Whole-brain Functional Connectivity Patterns Research Articles

Large-scale intrinsic connectivity is consistent across varying task demands.

Measuring whole-brain functional connectivity patterns based on task-free (‘resting-state’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisition is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns (commonly termed ‘resting state networks’). We employed a ‘steady-states’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing us to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis) using standard resting-state functional connectivity (FC) analysis, we show that the whole-brain network architecture characteristic of the resting-state is comparable across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Changes in functional connectivity were detected locally, within the active networks. But to identify these local changes, the contributions of different FC networks to the global intrinsic connectivity pattern had to be isolated. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.

Naltrexone Acutely Enhances Connectivity Between the Ventromedial Prefrontal Cortex and a Left Frontoparietal Network.

Naltrexone, an opioid receptor antagonist that is Food and Drug Administration approved for treating alcohol use disorder (AUD), reduces alcohol craving and intake. Despite known pharmacological properties, little is known regarding the effects of naltrexone on neural circuit function. Thus, a data-driven examination of the neural effects of naltrexone in human subjects may offer novel insight into its treatment mechanisms. Twenty-one alcohol using males (22 to 39) participated in a double-blind, placebo-controlled crossover study of the effects of naltrexone on brain voxel-wise functional connectivity (FC) using intersubject FC correlation mapping. We first cross-correlated the time series from each gray matter voxel to produce a 6,356×6,356 FC matrix for each subject and session. We then subtracted the placebo FC matrix from the naltrexone FC matrix. To identify brain regions demonstrating significant reconfiguration of whole-brain FC patterns following naltrexone treatment, we statistically quantified the consistency of patterns of voxel FC changes across subjects. Permutation testing identified significant clusters of voxels undergoing significant reconfiguration. Using the identified clusters in a seed-based FC analysis, we then compared the FC patterns of affected brain areas on placebo versus naltrexone in a paired t-test. Ridge regression analyses identified self-report measures, including substance use, that significantly predicted individual differences in FC among naltrexone-modulated regions. Two clusters in the rostral anterior cingulate cortex (rACC)/ventromedial prefrontal cortex (vmPFC) demonstrated significant modulation of FC by naltrexone. Using these 2 proximal clusters as a single seed, specific FC changes were identified in regions associated with a left frontoparietal network (increasing), as well as visual and motor regions (decreasing). Stronger FC between the rACC/vmPFC and this set of regions on placebo was associated with more external locus of control, whereas weaker connectivity was associated with greater substance use problems. Naltrexone strengthened these connections most among individuals who reported greater drinking to cope. Enhancing connectivity between the rACC/vmPFC, implicated in alcohol craving, and components of a left frontoparietal network involved in executive control may represent an effective strategy for the treatment of AUD.

Aberrant functional connectivity profiles of brain regions associated with salience and reward processing in female patients with borderline personality disorder.

Recent resting-state functional connectivity studies have shown significant group differences in several networks between patients suffering from borderline personality disorder (BPD) and healthy controls. However, reliable and consistent findings have not been reported yet. Several methodological factors might be responsible for the discrepant findings, including the heterogeneity of patient samples in terms of symptom severity. In the current study, we combined investigations of the whole-brain resting-state functional connectivity patterns of BPD patients with seed-based connectivity measures and then computed the correlation of connectivity measures with borderline symptom severity. Correlation-based connectivity analysis was performed on resting-state functional magnetic resonance imaging (fMRI) data from 26 female BPD patients and 26 healthy controls. Increased intrinsic connectivity was found in clusters involving part of the caudate nucleus and the left insula in the patient group, indicating greater integration of each region. Further seed-based connectivity analyses revealed that with the caudate seed, the patient data exhibited an increased resting-state functional connectivity in the bilateral ventral striatum and the midline prefrontal regions extending to the ACC, a network associated with reward processing. The left insula seed showed significantly increased connectivity with the bilateral fronto-orbital/insula, the inferior parietal lobule and the mid-cingulate cortex, a network involved in attention and salience encoding, in the patient population. Moreover, symptom severity, as assessed with the BSL-95 outside the scanner, was negatively correlated with the coupling of the insula and the striatum in the BPD group. Overall, an increased functional connectivity within two large-scale circuitries underlying reward and salience processing was evident in patients, as compared to healthy participants. When correlated with borderline symptom severity, a reduced connectivity between key regions belonging to the reward system and salience network was observed in the patients. These findings may be helpful for facilitating further understanding of the potential mechanisms underlying the BPD pathophysiology and thereby delineate potential treatment targets.

