A previous small study suggested that Brain Network Activation (BNA), a novel ERP-based brain network analysis, may have diagnostic utility in attention deficit hyperactivity disorder (ADHD). In this study we examined the diagnostic capability of a new advanced version of the BNA methodology on a larger population of adults with and without ADHD.

Subjects were unmedicated right-handed 18- to 55-year-old adults of both sexes with and without a DSM-IV diagnosis of ADHD. We collected EEG while the subjects were performing a response inhibition task (Go/NoGo) and then applied a spatio-temporal Brain Network Activation (BNA) analysis of the EEG data. This analysis produced a display of qualitative measures of brain states (BNA scores) providing information on cortical connectivity. This complex set of scores was then fed into a machine learning algorithm.

The BNA analysis of the EEG data recorded during the Go/NoGo task demonstrated a high discriminative capacity between ADHD patients and controls (AUC = 0.92, specificity = 0.95, sensitivity = 0.86 for the Go condition; AUC = 0.84, specificity = 0.91, sensitivity = 0.76 for the NoGo condition).

BNA methodology can help differentiate between ADHD and healthy controls based on functional brain connectivity. The data support the utility of the tool to augment clinical examinations by objective evaluation of electrophysiological changes associated with ADHD. Results also support a network-based approach to the study of ADHD.

Laboratory tasks to delineate anxiety disorder features are used to refine classification and inform our understanding of etiological mechanisms. The present study examines laboratory measures of response inhibition, specifically the inhibition of a pre-potent motor response, in clinical anxiety. Data on associations between anxiety and response inhibition remain inconsistent, perhaps because of dissociable effects of clinical anxiety and experimentally manipulated state anxiety. Few studies directly assess the independent and interacting effects of these two anxiety types (state v. disorder) on response inhibition. The current study accomplished this goal, by manipulating state anxiety in healthy and clinically anxious individuals while they complete a response inhibition task.

The study employs the threat-of-shock paradigm, one of the best-established manipulations for robustly increasing state anxiety. Participants included 82 adults (41 healthy; 41 patients with an anxiety disorder). A go/nogo task with highly frequent go trials was administered during alternating periods of safety and shock threat. Signal detection theory was used to quantify response bias and signal-detection sensitivity.

There were independent effects of anxiety and clinical anxiety on response inhibition. In both groups, heightened anxiety facilitated response inhibition, leading to reduced nogo commission errors. Compared with the healthy group, clinical anxiety was associated with excessive response inhibition and increased go omission errors in both the safe and threat conditions.

Response inhibition and its impact on go omission errors appear to be a promising behavioral marker of clinical anxiety. These results have implications for a dimensional view of clinical anxiety.