Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder

Biederman, Joseph; Hammerness, Paul; Sadeh, Boaz; Peremen, Ziv; Agarwal, Amit; Or-ly, Hadas; Stern, Yaki; Reches, Amit; Geva, Amir B.; Faraone, Stephen V.

doi:10.1017/s0033291716003329

Cited by 17 publications

(20 citation statements)

References 34 publications

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“…As for beta waves (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25), which are typically higher when a person is active, busy, anxious or concentrating for example, it is apparent that beta activity patterns are not unique in adult ADHD profiles and cannot be useful in differentiating ADHD in adulthood from normative populations based on evidence included in this review (23,31). Yet, notably, there is evidence of abnormal rightward beta asymmetry in inferior parietal regions, which is potentially an important feature in adult ADHD which may occur due to impaired capacity for top down task directed control over sensory encoding functions associated with attention (26,27).…”

Section: Discussionmentioning

confidence: 99%

“…When Ponomarev, Mueller, Candrian, Grin-Yatsenko, & Kropotov (54) investigated the performance of spectral analysis of resting EEG by calculating current source density (CSD) and group independent component analysis (gICA) in 96 adults with a diagnosis of ADHD, they found that these measures were more sensitive in distinguishing ADHD from control populations in comparison to raw EEG data in the front-central areas. Furthermore, Biederman et al (22) report an EPR study which employed brain network activation (BNA) analysis to achieve qualitative data on cortical connectivity to EEG data that was collected during a Go/No-Go task in adults with and without ADHD. They suggest BNA analysis produces an algorithm which is able to discriminate between ADHD and normative groups with a high level of accuracy.…”

Section: Eeg Analysismentioning

confidence: 99%

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EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis

Adamou¹,

Fullen

Jones

2020

Front. Psychiatry

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Background: Attention deficit hyperactivity disorder is a common neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. Since the development of the concept, a reliable biomarker to aid diagnosis has been sought. One potential method is the use of electroencephalogram to measure neuronal activity. The aim of this review is to provide an up to date synthesis of the literature surrounding the potential use of electroencephalogram for diagnosis of attention deficit hyperactivity disorder in adulthood. Methods: A search of PsycINFO, PubMed, and EMBASE was undertaken in February 2019 for peer-reviewed articles exploring electroencephalogram patterns in adults (18 years with no upper limit) diagnosed with attention deficit hyperactivity disorder. Results: Differences in electroencephalogram activity are potentially unique to adult attention deficit hyperactivity disorder populations. Strongest support was derived for elevated levels of both absolute and relative theta power, alongside the observation that alpha activity is able to typically differentiate between adult attention deficit hyperactivity disorder and normative populations. Conclusions: Electroencephalogram can have a use in clinical settings to aid adult attention deficit hyperactivity disorder diagnosis, but areas of inconsistency are apparent.

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Section: Discussionmentioning

confidence: 99%

Section: Eeg Analysismentioning

confidence: 99%

EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis

Adamou¹,

Fullen

Jones

2020

Front. Psychiatry

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“…The researchers used Gaussian process classifiers and whole activation pattern analysis and were able to predict the ADHD diagnosis with 77% accuracy. Likewise, in a study with adults with and without a diagnosis of ADHD, machine learning predicted the diagnosis with a specificity of .91 and sensitivity of .76 based on EEG metrics during a NoGo task measuring inhibition (Biederman et al 2017). These results support collecting physiological metrics during tasks required attention control to generate pattern recognition analysis for the accurate classification of ADHD.…”

Section: Eye Gaze and Machine Learning And Adhdmentioning

confidence: 60%

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Michalek

Jayawardena

Jayarathna

2019

Computational Models for Biomedical Reasoning and Problem Solving

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ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding and treatment of ADHD in adults. This chapter will outline a feasibility study in which eye tracking was used to measure eye gaze metrics during a WMC task for adults with and without ADHD and machine learning algorithms were applied to generate a feature set unique to the ADHD diagnosis. The chapter will summarize the purpose, methods, results, and impact of this study.

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“…This review included other existing methods proposed to diagnose ADHD, that were not covered in the former systematic review. To make the indirect comparison possible, this in-house scoping review included only diagnostic accuracy studies that reported the chosen metrics of diagnostic for methods based on; electroencephalography and eventrelated potentials (Marcano et al, 2017;Loo et al, 2016;Snyder et al, 2015;Mohammadi et al, 2016;Gloss et al, 2016;Biederman et al, 2017;Gamma and Kara, 2016;Marcano et al, 2018;Manouilenko et al, 2017), structural and functional neuroimaging (Iannaccone et al, 2015;Rangarajan et al, 2014;de Celis Alonso et al, 2017;Qureshi et al, 2017;Serrallach et al, 2016;Hasaneen et al, 2017;Tan et al, 2017b;Uddin et al, 2017), simulated virtual reality and computer games (Negut et al, 2017(Negut et al, , 2016Berger et al, 2017;Faraone et al, 2016), and peripheral biochemical markers (Faraone et al, 2014;Scassellati and Bonvicini, 2015;Scassellati et al, 2012;Thome et al, 2012). According with this complementary "scoping review", the method presented in this research, seems to outperforms all the diagnostic accuracy metrics reported in the trials scrutinised in the aforementioned review.…”

Section: The Inferential Activity (Both At Sensory-motor and Represenmentioning

confidence: 99%

Predictive Modelling of The Dynamic Patterns of Thinking in Attention-Deficit/Hyperactivity Disorder: Diagnostic Accuracy of Spatiotemporal Fractal Measures

Labra-Spröhnle¹,

Smith

Ahammer

et al. 2018

Preprint

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Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental condition characterized by executive function (EF) dynamics disturbances.Notwithstanding, current advances in translational neuroscience, no ADHD objective, clinically useful, diagnostic marker is available to date. Objectives: Using a customized definition of EF and a new clinical paradigm, we performed a prospective diagnostic accuracy trial to assess the diagnostic value of several fractal measures from the thinking processes or inferences in a cohort of ADHD children and typically developing controls. Method: We included children from age five to twelve diagnosed with a reference standard based on case history, physical and neurological examination, Conners 3 rd Edition, and DSM-V TM . The index test consisted of a computer-based inference task with a set of eight different instances of the "Battleships" game to be solved. A consecutive series of 18 cases and 18 controls (n = 36) recruited at the primary paediatrics service from the Nelson Marlborough Health in New Zealand underwent the reference standard and the index test. Several fractal measures were obtained from the inference task to produce supervised classification models. Results: Notably, the summarized logistic regression's predicted probabilities from the eight games played by each children yielded a 100% classification accuracy, sensitivity and specificity in both a training and an independent testing/validating cohort. Conclusions: From a translational vantage point the expeditious method and the robust results make this technique a promising candidate to develop a screening, diagnostic and monitoring system for ADHD, and may serve to assess other EF disturbances.

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Diagnostic utility of brain activity flow patterns analysis in attention deficit hyperactivity disorder

Cited by 17 publications

References 34 publications

EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis

EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis

Predicting ADHD Using Eye Gaze Metrics Indexing Working Memory Capacity

Predictive Modelling of The Dynamic Patterns of Thinking in Attention-Deficit/Hyperactivity Disorder: Diagnostic Accuracy of Spatiotemporal Fractal Measures

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