Jiook Cha

BACKGROUND: Fear generalization is thought to contribute to the development and maintenance of anxiety symptoms and accordingly has been the focus of recent research. Previously, we reported that in healthy individuals (N = 25) neural reactivity in the insula, anterior cingulate cortex (ACC), supplementary motor area (SMA), and caudate follow a generalization gradient with a peak response to a conditioned stimulus (CS) that declines with greater perceptual dissimilarity of generalization stimuli (GS) to the CS. In contrast, reactivity in the ventromedial prefrontal cortex (vmPFC), a region linked to fear inhibition, showed an opposite response pattern. The aim of the current study was to examine whether neural responses to fear generalization differ in generalized anxiety disorder (GAD). A second aim was to examine connectivity of primary regions engaged by the generalization task in the GAD group versus healthy group, using psychophysiological interaction analysis. METHODS: Thirty-two women diagnosed with GAD were scanned using the same generalization task as our healthy group. RESULTS: Individuals with GAD exhibited a less discriminant vmPFC response pattern suggestive of deficient recruitment of vmPFC during fear inhibition. Across participants, there was enhanced anterior insula (aINS) coupling with the posterior insula, ACC, SMA, and amygdala during presentation of the CS, consistent with a modulatory role for the aINS in the execution of fear responses. CONCLUSIONS: These findings suggest that deficits in fear regulation, rather than in the excitatory response itself, are more critical to the pathophysiology of GAD in the context of fear generalization.

Prenatal exposure to maternal depression is common and puts offspring at risk for developing a range of neuropsychiatric disorders. Despite its prevalence and adverse associations, neurobiological processes by which prenatal maternal depression (PMD) confers risk remain poorly understood. Maternal mood and fetal behavior were assessed between 34 and 37 gestational weeks. Using resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI, we examined functional and structural connectivity within amygdala-prefrontal circuits in 64 infants (mean age=5.8±1.7 weeks) with (n=20) and without (n=44) in utero exposure to PMD. Resting fMRI and diffusion MRI both indicated atypical amygdala-prefrontal connectivity in PMD-exposed infants: Resting fMRI indicated increased inverse, or negative, functional connectivity between the amygdala and the dorsal prefrontal cortex (PFC), bilaterally, and diffusion MRI indicated decreased structural connectivity between the right amygdala and the right ventral PFC. Spectral dynamic causal modeling supported these findings suggesting altered amygdala-PFC effective (or directed) connectivity in PMD-exposed infants. Last, path analyses supported a mechanistic account relating PMD to a third-trimester fetal behavior: PMD alters amygdala-PFC connectivity, which in turn, is associated with an increase in fetal heart rate reactivity to in utero perturbation. These data suggest that the maturation and coordination of central and peripheral physiology are altered by prenatal exposure to maternal depression. To the best of our knowledge, this is the first study to directly associate infant MRI measures with a behavior-fetal heart rate response, and supports hypotheses that PMD-associated variations in the development of amygdala-PFC circuits are relevant for future neurobehavioral maturation.

The ventromedial prefrontal cortex (vmPFC) plays a critical role in a number of evaluative processes, including risk assessment. Impaired discrimination between threat and safety is considered a hallmark of clinical anxiety. Here, we investigated the circuit-wide structural and functional mechanisms underlying vmPFC threat-safety assessment in humans. We tested patients with generalized anxiety disorder (GAD; n = 32, female) and healthy controls (n = 25, age-matched female) on a task that assessed the generalization of conditioned threat during fMRI scanning. The task consisted of seven rectangles of graded widths presented on a screen; only the midsize one was paired with mild electric shock [conditioned stimulus (CS)], while the others, safety cues, systematically varied in width by ±20, 40, and 60% [generalization stimuli (GS)] compared with the CS. We derived an index reflecting vmPFC functioning from the BOLD reactivity on a continuum of threat (CS) to safety (GS least similar to CS); patients with GAD showed less discrimination between threat and safety cues, compared with healthy controls (Greenberg et al., 2013b). Using structural, functional (i.e., resting-state), and diffusion MRI, we measured vmPFC thickness, vmPFC functional connectivity, and vmPFC structural connectivity within the corticolimbic systems. The results demonstrate that all three factors predict individual variability of vmPFC threat assessment in an independent fashion. Moreover, these neural features are also linked to GAD, most likely via an vmPFC fear generalization. Our results strongly suggest that vmPFC threat processing is closely associated with broader corticolimbic circuit anomalies, which may synergistically contribute to clinical anxiety.

Abstract Nationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years ( N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication ( n = 614) and “probable AD” with only diagnosis ( n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.