Brain Connectivity Patterns Research Articles

MRI correlates of chronic symptoms in mild traumatic brain injury.

Some veterans with a history of mild traumatic brain injury (mTBI) have reported experiencing auditory and visual dysfunction that persist beyond the acute phase of the incident. The etiology behind these symptoms is difficult to characterize, since mTBI is defined by negative imaging findings on current clinical imaging. There are several competing hypotheses that could explain functional deficits; one example is shear injury, which may manifest in diffusion-weighted magnetic resonance (MR) imaging (DWI). Herein, we explore this alternative hypothesis in a pilot study of multi-parametric MR imaging. Briefly, we consider a cohort of 8 mTBI patients relative to 22 control subjects using structural T1-weighted imaging (T1w) and connectivity with DWI. 1,344 metrics were extracted per subject from whole brain regions and connectivity patterns in sensory networks. For each set of imaging-derived metrics, the control subject metrics were embedded in a low-dimensional manifold with principal component analysis, after which mTBI subject metrics were projected into the same space. These manifolds were employed to train support vector machines (SVM) to classify subjects as controls or mTBI. Two of the SVMs trained achieved near-perfect accuracy averaged across four-fold cross-validation. Additionally, we present correlations between manifold dimensions and 22 self-reported mTBI symptoms and find that five principal components from the manifolds (one component from the T1w manifold and four components from the DWI manifold) are significantly correlated with symptoms (p<0.05, uncorrected). The novelty of this work is that the DWI and T1w imaging metrics seem to contain information critical for distinguishing between mTBI and control subjects. This work presents an analysis of the pilot phase of data collection of the Quantitative Evaluation of Visual and Auditory Dysfunction and Multi-Sensory Integration in Complex TBI Patients study and defines specific hypotheses to be tested in the full sample.

Examining brain maturation during adolescence using graph Laplacian learning based Fourier transform

BackgroundLongitudinal neuroimaging studies have demonstrated that adolescence is a crucial developmental period of continued brain growth and change. Motivated by both achievements in graph signal processing and recent evidence that some brain areas act as hubs connecting functionally specialized systems, we propose an approach to detect these regions from a spectral analysis perspective. In particular, as the human brain undergoes substantial development throughout adolescence, we evaluate functional network difference among age groups from functional magnetic resonance imaging (fMRI) measurements. New methodsWe treated these measurements as graph signals defined on the parcellated functional brain regions and proposed a graph Laplacian learning based Fourier transform (GLFT) to transform the original graph signals into the frequency domain. Eigen-analysis was conducted afterwards to study the behaviors of the corresponding brain regions, which enabled the characterization of brain maturation. ResultWe first evaluated our method on the synthetic data and then applied it to resting state and task fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) dataset, comprised of normally developing adolescents from 8 to 22 years of age. The method provided an accuracy of 94.9% in distinguishing different adolescent stages and we detected 13 hubs from resting state fMRI and 16 hubs from task fMRI related to brain maturation. Comparison with existing methodsThe proposed GLFT demonstrated its superiority over conventional graph Fourier transform and alternative graph Fourier transform with high predictive power. ConclusionThe method provides a powerful approach for extracting brain connectivity patterns and identifying hub regions.

Processing speed and attention training modifies autonomic flexibility: A mechanistic intervention study

Adaptation capacity is critical for maintaining cognition, yet it is understudied in groups at risk for dementia. Autonomic nervous system (ANS) is critical for neurovisceral integration and is a key contributor to adaptation capacity. To determine the central nervous system’s top-down regulation of ANS, we conducted a mechanistic randomized controlled trial study, using a 6-week processing speed and attention (PS/A)-targeted intervention. Eighty-four older adults with amnestic mild cognitive impairment (aMCI) were randomized to a 6-week PS/A-targeted intervention or an active control without PS/A. Utilizing repeated measures (i.e., PS/A test different from the intervention, resting and cognitive task-based ECG, and resting fMRI) at baseline, immediately post-intervention (post-test), and 6-month follow-up, we aimed to test whether PS/A causally influences vagal control of ANS via their shared central neural pathways in aMCI. We indexed vagal control of ANS using high-frequency heart rate variability (HF-HRV) extracted from ECG data. Functional brain connectivity patterns were extracted from fMRI using advanced statistical tools. Compared to the control group, the intervention group showed significant improvement in PS/A, HF-HRV, salience network (SN), central executive network (CEN), and frontal parietal network (FPN) connectivity at post-test; the effect on SN, CEN, and FPN remained at 6-month follow-up. Changes in PS/A and SN connectivity significantly predicted change in HF-HRV from baseline to post-test and/or 6-month-follow-up. Age, neurodegeneration, nor sex did not affect these relationships. This work provides novel support for top-down regulation of PS/A and associated SN on vagal control of ANS. Intervening PS/A may be a viable approach for promoting adaptation capacity in groups at risk for dementia.

