Cortico-subcortical interactions in overlapping communities of edge functional connectivity.

Both cortical and subcortical regions can be functionally organized into networks. Regions of the basal ganglia are extensively interconnected with the cortex via reciprocal connections that relay and modulate cortical function. Here we employ an edge-centric approach, which computes co-fluctuations among region pairs in a network to investigate the role and interaction of subcortical regions with cortical systems. By clustering edges into communities, we show that cortical systems and subcortical regions couple via multiple edge communities, with hippocampus and amygdala having a distinct pattern from striatum and thalamus. We show that the edge community structure of cortical networks is highly similar to one obtained from cortical nodes when the subcortex is present in the network. Additionally, we show that the edge community profile of both cortical and subcortical nodes can be estimates solely from cortico-subcortical interactions. Finally, we used a motif analysis focusing on edge community triads where a subcortical region coupled to two cortical regions and found that two community triads where one community couples the subcortex to the cortex were overrepresented. In summary, our results show organized coupling of the subcortex to the cortex that may play a role in cortical organization of primary sensorimotor/attention and heteromodal systems and puts forth the motif analysis of edge community triads as a promising method for investigation of communication patterns in networks.

Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials.

BACKGROUND: Digital phenotyping has been proposed as a novel assessment tool for clinical trials targeting negative symptoms in psychotic disorders (PDs). However, it is unclear which digital phenotyping measurements are most appropriate for this purpose. AIMS: Machine learning was used to address this gap in the literature and determine whether: (1) diagnostic status could be classified from digital phenotyping measures relevant to negative symptoms and (2) the 5 negative symptom domains (anhedonia, avolition, asociality, alogia, and blunted affect) were differentially classified by active and passive digital phenotyping variables. METHODS: Participants included 52 outpatients with a PD and 55 healthy controls (CN) who completed 6 days of active (ecological momentary assessment surveys) and passive (geolocation, accelerometry) digital phenotyping data along with clinical ratings of negative symptoms. RESULTS: Machine learning algorithms classifying the presence of a PD diagnosis yielded 80% accuracy for cross-validation in H2O AutoML and 79% test accuracy in the Recursive Feature Elimination with Cross Validation feature selection model. Models classifying the presence vs absence of clinically significant elevations on each of the 5 negative symptom domains ranged in test accuracy from 73% to 91%. A few active and passive features were highly predictive of all 5 negative symptom domains; however, there were also unique predictors for each domain. CONCLUSIONS: These findings suggest that negative symptoms can be modeled from digital phenotyping data recorded in situ. Implications for selecting the most appropriate digital phenotyping variables for use as outcome measures in clinical trials targeting negative symptoms are discussed.

Subject identification using edge-centric functional connectivity.

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture.

Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs 'edge time series' and 'edge functional connectivity' (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits.

Temporal fluctuations in the brain's modular architecture during movie-watching.

Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of inter-subject similarity, and suggest that moment-to-moment fluctuations in functional connectivity reflect shared cognitive processing across individuals.

Mathematically Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems Approach.

OBJECTIVE: Anhedonia, traditionally defined as a diminished capacity for pleasure, is a core symptom of schizophrenia (SZ). However, modern empirical evidence indicates that hedonic capacity may be intact in SZ and anhedonia may be better conceptualized as an abnormality in the temporal dynamics of emotion. METHOD: To test this theory, the current study used ecological momentary assessment (EMA) to examine whether abnormalities in one aspect of the temporal dynamics of emotion, sustained reward responsiveness, were associated with anhedonia. Two experiments were conducted in outpatients diagnosed with SZ (n = 28; n = 102) and healthy controls (n = 28; n = 71) who completed EMA reports of emotional experience at multiple time points in the day over the course of several days. Markov chain analyses were applied to the EMA data to evaluate stochastic dynamic changes in emotional states to determine processes underlying failures in sustained reward responsiveness. RESULTS: In both studies, Markov models indicated that SZ had deficits in the ability to sustain positive emotion over time, which resulted from failures in augmentation (ie, the ability to maintain or increase the intensity of positive emotion from time t to t+1) and diminution (ie, when emotions at time t+1 are opposite in valence from emotions at time t, resulting in a decrease in the intensity of positive emotion over time). Furthermore, in both studies, augmentation deficits were associated with anhedonia. CONCLUSIONS: These computational findings clarify how abnormalities in the temporal dynamics of emotion contribute to anhedonia.

Network Analysis Reveals Which Negative Symptom Domains Are Most Central in Schizophrenia vs Bipolar Disorder.

Network analysis was used to examine how densely interconnected individual negative symptom domains are, whether some domains are more central than others, and whether sex influenced network structure. Participants included outpatients with schizophrenia (SZ; n = 201), a bipolar disorder (BD; n = 46) clinical comparison group, and healthy controls (CN; n = 27) who were rated on the Brief Negative Symptom Scale. The mutual information measure was used to construct negative symptom networks. Groups were compared on macroscopic network properties to evaluate overall network connectedness, and microscopic properties to determine which domains were most central. Macroscopic analyses indicated that patients with SZ had a less densely connected negative symptom network than BD or CN groups, and that males with SZ had less densely connected networks than females. Microscopic analyses indicated that alogia and avolition were most central in the SZ group, whereas anhedonia was most central in BD and CN groups. In addition, blunted affect, alogia, and asociality were most central in females with SZ, and alogia and avolition were most central in males with SZ. These findings suggest that negative symptoms may be highly treatment resistant in SZ because they are not very densely connected. Less densely connected networks may make treatments less likely to achieve global reductions in negative symptoms because individual domains function in isolation with little interaction. Sex differences in centralities suggest that the search for pathophysiological mechanisms and targeted treatment development should be focused on different sets of symptoms in males and females.

