Altered Expression of Brain Network Dynamics in Affective and Psychotic Illnesses
Poster No:
1744
Submission Type:
Abstract Submission
Authors:
Carrisa Cocuzza1, Sidhant Chopra1, Ashlea Segal1, Rowena Chin1, Avram Holmes2
Institutions:
1Yale University, New Haven, CT, 2Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ
First Author:
Co-Author(s):
Introduction:
Emerging evidence suggests that spatiotemporal shifts in brain network interactions enable the adaptive recruitment of processing resources, a property of brain functioning which may be impaired in psychiatric illness [1,2]. Despite the importance of linking such brain dynamics to behavior, research has largely examined static brain connectivity patterns exhibited during resting-state. Thus, the clinical relevance of across-state network dynamics remains unclear. Further, a longstanding barrier in linking neurobiology to psychopathology is the insufficient explanatory power of discrete diagnostic boundaries [3]. Thus, investigating transdiagnostic data with densely measured symptomatology is critical for the advancement of clinical neuroscience.
Methods:
We examined neuroimaging and behavioral data from the Transdiagnostic Connectome Project [4], which used the Human Connectome Project's processing pipelines [5]. N=203 participants (129 with affective/psychotic diagnoses, 74 without diagnoses) whole-brain functional connectomes from resting- and task-state (Stroop; emotional face matching) fMRI were decomposed using nonnegative matrix factorization (NMF) to identify dynamical constraints ("motifs") on network interactions across states [6]. We applied dimensional phenotyping [3] to 94 subscale measures given by 28 self-report and clinician-assessed tests that span psychopathology. We used agglomerative clustering on individual difference correlations of all pairs of measures and the elbow method to optimize the number of clusters. Probabilistic principal component (PC) analysis estimated each participant's expression of each cluster; expression patterns can be considered phenotypic fingerprints.
Results:
We found five phenotypic clusters with the following ontology: cluster (1) represents internalizing traits; (2) externalizing traits; (3) negative valence constructs; (4) positive valence constructs; (5) cognitive ability (Fig 1). This pattern is consistent with two prominent frameworks: the Hierarchical Taxonomy of Psychopathology [7] and the Research Domain Criteria [8]. Phenotypic fingerprints were variably expressed across participants, suggesting that cognitive features vary along a continuum of health and disease. We used NMF results in a preliminary case-control analysis (Fig 2). In patients, motif expression was less variable across task contexts, suggesting that dedifferentiated (i.e., flattened) network dynamics are linked with failures to meet varied cognitive demands. We extended this to dimensional phenotypes, and found that internalizing and externalizing traits were positively and negatively correlated with across-state variability in expression of motif one, respectively. Motif one also exhibited the greatest network efficiency [9], suggesting that the extent that motif one confers information flow is a dynamical constraint that is dissociably linked with internalizing/externalizing traits. Individual differences in negative valence were positively linked with variability in motif five; positive valence with motif four; and cognitive ability with motif three. This provides novel evidence that dynamical constraints upon across-state network interactions are differentiably linked to the phenotypic hierarchy.
·Figure 1. Dimensional phenotyping reveals hierarchically-expressed clinical fingerprints across health and disease.
·Figure 2. Dynamic constraints upon across-state shifts in brain network interactions link with phenotypic fingerprints.
Conclusions:
Brain network dynamics were dedifferentiated transdiagnostically, suggesting there is a failure to flexibly recruit and coordinate processing resources to meet increased task demands in patients. The motifs constraining these dynamics were linked in a distinct manner across phenotypic dimensions. Interestingly, dedifferentiated network dynamics were associated with externalizing traits, suggesting that failures to coordinate dynamic processes are linked with individual differences in a specific set of psychiatric features, including disinhibition, dysregulated attention, and noncooperation [10]. Our findings emphasize the importance of uncovering the dynamical constraints upon brain network reconfigurations in health and disease.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Higher Cognitive Functions Other
Modeling and Analysis Methods:
Classification and Predictive Modeling
fMRI Connectivity and Network Modeling 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Affective Disorders
Computational Neuroscience
FUNCTIONAL MRI
Modeling
Psychiatric
Other - Psychosis, Transdiagnostic, Network Dynamics, Whole-Brain, Dimensional Phenotyping
1|2Indicates the priority used for review
Provide references using author date format
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