuitry in psychosis. However, how exactly the cerebellum contributes to AVH remains unclear.MethodsA systematic search of electronic databases identified a broad range of cerebellar neuroimaging studies in psychotic patients, reporting volume, structural connectivity, or resting-state functional connectivity data. A total of 22 studies were selected for review: 11 focused on the specific effects of AVH and 11 probed the effects of aggregated positive symptom scores. Meta-analysis was used to probe the consistency of cerebellar differences and their relationship with sociodemographic and clinical measures. An exploratory activation likelihood estimation (ALE) analysis tested the regional specificity of cerebellar differences in patients with such symptoms.ResultsCerebellar differences were more consistently associated with AVH than with aggregated positive symptom measures, particularly when considering resting-state functional connectivity data. These differences were not moderated by age, sex, medication, or symptom severity. The ALE meta-analysis revealed a spatial convergence of these differences in lobules V–VI and crus I.ConclusionsCerebellar dysconnectivity might indicate a specific liability for AVH, particularly in sensorimotor (lobules V–VI) and cognitive (crus I) cerebellar zones. These abnormalities may contribute to altered sensory feedback processing and, consequently, affect higher level cognitive functions (eg, cognitive control) in AVH.

Structural and Functional Default Mode Network Connectivity and Antipsychotic Treatment Response in Medication-Naïve First Episode Psychosis Patients.

IntroductionOnly a few studies have comprehensively characterized default mode network (DMN) pathology on a structural and functional level, and definite conclusions cannot be drawn due to antipsychotic medication exposure and illness chronicity. The objective of this study was to characterize DMN pathology in medication-naïve first episode psychosis (FEP) patients, and determine if DMN structural and functional connectivity (FC) have potential utility as a predictor for subsequent antipsychotic treatment response. MethodsDiffusion imaging and resting state FC data from 42 controls and 52 FEP were analyzed. Patients then received 16 weeks of antipsychotic treatment. Using region of interest analyses, we quantified FC of the DMN and structural integrity of the white matter tracts supporting DMN function. We then did linear regressions between DMN structural and FC indices and antipsychotic treatment response. ResultsWe detected reduced DMN fractional anisotropy and axial diffusivity in FEP compared to controls. No DMN FC abnormalities nor correlations between DMN structural and FC were found. Finally, DMN fractional anisotropy and radial diffusivity were associated with response to treatment. ConclusionOur study highlights the critical role of the DMN in the pathophysiology suggesting that axonal damage may already be present in FEP patients. We also demonstrated that DMN pathology is clinically relevant, as greater structural DMN alterations were associated with a less favorable clinical response to antipsychotic medications.

Evaluation of spontaneous regional brain activity in weight-recovered anorexia nervosa

Whereas research using structural magnetic resonance imaging (sMRI) reports sizable grey matter reductions in patients suffering from acute anorexia nervosa (AN) to be largely reversible already after short-term weight gain, many task-based and resting-state functional connectivity (RSFC) studies suggest persistent brain alterations even after long-term weight rehabilitation. First investigations into spontaneous regional brain activity using voxel-wise resting-state measures found widespread abnormalities in acute AN, but no studies have compared intrinsic brain activity properties in weight-recovered individuals with a history of AN (recAN) with healthy controls (HCs). SMRI and RSFC data were analysed from a sample of 130 female volunteers: 65 recAN and 65 pairwise age-matched HC. Cortical grey matter thickness was assessed using FreeSurfer software. Fractional amplitude of low-frequency fluctuations (fALFFs), mean-square successive difference (MSSD), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VHMC), and degree centrality (DC) were calculated. SMRI and RSFC data were analysed from a sample of 130 female volunteers: 65 recAN and 65 pairwise age-matched HCs. Cortical grey matter thickness was assessed using FreeSurfer software. Fractional amplitude of low-frequency fluctuations (fALFF), mean-square successive difference (MSSD), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VHMC), and degree centrality (DC) were calculated. Abnormal regional homogeneity found in acute AN seems to normalize in recAN, supporting assumptions of a state rather than a trait marker. Aberrant fALFF values in the cerebellum and the infertior temporal gyrus could possibly hint towards trait factors or a scar (the latter, e.g., from prolonged periods of undernutrition), warranting further longitudinal research.