Network Analysis Reveals the Latent Structure of Negative Symptoms in Schizophrenia.

Prior studies using exploratory factor analysis provide evidence that negative symptoms are best conceptualized as 2 dimensions reflecting diminished motivation and expression. However, the 2-dimensional model has yet to be evaluated using more complex mathematical techniques capable of testing structure. In the current study, network analysis was applied to evaluate the latent structure of negative symptoms using a community-detection algorithm. Two studies were conducted that included outpatients with schizophrenia (SZ; Study 1: n = 201; Study 2: n = 912) who were rated on the Brief Negative Symptom Scale (BNSS). In both studies, network analysis indicated that the 13 BNSS items divided into 6 negative symptom domains consisting of anhedonia, avolition, asociality, blunted affect, alogia, and lack of normal distress. Separation of these domains was statistically significant with reference to a null model of randomized networks. There has been a recent trend toward conceptualizing the latent structure of negative symptoms in relation to 2 distinct dimensions reflecting diminished expression and motivation. However, the current results obtained using network analysis suggest that the 2-dimensional conceptualization is not complex enough to capture the nature of the negative symptom construct. Similar to recent confirmatory factor analysis studies, network analysis revealed that the latent structure of negative symptom is best conceptualized in relation to the 5 domains identified in the 2005 National Institute of Mental Health consensus development conference (anhedonia, avolition, asociality, blunted affect, and alogia) and potentially a sixth domain consisting of lack of normal distress. Findings have implications for identifying pathophysiological mechanisms and targeted treatments.

An ecological momentary assessment evaluation of emotion regulation abnormalities in schizophrenia.

BACKGROUND: Prior studies using self-report questionnaires and laboratory-based methods suggest that schizophrenia is characterized by abnormalities in emotion regulation (i.e. using strategies to increase or decrease the frequency, duration, or intensity of negative emotion). However, it is unclear whether these abnormalities reflect poor emotion regulation effort or adequate effort, but limited effectiveness. It is also unclear whether dysfunction results primarily from one of the three stages of the emotion regulation process: identification, selection, or implementation. METHOD: The current study used ecological momentary assessment (EMA) to address these questions in the context of everyday activities. Participants included 28 outpatients diagnosed with schizophrenia (SZ) and 28 demographically matched healthy controls (CN) who completed 6 days of EMA reports of in-the-moment emotional experience, emotion regulation strategy use, and context. RESULTS: Results indicated that SZ demonstrated adequate emotion regulation effort, but poor effectiveness. Abnormalities were observed at each of the three stages of the emotion regulation process. At the identification stage, SZ initiated emotion regulation efforts at a lower threshold of negative emotion intensity. At the selection stage, SZ selected more strategies than CN and strategies attempted were less contextually appropriate. At the implementation stage, moderate to high levels of effort were ineffective at decreasing negative emotion. CONCLUSIONS: Findings suggest that although SZ attempt to control their emotions using various strategies, often applying more effort than CN, these efforts are unsuccessful; emotion regulation abnormalities may result from difficulties at the identification, selection, and implementation stages.

Sensitivity of the Positive and Negative Syndrome Scale (PANSS) in Detecting Treatment Effects via Network Analysis.

Objective: The Positive and Negative Syndrome Scale is a primary outcome measure in clinical trials examining the efficacy of antipsychotic medications. Although the Positive and Negative Syndrome Scale has demonstrated sensitivity as a measure of treatment change in studies using traditional univariate statistical approaches, its sensitivity to detecting network-level changes in dynamic relationships among symptoms has yet to be demonstrated using more sophisticated multivariate analyses. In the current study, we examined the sensitivity of the Positive and Negative Syndrome Scale to detecting antipsychotic treatment effects as revealed through network analysis. Design: Participants included 1,049 individuals diagnosed with psychotic disorders from the Phase I portion of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) study. Of these participants, 733 were clinically determined to be treatment-responsive and 316 were found to be treatment-resistant. Item level data from the Positive and Negative Syndrome Scale were submitted to network analysis, and macroscopic, mesoscopic, and microscopic network properties were evaluated for the treatment-responsive and treatment-resistant groups at baseline and post-phase I antipsychotic treatment. Results: Network analysis indicated that treatment-responsive patients had more densely connected symptom networks after antipsychotic treatment than did treatment-responsive patients at baseline, and that symptom centralities increased following treatment. In contrast, symptom networks of treatment-resistant patients behaved more randomly before and after treatment. Conclusions: These results suggest that the Positive and Negative Syndrome Scale is sensitive to detecting treatment effects as revealed through network analysis. Its findings also provide compelling new evidence that strongly interconnected symptom networks confer an overall greater probability of treatment responsiveness in patients with psychosis, suggesting that antipsychotics achieve their effect by enhancing a number of central symptoms, which then facilitate reduction of other highly coupled symptoms in a network-like fashion.

Pattern discrimination of neuroelectric waveforms evoked by synchronized visuo-motor integration.

Today, there is a significant demand for fast, accurate, and automated methods for the discrimination of latent patterns in neuroelectric waveforms. One of the main challenges is the development of efficient feature extraction tools to utilize the rich spatio-temporal information inherent in large scale human electrocortical activity. In this paper, our aim is to isolate the most suitable feature extraction method for accurate classification of EEG data related to distinct modes of sensorimotor integration. Our results demonstrate that with some user-dependent input for feature space constraint, a simple classification framework can be developed to accurately distinguish between brain electrical activity patterns during two distinct conditions.