Identification of Tendency to Alcohol Misuse From the Structural Brain Networks.

The propensity to engage in risky behaviors including excessive alcohol consumption may impose increased medical, emotional, and psychosocial burdens. Personality and behavioral traits of individuals may contribute in part to the involvement in risky behaviors, and therefore the classification of one’s traits may help identify those who are at risk for future onset of the addictive disorder and related behavioral issues such as alcohol misuse. Personality and behavioral characteristics including impulsivity, anger, reward sensitivity, and avoidance were assessed in a large sample of healthy young adults (n = 475). Participants also underwent diffusion tensor imaging for the analysis of structural brain networks. A data-driven clustering using personality and behavioral traits of the participants identified four subtypes. As compared with individuals clustered into the neutral type, individuals with a high level of impulsivity (A subtype) and those with high levels of reward sensitivity, impulsivity, anger, and avoidance (B subtype) showed significant associations with problem drinking. In contrast, individuals with high levels of impulsivity, anger, and avoidance but not reward sensitivity (C subtype) showed a pattern of social drinking that was similar to those of the neutral subtype. Furthermore, logistic regression analysis with ridge estimators was applied to demonstrate the neurobiological relevance for the identified subtypes according to distinct patterns of structural brain connectivity within the addiction circuitry [neutral vs. A subtype, the area under the receiver operator characteristic curve (AUC) = 0.74, 95% CI = 0.67–0.81; neutral vs. B subtype, AUC = 0.74, 95% CI = 0.66–0.82; neutral vs. C subtype, AUC = 0.77, 95% CI = 0.70–0.84]. The current findings enable the characterization of individuals according to subtypes based on personality and behavioral traits that are also corroborated by neuroimaging data and may provide a platform to better predict individual risks for addictive disorders.

Functional connectivity predicts changes in attention observed across minutes, days, and months

The ability to sustain attention differs across people and changes within a single person over time. Although recent work has demonstrated that patterns of functional brain connectivity predict individual differences in sustained attention, whether these same patterns capture fluctuations in attention within individuals remains unclear. Here, across five independent studies, we demonstrate that the sustained attention connectome-based predictive model (CPM), a validated model of sustained attention function, generalizes to predict attentional state from data collected across minutes, days, weeks, and months. Furthermore, the sustained attention CPM is sensitive to within-subject state changes induced by propofol as well as sevoflurane, such that individuals show functional connectivity signatures of stronger attentional states when awake than when under deep sedation and light anesthesia. Together, these results demonstrate that fluctuations in attentional state reflect variability in the same functional connectivity patterns that predict individual differences in sustained attention.

Brain connectivity markers for the identification of effective contacts in subthalamic nucleus deep brain stimulation.

