Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-19T06:48:53.596Z Has data issue: false hasContentIssue false
  • English
  • Français

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: [email protected])
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bullmore, E, Sporns, O (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience 10, 186–198.Google Scholar
Buzsaki, G, Draguhn, A (2004). Neuronal oscillations in cortical networks. Science 304, 1926–1929.CrossRefGoogle ScholarPubMed
Cortese, S, Kelly, C, Chabernaud, C, Proal, E, Di Martino, A, Milham, MP, Castellanos, FX (2012). Toward systems neuroscience of ADHD: a meta-analysis of 55 fMRI studies. American Journal of Psychiatry 169, 1038–1055.Google Scholar
Delorme, A, Makeig, S (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods 134, 9–21.Google Scholar
Dimoska, A, Johnstone, SJ, Barry, RJ, Clarke, AR (2003). Inhibitory motor control in children with attention-deficit/hyperactivity disorder: event-related potentials in the stop-signal paradigm. Biological Psychiatry 54, 1345–1354.Google Scholar
Engel, AK, Fries, P, Singer, W (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews. Neuroscience 2, 704–716.Google Scholar
Faraone, SV, Asherson, P, Banaschewski, T, Biederman, J, Buitelaar, JK, Ramos-Quiroga, JA, Rohde, LA, Sonuga-Barke, EJ, Tannock, R, Franke, B (2015). Attention-deficit/hyperactivity disorder. Nature reviews. Disease primers 1, 15020.Google Scholar
Faraone, SV, Biederman, J (2005). What is the prevalence of adult ADHD? Results of a population screen of 966 adults. Journal of Attention Disorders 9, 384–391.Google Scholar
Faraone, SV, Sergeant, J, Gillberg, C, Biederman, J (2003). The Worldwide Prevalence of ADHD: is it an American Condition? World Psychiatry 2, 104–113.Google Scholar
Fassbender, C, Schweitzer, JB (2006). Is there evidence for neural compensation in attention deficit hyperactivity disorder? A review of the functional neuroimaging literature. Clinical Psychology Review 26, 445–465.Google Scholar
First, M, Spitzer, R, Gibbon, M, Williams, J (1997). Structured Clinical Interview for DSM-IV Axis I Disorders. American Psychiatric Press: Washington, DC.Google Scholar
Fries, P (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in Cognitive Sciences 9, 474–480.CrossRefGoogle ScholarPubMed
Gambino, B (1997). The correction for bias in prevalence estimation with screening tests. Journal of Gambling Studies 13, 343–351.Google Scholar
Greimel, E, Wanderer, S, Rothenberger, A, Herpertz-Dahlmann, B, Konrad, K, Roessner, V (2011). Attentional performance in children and adolescents with tic disorder and co-occurring attention-deficit/hyperactivity disorder: new insights from a 2 × 2 factorial design study. Journal of Abnormal Child Psychology 39, 819–828.Google Scholar
Groom, MJ, Jackson, GM, Calton, TG, Andrews, HK, Bates, AT, Liddle, PF, Hollis, C (2008). Cognitive deficits in early-onset schizophrenia spectrum patients and their non-psychotic siblings: a comparison with ADHD. Schizophrenia Research 99, 85–95.Google Scholar
Hakvoort Schwerdtfeger, RM, Alahyane, N, Brien, DC, Coe, BC, Stroman, PW, Munoz, DP (2012). Preparatory neural networks are impaired in adults with attention-deficit/hyperactivity disorder during the antisaccade task. NeuroImage: Clinical 2, 63–78.CrossRefGoogle ScholarPubMed
Hamilton, M (1959). The assessment of anxiety states by rating. British journal of medical psychology. 32, 50–55.CrossRefGoogle ScholarPubMed
Hamilton, M (1960). A rating scale for depression. Journal of neurology, neurosurgery, and psychiatry 23, 56–62.Google Scholar
Himelstein, J, Halperin, JM (2000). Neurocognitive functioning in adults with attention-deficit/hyperactivity disorder. CNS Spectrums 5, 58–64.Google Scholar
Hollingshead, AB (1975). Four Factor Index of Social Status. Yale Press: New Haven, CT.Google Scholar
Johnstone, SJ, Dimoska, A, Smith, JL, Barry, RJ, Pleffer, CB, Chiswick, D, Clarke, AR (2007). The development of stop-signal and Go/Nogo response inhibition in children aged 7–12 years: performance and event-related potential indices. International Journal of Psychophysiology 63, 25–38.Google Scholar
