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

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. Therefore, the present study describes the development and preliminary psychometric study of the Neural Network Symptomatology Inventory (NNSI). : Two different samples were recruited (sample 1: = 214, Mage = 21.0, SD = 7.10; sample 2: = 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. EFA and CFA suggested a 4-factor model with adequate goodness-of-fit indices (χ2(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. 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.