Functional Connectivity Data Research Articles

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia.

Background:Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke.Methods:Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data.Results:The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68–0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87).Conclusions:While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping.

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behaviour maps of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage lesion-behaviour maps based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each lesion-behaviour map through the use of structural and functional lesion network mapping, which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on lesion-behaviour map results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the lesion-behaviour maps and structural and functional lesion network mapping and evaluated how much variance each could explain in 12-month behavioural outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both structural and functional lesion network maps were able to predict variance in 12-month outcomes beyond lesion-behaviour mapping. Functional lesion network mapping performed best for the prediction of language deficits, and structural lesion network mapping performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.

White matter volume loss drives cortical reshaping after thalamic infarcts

ObjectiveThe integration of somatosensory, ocular motor and vestibular signals is necessary for self-location in space and goal-directed action. We aimed to detect remote changes in the cerebral cortex after thalamic infarcts to reveal the thalamo-cortical connections necessary for multisensory processing and ocular motor control. MethodsThirteen patients with unilateral ischemic thalamic infarcts presenting with vestibular, somatosensory, and ocular motor symptoms were examined longitudinally in the acute phase and after six months. Voxel- and surface-based morphometry were used to detect changes in vestibular and multisensory cortical areas and known hubs of central ocular motor processing. The results were compared with functional connectivity data in 50 healthy volunteers. ResultsPatients with paramedian infarcts showed impaired saccades and vestibular perception, i.e., tilts of the subjective visual vertical (SVV). The most common complaint in these patients was double vision or vertigo / dizziness. Posterolateral thalamic infarcts led to tilts of the SVV and somatosensory deficits without vertigo. Tilts of the SVV were higher in paramedian compared to posterolateral infarcts (median 11.2° vs 3.8°). Vestibular and ocular motor symptoms recovered within six months. Somatosensory deficits persisted. Structural longitudinal imaging showed significant volume reduction in subcortical structures connected to the infarcted thalamic nuclei (vestibular nuclei region, dentate nucleus region, trigeminal root entry zone, medial lemniscus, superior colliculi). Volume loss was evident in connections to the frontal, parietal and cingulate lobes. Changes were larger in the ipsilesional hemisphere but were also detected in homotopical regions contralesionally. The white matter volume reduction led to deformation of the cortical projection zones of the infarcted nuclei. ConclusionsWhite matter volume loss after thalamic infarcts reflects sensory input from the brainstem as well the cortical projections of the main affected nuclei for sensory and ocular motor processing. Changes in the cortical geometry seem not to reflect gray matter atrophy but rather reshaping of the cortical surface due to the underlying white matter atrophy.

Threat imminence reveals links among unfolding of anticipatory physiological response, cortical-subcortical intrinsic functional connectivity, and anxiety

Excessive expression of fear responses in anticipation of threat occurs in anxiety, but understanding of underlying pathophysiological mechanisms is limited. Animal research indicates that threat-anticipatory defensive responses are dynamically organized by threat imminence and rely on conserved circuitry. Insight from basic neuroscience research in animals on threat imminence could guide mechanistic research in humans mapping abnormal function in this circuitry to aberrant defensive responses in pathological anxiety.50 pediatric anxiety patients and healthy-comparisons (33 females) completed an instructed threat-anticipation task whereby cues signaled delivery of painful (threat) or non-painful (safety) thermal stimulation. Temporal changes in skin-conductance indexed anxiety effects on anticipatory responding as function of threat imminence. Multivariate network analyses of resting-state functional connectivity data from a subsample were used to identify intrinsic-function correlates of anticipatory-response dynamics, within a specific, distributed network derived from translational research on defensive responding.By considering threat imminence, analyses revealed specific anxiety effects. Importantly, pathological anxiety was associated with excessive deployment of anticipatory physiological response as threat, but not safety, outcomes became more imminent. Magnitude of increase in threat-anticipatory physiological responses corresponded with magnitude of intrinsic connectivity within a cortical-subcortical circuit. Moreover, more severe anxiety was associated with stronger associations between anticipatory physiological responding and connectivity that ventromedial prefrontal cortex showed with hippocampus and basolateral amygdala, regions implicated in animal models of anxiety.These findings link basic and clinical research, highlighting variations in intrinsic function in conserved defensive circuitry as a potential pathophysiological mechanism in anxiety.

