Background: We report a randomized controlled clinical trial of neurofeedback therapy intervention for ADHD/ADD in adults. We focus on internal mechanics of neurofeedback learning, to elucidate the primary role of cortical self-regulation in neurofeedback. We report initial results; more extensive analysis will follow.Methods: Trial has two phases: intervention and follow-up. The intervention consisted of neurofeedback treatment, including intake and outtake measurements, using a waiting-list control group. Treatment involved ~40 h-long sessions 2–5 times per week. Training involved either theta/beta or sensorimotor-rhythm regimes, adapted by adding a novel “inverse-training” condition to promote self-regulation. Follow-up (ongoing) will consist of self-report and executive function tests.Setting: Intake and outtake measurements were conducted at University of Helsinki. Treatment was administered at partner clinic Mental Capital Care, Helsinki.Randomization: We randomly allocated half the sample then adaptively allocated the remainder to minimize baseline differences in prognostic variables.Blinding: Waiting-list control design meant trial was not blinded.Participants: Fifty-four adult Finnish participants (mean age 36 years; 29 females) were recruited after screening by psychiatric review. Forty-four had ADHD diagnoses, 10 had ADD.Measurements: Symptoms were assessed by computerized attention test (T.O.V.A.) and self-report scales, at intake and outtake. Performance during neurofeedback trials was recorded.Results: Participants were recruited and completed intake measurements during summer 2012, before assignment to treatment and control, September 2012. Outtake measurements ran April-August 2013. After dropouts, 23 treatment and 21 waiting-list participants remained for analysis.Initial analysis showed that, compared to waiting-list control, neurofeedback promoted improvement of self-reported ADHD symptoms, but did not show transfer of learning to T.O.V.A. Comprehensive analysis will be reported elsewhere.Trial Registration: “Computer Enabled Neuroplasticity Treatment (CENT),” ISRCTN13915109.

for data gathering; Otto Lappi, Alina Leminen and Eeva Palomäki for comments to the manuscript. Conflict of InterestAuthors report no conflict of interest 15. Funding sources This work was supported by the TEKES (presently Business Finland) grant number 440078.

Attention-deficit hyperactivity disorder (ADHD) in adults is understudied, especially regarding neural mechanisms such as oscillatory control of attention sampling. We report an EEG study of such cortical oscillations, in ADHD-diagnosed adults taking a continuous performance test that measures the ability to sustain attention and inhibit impulsivity for a prolonged period of time. We recorded 53 adults (28f, 25m, aged 18-60), and 18 matched healthy controls, using 128-channel EEG. We analysed features with established links to neural correlates of attention: event-related (de)synchronisation (ERS), alpha and theta frequency band activation, and stimulus-locking indices; in frontal and parietal scalp regions. Test performance distinguished healthy controls from ADHD adults. The ADHD group manifested significantly less parietal 4 Hz theta ERS during correct inhibition trials, in addition to having greater sensitivity to targets in stimulus-locking measures. Our results suggest that ADHD adults have impaired attention sampling in relational categorisation tasks.

This paper presents a compact procedure for classifying the importance of elements in a user interface based on the visual walkthrough method. This method was used during a usability evaluation of an information service for healthcare professionals. Paper printouts were given to users who were asked to highlight the parts of the system they consider most important for them. This method proved to be a quick and useful way to understand which parts of complex user interfaces are the most important for users. In addition, heat maps were constructed based on these answers and they proved to be an easy way to visualise the results both for the evaluators and the different stakeholders. These heat maps could be formed right after the last test session, on the spot of the actual test session.