rsFC Data Research Articles

Language and Attention Networks Have Distinct Roles in Language Improvement Following an Intensive Comprehensive Aphasia Program.

Attention is known to play an important role in language, and attentional deficits have been associated with language impairments in people with aphasia (PWA). A prior study by our laboratory indicated that behavioral measures for PWA participating in an intensive comprehensive aphasia program (ICAP) clustered into 1 language and 1 attention-related factor, with each factor correlated with independent resting state functional connectivity (rsFC) networks. The present study includes additional attention measures and participants to better assess the relationship between attention, language, and rsFC. PWA participated in 120 hours of ICAP therapy over 4 weeks, between April 2018 and August 2022. Participants were evaluated with the Western Aphasia Battery and Conners' Continuous Performance Test, pre- and post-ICAP. rsFC data were collected during functional magnetic resonance imaging scans pre- and post-ICAP. Principal component analysis identified behavioral score associations pre- and post-ICAP. FC matrices and graph measures were evaluated for both PWA and healthy controls. Twenty-three PWA participated (19 included in the final analysis). Data for 179 age and sex-matched healthy controls were taken from a public data set. The principal component analysis indicated 1 language and 2 attention-related principal components (PCs). The attention PCs were labeled Attention Accuracy and Attention Rate, in accordance with their best-associated behavioral measures. Importantly, each PC was associated with distinct networks, including higher-order networks, so-called because of their involvement in higher-order cognition. Notably, the language PC was significantly correlated with the ventral attention-memory-retrieval network rsFC (r=0.67, P=0.003) and the default mode-frontoparietal network rsFC (r=-0.56, P=0.019). Attention Accuracy was significantly correlated with cingulo-opercular-subcortical network rsFC (r=0.60, P=0.011). Attention Rate was significantly negatively correlated with visual-somatomotor network rsFC (r=-0.74, P=0.0007). This study supports the notion that language improvements for PWAs participating in an ICAP are associated with distinct network changes. Importantly, these networks are not restricted to language networks but also include attention, and task-control networks.

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Acute Exercise Improves Large-Scale Brain Network Segregation in Healthy Older Adults.

Introduction: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks. Methods: To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN). Results: We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN. Conclusion: These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.

Association between resting-state connectivity patterns in the defensive system network and treatment response in spider phobia—a replication approach

Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

Testing bidirectional relationships between functional brain networks and preclinical Alzheimer disease biomarkers

AbstractBackgroundResting‐state functional connectivity (RSFC) provides estimates of brain networks using fMRI. There are conflicting reports regarding the effects of amyloid‐β (Aβ) and tau on RSFC networks in cognitively normal participants. These inconsistencies might be explained by a recently‐proposed theoretical model that Aβ and tau have an interactive effect on RSFC, marked first by hyper‐connectivity in association with Aβ, then by hypo‐connectivity in association with tau. Although there is preliminary support for this model, independent validations and further characterizations of the effects of Aβ and tau on RSFC are needed.MethodWe analyzed RSFC, Aβ PET, and tau PET data from the Knight Alzheimer Disease Research Center (ADRC). All participants were cognitively normal (Clinical Dementia Rating®: 0). RSFC was collected on two scanners in partly‐overlapping samples (PETMR N: 190, TRIO N: 149). RSFC data were processed with framewise censoring and global signal regression. RSFC networks were defined using 298 regions of interest. Aβ was measured with PET PiB and AV‐45 tracers, which were harmonized using a Centiloid conversion. Tau was measured with a PET AV‐1451 tracer and was summarized in pre‐defined regions. Main effect and interaction relationships between Aβ, tau PET, and RSFC were tested with linear regression models, controlling for age, sex, education, and head motion as covariates, with a false discovery rate correction.ResultWe did not observe a bidirectional interaction effect in any of the hypothesized networks (default mode [DMN], salience [SAL], frontoparietal [FPN]). Aβ was positively associated with RSFC, independent of tau, in the DMN and SAL, while tau was negatively associated with RSFC, independent of Aβ, in the SAL only. Exploratory analyses of FC within and between all networks did not identify clear patterns of interactive biomarker effects, nor consistent patterns of main effect relationships within any of the other networks.ConclusionThese results do not support an interactive model of RSFC in preclinical AD. Inconsistencies with previous interactions may be driven by sample‐specific or methodological differences, or the low reliability of FC and brain‐behavior relationships. Prominent biomarker relationships in the DMN and SAL are consistent with regional overlap with Aβ deposition in early AD.