Neonatal motor functional connectivity and motor outcomes at age two years in very preterm children with and without high-grade brain injury

Preterm-born children have high rates of motor impairments, but mechanisms for early identification remain limited. We hypothesized that neonatal motor system functional connectivity (FC) would relate to motor outcomes at age two years; currently, this relationship is not yet well-described in very preterm (VPT; born <32 weeks’ gestation) infants with and without brain injury.We recruited 107 VPT infants – including 55 with brain injury (grade III–IV intraventricular hemorrhage, cystic periventricular leukomalacia, post-hemorrhagic hydrocephalus) – and collected FC data at/near term-equivalent age (35–45 weeks postmenstrual age). Correlation coefficients were used to calculate the FC between bilateral motor and visual cortices and thalami. At two years corrected-age, motor outcomes were assessed with the Bayley Scales of Infant and Toddler Development, 3rd edition. Multiple imputation was used to estimate missing data, and regression models related FC measures to motor outcomes.Within the brain-injured group only, interhemispheric motor cortex FC was positively related to gross motor outcomes. Thalamocortical and visual FC were not related to motor scores. This suggests neonatal alterations in motor system FC may provide prognostic information about impairments in children with brain injury.

Differentiating Between Migraine and Post-traumatic Headache Using a Machine Learning Classifier

The objective was to develop classification models differentiating persistent PTH (PPTH) and migraine using clinical data and MRI-based measures of brain structure and functional connectivity. PTH and migraine commonly have similar phenotypes. Furthermore, migraine is a risk factor for developing PTH, sometimes making it difficult to differentiate PTH from exacerbation of migraine symptoms. Thirty-four individuals with migraine without history of TBI and 48 individuals with mild TBI attributed to PPTH but without history of migraine or prior frequent tension type headache were included. Subjects completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities and cognitive function and underwent MRI imaging during the same day. Clinical features and structural brain measures from T1-weighted imaging, diffusion tensor imaging and functional resting-state measures were included as potential variables. A classifier using ridge logistic regression of principal components (PC) was fit. Since PCs can hinder identification of significant variables in a model, a second regression model was fit directly to the data. In the non-PC based model, input variables were selected based on lowest t-test or chi-square p-value by modality. Average accuracy was calculated using leave-one-out cross validation. The importance of variables to the classifier were examined. The PC-based classifier achieved an average classification accuracy of 85%. The non-PC based classifier achieved an average classification accuracy of 74.4%. Both classifiers were more accurate at classifying migraine subjects than PPTH. The PC-based model incorrectly classified 9/48 (18.8%) PPTH subjects compared to 3/34 (8.8%) migraine patients, whereas the non-PC classifier incorrectly classed 16/48 (33.3%) vs 5/34 (14.7%) of migraine subjects. Important variables in the non-PC model included static and dynamic functional connectivity values, several questions from the Beck Depression Inventory, and worsening symptoms and headaches with mental activity. Multivariate models including clinical characteristics, functional connectivity, and brain structural data accurately classify and differentiate PPTH vs migraine.

Application of artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases

The first two decades of the 21st century have seen great advances in artificial intelligence and machine learning. The techniques have found their way into everyday life, for example in smartphones, search engines, digital customer assistants, motion control systems, and biomedical devices. The aims of this paper are to outline the possibilities for using artificial intelligence and machine learning techniques in supporting the diagnosis and treatment of neurological diseases, and to discuss selected applications of these techniques based on the most recent published reports. First, contemporary definitions of artificial intelligence and machine learning are presented. This is followed by a review of the most important techniques for intelligent data processing: search methods, mathematical logic, probabilistic methods, classifiers, and artificial neural networks (including deep and convolutional networks). Areas of application of these techniques in medicine are identified, including disease diagnosis and support of treatment as well as monitoring and prediction of changes in health status. The role of artificial intelligence and machine learning in neuroscience is presented, together with examples of diagnostic applications based on anatomical, morphological and functional brain connectivity data. Sample applications of intelligent techniques in supporting the treatment (including surgical management) of nervous system diseases are also described. Ambient smart devices monitoring the health status of patients with chronic neurological conditions are discussed, and selected projects based on smart techniques to support early detection of symptoms of neurodegenerative disorders are described. The conclusions highlight the potential of the techniques, as well as the challenges and risks associated with them. A possible synergy between intelligent systems and actions taken by medical staff is outlined as a way to improve the safety and quality of life of patients with acute and chronic neurological diseases.