The clinical benefit of deep brain stimulation (DBS) for Parkinson's disease (PD) is relevant to the tracts adjacent to the stimulation site, but it remains unclear what connectivity pattern is associated with effective DBS. The aim of this study was to identify clinically effective electrode contacts on the basis of brain connectivity markers derived from diffusion tensor tractography. We reviewed 77 PD patients who underwent bilateral subthalamic nucleus DBS surgery. The patients were assigned into the training (n = 58) and validation (n = 19) groups. According to the therapeutic window size, all contacts were classified into effective and ineffective groups. The whole‐brain connectivity of each contact's volume of tissue activated was estimated using tractography with preoperative diffusion tensor data. Extracted connectivity features were put into an all‐relevant feature selection procedure within cross‐validation loops, to identify features with significant discriminative power for contact classification. A total of 616 contacts on 154 DBS leads were discriminated, with 388 and 228 contacts being classified as effective and ineffective ones, respectively. After the feature selection, the connectivity of contacts with the thalamus, pallidum, hippocampus, primary motor area, supplementary motor area and superior frontal gyrus was identified to significantly contribute to contact classification. Based on these relevant features, the random forest model constructed from the training group achieved an accuracy of 84.9% in the validation group, to discriminate effective contacts from the ineffective. Our findings advanced the understanding of the specific brain connectivity patterns associated with clinical effective electrode contacts, which potentially guided postoperative DBS programming.

Early childhood developmental functional connectivity of autistic brains with non-negative matrix factorization.

Autism spectrum disorder (ASD) is associated with altered patterns of over- and under-connectivity of neural circuits. Age-related changes in neural connectivities remain unclear for autistic children as compared with normal children. In this study, a parts-based network-decomposition technique, known as non-negative matrix factorization (NMF), was applied to identify a set of possible abnormal connectivity patterns in brains affected by ASD, using resting-state electroencephalographic (EEG) data. Age-related changes in connectivities in both inter- and intra-hemispheric areas were studied in a total of 256 children (3-6 years old), both with and without ASD. The findings included the following: (1) the brains of children affected by ASD were characterized by a general trend toward long-range under-connectivity, particularly in interhemispheric connections, combined with short-range over-connectivity; (2) long-range connections were often associated with slower rhythms (δ and θ), whereas synchronization of short-range networks tended to be associated with faster frequencies (α and β); and (3) the α-band specific patterns of interhemispheric connections in ASD could be the most prominent during early childhood neurodevelopment. Therefore, NMF would be useful for further exploring the early childhood developmental functional connectivity of children aged 3-6 with ASD as well as with typical development. Additionally, long-range interhemispheric alterations in connectivity may represent a potential biomarker for the identification of ASD.

Dynamic spatiotemporal patterns of brain connectivity reorganize across development.

Late human development is characterized by the maturation of high-level functional processes, which rely on reshaping of white matter connections, as well as synaptic density. However, the relationship between the whole-brain dynamics and the underlying white matter networks in neurodevelopment is largely unknown. In this study, we focused on how the structural connectome shapes the emerging dynamics of cerebral development between the ages of 6 and 33 years, using functional and diffusion magnetic resonance imaging combined into a spatiotemporal connectivity framework. We defined two new measures of brain dynamics, namely the system diversity and the spatiotemporal diversity, which quantify the level of integration/segregation between functional systems and the level of temporal self-similarity of the functional patterns of brain dynamics, respectively. We observed a global increase in system diversity and a global decrease and local refinement in spatiotemporal diversity values with age. In support of these findings, we further found an increase in the usage of long-range and inter-system white matter connectivity and a decrease in the usage of short-range connectivity with age. These findings suggest that dynamic functional patterns in the brain progressively become more integrative and temporally self-similar with age. These functional changes are supported by a greater involvement of long-range and inter-system axonal pathways.

Brain network topology predicts participant adherence to mental training programs.

Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configurations of brain connections in resting-state fMRI scans can be used to predict adherence to two programs: meditation and creative writing. Results indicate that greater system segregation and clustering predict the number of practice sessions and class participation in both programs at a wide range of network thresholds (corrected p value < 0.05). At a local level, regions in subcortical circuitry such as striatum and accumbens predicted adherence in all subjects. Furthermore, there were also some important distinctions between groups: Adherence to meditation was predicted by connectivity within local network of the anterior insula and default mode network; and in the writing program, adherence was predicted by network neighborhood of frontal and temporal regions. Four machine learning methods were applied to test the robustness of the brain metric for classifying individual capacity for adherence and yielded reasonable accuracy. Overall, these findings underscore the fact that adherence and the ability to perform prescribed exercises is associated with organizational patterns of brain connectivity.