Testing bidirectional relationships between functional brain networks and preclinical Alzheimer disease biomarkers

AbstractBackgroundResting‐state functional connectivity (RSFC) provides estimates of brain networks using fMRI. There are conflicting reports regarding the effects of amyloid‐ß (Aß) and tau on RSFC networks in cognitively normal participants. These inconsistencies might be explained by a recently‐proposed theoretical model that Aß and tau have an interactive effect on RSFC, marked first by hyper‐connectivity in association with Aß, then by hypo‐connectivity in association with tau. Although there is preliminary support for this model, independent validations and further characterizations of the effects of Aß and tau on RSFC are needed.MethodWe analyzed RSFC, Aß PET, and tau PET data from the Knight Alzheimer Disease Research Center (ADRC). All participants were cognitively normal (Clinical Dementia Rating®: 0). RSFC was collected on two scanners in partly‐overlapping samples (PETMR N: 190, TRIO N: 149). RSFC data were processed with framewise censoring and global signal regression. RSFC networks were defined using 298 regions of interest. Aß was measured with PET PiB and AV‐45 tracers, which were harmonized using a Centiloid conversion. Tau was measured with a PET AV‐1451 tracer and was summarized in pre‐defined regions. Main effect and interaction relationships between Aß, tau PET, and RSFC were tested with linear regression models, controlling for age, sex, education, and head motion as covariates, with a false discovery rate correction.ResultWe did not observe a bidirectional interaction effect in any of the hypothesized networks (default mode [DMN], salience [SAL], frontoparietal [FPN]). Aß was positively associated with RSFC, independent of tau, in the DMN and SAL, while tau was negatively associated with RSFC, independent of Aß, in the SAL only. Exploratory analyses of FC within and between all networks did not identify clear patterns of interactive biomarker effects, nor consistent patterns of main effect relationships within any of the other networks.ConclusionThese results do not support an interactive model of RSFC in preclinical AD. Inconsistencies with previous interactions may be driven by sample‐specific or methodological differences, or the low reliability of FC and brain‐behavior relationships. Prominent biomarker relationships in the DMN and SAL are consistent with regional overlap with Aß deposition in early AD.

Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach.

Abnormal resting-state functional connectivity (rs-FC) and brain structure have emerged as pathological hallmarks of fibromyalgia (FM). This study investigated and compared the accuracy of network rs-FC and brain structural features in identifying FM with a machine learning (ML) approach. Twenty-six FM patients and thirty healthy controls were recruited. Clinical presentation was measured by questionnaires. After MRI acquisitions, network rs-FC z-score and network-based gray matter volume matrices were exacted and preprocessed. The performance of feature selection and classification methods was measured. Correlation analyses between predictive features in final models and clinical data were performed. The combination of the recursive feature elimination (RFE) selection method and support vector machine (rs-FC data) or logistic regression (structural data), after permutation importance feature selection, showed high performance in distinguishing FM patients from pain-free controls, in which the rs-FC ML model outperformed the structural ML model (accuracy: 0.91 vs. 0.86, AUC: 0.93 vs. 0.88). The combined rs-FC and structural ML model showed the best performance (accuracy: 0.95, AUC: 0.95). Additionally, several rs-FC features in the final ML model correlated with FM's clinical data. In conclusion, ML models based on rs-FC and brain structural MRI features could effectively differentiate FM patients from pain-free subjects.

Functional connectivity in reward circuitry and symptoms of anhedonia as therapeutic targets in depression with high inflammation: evidence from a dopamine challenge study

