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Neurofeedback Efficacy in Pediatric Neurological and Developmental Disorders: ADHD, Autism, Epilepsy, and Beyond
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March 7, 2025
Autism Spectrum
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Showing 6 of 50Individual brain regulation as learned via neurofeedback is related to affective changes in adolescents with autism spectrum disorder
Klöbl, Manfred, Prillinger, Karin, Diehm, Robert, Doganay, Kamer, Lanzenberger, Rupert, Poustka, Luise, Plener, Paul, Konicar, Lilian (2023) · Child and Adolescent Psychiatry and Mental Health
Abstract Background Emotions often play a role in neurofeedback (NF) regulation strategies. However, investigations of the relationship between the induced neuronal changes and improvements in affective domains are scarce in electroencephalography-based studies. Thus, we extended the findings of the first study on slow cortical potential (SCP) NF in autism spectrum disorder (ASD) by linking affective changes to whole-brain activity during rest and regulation. Methods Forty-one male adolescents with ASD were scanned twice at rest using functional magnetic resonance imaging. Between scans, half underwent NF training, whereas the other half received treatment as usual. Furthermore, parents reported on their child’s affective characteristics at each measurement. The NF group had to alternatingly produce negative and positive SCP shifts during training and was additionally scanned using functional magnetic resonance imaging while applying their developed regulation strategies. Results No significant treatment group-by-time interactions in affective or resting-state measures were found. However, we found increases of resting activity in the anterior cingulate cortex and right inferior temporal gyrus as well as improvements in affective characteristics over both groups. Activation corresponding to SCP differentiation in these regions correlated with the affective improvements. A further correlation was found for Rolandic operculum activation corresponding to positive SCP shifts. There were no significant correlations with the respective achieved SCP regulation during NF training. Conclusion SCP NF in ASD did not lead to superior improvements in neuronal or affective functioning compared to treatment as usual. However, the affective changes might be related to the individual strategies and their corresponding activation patterns as indicated by significant correlations on the whole-brain level. Trial registration This clinical trial was registered at drks.de (DRKS00012339) on 20th April, 2017.
View Full Paper →A comparison between classical and new proposed feature selection methods for attention level recognition in disordered children
Rady, Radwa Magdy, Moussa, Nancy Diaa, El Salmawy, Doaa Hanafy, M Rizk, M R, Alim, Onsy Abdel (2022) · Alexandria Engineering Journal
Lack of attention is a chronic behavior in ADHD (Attention Deficit Hyperactivity Disorder) and ASD (Autism Spectrum Disorder). Our goal is to develop a reliable method for the detection of inattention with high accuracy and low time consumption to be used in real time neurofeedback. The new applied methods for inattention in children are EMD (Empirical Mode Decomposition) with difference time series (Dt) and MRA (Multi Resolution Analysis). EMD is a method of breaking down a signal into ‘modes’ (IMFs) representing its different frequency components. Furthermore, MRA strikes balance between temporal and frequency resolution through localizing the EEG signal in frequency domain of interest (beta range) by wavelet decomposition or EMD and then retains time domain information using FD. As the results demonstrate, in intermediate and severe level cases of inattention, EMD_Dt technique is the most accurate. In mild level cases of inattention MRA (wavelet + FD) technique performance is better than EMD_Dt. However, the time consumption of the MRA (wavelet + FD) technique is fifteen times larger than EMD_Dt technique. EMD_Dt is the best technique as it requires less processing time which is the most important factor in neurofeedback, furthermore, clinician concerned more with severe and intermediate level of inattention to be treated.