Association of Intrinsic Brain Architecture With Changes in Attentional and Mood Symptoms During Development

Understanding the neurodevelopmental trajectory of psychiatric symptoms is important for improving early identification, intervention, and prevention of mental disorders. To test whether the strength of the coupling of activation between specific brain regions, as measured by resting-state functional magnetic resonance imaging (fMRI), predicted individual children's developmental trajectories in terms of attentional problems characteristic of attention-deficit/hyperactivity disorder and internalizing problems characteristics of major depressive disorder (MDD). A community cohort of 94 children was recruited from Vanderbilt University between 2010 and 2013. They were followed up longitudinally for 4 years and the data were analyzed from 2016 to 2019. Based on preregistered hypotheses and an analytic plan, we examined whether specific brain connectivity patterns would be associated with longitudinal changes in scores on the Child Behavior Checklist (CBCL), a parental-report assessment used to screen for emotional, behavioral, and social problems and to predict psychiatric illnesses. We used the strength of resting-state fMRI connectivity at age 7 years to predict subsequent changes in CBCL measures 4 years later and investigated the mechanisms of change by associating brain connectivity changes with changes in the CBCL. We analyzed data from a longitudinal brain development study involving children assessed at age 7 years (n = 94; 41 girls [43.6%]) and 11 years (n = 54; 32 girls [59.3%]). As predicted, less positive coupling at age 7 years between the medial prefrontal cortex and dorsolateral prefrontal cortex (DLPFC) was associated with a decrease in attentional symptoms by age 11 years (t49 = 2.38; P = .01; β = 0.32). By contrast, a less positive coupling between a region implicated in mood, the subgenual anterior cingulate cortex (sgACC), and DLPFC at age 7 years was associated with an increase in internalizing (eg, anxiety/depression) behaviors by age 11 years (t49 = -2.4; P = .01; β = -0.30). Logistic regression analyses revealed that sgACC-DLPFC connectivity was a more accurate predictor than baseline CBCL measures for progression to a subclinical score on internalization (t50 = -2.61; P = .01; β = -0.29). We then replicated and extended the sgACC-DLPFC result in an independent sample of children with (n = 25) or without (n = 18) familial risk for MDD. These resting-state fMRI metrics are promising biomarkers for the early identification of children at risk of developing MDD or attention-deficit/hyperactivity disorder.

Dissociable neural effects of temporal expectations due to passage of time and contextual probability

The human brain is equipped with complex mechanisms to track the changing probability of events in time. While the passage of time itself usually leads to a mounting expectation, context can provide additional information about when events are likely to happen. In this study we dissociate these two sources of temporal expectation in terms of their neural correlates and underlying brain connectivity patterns. We analysed magnetoencephalographic (MEG) data acquired from N = 24 healthy participants listening to auditory stimuli. These stimuli could be presented at different temporal intervals but occurred most often at intermediate intervals, forming a contextual probability distribution. Evoked MEG response amplitude was sensitive to both passage of time (time elapsed since the cue) and contextual probability, albeit at different latencies: the effects of passage of time were observed earlier than the effects of context. The underlying sources of MEG activity were also different across the two types of temporal prediction: the effects of passage of time were localised to early auditory regions and superior temporal gyri, while context was additionally linked to activity in inferior parietal cortices. Finally, these differences were modelled using biophysical (dynamic causal) modelling: passage of time was explained in terms of widespread gain modulation and decreased prediction error signalling at lower levels of the hierarchy, while contextual expectation led to more localised gain modulation and decreased prediction error signalling at higher levels of the hierarchy. These results present a comprehensive account of how independent sources of temporal prediction may be differentially expressed in cortical circuits.

Environmental enrichment effects after early stress on behavior and functional brain networks in adult rats.

