Neural mechanism of critical self-judgment as a trans-diagnostic therapeutic target: a secondary data analysis study from the RDoC perspective

NIH Pandemic-Era Grants

Pandemic Era Grants

2024

Document text

Principal Investigator: Diane  Joss
Organization: CAMBRIDGE HEALTH ALLIANCE
Fiscal Year: 2024
Award: $82,000
Funding agency: National Institute of Mental Health

Project Summary
Critical self-judgment is a common phenomenon underlying a wide range of psychological disorders. The
proposed project aims to investigate the neural mechanism of critical self-judgment by analyzing fMRI and
questionnaire data from a total of 203 subjects collected from 5 previous projects that all included mindfulness
or compassion-based interventions for various conditions, e.g., depression, anxiety, trauma, stress and chronic
pain. The analyses will particularly evaluate the effective and functional connectivity among the amygdala, the
hippocampus, inferior frontal gyrus and precuneus, to test the central hypothesis that critical self-judgment is
characterized by excessive influence from negative valence on the left Inferior Frontal Gyrus-Pars Triangularis
(IFGPT), a key area for internal speech and self-awareness, which further influences the precuneus (PCu) of
the default mode network (DMN), a neural circuitry that supports self-referential processes.
Aim 1: To elucidate the effective connectivity in the neural circuitry during critical self-judgment. Using
fMRI data from a self-reappraisal task that included self-critical vs. self-reassurance conditions, Granger
Causality analysis will be conducted with fMRI time series from regions of interest of the hypothesized neural
circuitry to test whether there is significant effective connectivity in the directions of amygdala→IFGPT and
IFGPT→PCu during self-critical condition. Similar analyses with the self-reassurance condition will test whether
there is significant effective connectivity in the direction of MPFC→amygdala and IFGPT→amygdala, reflecting
frontal inhibition of negative affect.
Aim 2: To identify features from resting state fMRI that are associated with trait critical self-judgment.
Through analyzing resting state fMRI data with the CONN toolbox, we will extract Resting State Functional
Connectivity (RSFC) from regions of interest in the proposed neural circuitry, then use linear mixed effects
models to test whether the severity of trait critical self-judgment is collectively predicted by RSFC of
hippocampus-IFGPT, amygdala-IFGPT, IFGPT-PCu, hippocampus-PCu and amygdala-PCu.
Aim 3: To investigate whether post-intervention changes in trait critical self-judgment are predicted by
changes in the proposed neural circuitry. By analyzing post-intervention changes in fMRI metrics and trait
critical self-judgment severity, we will test whether post-intervention changes in trait critical self-judgment are
predicted by post-intervention changes in RSFC of hippocampal-IFGPT, amygdala-IFGPT, IFGPT-PCu,
hippocampus-PCu and amygdala-PCu.

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