View Full Paper →A new error-monitoring brain-computer interface based on reinforcement learning for people with autism spectrum disorders
Pires, Gabriel, Cruz, Aniana, Jesus, Diogo, Yasemin, Mine, Nunes, Urbano J., Sousa, Teresa, Castelo-Branco, Miguel (2022) · Journal of Neural Engineering
Objective.Brain-computer interfaces (BCIs) are emerging as promising cognitive training tools in neurodevelopmental disorders, as they combine the advantages of traditional computerized interventions with real-time tailored feedback. We propose a gamified BCI based on non-volitional neurofeedback for cognitive training, aiming at reaching a neurorehabilitation tool for application in autism spectrum disorders (ASDs).Approach.The BCI consists of an emotional facial expression paradigm controlled by an intelligent agent that makes correct and wrong actions, while the user observes and judges the agent's actions. The agent learns through reinforcement learning (RL) an optimal strategy if the participant generates error-related potentials (ErrPs) upon incorrect agent actions. We hypothesize that this training approach will allow not only the agent to learn but also the BCI user, by participating through implicit error scrutiny in the process of learning through operant conditioning, making it of particular interest for disorders where error monitoring processes are altered/compromised such as in ASD. In this paper, the main goal is to validate the whole methodological BCI approach and assess whether it is feasible enough to move on to clinical experiments. A control group of ten neurotypical participants and one participant with ASD tested the proposed BCI approach.Main results.We achieved an online balanced-accuracy in ErrPs detection of 81.6% and 77.1%, respectively for two different game modes. Additionally, all participants achieved an optimal RL strategy for the agent at least in one of the test sessions.Significance.The ErrP classification results and the possibility of successfully achieving an optimal learning strategy, show the feasibility of the proposed methodology, which allows to move towards clinical experimentation with ASD participants to assess the effectiveness of the approach as hypothesized.
View Full Paper →Characterizing the ASD–ADHD phenotype: measurement structure and invariance in a clinical sample
Krakowski, Aneta D., Cost, Katherine Tombeau, Szatmari, Peter, Anagnostou, Evdokia, Crosbie, Jennifer, Schachar, Russell, Duku, Eric, Georgiades, Stelios, Ayub, Muhammad, Kelley, Elizabeth, Nicolson, Rob, Pullenayegum, Eleanor, Barnett‐Tapia, Carolina (2022) · Journal of Child Psychology and Psychiatry
Background: Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) have considerable overlap, supporting the need for a dimensional framework that examines neurodevelopmental domains which cross traditional diagnostic boundaries. In the following study, we use factor analysis to deconstruct the ASD–ADHD phenotype into its underlying phenotypic domains and test for measurement invariance across adaptive functioning, age, gender and ASD/ADHD clinical diagnoses. Methods: Participants included children and youth (aged 3–20 years) with a clinical diagnosis of ASD (n = 727) or ADHD (n = 770) for a total of 1,497 participants. Parents of these children completed the Social Communication Questionnaire (SCQ), a measure of autism symptoms, and the Strengths and Weaknesses of ADHD and Normal Behaviour (SWAN) questionnaire, a measure of ADHD symptoms. An exploratory factor analysis (EFA) was performed on combined SCQ and SWAN items. This was followed by a confirmatory factor analysis (CFA) and tests of measurement invariance. Results: EFA revealed a four-factor solution (inattention, hyperactivity/impulsivity, social-communication, and restricted, repetitive, behaviours and interests (RRBI)) and a CFA confirmed good model fit. This solution also showed good model fit across subgroups of interest. Conclusions: Our study shows that a combined ASD–ADHD phenotype is characterized by two latent ASD domains (social communication and RRBIs) and two latent ADHD domains (inattention and hyperactivity/impulsivity). We established measurement invariance of the derived measurement model across adaptive functioning, age, gender and ASD/ADHD diagnoses
View Full Paper →Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventure
Hoogman, Martine, Rooij, Daan, Klein, Marieke, Boedhoe, Premika, Ilioska, Iva, Li, Ting, Patel, Yash, Postema, Merel C., Zhang‐James, Yanli, Anagnostou, Evdokia, Arango, Celso, Auzias, Guillaume, Banaschewski, Tobias, Bau, Claiton H. D., Behrmann, Marlene, Bellgrove, Mark A., Brandeis, Daniel, Brem, Silvia, Busatto, Geraldo F., Calderoni, Sara, Calvo, Rosa, Castellanos, Francisco X., Coghill, David, Conzelmann, Annette, Daly, Eileen, Deruelle, Christine, Dinstein, Ilan, Durston, Sarah, Ecker, Christine, Ehrlich, Stefan, Epstein, Jeffery N., Fair, Damien A., Fitzgerald, Jacqueline, Freitag, Christine M., Frodl, Thomas, Gallagher, Louise, Grevet, Eugenio H., Haavik, Jan, Hoekstra, Pieter J., Janssen, Joost, Karkashadze, Georgii, King, Joseph A., Konrad, Kerstin, Kuntsi, Jonna, Lazaro, Luisa, Lerch, Jason P., Lesch, Klaus‐Peter, Louza, Mario R., Luna, Beatriz, Mattos, Paulo, McGrath, Jane, Muratori, Filippo, Murphy, Clodagh, Nigg, Joel T., Oberwelland‐Weiss, Eileen, O'Gorman Tuura, Ruth L., O'Hearn, Kirsten, Oosterlaan, Jaap, Parellada, Mara, Pauli, Paul, Plessen, Kerstin J., Ramos‐Quiroga, J. Antoni, Reif, Andreas, Reneman, Liesbeth, Retico, Alessandra, Rosa, Pedro G. P., Rubia, Katya, Shaw, Philip, Silk, Tim J., Tamm, Leanne, Vilarroya, Oscar, Walitza, Susanne, Jahanshad, Neda, Faraone, Stephen V., Francks, Clyde, Heuvel, Odile A., Paus, Tomas, Thompson, Paul M., Buitelaar, Jan K., Franke, Barbara (2022) · Human Brain Mapping
Neuroimaging has been extensively used to study brain structure and function in individuals with attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) over the past decades. Two of the main shortcomings of the neuroimaging literature of these disorders are the small sample sizes employed and the heterogeneity of methods used. In 2013 and 2014, the ENIGMA-ADHD and ENIGMA-ASD working groups were respectively, founded with a common goal to address these limitations. Here, we provide a narrative review of the thus far completed and still ongoing projects of these working groups. Due to an implicitly hierarchical psychiatric diagnostic classification system, the fields of ADHD and ASD have developed largely in isolation, despite the considerable overlap in the occurrence of the disorders. The collaboration between the ENIGMA-ADHD and -ASD working groups seeks to bring the neuroimaging efforts of the two disorders closer together. The outcomes of case–control studies of subcortical and cortical structures showed that subcortical volumes are similarly affected in ASD and ADHD, albeit with small effect sizes. Cortical analyses identified unique differences in each disorder, but also considerable overlap between the two, specifically in cortical thickness. Ongoing work is examining alternative research questions, such as brain laterality, prediction of case–control status, and anatomical heterogeneity. In brief, great strides have been made toward fulfilling the aims of the ENIGMA collaborations, while new ideas and follow-up analyses continue that include more imaging modalities (diffusion MRI and resting-state functional MRI), collaborations with other large databases, and samples with dual diagnoses.
View Full Paper →Effectivity of ILF Neurofeedback on Autism Spectrum Disorder—A Case Study
Rauter, Alexandra, Schneider, Horst, Prinz, Wolfgang (2022) · Frontiers in Human Neuroscience
Autism spectrum disorder (ASD) is a neural and mental developmental disorder that impacts brain connectivity and information processing. Although application of the infra-low frequency (ILF) neurofeedback procedure has been shown to lead to significant changes in functional connectivity in multiple areas and neuronal networks of the brain, rather limited data are available in the literature for the efficacy of this technique in a therapeutic context to treat ASD. Here we present the case study of a 5-year-old boy with ASD, who received a treatment of 26 sessions of ILF neurofeedback over a 6-month period. A systematic and quantitative tracking of core ASD symptoms in several categories was used to document behavioral changes over time. The ILF neurofeedback intervention decreased the average symptom severity of every category to a remarkable degree, with the strongest effect (80 and 77% mean severity reduction) for physical and sleep symptoms and the lowest influence on behavioral symptoms (15% mean severity reduction). This case study is representative of clinical experience, and thus shows that ILF neurofeedback is a practical and effective therapeutic instrument to treat ASD in children.
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