Introducing a Neuroscience-Based Assessment Instrument: Development and Psychometric Study of the Neural Networks Symptomatology Inventory

Background: Neuroscience research methods contribute to the understanding of the underlying neural impairments associated with psychopathology. Previous research suggested that impairments in Default Mode Network, Fronto-Parietal Executive Network, Amygdaloid-Hippocampal Memory Network, and Attentional Salience Network are present in different psychopathological symptoms. However, a self-report measure based on this evidence is lacking. Aims: Therefore, the present study describes the development and preliminary psychometric study of the Neural Network Symptomatology Inventory (NNSI). Method: Two different samples were recruited (sample 1: N = 214, Mage = 21.0, SD = 7.10; sample 2: N = 194, Mage = 21.5, SD = 8.41) and responded to self-report instruments in a cross-sectional design. Standard methodologies to scale development and psychometric study were applied: Item development, Exploratory (EFA), Confirmatory Factor Analysis (CFA), and Pearson correlations. Results: EFA and CFA suggested a 4-factor model with adequate goodness-of-fit indices (chi 2(449) = 808,9841, TLI = .89, CFI = .92, RMSEA = .048 (.042-.053). All NNSI subscales correlated positively with psychopathological domains and correlated negatively with psychological well-being. Conclusions: This preliminary study suggests that NNSI may be a valid instrument to assess symptomatology associated with complex neural network impairments. Nevertheless, further research is required to deepen and improve NNSI psychometric characteristics.