Decoding sound categories based on whole-brain functional connectivity patterns.

2Sound decoding is important for patients with sensory loss, such as the blind. Previous studies on sound categorization were conducted by estimating brain activity using univariate analysis or voxel-wise multivariate decoding methods and suggested some regions were sensitive to auditory categories. It is proposed that feedback connections between brain areas may facilitate auditory object selection. Therefore, it is important to explore whether functional connectivity among regions can be used to decode sound category. In this study, we constructed whole-brain functional connectivity patterns when subjects perceived four different sound categories and combined them with multivariate pattern classification analysis for sound decoding. The categorical discriminative networks and regions were determined based on the weight maps. Results showed that a high accuracy in multi-category classification was obtained based on the whole-brain functional connectivity patterns and the results were verified by different preprocessing parameters. Insight into the category discriminative functional networks showed that contributive connections crossed the left and right brain, and ranged from primary regions to high-level cognitive regions, which provide new evidence for the distributed representation of auditory object. Further analysis of brain regions in the discriminative networks showed that superior temporal gyrus and Heschl's gyrus significantly contributed to discriminating sound categories. Together, the findings reveal that functional connectivity based multivariate classification method provides rich information for auditory category decoding. The successful decoding results implicate the interactive properties of the distributed brain areas in auditory sound representation.

Identifying mild-moderate Parkinson’s disease using whole-brain functional connectivity

ObjectiveOur study aims to extract significant disorder-associated patterns from whole brain functional connectivity to distinguish mild-moderate Parkinson’s disease (PD) patients from controls. MethodsResting-state fMRI data were measured from thirty-six PD individuals and thirty-five healthy controls. Multivariate pattern analysis was applied to investigate whole-brain functional connectivity patterns in individuals with ‘mild-moderate’ PD. Additionally, the relationship between the asymmetry of functional connectivity and the side of the initial symptoms was also analyzed. ResultsIn a leave-one-out cross-validation, we got the generalization rate of 80.28% for distinguishing PD patients from controls. The most discriminative functional connectivity was found in cortical networks that included the default mode, sensorimotor and attention networks. Compared to patients with the left side initially affected, an increased abnormal functional connectivity was found in patients in whom the right side was initially affected. ConclusionsOur results indicated that discriminative functional connectivity is likely associated with disturbances of cortical networks involved in sensorimotor control and attention. The spatiotemporal patterns of motor asymmetry may be related to the lateralized dysfunction on the early stages of PD. SignificanceThis study identifies discriminative functional connectivity that is associated with disturbances of cortical networks. Our results demonstrated new evidence regarding the functional brain changes related to the unilateral motor symptoms of early PD.

Association between resting-state brain functional connectivity and cortisol levels in unmedicated major depressive disorder

Disturbed hypothalamus–pituitary–adrenal axis function, which leads to excessive and prolonged hypercortisolemia, is a core feature of major depressive disorder (MDD). However, the relationships between depression, brain structure and function, and cortisol levels are unclear. The current study examined the whole-brain functional connectivity pattern of patients with MDD and evaluated the association between functional connectivity and serum cortisol levels in MDD. A total of 93 unmedicated patients with MDD and 139 healthy control subjects underwent resting-state functional magnetic resonance imaging. Voxel-wise whole-brain connectivity was analyzed by using a graph theory approach: functional connectivity strength (FCS). A seed-based resting-state functional connectivity analysis was further performed to investigate abnormal functional connectivity patterns of those regions with changed FCS. Morning blood samples were drawn for cortisol measurements in some subjects (including 53 MDD patients and 30 controls). The MDD patients had a significantly lower FCS in the left posterior lobes of the cerebellum (mainly lobule Crus II) (p < 0.05, TFCE corrected). The seed-based functional connectivity analysis revealed decreased functional connectivity between the left posterior cerebellum and the left medial orbitofrontal cortex (OFC) (p < 0.05, TFCE corrected). Moreover, the functional connectivity between the left posterior cerebellum and the left medial OFC were significantly positively correlated with the serum cortisol levels in MDD patients. Our results suggest that cerebellar dysconnectivity, in particular distributed cerebellar–OFC functional connectivity, may be associated with serum cortisol levels in MDD patients.