Connectome-Based Predictive Modeling of Creativity Anxiety

While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or “edges” that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.

Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.

Autism spectrum disorders (ASDs) are heterogeneous neurodevelopmental conditions. In fMRI studies, including most machine learning studies seeking to distinguish ASD from typical developing (TD) samples, cohorts differing in gender and symptom severity composition are often treated statistically as one "ASD group". Using resting-state functional connectivity (FC) data, we implemented random forest to build diagnostic classifiers in 4 ASD samples including a total of 656 participants (NASD = 306, NTD = 350, ages 6-18). Groups were manipulated to titrate heterogeneity of gender and symptom severity and partially overlapped. Each sample differed on inclusionary criteria: (1) all genders, unrestricted severity range; (2) only male participants, unrestricted severity; (3) all genders, higher severity only; (4) only male participants, higher severity. Each set consisted of 200 participants per group (ASD, TD; matched on age and head motion), 160 for training and 40 for validation. FMRI time series from 237 regions of interest (ROIs) were Pearson correlated in a 237×237 FC matrix and classifiers were built using random forest in training samples. Classification accuracies in validation samples were 62.5%, 65%, 70% and 73.75%, respectively for samples 1-4. Connectivity within cingulo-opercular task control (COTC) network, and between COTC ROIs and default mode and dorsal attention network contributed overall most informative features, but features differed across sets. Findings suggest that diagnostic classifiers vary depending on ASD sample composition. Specifically, greater homogeneity of samples regarding gender and symptom severity enhances classifier performance. However, given the true heterogeneity of ASDs, performance metrics alone may not adequately reflect classifier utility.

Lesion network mapping demonstrates that mind-wandering is associated with the default mode network.

Functional neuroimaging research has consistently associated brain structures within the default mode network (DMN) and frontoparietal network (FPN) with mind-wandering. Targeted lesion research has documented impairments in mind-wandering after damage to the medial prefrontal cortex (mPFC) and hippocampal regions associated with the DMN. However, no lesion studies to date have applied lesion network mapping to identify common networks associated with deficits in mind-wandering. In lesion network mapping, resting-state functional connectivity data from healthy participants are used to infer which brain regions are functionally connected to each lesion location from a sample with brain injury. In the current study, we conducted a lesion network mapping analysis to test the hypothesis that lesions affecting the DMN and FPN would be associated with diminished mind-wandering. We assessed mind-wandering frequency on the Imaginal Processes Inventory (IPI) in participants with brain injury (n=29) and healthy comparison participants without brain injury (n=19). Lesion network mapping analyses showed the strongest association of reduced mind-wandering with the left inferior parietal lobule within the DMN. In addition, traditional lesion symptom mapping results revealed that reduced mind-wandering was associated with lesions of the dorsal, ventral, and anterior sectors of mPFC, parietal lobule, and inferior frontal gyrus in the DMN (p<0.05 uncorrected). These findings provide novel lesion support for the role of the DMN in mind-wandering and contribute to a burgeoning literature on the neural correlates of spontaneous cognition.

Effects of TPH2 gene variation and childhood trauma on the clinical and circuit-level phenotype of functional movement disorders

BackgroundFunctional movement disorders (FMDs), part of the wide spectrum of functional neurological disorders (conversion disorders), are common and often associated with a poor prognosis. Nevertheless, little is known about their...

Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model.

Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity. Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders. Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively. The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

S147. FUNCTIONAL BRAIN CONNECTIVITY DATA IMPROVE CLINICAL OUTCOME PREDICTION IN YOUTH AT RISK FOR PSYCHOSIS

BackgroundImproved outcome prediction in individuals at high risk for psychosis may facilitate targeted early intervention. Studies suggest that improved outcome prediction may be achieved through the use of neurocognitive or neuroimaging data, on their own or in addition to clinical data. This study examines whether adding resting-state functional connectivity data to validated clinical predictors of psychosis improve outcome prediction in the prodromal stage.MethodsThis study involves 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome after one-year follow-up, participants were separated into three outcome categories: good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Resting-state fMRI data were acquired for each participant and processed using the Conn toolbox, including rigorous motion correction. Multinomial logistic regression analysis and leave-one-out cross-validation were used to assess the performance of three prediction models: 1) a clinical-only model using validated clinical predictors from the NAPLS-2 psychosis-risk calculator, 2) an fMRI-only model using measures of functional connectome organization and within/between-network connectivity among established resting-state networks, and 3) a combined clinical and fMRI prediction model. Model performance was assessed using the harmonic mean of the positive predictive value and sensitivity for each outcome category. This F1 measure was compared to expected chance-levels using a permutation test with 1,000 sampled permutations in order to evaluate the statistical significance of the model’s prediction.ResultsThe clinical-only prediction model failed to achieve a significant level of outcome prediction (F1 = 0.32, F1-chance = 0.26 □ 0.06, p = .154). The fMRI-only model did predict clinical outcome to a significant degree (F1 = 0.41, F1-chance = 0.29 □ 0.06, p = .016), but the combined clinical and fMRI prediction model showed the best performance (F1 = 0.46, F1-chance = 0.29 □ 0.06, p < .001). On average, positive predictive values (reflecting the probability that an outcome label predicted by the model was correct) were 39% better than chance-level and 32% better than the clinical-only model. Analyzing the contribution of individual predictor variables showed that GAF functional decline, a family history of psychosis, and performance on the Hopkins Verbal Learning Test were the most influential clinical predictors, whereas modular connectome organization, default-mode and fronto-parietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, cerebellum, and sensorimotor networks were the leading fMRI predictors.DiscussionThis study’s findings suggest that functional brain abnormalities reflected by alterations in resting-state functional connectivity precede and may drive subsequent changes in clinical functioning. Moreover, the findings show that markers of functional brain connectivity may be useful for improving early identification and clinical decision-making in prodromal psychosis.

Peripheral serotonin transporter DNA methylation is linked to increased salience network connectivity in females with anorexia nervosa

Epigenetic variation in the serotonin transporter gene (SLC6A4) has been shown to modulate the functioning of brain circuitry associated with the salience network and may heighten the risk for mental illness. This study is, to our knowledge, the first to test this epigenome–brain–behaviour pathway in patients with anorexia nervosa. We obtained resting-state functional connectivity (rsFC) data and blood samples from 55 acutely underweight female patients with anorexia nervosa and 55 age-matched female healthy controls. We decomposed imaging data using independent component analysis. We used bisulfite pyrosequencing to analyze blood DNA methylation within the promoter region of SLC6A4. We then explored salience network rsFC patterns in the group × methylation interaction. We identified a positive relationship between SLC6A4 methylation levels and rsFC between the dorsolateral prefrontal cortex and the salience network in patients with anorexia nervosa compared to healthy controls. Increased rsFC in the salience network mediated the link between SLC6A4 methylation and eating disorder symptoms in patients with anorexia nervosa. We confirmed findings of rsFC alterations for CpG-specific methylation at a locus with evidence of methylation correspondence between brain and blood tissue. This study was cross-sectional in nature, the sample size was modest for the method and methylation levels were measured peripherally, so findings cannot be fully generalized to brain tissue. This study sheds light on the neurobiological process of how epigenetic variation in the SLC6A4 gene may relate to rsFC in the salience network that is linked to psychopathology in anorexia nervosa.