Classification of emotion categories based on functional connectivity patterns of the human brain

Neurophysiological and psychological models posit that emotions depend on connections across wide-spread corticolimbic circuits. While previous studies using pattern recognition on neuroimaging data have shown differences between various discrete emotions in brain activity patterns, less is known about the differences in functional connectivity. Thus, we employed multivariate pattern analysis on functional magnetic resonance imaging data (i) to develop a pipeline for applying pattern recognition in functional connectivity data, and (ii) to test whether connectivity patterns differ across emotion categories. Six emotions (anger, fear, disgust, happiness, sadness, and surprise) and a neutral state were induced in 16 participants using one-minute-long emotional narratives with natural prosody while brain activity was measured with functional magnetic resonance imaging (fMRI). We computed emotion-wise connectivity matrices both for whole-brain connections and for 10 previously defined functionally connected brain subnetworks and trained an across-participant classifier to categorize the emotional states based on whole-brain data and for each subnetwork separately. The whole-brain classifier performed above chance level with all emotions except sadness, suggesting that different emotions are characterized by differences in large-scale connectivity patterns. When focusing on the connectivity within the 10 subnetworks, classification was successful within the default mode system and for all emotions. We thus show preliminary evidence for consistently different sustained functional connectivity patterns for instances of emotion categories particularly within the default mode system.

Dissect Relationships Between Gene Co-expression and Functional Connectivity in Human Brain.

Although recent evidence indicates an association between gene co-expression and functional connectivity in human brain, specific association patterns remain largely unknown. Here, using neuroimaging-based functional connectivity data of living brains and brain-wide gene expression data of postmortem brains, we performed comprehensive analyses to dissect relationships between gene co-expression and functional connectivity. We identified 125 connectivity-related genes (20 novel genes) enriched for dendrite extension, signaling pathway and schizophrenia, and 179 gene-related functional connections mainly connecting intra-network regions, especially homologous cortical regions. In addition, 51 genes were associated with connectivity in all brain functional networks and enriched for action potential and schizophrenia; in contrast, 51 genes showed network-specific modulatory effects and enriched for ion transportation. These results indicate that functional connectivity is unequally affected by gene expression, and connectivity-related genes with different biological functions are involved in connectivity modulation of different networks.

Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning

Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients.Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls.Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%.Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.

The forceps minor white matter modulates cingulo‐opercular functional connectivity change in subjective cognitive decline

AbstractBackgroundThe ‘cingulo‐opercular’ network (CON) functional connectivity (FC) decreases in aging and mild cognitive impairment. However, little is known about the cross‐sectional differences or longitudinal changes in CON FC in subjective cognitive decline (SCD). Frontal white‐matter tracts such as the forceps minor exhibit alterations in SCD and, thus, might play a relevant role for CON FC in SCD. Thus, we tested (i) whether baseline SCD predicts CON FC decrease over three years and (ii) whether white matter in the forceps minor modulates that effect.MethodSixty‐nine healthy older participants (13 males; 68.33 ± 7.95 years old) underwent resting‐state functional and diffusion‐weighted magnetic resonance imaging. Forty‐nine of those participants were further enrolled in 2 follow‐ups, about 18 months apart each. The degree of SCD was assessed at baseline with the memory functioning questionnaire (MFQ; greater score indicating more SCD). After imaging data preprocessing, we obtained the average CON FC (correlation between 5 regions) for each time point and the neurite density of the forceps minor (‘structural connectivity’, SC), which was then averaged across time points. A latent growth curve model was fit to the longitudinal FC data, setting the intercept at the last time point. The model tested whether baseline MFQ predicted FC after 3 years (latent intercept) and/or its rate of change (latent slope) and whether the interaction between SC and MFQ predicted the latent intercept of FC.ResultWe did not find evidence of baseline MFQ as a predictor of CON FC after 3 years (b = ‐0.19, 95% confidence interval, CI, [‐0.49, 0.11], p = 0.211) or its rate of change (b = ‐0.17, [‐0.37, 0.03], p = 0.093). However, we found that SC (averaged across all time points) modified the relation between MFQ and FC after 3 years (intercept, b = 0.20, [0.03, 0.37], p = 0.020; FIG).ConclusionGreater SCD predicts lower CON FC after 3 years in individuals with relatively low neurite density in the forceps minor. This evidence implies that CON FC alterations in SCD are dependent upon the forceps minor’s white matter and that these alterations might be associated with attentional or executive‐function changes in SCD.