Increased inflammation in major depressive disorder (MDD) has been associated with low functional connectivity (FC) in corticostriatal reward circuits and symptoms of anhedonia, relationships which may involve the impact of inflammation on synthesis and release of dopamine. To test this hypothesis while establishing a platform to examine target engagement of potential therapies in patients with increased inflammation, medically stable unmedicated adult MDD outpatients enrolled to have a range of inflammation (as indexed by plasma C-reactive protein [CRP] levels) were studied at two visits involving acute challenge with the dopamine precursor levodopa (L-DOPA; 250 mg) and placebo (double-blind, randomized order ~1-week apart). The primary outcome of resting-state (rs)FC in a classic ventral striatum to ventromedial prefrontal cortex reward circuit was calculated using a targeted, a priori approach. Data available both pre- and post-challenge (n = 31/40) established stability of rsFC across visits and determined CRP > 2 mg/L as a cut-point for patients exhibiting positive FC responses (post minus pre) to L-DOPA versus placebo (p < 0.01). Higher post-L-DOPA FC in patients with CRP > 2 mg/L was confirmed in all patients (n = 40) where rsFC data were available post-challenge (B = 0.15, p = 0.006), and in those with task-based (tb)FC during reward anticipation (B = 0.15, p = 0.013). While effort-based motivation outside the scanner positively correlated with rsFC independent of treatment or CRP, change in anhedonia scores negatively correlated with rsFC after L-DOPA only in patients with CRP > 2 mg/L (r = -0.56, p = 0.012). FC in reward circuitry should be further validated in larger samples as a biomarker of target engagement for potential treatments including dopaminergic agents in MDD patients with increased inflammation.

Resting-State Functional Connectivity following Phonological Component Analysis: The Combined Action of Phonology and Visual Orthographic Cues.

Anomia is the most frequent and pervasive symptom for people with aphasia (PWA). Phonological component analysis (PCA) is a therapy incorporating phonological cues to treat anomia. Investigations of neural correlates supporting improvements following PCA remain scarce. Resting-state functional connectivity (rsFC) as a marker of therapy-induced neuroplasticity has been reported by our team. The present study explores the efficacy of PCA in French and associated therapy-induced neuroplasticity using whole-brain rsFC analysis. Ten PWA participated in a pre-/post-PCA fMRI study with cognitive linguistic assessments. PCA was delivered in French following the standard procedure. PCA led to significant improvement with trained and untrained items. PCA also led to changes in rsFC between distributed ROIs in the semantic network, visual network, and sub-cortical areas. Changes in rsFC can be interpreted within the frame of the visual and phonological nature of PCA. Behavioral and rsFC data changes associated with PCA in French highlight its efficacy and point to the importance of phonological and orthographic cues to consolidate the word-retrieval strategy, contributing to generalization to untrained words.

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.

Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment

ObjectiveTo explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. MethodThis study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer’s disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant’s AVLT-DR score. ResultsThe negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. ConclusionOur findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.

Contributions From Resting State Functional Connectivity and Familial Risk to Early Adolescent-Onset MDD: Results From the Adolescent Brain Cognitive Development Study

Abstract Background Family history of Major Depressive Disorder (MDD) is a robust predictor of MDD onset, especially in early adolescence. We examined the relationships between familial risk for depression and alterations to resting state functional connectivity (rsFC) within the default mode network (wDMN) and between the DMN and the left/right hippocampus (DMN-LHIPP/DMN-RHIPP) to the risk for early adolescent MDD onset. Methods We examined 9403 youth aged nine to eleven from the Adolescent Brain Cognitive Development study. Depressive symptoms were measured with the parent-reported Child Behavior Checklist. Both youth and their parents completed the Kiddie Schedule for Affective Disorders and Schizophrenia, which provided MDD diagnoses. A family history screen was administered to determine familial risk for depression. Youth underwent a resting state functional magnetic resonance imaging scan, providing us with rsFC data. Results Negative wDMN rsFC was associated with child-reported current depression, both child- and parent-reported past depression, and parent-reported current depressive symptoms. No difference was found in wDMN, DMN-LHIPP or DMN-RHIPP rsFC in children with or without familial risk for depression. Familial risk for depression interacted with wDMN rsFC in association with child-reported past MDD diagnosis and parent-reported current depressive symptoms. Limitations Information such as length of depressive episodes and age of onset of depression was not collected. Conclusions Altered wDMN rsFC in youth at familial risk for depression may be associated with increased risk for MDD onset in adolescence, but longitudinal studies are needed to test this hypothesis.