Kiefer, AW, Barber Foss, K, Reches, A, Gadd, B, Gordon, M, Rushford, K, Laufer, I, Weiss, M, Myer, GD (2015). Brain network activation as a novel biomarker for the return-to-play pathway following sport-related brain injury. Frontiers in Neurology 6, 243.Google Scholar
Konrad, K, Eickhoff, SB (2010). Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Mapping 31, 904–916.CrossRefGoogle ScholarPubMed
Makris, N, Biederman, J, Valera, EM, Bush, G, Kaiser, J, Kennedy, DN, Caviness, VS, Faraone, SV, Seidman, LJ (2007). Cortical thinning of the attention and executive function networks in adults with attention-deficit/hyperactivity disorder. Cerebral Cortex 17, 1364–1375.Google Scholar
Muller, V, Anokhin, AP (2012). Neural synchrony during response production and inhibition. PLoS ONE 7, e38931.Google Scholar
Nieuwenhuis, S, Yeung, N, Cohen, JD (2004). Stimulus modality, perceptual overlap, and the go/no-go N2. Psychophysiology 41, 157–160.Google Scholar
Orvaschel, H (1994). Schedule for Affective Disorders and Schizophrenia for School-Age Children Epidemiologic Version. Nova Southeastern University, Center for Psychological Studies: Ft. Lauderdale.Google Scholar
Pandey, AK, Kamarajan, C, Tang, Y, Chorlian, DB, Roopesh, BN, Manz, N, Stimus, A, Rangaswamy, M, Porjesz, B (2012). Neurocognitive deficits in male alcoholics: an ERP/sLORETA analysis of the N2 component in an equal probability Go/NoGo task. Biological Psychiatry 89, 170–182.Google Scholar
Polanczyk, G, de Lima, MS, Horta, BL, Biederman, J, Rohde, LA (2007). The worldwide prevalence of ADHD: a systematic review and metaregression analysis. American Journal of Psychiatry 164, 942–948.Google Scholar
Reches, A, Kerem, D, Gal, N, Laufer, I, Shani-Hershkovitch, R, Dickman, D, Geva, AB (2013 a). A Novel ERP Pattern Analysis Method for Revealing Invariant Reference Brain Network Models. Functional Neurology, Rehabilitation, and Ergonomics 3, 295–317.Google Scholar
Reches, A, Laufer, I, Ziv, K, Cukierman, G, McEvoy, K, Ettinger, M, Knight, RT, Gazzaley, A, Geva, AB (2013 b). Network dynamics predict improvement in working memory performance following donepezil administration in healthy young adults. NeuroImage 88C, 228–241.Google Scholar
Reches, A, Levy-Cooperman, N, Laufer, I, Shani-Hershkovitch, R, Ziv, K, Kerem, D, Gal, N, Stern, Y, Cukierman, G, Romach, MK, Sellers, EM, Geva, AB (2014). Brain Network Activation (BNA) reveals scopolamine-induced impairment of visual working memory. Journal of Molecular Neuroscience 54, 59–70.Google Scholar
Reches, A, Nir, RR, Shram, MJ, Dickman, D, Laufer, I, Shani-Hershkovich, R, Stern, Y, Weiss, M, Yarnitsky, D, Geva, AB (2016). A novel electroencephalography-based tool for objective assessment of network dynamics activated by nociceptive stimuli. European Journal of Pain 20, 250–262.Google Scholar
Shahaf, G, Reches, A, Pinchuk, N, Fisher, T, Ben Bashat, G, Kanter, A, Tauber, I, Kerem, D, Laufer, I, Aharon-Peretz, J, Pratt, H, Geva, AB (2012). Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder. Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology 123, 1568–1580.Google Scholar
Simmonds, DJ, Pekar, JJ, Mostofsky, SH (2008). Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia 46, 224–232.Google Scholar
Smith, JL, Johnstone, SJ, Barry, RJ (2004). Inhibitory processing during the Go/NoGo task: an ERP analysis of children with attention-deficit/hyperactivity disorder. Clinical Neurophysiology 115, 1320–1331.Google Scholar
Stern, Y, Reches, A, Geva, AB (2016). Brain network activation analysis utilizing spatiotemporal features for event related potentials classification. Frontiers in computational neuroscience 10, 1–11.Google Scholar
Vapnik, V (1995). The Nature of Statistical Learning Theory. Springer-Varlag: New York.CrossRefGoogle Scholar
Supplementary material: File

Biederman supplementary material

Biederman supplementary material

Download Biederman supplementary material(File)
File 305.7 KB