Wearable Neurofeedback Training for Boosting Attention Regulation at the Wheel

2021 ◽  
pp. 643-647
Author(s):  
Davide Crivelli ◽  
Laura Angioletti ◽  
Michela Balconi
Author(s):  
Holger Gevensleben ◽  
Gunther H. Moll ◽  
Hartmut Heinrich

Im Rahmen einer multizentrischen, randomisierten, kontrollierten Studie evaluierten wir die klinische Wirksamkeit eines Neurofeedback-Trainings (NF) bei Kindern mit einer Aufmerksamkeitsdefizit-/Hyperaktivitätsstörung (ADHS) und untersuchten die einem erfolgreichen Training zugrunde liegenden neurophysiologischen Wirkmechanismen. Als Vergleichstraining diente ein computergestütztes Aufmerksamkeitstraining, das dem Setting des Neurofeedback-Trainings in den wesentlichen Anforderungen und Rahmenbedingungen angeglichen war. Auf Verhaltensebene (Eltern- und Lehrerbeurteilung) zeigte sich das NF-Training nach Trainingsende dem Kontrolltraining sowohl hinsichtlich der ADHS-Kernsymptomatik als auch in assoziierten Bereichen überlegen. Für das Hauptzielkriterium (Verbesserung im FBB-HKS Gesamtwert) ergab sich eine mittlere Effektstärke (von 0.6). Sechs Monate nach Trainingsende (follow-up) konnte das gleiche Ergebnismuster gefunden werden. Die Ergebnisse legen somit den Schluss nahe, dass NF einen klinisch wirksamen Therapiebaustein zur Behandlung von Kindern mit ADHS darstellt. Auf neurophysiologischer Ebene (EEG; ereignisbezogene Potentiale, EPs) konnten für die beiden Neurofeedback-Protokolle Theta/Beta-Training und Training langsamer kortikaler Potentiale spezifische Effekte aufgezeigt werden. So war für das Theta/Beta-Training beispielsweise die Abnahme der Theta-Aktivität mit einer Reduzierung der ADHS-Symptomatik assoziiert. Für das SCP-Training wurde u. a. im Attention Network Test eine Erhöhung der kontingenten negativen Variation beobachtet, die die mobilisierten Ressourcen bei Vorbereitungsprozessen widerspiegelt. EEG- und EP-basierte Prädiktorvariablen konnten ermittelt werden. Der vorliegende Artikel bietet einen Gesamtüberblick über die in verschiedenen Publikationen unserer Arbeitsgruppe beschriebenen Ergebnisse der Studie und zeigt zukünftige Fragestellungen auf.


2012 ◽  
Vol 37 (03) ◽  
Author(s):  
A Trinker ◽  
HF Unterrainer ◽  
N Lackner ◽  
A Novosel ◽  
M Dunitz-Scheer ◽  
...  

2020 ◽  
Author(s):  
Da-Wei Zhang ◽  
Stuart J. Johnstone ◽  
Hui Li ◽  
Xiangsheng Li ◽  
Li Sun

The current study used behavioral and electroencephalograph measures to compare the transferability of cognitive training (CT), neurofeedback training (NFT), and CT combined with NFT in children with AD/HD. Following a multiple-baseline single-case experimental design, twelve children were randomized to a training condition. Each child completed a baseline phase, followed by an intervention phase. The intervention phase consisted of 20 sessions of at-home training. Tau-U analysis and standardized visual analysis were adopted to detect effects. CT improved inhibitory function, and NFT showed improved alpha activity and working memory. The combined condition, who was a reduced 'dose' of CT and NFT, did not show any improvements. The three conditions did not alleviate AD/HD symptoms. While CT and NFT may have near transfer effects, considering the lack of improvement in symptoms, this study does not support CT and NFT on their own as a treatment for children with AD/HD.


2019 ◽  
Vol XVI (4) ◽  
pp. 67-79
Author(s):  
Muhammad Abul Hasan ◽  
Matthew Fraser ◽  
Saad Ahmed Qazi

Neurofeedback (NF) training has been used for the treatment of neuropathic pain. This paper presents the results of assessment of the learning ability of five patients having neuropathic pain. The following two types of baselines were adopted: Baseline 1 refers to power on Day 1 in PreNF state; and Baseline 2 refers to power recorded on each training day in PreNF state. The result of the study demonstrated that not only the baseline its selection is also important to demonstrate the validity of training protocol. It was also found that Baseline 2 can be used to define cut-off time for training (when training should be stopped). All five patients can be classified as learner and alpha band was found to be most relevant for NF training.


2021 ◽  
pp. 1-9 ◽  
Author(s):  
Isabella Vainieri ◽  
Joanna Martin ◽  
Anna-Sophie Rommel ◽  
Philip Asherson ◽  
Tobias Banaschewski ◽  
...  

Abstract Background A recent genome-wide association study (GWAS) identified 12 independent loci significantly associated with attention-deficit/hyperactivity disorder (ADHD). Polygenic risk scores (PRS), derived from the GWAS, can be used to assess genetic overlap between ADHD and other traits. Using ADHD samples from several international sites, we derived PRS for ADHD from the recent GWAS to test whether genetic variants that contribute to ADHD also influence two cognitive functions that show strong association with ADHD: attention regulation and response inhibition, captured by reaction time variability (RTV) and commission errors (CE). Methods The discovery GWAS included 19 099 ADHD cases and 34 194 control participants. The combined target sample included 845 people with ADHD (age: 8–40 years). RTV and CE were available from reaction time and response inhibition tasks. ADHD PRS were calculated from the GWAS using a leave-one-study-out approach. Regression analyses were run to investigate whether ADHD PRS were associated with CE and RTV. Results across sites were combined via random effect meta-analyses. Results When combining the studies in meta-analyses, results were significant for RTV (R2 = 0.011, β = 0.088, p = 0.02) but not for CE (R2 = 0.011, β = 0.013, p = 0.732). No significant association was found between ADHD PRS and RTV or CE in any sample individually (p > 0.10). Conclusions We detected a significant association between PRS for ADHD and RTV (but not CE) in individuals with ADHD, suggesting that common genetic risk variants for ADHD influence attention regulation.


IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Meiyun Xia ◽  
Pengfei Xu ◽  
Yuanbin Yang ◽  
Wenyu Jiang ◽  
Zehua Wang ◽  
...  

2021 ◽  
Author(s):  
Yue Hou ◽  
Shuqin Zhang ◽  
Ning Li ◽  
Zhaoyang Huang ◽  
Li Wang ◽  
...  

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