Contributions from resting state functional connectivity and familial risk to early adolescent-onset MDD: Results from the Adolescent Brain Cognitive Development study

BackgroundFamily history of Major Depressive Disorder (MDD) is a robust predictor of MDD onset, especially in early adolescence. We examined the relationships between familial risk for depression and alterations to resting state functional connectivity (rsFC) within the default mode network (wDMN) and between the DMN and the left/right hippocampus (DMN-LHIPP/DMN-RHIPP) to the risk for early adolescent MDD onset. MethodsWe examined 9403 youth aged nine to eleven from the Adolescent Brain Cognitive Development study. Depressive symptoms were measured with the parent-reported Child Behavior Checklist. Both youth and their parents completed the Kiddie Schedule for Affective Disorders and Schizophrenia, which provided MDD diagnoses. A family history screen was administered to determine familial risk for depression. Youth underwent a resting state functional magnetic resonance imaging scan, providing us with rsFC data. ResultsNegative wDMN rsFC was associated with child-reported current depression, both child- and parent-reported past depression, and parent-reported current depressive symptoms. No difference was found in wDMN, DMN-LHIPP or DMN-RHIPP rsFC in children with or without familial risk for depression. Familial risk for depression interacted with wDMN rsFC in association with child-reported past MDD diagnosis and parent-reported current depressive symptoms. LimitationsInformation such as length of depressive episodes and age of onset of depression was not collected. ConclusionsAltered wDMN rsFC in youth at familial risk for depression may be associated with increased risk for MDD onset in adolescence, but longitudinal studies are needed to test this hypothesis.

93096 Does gender matter? Gender differences in the relationship between resting-state functional connectivity and emotion regulation in alcohol use disorder.

ABSTRACT IMPACT: Our research has the potential to impact human health by identifying gender specific neural markers of emotion regulation in alcohol use disorder. OBJECTIVES/GOALS: Emotion dysregulation is known to be mediated by altered functional organization of the limbic system in addiction. This preliminary study sought to identify gender effects in the association between emotion regulation and resting-state functional connectivity (rsFC) of a negative affect network. METHODS/STUDY POPULATION: 55 individuals receiving treatment for alcohol use disorder (˜2 weeks of abstinence) were recruited for this study and included in this analysis (N=55; Age: M=41.78, SD=10.66; 21 females). RsFC within a network involved in the withdrawal/negative affect stage of addiction and Personality Inventory for DSM-5 (PID-5) metrics were collected from all participants. RsFC data were preprocessed using the Human Connectome Project pipelines. Correlations between (a) rsFC within the withdrawal/negative affect network and the (b) scores of the negative affect subscale of the PID-5 instrument were conducted for each gender separately. RESULTS/ANTICIPATED RESULTS: Independent samples t-test showed a statistically significant gender difference in the PID-5 negative affect scores (Males: M=1.02, SD=0.66; Females: M=1.53, SD=0.51); t(55)=-3.002, p=0.004. Only females showed a significant correlation between rsFC within the withdrawal/negative affect network and negative affect scores of the PID-5 (r=0.51, p&lt;0.05). Fisher r-to-z test showed significant gender differences (z=-1.91; p=0.03, 1-tailed) in correlations coefficients representing the relationship between rsFC of the withdrawal/negative affect network and negative affect (PID-5 subscale). DISCUSSION/SIGNIFICANCE OF FINDINGS: Preliminary findings suggest that the relationship between neural networks mediating emotion regulation and negative affect is only found in females. These results provide valuable data to inform personalized chemical dependency treatment that targets emotion regulation specific to females.

Repetitive deep TMS for the reduction of body weight: Bimodal effect on the functional brain connectivity in “diabesity”

Background and aimsDeep repetitive Transcranial Magnetic Stimulation (deep rTMS) over the bilateral insula and prefrontal cortex (PFC) can promote weight-loss in obesity, preventing cardiometabolic complications as Type 2 Diabetes (T2D). To investigate the changes in the functional brain integration after dTMS, we conducted a resting-state functional connectivity (rsFC) study in obesity. Methods and resultsThis preliminary study was designed as a randomized, double-blind, sham-controlled study: 9 participants were treated with high-frequency stimulation (realTMS group), 8 were sham-treated (shamTMS group). Out of the 17 enrolled patients, 6 were affected by T2D. Resting-state fMRI scans were acquired at baseline (T0) and after the 5-week intervention (T1). Body weight was measured at three time points [T0, T1, 1-month follow-up visit (FU1)]. A mixed-model analysis showed a significant group-by-time interaction for body weight (p = .04), with a significant decrease (p < .001) in the realTMS group. The rsFC data revealed a significant increase of degree centrality for the realTMS group in the medial orbitofrontal cortex (mOFC) and a significant decrease in the occipital pole. ConclusionAn increase of whole-brain functional connections of the mOFC, together with the decrease of whole-brain functional connections with the occipital pole, may reflect a brain mechanism behind weight-loss through a diminished reactivity to bottom-up visual–sensory processes in favor of increased reliance on top-down decision-making processes. Trial registration numberClinicalTrials.gov NCT03009695.