Structure and Dynamics of Large-Scale Cognitive Networks in Huntington's Disease.

Huntington's disease is a neurodegenerative disorder characterized by clinical alterations in the motor, behavioral, and cognitive domains. However, the structure and disruptions to large-scale brain cognitive networks have not yet been established. We aimed to profile changes in large-scale cognitive networks in premanifest and symptomatic patients with Huntington's disease. We prospectively recruited premanifest and symptomatic Huntington's disease mutation carriers as well as healthy controls. Clinical and sociodemographic data were obtained from all participants, and resting-state functional connectivity data, using both time-averaged and dynamic functional connectivity, was acquired from whole-brain and cognitively oriented brain parcellations. A total of 64 gene mutation carriers and 23 healthy controls were included; 21 patients with Huntington's disease were classified as premanifest and 43 as symptomatic Huntington's disease. Compared with healthy controls, patients with Huntington's disease showed decreased network connectivity within the posterior hubs of the default-mode network and the medial prefrontal cortex, changes that correlated with cognitive (t=2.25, P=0.01) and disease burden scores (t=-2.42, P=0.009). The salience network showed decreased functional connectivity between insular and supramarginal cortices and also correlated with cognitive (t=2.11, P=0.02) and disease burden scores (t=-2.35, P=0.01). Dynamic analyses showed that network variability was decreased for default-central executive networks, a feature already present in premanifest mutation carriers (dynamic factor 8, P=0.02). Huntington's disease shows an early and widespread disruption of large-scale cognitive networks. Importantly, these changes are related to cognitive and disease burden scores, and novel dynamic functional analyses uncovered subtler network changes even in the premanifest stages.

Attention deficit/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging

Attention deficit/hyperactivity disorder is a neurological disorder characterized by inattention, hyperactivity and impulsivity. Since the resting functional magnetic resonance imaging (rs-fMRI) can reflect the brain activity state, automatic diagnosis of attention deficit/hyperactivity disorder (ADHD) based on re-fMRI has become a critical method which can relieve the pressure of doctors meanwhile improve the efficiency and accuracy of diagnosis. Up to now, the traditional diagnosis of ADHD based on rs-fMRI converts rs-fMRI into functional connectivity or low-dimensional data for classification which will lead to original information loss since rs-fMRI contains 3D spatial and 1D temporal information. In this paper, we propose a deep learning model consisting of the spatio-temporal feature extraction and classifier. The nested residual convolutional denoising autoencoder (NRCDAE) is exploited to reduce the spatial dimension of rs-fMRI and extract the 3D spatial features. Then, the 3D convolutional gated recurrent unit (GRU) is adopted to extract the spatial and temporal features simultaneously. The spatio-temporal features extracted are sent into the sigmoid classifier for classification. The training and testing are carried out across five sites of ADHD-200 and the results of experimental show that the proposed method can effectively improve the classification performance between ADHD and typically developing (TD) in the cross-site test set from 1.14% to 10.9% compared with other methods. To our knowledge, the 3D convolutional GRU is first employed to extract the features of the fMRI and the model has better generalization than other models.

Parental Education and Functional Connectivity between Nucleus Accumbens (NAcc) and Frontoparietal Network (FPN)

