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

Published online by Cambridge University Press:  09 January 2017

J. Biederman ,
P. Hammerness ,
B. Sadeh ,
Z. Peremen ,
A. Amit ,
H. Or-ly ,
Y. Stern ,
A. Reches ,
A. Geva  and
S. V. Faraone
J. Biederman*
Affiliation:
Massachusettes General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA
P. Hammerness
Affiliation:
Massachusettes General Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA Boston Children's Hospital, Boston, MA, USA
B. Sadeh
Affiliation:
ElMindA Ltd, Herzliya, Israel
Z. Peremen
Affiliation:
ElMindA Ltd, Herzliya, Israel Tel Aviv University, Tef-Aviv, Israel
A. Amit
Affiliation:
ElMindA Ltd, Herzliya, Israel
H. Or-ly
Affiliation:
ElMindA Ltd, Herzliya, Israel
Y. Stern
Affiliation:
ElMindA Ltd, Herzliya, Israel
A. Reches
Affiliation:
ElMindA Ltd, Herzliya, Israel
A. Geva
Affiliation:
ElMindA Ltd, Herzliya, Israel Ben Gurion University, Beer-Sheva, Israel
S. V. Faraone
Affiliation:
SUNY Upstate Medical University, Syracuse, NY, USA KG Jebsen Centre for Research on Neuropsychiatric Disorders, Bergen, Norway
*
*Address for correspondence: Dr J. Biederman, Massachusetts General Hospital, 55 Fruit St., Warren Building 705, Boston, MA 02114, USA. (Email: jbiederman@partners.org)
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Abstract

Background

A previous small study suggested that Brain Network Activation (BNA), a novel ERP-based brain network analysis, may have diagnostic utility in attention deficit hyperactivity disorder (ADHD). In this study we examined the diagnostic capability of a new advanced version of the BNA methodology on a larger population of adults with and without ADHD.

Method

Subjects were unmedicated right-handed 18- to 55-year-old adults of both sexes with and without a DSM-IV diagnosis of ADHD. We collected EEG while the subjects were performing a response inhibition task (Go/NoGo) and then applied a spatio-temporal Brain Network Activation (BNA) analysis of the EEG data. This analysis produced a display of qualitative measures of brain states (BNA scores) providing information on cortical connectivity. This complex set of scores was then fed into a machine learning algorithm.

Results

The BNA analysis of the EEG data recorded during the Go/NoGo task demonstrated a high discriminative capacity between ADHD patients and controls (AUC = 0.92, specificity = 0.95, sensitivity = 0.86 for the Go condition; AUC = 0.84, specificity = 0.91, sensitivity = 0.76 for the NoGo condition).

Conclusions

BNA methodology can help differentiate between ADHD and healthy controls based on functional brain connectivity. The data support the utility of the tool to augment clinical examinations by objective evaluation of electrophysiological changes associated with ADHD. Results also support a network-based approach to the study of ADHD.

Keywords

Attention deficit hyperactivity disorder (ADHD)brain networksBrain Network Activation (BNA)diagnosticsevent-related potentials (ERPs)Go/NoGoresponse inhibitionspatio-temporal parcellation analysis
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 7 , May 2017 , pp. 1259 - 1270
Copyright
Copyright © Cambridge University Press 2017 

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