Brain features of nearly drug-naïve female monozygotic twins with first-episode schizophrenia and the classification accuracy of brain feature patterns: A pilot study.

BackgroundData on differences in brain features between monozygotic (MZ) twins with and without schizophrenia are scarce.MethodsWe compared brain features of female MZ twins with and without first‐episode schizophrenia and healthy controls (n = 20 each). Voxel‐based morphometry and tract‐based spatial statistics were used to analyze differences in brain structure. Whole‐brain effective connectivity (EC) and functional connectivity (FC) networks were constructed using resting‐state functional magnetic resonance imaging (rs‐fMRI) data.ResultsFemale twins with schizophrenia exhibited abnormal gray matter volume (GMV) in the basal ganglia and prefrontal and parietal cortices, impairments in the arcuate fasciculus, and significant disruptions (primarily decreases) in nine EC networks. They exhibited rs‐EC alterations involving the limbic areas and subcortex. Combined rs‐EC and rs‐FC data distinguished twins with first‐episode schizophrenia with high accuracy. Combined consideration of structural and functional features enabled the distinction of female MZ twins with schizophrenia from those without schizophrenia and healthy controls with 100% accuracy.ConclusionsFemale MZ twins with schizophrenia exhibited increased GMV, white matter impairment, and disruptions in EC and FC networks. The combination of rs‐EC + rs‐FC data could distinguish female twins with schizophrenia from twins without schizophrenia and healthy controls with 97.4% accuracy, and the addition of structural brain features yielded a 100% accuracy rate. These findings may provide pivotal insight for further study of the mechanisms underlying schizophrenia.

Mindfulness-based intervention to decrease mood lability in at-risk youth: Preliminary evidence for changes in resting state functional connectivity

Background: In youth at familial risk for bipolar disorder (BD), mood lability is an important precursor to BD onset. Previous work in adults indicates that mindfulness-based interventions (MBI) may improve emotion regulation, in part by increasing resting-state functional connectivity (rsFC) between posterior cingulate cortex (PCC) and executive control network (ECN). In this pilot study, we assessed effects of an MBI on PCC-ECN rsFC and mood lability in at-risk youth.Methods: We recruited 35 youth (10–14 years old) with a first-degree family history of BD and mood lability, and 21 age-matched healthy controls. Eligible at-risk youth were scanned pre/post an 8-week MBI and assessed three months later. Healthy controls were scanned at matched timepoints but did not participate in the MBI. The MBI used age-appropriate strategies to promote non-judgmental, present-moment awareness. We assessed pre/post changes in PCC-ECN rsFC and how rsFC changes were related to mood outcomes.Results: Twenty at-risk youth were scanned pre/post MBI; 16 had high-quality rsFC data. Following MBI, at-risk youth showed increased rsFC between PCC and left dorsolateral prefrontal cortex (DLPFC) (BA 9; k = 28; corrected p=.006); healthy controls did not show this increase. Following MBI, at-risk youth reported more mindfulness (F = 7.15, p=.003), less mood lability (F = 7.2, p=.002), and less suppression of negative emotions (F = 5.05, p=.01). PCC-DLPFC rsFC increases predicted less mood lability (t=−2.25, p=.04) and less emotion suppression (t=−2.75, p=.02) at follow-up.Limitations: Small sample and lack of a control intervention.Conclusions: PCC-DLPFC rsFC may be a clinically meaningful neural target of an MBI in at-risk youth, related to improvements in mood lability.