Image categorization from functional magnetic resonance imaging using functional connectivity

BackgroundPrevious studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given region or across multiple voxels, and utilize the relationships among voxels to decipher the category of a stimulus image. Yet, the functional connectivity (FC) patterns across regions of interest in response to image categories, and their potential contributions to category classification are largely unknown. New methodWe investigated whole-brain FC patterns in response to 4 image category stimuli (cats, faces, houses, and vehicles) using fMRI in healthy adult volunteers, and classified FC patterns using machine learning framework (Support Vector Machine [SVM] and Random Forest). We further examined the FC robustness and the influence of the window length on FC patterns for neural decoding. ResultsThe average one-vs.-one classification accuracy of the two classification models were 74% within subjects and 80% between subjects, which are higher than the chance level (50%). The Random Forest results were better than SVM results, and the 48-s FC results were better than the 24-s FC results. Comparison with existing method(s)We compared the classification performance of our FC patterns with two other existing methods, inter-block and intra-block, without overlapping temporal information. ConclusionsWhole-brain FC patterns for different window lengths (24 and 48 s) can predict images categories with high accuracy. These results reveal novel mechanisms underlying the representation of categorical information in large-scale FC patterns in the human brain.

Age- and performance-related patterns of whole-brain functional connectivity during context memory tasks: An adult lifespan fMRI study

Age- and performance-related patterns of whole-brain functional connectivity during context memory tasks: An adult lifespan fMRI study

F158. FUNCTIONAL CONNECTIVITY DIVERSITY OF THE INSULA CORTEX IN SCHIZOPHRENIA: SUBREGIONS OR CONTINUA?

BackgroundThe function, cytoarchitecture and connectivity of the insula cortex are diverse. Cluster analyses have been applied to functional magnetic resonance imaging (MRI) connectivity data to parse this diversity and subdivide the insula into discrete subregions. However, the number of subregions comprising the insula remains vexed and whether these putative subregions are disturbed in neuropsychiatric illness are unknown. The present study aimed to (i) rigorously evaluate the number of subregions (if any) into which the insula can be subdivided based on topographic variation in whole-brain patterns of insula functional connectivity; and, (ii) establish whether the connectional topography of the insula is altered in schizophrenia.MethodsTwo alternative models explaining the heterogeneity of insula connectivity were tested: (Model i) insula comprising discrete subregions, each associated with a distinct connectivity fingerprint; and, (Model ii) connectivity varying as a continuum across insula, without marked boundaries. Cluster analysis was used to delineate discrete subregions, and a novel gradient-based method was developed to evaluate whether connectivity varied continuously across the insula. These models were tested in a sample of individuals with schizophrenia (N=49), healthy comparison individuals (N=52) and an independent validation cohort from the Human Connectome Project (N=50).ResultsCluster analyses indicated that the insula comprised anterior and posterior subregions, with significantly less differentiation in connectivity patterns between these two clusters in the schizophrenia group (right: P=.0038; left: P=.002). The anterior insula was more strongly connected to the sensory-motor, occipital/parietal cortex and posterior lobe of cerebellum in the schizophrenia group, whereas the connectivity between the posterior insula and prefrontal cortex and thalamus was stronger in the patients (PFWE<.05). The dysconnectivity between anterior insula and anterior cingulate cortex was correlated with the severity of emotion withdrawal (Jonckeere-Terpstra test; JT=-3.74, P<.001). Most importantly however, for the majority of individuals in both datasets, the degree of cluster separation between insula subregions identified with cluster analyses was not significantly improved compared to clusters delineated in null data that was generated from white matter, where no clusters were expected. Modeling patterns of insula connectivity as continua of variation across a rostrocaudal axis was found to provide a more parsimonious model than using distinct subregions segregated by sharp boundaries. The variation in connectivity across this rostrocaudal axis was significantly reduced in schizophrenia patients (P=0.02).DiscussionThis is the first study that comprehensively investigate the potential differences in connectional pathology of insula between its anterior and posterior aspects. We conclude that the connectional diversity of the insula inferred from resting-state functional connectivity should be conceptualized as continua of variation, rather than discrete subregions. We posit that the reduced differentiation between the anterior and posterior insula in schizophrenia may impact on the ability in discriminating self-generated from externally-generated sensory information, possibly contributing to hallucinations in the disorder.

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging (fMRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist fMRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions.

Functional connectivity pattern underlies individual differences in independent self-construal.