Introduction: While previous studies have indicated an association between socioeconomic status (SES) and children’s neuroimaging measures, weaker SES effects are shown for Black than White families. This is, in part, due to processes such as stratification, racism, marginalization, and othering of Black people in the US, which act as barriers to translating SES resources into health outcomes. Purpose: This study had two aims: First, to test the association between parental education and the nucleus accumbens (NAcc) resting-state functional connectivity (rsFC) with the frontoparietal network (FPN) 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 of 7959 US pre-adolescents who were between 9 and 10 years old. The main outcome was the NAcc resting-state functional connectivity with FPN. Parental education was considered as an independent variable. Family structure, sex and age were the study covariates. Finally, race was regarded as the moderator. We used mixed-effects regression for data analysis with and without interaction terms between parental education and race. Results: Parental education was associated with higher NAcc resting-state functional connectivity with FPN. Race showed a statistically significant interaction with parental education, suggesting that the effects of parental education on NAcc rsFC with FPN was significantly weaker for Black pre-adolescents compared to White pre-adolescents. Conclusions: In line with Minorities’ Diminished Returns (MDRs), the association between parental education and pre-adolescents’ NAcc rsFC with FPN is weaker in Black pre-adolescents compared to their White counterparts. This finding is of interest because FPN’s rsFC with NAcc may have a role in cognitive flexibility and reward processing. The weaker links between SES indicators and children’s neuroimaging findings for Black than for White families may reflect the racialization of Blacks in the US. Social stratification, racism, and discrimination may minimize the returns of SES in Black communities.

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.

Correlations between hippocampal functional connectivity, structural changes, and clinical data in patients with relapsing-remitting multiple sclerosis: a case-control study using multimodal magnetic resonance imaging.

Multiple sclerosis is associated with structural and functional brain alterations leading to cognitive impairments across multiple domains including attention, memory, and the speed of information processing. The hippocampus, which is a brain important structure involved in memory, undergoes microstructural changes in the early stage of multiple sclerosis. In this study, we analyzed hippocampal function and structure in patients with relapsing-remitting multiple sclerosis and explored correlations between the functional connectivity of the hippocampus to the whole brain, changes in local brain function and microstructure, and cognitive function at rest. We retrospectively analyzed data from 20 relapsing-remitting multiple sclerosis patients admitted to the Department of Neurology at the China-Japan Union Hospital of Jilin University, China, from April 2015 to November 2019. Sixteen healthy volunteers were recruited as the healthy control group. All participants were evaluated using a scale of extended disability status and the Montreal cognitive assessment within 1 week before and after head diffusion tensor imaging and functional magnetic resonance imaging. Compared with the healthy control group, the patients with relapsing-remitting multiple sclerosis had lower Montreal cognitive assessment scores and regions of simultaneously enhanced and attenuated whole-brain functional connectivity and local functional connectivity in the bilateral hippocampus. Hippocampal diffusion tensor imaging data showed that, compared with the healthy control group, patients with relapsing-remitting multiple sclerosis had lower hippocampal fractional anisotropy values and higher mean diffusivity values, suggesting abnormal hippocampal structure. The left hippocampus whole-brain functional connectivity was negatively correlated with the Montreal cognitive assessment score (r = -0.698, P = 0.025), and whole-brain functional connectivity of the right hippocampus was negatively correlated with extended disability status scale score (r = -0.649, P = 0.042). The mean diffusivity value of the left hippocampus was negatively correlated with the Montreal cognitive assessment score (r = -0.729, P = 0.017) and positively correlated with the extended disability status scale score (r = 0.653, P = 0.041). The right hippocampal mean diffusivity value was positively correlated with the extended disability status scale score (r = 0.684, P = 0.029). These data suggest that the functional connectivity and presence of structural abnormalities in the hippocampus in patients with relapse-remission multiple sclerosis are correlated with the degree of cognitive function and extent of disability. This study was approved by the Ethics Committee of China-Japan Union Hospital of Jilin University, China (approval No. 201702202) on February 22, 2017.

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.

Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork

Recently, the dimensional approach has attracted much attention, bringing a paradigm shift to a continuum of understanding of different psychiatric disorders. In line with this new paradigm, we examined whether there was common functional connectivity related to various psychiatric disorders in an unsupervised manner without explicitly using diagnostic label information. To this end, we uniquely applied a newly developed network-based multiple clustering method to resting-state functional connectivity data, which allowed us to identify pairs of relevant brain subnetworks and subject cluster solutions accordingly. Thus, we identified four subject clusters, which were characterized as major depressive disorder (MDD), young healthy control (young HC), schizophrenia (SCZ)/bipolar disorder (BD), and autism spectrum disorder (ASD), respectively, with the relevant brain subnetwork represented by the cerebellum-thalamus-pallidum-temporal circuit. The clustering results were validated using independent datasets. This study is the first cross-disorder analysis in the framework of unsupervised learning of functional connectivity based on a data-driven brain subnetwork.

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.