Early life stress is associated with long-term and pervasive adverse effects on neuroendocrine development, affecting normal cognitive and emotional development. Experimental manipulations like environmental enrichment (EE) may potentially reverse the effects of early life stress induced by maternal separation (MS) paradigm in rodents. However, the functional brain networks involved in the effects of EE after prolonged exposure to MS have not yet been investigated. In order to evaluate possible changes in brain functional connectivity induced by EE after MS, quantitative cytochrome c oxidase (CCO) histochemistry was applied to determine regional brain oxidative metabolism in adult male rats. Unexpectedly, results show that prolonged MS during the entire weaning period did not cause any detrimental effects on spatial learning and memory, including depressive-like behavior evaluated in the forced-swim test, and decreased anxiety-like behavior. However, EE seemed to alter anxiety- and depression-like behaviors in both control and MS groups, but improved spatial memory in the latter groups. Analysis of brain CCO activity showed significantly lower metabolic capacity in most brain regions selected in EE groups probably associated with chronic stress, but no effects of MS on brain metabolic capacity. In addition, principal component analysis of CCO activity revealed increased large-scale functional brain connectivity comprising at least three main networks affected by EE in both MS and control groups. Moreover, EE induced a pattern of functional brain connectivity associated with stress and anxiety-like behavior as compared with non-enriched groups. In conclusion, EE had differential effects on cognition and emotional behavior irrespective of exposure to MS. In view of the remarkable effects of EE on brain function and behavior, implementation of rodent housing conditions should be optimized by evaluating the balance between scientific validity and animal welfare.

The Power of Smiling: The Adult Brain Networks Underlying Learned Infant Emotionality.

The perception of infant emotionality, one aspect of temperament, starts to form in infancy, yet the underlying mechanisms of how infant emotionality affects adult neural dynamics remain unclear. We used a social reward task with probabilistic visual and auditory feedback (infant laughter or crying) to train 47 nulliparous women to perceive the emotional style of six different infants. Using functional neuroimaging, we subsequently measured brain activity while participants were tested on the learned emotionality of the six infants. We characterized the elicited patterns of dynamic functional brain connectivity using Leading Eigenvector Dynamics Analysis and found significant activity in a brain network linking the orbitofrontal cortex with the amygdala and hippocampus, where the probability of occurrence significantly correlated with the valence of the learned infant emotional disposition. In other words, seeing infants with neutral face expressions after having interacted and learned their various degrees of positive and negative emotional dispositions proportionally increased the activity in a brain network previously shown to be involved in pleasure, emotion, and memory. These findings provide novel neuroimaging insights into how the perception of happy versus sad infant emotionality shapes adult brain networks.

Fronto-parietal engagement in response inhibition is inversely scaled with attention-deficit/hyperactivity disorder symptom severity.

Impaired response inhibition is one of the most consistent findings in attention deficit hyperactivity disorder (ADHD). However, the underlying brain mechanisms are not clear. This study aimed to underpin atypical inhibition-related brain activation and connectivity patterns in ADHD using a novel Go/No-go task design, and to determine its association with clinical symptoms of the disorder. Forty-eight adults with ADHD performed a Go/No-go task in which target frequency was manipulated during functional MRI. Specific inhibition-related brain activation was correlated with ADHD symptom severity, to assess the relationship of individual differences in engagement of inhibition-related brain circuits with the magnitude of every-day functioning impairments. Finally, generalized psychophysical interaction analyses were carried out to examine whether not only engagement but also functional connectivity between regions implicated in response inhibition is related to symptom severity. We found no evidence for the expected parietal modulation by increased demand for inhibition at the group-level results. However, this lack of modulation was mediated by individual differences in ADHD symptom severity - increased engagement of the intraparietal sulcus (IPS) in inhibition-demanding events was evident in individuals with less severe symptoms but dissipated with increase in symptomatology. Similarly, functional connectivity between the IPS and the right inferior frontal gyrus (rIFG) was elevated under high inhibitory demand conditions, but this effect diminished with increased symptom severity. The results highlight the importance of IPS engagement in response inhibition and suggest that IPS modulation may be driven by top-down control from the IFG. Moreover, the current findings force the point of treating ADHD as a continuum whereby brain correlates are scaled with severity of the disorder, and point to the potential use of individual differences in the modulation of IPS activation and connectivity as a neuromarker of ADHD.