Independent vs interdependent self-construal is a concept that reflects how people perceive the relationship between self and other people, which has been extensively examined across disciplines. However, little evidence on the whole-brain functional connectivity (FC) pattern of independent vs interdependent self-construal has been reported. Here, in a sample of 51 healthy participants, we used resting-state functional magnetic resonance imaging and voxel-based FC analysis (i.e. FC strength and seed-based FC) by measuring the temporal correlation of blood oxygen level-dependent signals between spatially separate brain regions to investigate the neural mechanism of independent vs interdependent self-construal. First, we found that FC strength of bilateral posterior cingulate cortex and precuneus, and left inferior frontal gyrus were positively correlated with the independent vs interdependent score. Seed-based FC analysis with these three regions as seeds revealed that, FC within default mode network and executive control network was positively correlated with the independent vs interdependent score. Negative correlation with independent vs interdependent score was shown in the connections between default mode network and executive control regions. Taking together, our results provide a comprehensive FC architecture of the independent vs interdependent self-construal and advance the understanding of the interplay between culture, mind and brain.

Altered cerebellar functional connectivity in remitted bipolar disorder: A resting-state functional magnetic resonance imaging study.

Several recent studies have reported a strong association between the cerebellar structural and functional abnormalities and psychiatric disorders. However, there are no studies to investigate possible changes in cerebellar functional connectivity in bipolar disorder. This study aimed to examine the whole-brain functional connectivity pattern of patients with remitted bipolar disorder II, in particular in the cerebellum. A total of 25 patients with remitted bipolar disorder II and 25 controls underwent resting-state functional magnetic resonance imaging and neuropsychological tests. Voxel-wise whole-brain connectivity was analyzed using a graph theory approach: functional connectivity strength. A seed-based resting-state functional connectivity analysis was further performed to investigate abnormal functional connectivity pattern of those regions with changed functional connectivity strength. Remitted bipolar disorder II patients had significantly decreased functional connectivity strength in the bilateral posterior lobes of cerebellum (mainly lobules VIIb/VIIIa). The seed-based functional connectivity analyses revealed decreased functional connectivity between the right posterior cerebellum and the default mode network (i.e. right posterior cingulate cortex/precuneus and right superior temporal gyrus), bilateral hippocampus, right putamen, left paracentral lobule and bilateral posterior cerebellum and decreased functional connectivity between the left posterior cerebellum and the right inferior parietal lobule and bilateral posterior cerebellum in patients with remitted bipolar disorder II. Our results suggest that cerebellar dysconnectivity, in particular distributed cerebellar-cerebral functional connectivity, might be associated with the pathogenesis of bipolar disorder.

Convergent and divergent functional connectivity patterns in patients with long-term left-sided and right-sided deafness.

Cortical reorganization may be induced in long-term single-sided deafness (SD); however, the influence of the deafness side on the functional changes remains poorly understood. Here, we investigated whole-brain functional connectivity patterns in long-term SD patients. The normalized voxel-based functional connectivity strength (FCS) was determined using resting-state fMRI (rs-fMRI) in 17 left-sided deafness (LD) patients, 21 right-sided deafness (RD) patients and 21 healthy controls (HCs). Relative to the HCs, both the LD and RD patients exhibited a reduction in the FCS in the ipsilateral visual cortex. However, compared to that in the HCs, a significantly higher FCS was observed in some regions in the salience and default-mode networks in the RD patients, but this FCS alternation pattern was not observed in the LD patients. A direct comparison of the two patient groups revealed a significantly increased FCS in the supplemental motor area in the LD group. Altogether, the long-term SD groups with LD and RD exhibited convergent and divergent functional connectivity patterns in whole-brain networks, providing promising evidence that the functional changes in long-term SD are highly deafness-side-dependent.

Replicability of time-varying connectivity patterns in large resting state fMRI samples

The past few years have seen an emergence of approaches that leverage temporal changes in whole-brain patterns of functional connectivity (the chronnectome). In this chronnectome study, we investigate the replicability of the human brain's inter-regional coupling dynamics during rest by evaluating two different dynamic functional network connectivity (dFNC) analysis frameworks using 7 500 functional magnetic resonance imaging (fMRI) datasets. To quantify the extent to which the emergent functional connectivity (FC) patterns are reproducible, we characterize the temporal dynamics by deriving several summary measures across multiple large, independent age-matched samples. Reproducibility was demonstrated through the existence of basic connectivity patterns (FC states) amidst an ensemble of inter-regional connections. Furthermore, application of the methods to conservatively configured (statistically stationary, linear and Gaussian) surrogate datasets revealed that some of the studied state summary measures were indeed statistically significant and also suggested that this class of null model did not explain the fMRI data fully. This extensive testing of reproducibility of similarity statistics also suggests that the estimated FC states are robust against variation in data quality, analysis, grouping, and decomposition methods. We conclude that future investigations probing the functional and neurophysiological relevance of time-varying connectivity assume critical importance.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Comparing test-retest reliability of dynamic functional connectivity methods