Working Memory Training Is Associated with Changes in Resting State Functional Connectivity in Children Who Were Born Extremely Preterm: a Randomized Controlled Trial

Children born extremely preterm (EP; < 28 weeks of gestation) or extremely low birth weight (ELBW; < 1000 g) are at increased risk of working memory deficits compared with their term-born peers and may benefit from working memory training. This study aimed to determine whether Cogmed Working Memory Training®, compared with a placebo training program, was associated with changes in resting-state functional connectivity (rsfc) and whether these changes correlated with working memory performance in EP/ELBW children. Twenty-one 7-year-old EP/ELBW children were enrolled in a double-blinded randomized controlled trial and had magnetic resonance imaging (MRI) assessments (Cogmed, n = 12; placebo (a non-adaptive version of Cogmed), n = 9). Prior to training (baseline) and 2 weeks post-training, all children received a cognitive assessment, inclusive of immediate memory and working memory measures and an MRI. The Cogmed Improvement Index was used as a measure of improvement in trained activities in the Cogmed group. Resting-state functional MRI was used to measure training-related changes in intra- and inter-network rsfc. The networks assessed include the default mode network, the left and right central executive networks, the bilateral executive network, the dorsal attention network, and the salience network. rsfc data were compared between treatment groups and investigated in relation to changes in working memory performance. There was little evidence of differences in intra- or inter-network rsfc strength changes from baseline to post-training between treatment groups. In the Cogmed group, working memory performance was associated with increased rsfc from baseline to post-training within the precuneus network, but not in the placebo group. In the Cogmed group, results that did not survive multiple comparison correction further showed that improvement in trained activities was associated with increased rsfc between the left central and bilateral executive networks, and with decreased rsfc within the right central executive network and between the right central executive and salience networks. Changes in rsfc may facilitate working memory performance following Cogmed training. Further studies are needed to investigate how changes in rsfc are associated with behavioral changes to better support working memory in vulnerable groups.

Intrinsic functional connectivity correlates of person-level risk for bipolar disorder in offspring of affected parents.

Offspring of parents with bipolar disorder (OBP) are at increased risk to develop bipolar disorder (BD). Alterations in resting-state functional connectivity (rsFC) have been identified in OBP; however, replication has been limited and correlation with person-level risk is unknown. A recent study found reduced rsFC between left inferior frontal gyrus (IFG) and clusters in the left insula (LINS), lentiform nucleus (LENT), and midcingulate cortex (MCING) in OBP (Roberts et al. 2017); here, we aim to extend these findings to at-risk youth. We scanned a subset of the Pittsburgh Bipolar Offspring Study, a longitudinal study of OBP and community controls. Twenty-four OBP, 20 offspring of control parents with non-bipolar psychopathology (OCP), and 27 healthy controls (HC) had acceptable rsFC data. After preprocessing steps, we assessed group differences in seed-based rsFC between the IFG and target clusters (LINS, LENT, MCING) using multivariate regression. Next, we tested whether rsFC correlated with person-level risk score and with other dimensional measures. We did not find group differences in rsFC between IFG and target regions. Within OBP, risk score negatively correlated with IFG-LINS rsFC (p = 0.002). Across groups, mood lability correlated negatively with rsFC between IFG and target regions (p = 0.0002), due to negative correlation with IFG-LINS (p = 0.0003) and IFG-MCING (p = 0.001) rsFC. While group-level differences were not replicated, IFG-LINS rsFC was negatively correlated with a person-level risk score in OBP and with mood lability (a predictor of BD) across the sample. Thus, IFG-LINS rsFC might constitute a risk marker, within OBP, for the development of BD.

Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting-state fMRI.

Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n=108; 63 male) and matched nonsmoking controls (n=108; 63 male) and multi-dimensional scaling to visualize the heterogeneity of NUD in individual smokers based on rsFC measures. Machine-based learning models identified five resting-state networks that played a role in distinguishing smokers from controls: the posterior and anterior default mode networks, the sensorimotor network, the salience network and the right executive control network. The classification method constructed classifiers with an average correct classification rate of 88.1 percent and an average area under the curve of 0.93. Compared with controls, individuals with NUD had weaker functional connectivity measures within these networks (P<0.05, false discovery rate corrected). Further, multi-dimensional scaling visualization demonstrated that controls were similar to each other whereas individuals with NUD had less similarity to controls and to other individuals with NUD. Our findings build upon previous literature demonstrating that machine learning-based approaches to classifying rsFC data offer a valuable technique to understanding network-level differences in nicotine-related neurobiology and extend previous findings by improving classification accuracy and demonstrating the heterogeneity in resting-state networks of individuals with NUD.