Disrupted dynamic local brain functional connectivity patterns in generalized anxiety disorder

Previous studies have reported abnormalities in static brain activity and connectivity in patients with generalized anxiety disorder (GAD). However, the dynamic patterns of brain connectivity in patients with GAD have not been fully explored. In this study, we aimed to investigate the dynamic local brain functional connectivity in patients with GAD using dynamic regional phase synchrony (DRePS), a newly developed method for assessing intrinsic dynamic local functional connectivity. Seventy-four patients with GAD and 74 healthy controls (HCs) were enrolled and underwent resting-state functional magnetic resonance imaging. Compared to the HCs, patients with GAD exhibited decreased DRePS values in the bilateral caudate, left hippocampus, left anterior insula, left inferior frontal gyrus, and right fusiform gyrus extending to inferior temporal gyrus. The DRePS value of the left hippocampus was negatively correlated with the Hamilton Anxiety Rating Scale scores. Moreover, these abnormal DRePS patterns could be used to distinguish patients with GAD from HCs in an independent sample (18 patients with GAD and 21 HCs). Our findings provide further evidence on brain dysfunction in GAD from the perspective of the dynamic behaviour of local connections, suggesting that patients with GAD may have an insufficient brain adaptation. This study provides new insights into the neurocognitive mechanism of GAD and could potentially inform the diagnosis and treatment of this disease. Future studies on GAD could benefit from combining the DRePS method with task-related functional magnetic resonance imaging and non-invasive brain stimulation.

Association of Inflammation With Pronociceptive Brain Connections in Rheumatoid Arthritis Patients With Concomitant Fibromyalgia.

Rheumatoid arthritis (RA) patients with concomitant fibromyalgia (FM) exhibit alterations in brain connectivity synonymous with central sensitization. This study was undertaken to investigate how peripheral inflammation, the principal nociceptive stimulus in RA, interacts with brain connectivity in RA patients with FM. RA patients with concomitant FM and those without FM (FM+ and FM-, respectively; n = 27 per group) underwent functional connectivity magnetic resonance imaging. Seed-to-whole-brain functional connectivity analyses were conducted using seeds from the left mid/posterior insula and left inferior parietal lobule (IPL), which are regions that have been previously linked to FM symptoms and inflammation, respectively. The association between functional connectivity and erythrocyte sedimentation rate (ESR) was assessed in each group separately, followed by post hoc analyses to test for interaction effects. Cluster-level, family-wise error (FWE) rates were considered significant if the P value was less than 0.05. The group of RA patients with FM and those without FM did not differ by age, sex, or ESR (P > 0.2). In FM+ RA patients, increased functional connectivity of the insula-left IPL, left IPL-dorsal anterior cingulate, and left IPL-medial prefrontal cortex regions correlated with higher levels of ESR (all FWE-corrected P < 0.05). Post hoc interaction analyses largely confirmed the relationship between ESR and connectivity changes as FM scores increased. We report the first neurobiologic evidence that FM in RA may be linked to peripheral inflammation via pronociceptive patterns of brain connectivity. In patients with such "bottom-up" pain centralization, concomitant symptoms may partially respond to antiinflammatory treatments.

Cognitive and functional status in late-onset Lennox–Gastaut syndrome: Variation on a classic phenotype

Lennox–Gastaut syndrome (LGS) denotes a refractory epileptic encephalopathy of childhood onset with the triad of generalized slow spike–wave (GSSW) on interictal scalp electroencephalogram (EEG), multiple seizure types, and intellectual impairment. The neurobiology of LGS is said to sustain abnormal patterns of brain activity and connectivity that ultimately impair normal cerebral developmental mechanisms. However, we describe eight patients from our combined practice who presented with electroclinical findings consistent with LGS but without significant cognitive impairment. All patients fulfilled the other criteria of LGS with multiple seizure types (three or more of generalized tonic–clonic, atonic, tonic, myoclonic, and atypical absence) and GSSW activity on EEG. Four subjects completed high school, two completed some college, two acquired college degrees, and all performed basic and instrumental activities of daily living (ADL) independently. Magnetic resonance imaging (MRI) was normal in all patients. We speculate that a variation of the classic phenotype of LGS can present with preserved cognitive and functional status, often with onset in the second decade of life, and associated with normal brain imaging.

Similarity of individual functional brain connectivity patterns formed by music listening quantified with a data-driven approach.