Due to the dynamic, condition-dependent nature of brain activity, interest in estimating rapid functional connectivity (FC) changes that occur during resting-state functional magnetic resonance imaging (rs-fMRI) has recently soared. However, studying dynamic FC is methodologically challenging, due to the low signal-to-noise ratio of the blood oxygen level dependent (BOLD) signal in fMRI and the massive number of data points generated during the analysis. Thus, it is important to establish methods and summary measures that maximize reliability and the utility of dynamic FC to provide insight into brain function. In this study, we investigated the reliability of dynamic FC summary measures derived using three commonly used estimation methods - sliding window (SW), tapered sliding window (TSW), and dynamic conditional correlations (DCC) methods. We applied each of these techniques to two publicly available rs-fMRI test-retest data sets - the Multi-Modal MRI Reproducibility Resource (Kirby Data) and the Human Connectome Project (HCP Data). The reliability of two categories of dynamic FC summary measures were assessed, specifically basic summary statistics of the dynamic correlations and summary measures derived from recurring whole-brain patterns of FC (“brain states”). The results provide evidence that dynamic correlations are reliably detected in both test-retest data sets, and the DCC method outperforms SW methods in terms of the reliability of summary statistics. However, across all estimation methods, reliability of the brain state-derived measures was low. Notably, the results also show that the DCC-derived dynamic correlation variances are significantly more reliable than those derived using the non-parametric estimation methods. This is important, as the fluctuations of dynamic FC (i.e., its variance) has a strong potential to provide summary measures that can be used to find meaningful individual differences in dynamic FC. We therefore conclude that utilizing the variance of the dynamic connectivity is an important component in any dynamic FC-derived summary measure.

Whole-brain resting-state functional connectivity identified major depressive disorder: A multivariate pattern analysis in two independent samples

Backgroundthere has been a recent increase in the use of connectome-based multivariate pattern analysis (MVPA) of resting-state functional magnetic resonance imaging (fMRI) data aimed at distinguishing patients with major depressive disorder (MDD) from healthy controls (HCs). However, the validity of this method needs to be confirmed in independent samples. Methodwe used resting-state fMRI to explore whole-brain functional connectivity (FC) patterns characteristic of MDD and to confirm the effectiveness of MVPA in distinguishing MDD versus HC groups in two independent samples. The first sample set included 29 MDD patients and 33 HCs and second sample set included 46 MDD patients and 57 HCs. Resultsfor the first sample, we obtained a correct classification rate of 91.9% with a sensitivity of 89.6% and specificity of 93.9%. For the second sample, we observed a correct classification rate of 86.4% with a sensitivity of 84.8% and specificity of 87.7%. With both samples, we found that the majority of consensus FCs used for MDD identification were located in the salience network, default mode network, the cerebellum, visual cortical areas, and the affective network. Limitationwe did not analyze potential structural differences between the groups. Conclusionresults suggest that whole-brain FC patterns can be used to differentiate depressed patients from HCs and provide evidence for the potential use of connectome-based MVPA as a complementary tool in the clinical diagnosis of MDD.

Dynamic Connectivity Patterns in Conscious and Unconscious Brain.

Brain functional connectivity undergoes dynamic changes from the awake to unconscious states. However, how the dynamics of functional connectivity patterns are linked to consciousness at the behavioral level remains elusive. In this study, we acquired resting-state functional magnetic resonance imaging data during wakefulness and graded levels of consciousness in rats. Data were analyzed using a dynamic approach combining the sliding window method and k-means clustering. Our results demonstrate that whole-brain networks contained several quasi-stable patterns that dynamically recurred from the awake state into anesthetized states. Remarkably, two brain connectivity states with distinct spatial similarity to the structure of anatomical connectivity were strongly biased toward high and low consciousness levels, respectively. These results provide compelling neuroimaging evidence linking the dynamics of whole-brain functional connectivity patterns and states of consciousness at the behavioral level.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention.

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention. Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention.