Topography of the Anxious Self: Abnormal Rest-Task Modulation in Social Anxiety Disorder

Social anxiety disorder (SAD) is characterized by social anxiety/fear, self-attention, and interoception. Functional magnetic resonance imaging studies demonstrate increased activity during symptom-sensitive tasks in regions of the default-mode network (DMN), amygdala (AMG), and salience network (SN). What is the source of this task-unspecific symptom-sensitive hyperactivity in DMN? We address this question by probing SAD resting state (rs) changes in DMN including their relation to other regions as possible source of task-unspecific hyperactivity in the same regions. Our findings show the following: (1) rs-hypoconnectivity within-DMN regions; (2) rs-hyperconnectivity between DMN and AMG/SN; (3) task-evoked hyperactivity in the abnormal rs-regions of DMN and AMG/SN during different symptom-sensitive tasks; (4) negative relationship of rest and task changes in especially anterior DMN regions as their rs-hypoconnectivity is accompanied by task-unspecific hyperactivity; (5) abnormal top-down/bottom-up modulation between anterior DMN regions and AMG during rest and task. Findings demonstrate that rs-hypoconnectivity among DMN regions is negatively related to task-unspecific hyperactivity in DMN and AMG/SN. We propose a model of “Topography of the Anxious Self” in SAD (TAS-SAD). Abnormal DMN-AMG/SN topography during rest, as trait feature of an “unstable social self”, is abnormally aggravated during SAD-sensitive situations resulting in task-related hyperactivity in the same regions with an “anxious self” as state feature.

Continuous chromatin state feature annotation of the human epigenome

Semi-automated genome annotation (SAGA) methods are widely used to understand genome activity and gene regulation. These methods take as input a set of sequencing-based assays of epigenomic activity (such as ChIP-seq measurements of histone modification and transcription factor binding), and output an annotation of the genome that assigns a chromatin state label to each genomic position. Existing SAGA methods have several limitations caused by the discrete annotation framework: such annotations cannot easily represent varying strengths of genomic elements, and they cannot easily represent combinatorial elements that simultaneously exhibit multiple types of activity. To remedy these limitations, we propose an annotation strategy that instead outputs a vector of chromatin state features at each position rather than a single discrete label. Continuous modeling is common in other fields, such as in topic modeling of text documents. We propose a method, epigenome-ssm, that uses a Kalman filter state space model to efficiently annotate the genome with chromatin state features. We show that chromatin state features from epigenome-ssm are more useful for several downstream applications than both continuous and discrete alternatives, including their ability to identify expressed genes and enhancers. Therefore, we expect that these continuous chromatin state features will be valuable reference annotations to be used in visualization and downstream analysis.