This study aims to explore the similarities in functional connectivity (FC) patterns in individuals when listening to different music genres and, in comparison, to the spoken word, using a novel data-driven approach. Our model and findings can potentially be utilized for evaluating the neurological effects of therapeutic music interventions. Twelve healthy volunteers listened to seven different sound tracks while undergoing functional magnetic resonance imaging (fMRI) scans: music of the volunteer's choice with positive emotional attachment, two selections of unfamiliar classical music, one classical piece repeated with visual guidance and three spoken language tracks. FC network graphs were created, and selected graph properties were evaluated toward their commonalities across sound tracks. For comparison, FC patterns represented by the graph adjacency matrices were directly compared for high and low BOLD activation during listening. Graph properties averaged across subjects showed similar values for the same sound track compared to different sound tracks (p < 0.003). For high BOLD activation involving most areas in the auditory cortex, FC patterns for the same sound track correlated highly (0.74 ± 0.11), whereas FC patterns for different sound tracks did not (0.09 ± 0.07; p < 6e-5). For low BOLD activation involving additional brain regions, correlation of FC patterns for the sound tracks was still higher (0.43 ± 0.07) than for different sound tracks (0.09 ± 0.05; p < 8e-6). Similar music creates similar functional activation and connectivity patterns in the brain of healthy individuals as does listening to the spoken word. Direct comparison of FC patterns yielded higher correlations than indirect comparisons of graph properties derived from corresponding FC networks.

Abnormal dynamic functional connectivity density in patients with generalized anxiety disorder

BackgroundNumerous studies have revealed the abnormal static functional connectivity (FC) among different brain regions in patients with generalized anxiety disorder (GAD). However, little is known about the dynamic changes of FC in patients with GAD. MethodsThis study investigated the whole-brain dynamic changes of FC in patients with GAD by combining global FC density (FCD) and sliding window correlation analyses. The standard deviation of dynamic FCD (dFCD) was calculated to evaluate its temporal variability along time. Support vector regression was then employed to predict the symptom severity of patients based on abnormal dynamic connectivity patterns. ResultsThe abnormal dFCD variability between 81 GAD patients and 80 healthy controls showed that the patients had higher dFCD variability in the bilateral dorsomedial prefrontal cortex (dmPFC) and left hippocampus while lower dFCD variability in the right postcentral gyrus. The abnormal dFCD variability of the left dmPFC is an important feature for anxiety prediction. LimitationsThe selection of sliding window length remains controversial, and most of our patients have been treated with medications. Future studies are expected to rule out the potential confounding effects from applying different parameters of the sliding window and recruiting large samples of medication-free patients. ConclusionThe altered patterns of time-varying brain connectivity in the frontolimbic and sensorimotor areas may reflect abnormal dynamic neural communication between these regions and other regions of the brain, which may deepen our understanding of the disease.

Effect of Intrinsic Patterns of Functional Brain Connectivity in Moderating Antidepressant Treatment Response in Major Depression.

Major depressive disorder is associated with aberrant resting-state functional connectivity across multiple brain networks supporting emotion processing, executive function, and reward processing. The purpose of this study was to determine whether patterns of resting-state connectivity between brain regions predict differential outcome to antidepressant medication (sertraline) compared with placebo. Participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study underwent structural and resting-state functional MRI at baseline. Participants were then randomly assigned to receive either sertraline or placebo treatment for 8 weeks (N=279). A region of interest-based approach was utilized to compute functional connectivity between brain regions. Linear mixed-model intent-to-treat analyses were used to identify brain regions that moderated (i.e., differentially predicted) outcomes between the sertraline and placebo arms. Prediction of response to sertraline involved several within- and between-network connectivity patterns. In general, higher connectivity within the default mode network predicted better outcomes specifically for sertraline, as did greater between-network connectivity of the default mode and executive control networks. In contrast, both placebo and sertraline outcomes were predicted (in opposite directions) by between-network hippocampal connectivity. This study identified specific functional network-based moderators of treatment outcome involving brain networks known to be affected by major depression. Specifically, functional connectivity patterns of brain regions between and within networks appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.