Erich Studerus1, Salvatore Corbisiero2, Nadine Mazzariello1, Sarah Ittig1, Letizia Leanza1, Laura Egloff1, Katharina Beck1, Ulrike Heitz1, Christina Andreou1, Rolf-Dieter Stieglitz3, Anita Riecher-Rössler4. 1. Center for Gender Research and Early Detection, University of Basel Psychiatric Hospital, Basel, Switzerland. 2. Clinical Psychology and Psychiatry, University of Basel Psychiatric Hospital, Basel, Switzerland. 3. Clinical Psychology and Psychiatry, University of Basel Psychiatric Hospital, Basel, Switzerland; University of Basel Psychiatric Hospital, Basel, Switzerland. 4. Center for Gender Research and Early Detection, University of Basel Psychiatric Hospital, Basel, Switzerland. Electronic address: anita.riecher@upkbs.ch.
Abstract
BACKGROUND: Patients with an at-risk mental state (ARMS) for psychosis and patients with attention-deficit/hyperactivity disorder (ADHD) have many overlapping signs and symptoms and hence can be difficult to differentiate clinically. The aim of this study was to investigate whether the differential diagnosis between ARMS and adult ADHD could be improved by neuropsychological testing. METHODS: 168 ARMS patients, 123 adult ADHD patients and 109 healthy controls (HC) were recruited via specialized clinics of the University of Basel Psychiatric Hospital. Sustained attention and impulsivity were tested with the Continuous Performance Test, verbal learning and memory with the California Verbal Learning Test, and problem solving abilities with the Tower of Hanoi Task. Group differences in neuropsychological performance were analyzed using generalized linear models. Furthermore, to investigate whether adult ADHD and ARMS can be correctly classified based on the pattern of cognitive deficits, machine learning (i.e. random forests) was applied. RESULTS: Compared to HC, both patient groups showed deficits in attention and impulsivity and verbal learning and memory. However, in adult ADHD patients the deficits were comparatively larger. Accordingly, a machine learning model predicted group membership based on the individual neurocognitive performance profile with good accuracy (AUC = 0.82). CONCLUSIONS: Our results are in line with current meta-analyses reporting that impairments in the domains of attention and verbal learning are of medium effect size in adult ADHD and of small effect size in ARMS patients and suggest that measures of these domains can be exploited to improve the differential diagnosis between adult ADHD and ARMS patients.
BACKGROUND:Patients with an at-risk mental state (ARMS) for psychosis and patients with attention-deficit/hyperactivity disorder (ADHD) have many overlapping signs and symptoms and hence can be difficult to differentiate clinically. The aim of this study was to investigate whether the differential diagnosis between ARMS and adult ADHD could be improved by neuropsychological testing. METHODS: 168 ARMS patients, 123 adult ADHDpatients and 109 healthy controls (HC) were recruited via specialized clinics of the University of Basel Psychiatric Hospital. Sustained attention and impulsivity were tested with the Continuous Performance Test, verbal learning and memory with the California Verbal Learning Test, and problem solving abilities with the Tower of Hanoi Task. Group differences in neuropsychological performance were analyzed using generalized linear models. Furthermore, to investigate whether adult ADHD and ARMS can be correctly classified based on the pattern of cognitive deficits, machine learning (i.e. random forests) was applied. RESULTS: Compared to HC, both patient groups showed deficits in attention and impulsivity and verbal learning and memory. However, in adult ADHDpatients the deficits were comparatively larger. Accordingly, a machine learning model predicted group membership based on the individual neurocognitive performance profile with good accuracy (AUC = 0.82). CONCLUSIONS: Our results are in line with current meta-analyses reporting that impairments in the domains of attention and verbal learning are of medium effect size in adult ADHD and of small effect size in ARMS patients and suggest that measures of these domains can be exploited to improve the differential diagnosis between adult ADHD and ARMS patients.
Authors: Jan Buitelaar; Sven Bölte; Daniel Brandeis; Arthur Caye; Nina Christmann; Samuele Cortese; David Coghill; Stephen V Faraone; Barbara Franke; Markus Gleitz; Corina U Greven; Sandra Kooij; Douglas Teixeira Leffa; Nanda Rommelse; Jeffrey H Newcorn; Guilherme V Polanczyk; Luis Augusto Rohde; Emily Simonoff; Mark Stein; Benedetto Vitiello; Yanki Yazgan; Michael Roesler; Manfred Doepfner; Tobias Banaschewski Journal: Front Behav Neurosci Date: 2022-07-06 Impact factor: 3.617
Authors: Lisa Hochstrasser; Erich Studerus; Anita Riecher-Rössler; Benno G Schimmelmann; Martin Lambert; Undine E Lang; Stefan Borgwardt; Rolf-Dieter Stieglitz; Christian G Huber Journal: Sci Rep Date: 2022-04-22 Impact factor: 4.996