Access the full text.
Sign up today, get DeepDyve free for 14 days.
Background: Among the most robust neural abnormalities differentiating individuals with Attention-Deficit/ Hyperactivity Disorder (ADHD) from typically developing controls are elevated levels of slow oscillatory activity (e.g., theta) and reduced fast oscillatory activity (e.g., alpha and beta) during resting-state electroencephalography (EEG). However, studies of resting state EEG in adults with ADHD are scarce and yield inconsistent findings. Methods: EEG profiles, recorded during a resting-state with eyes-open and eyes-closed conditions, were compared for college students with ADHD (n = 18) and a nonclinical comparison group (n = 17). Results: The ADHD group showed decreased power for fast frequencies, especially alpha. This group also showed increased power in the slow frequency bands, however, these effects were strongest using relative power computations. Furthermore, the theta/beta ratio measure was reliably higher for the ADHD group. All effects were more pronounced for the eyes-closed compared to the eyes-open condition. Measures of intra-individual variability suggested that brains of the ADHD group were less variable than those of controls. Conclusions: The findings of this pilot study reveal that college students with ADHD show a distinct neural pattern during resting state, suggesting that oscillatory power, especially alpha, is a useful index for reflecting differences in neural communication of ADHD in early adulthood. Keywords: Quantitative Electroencephalography (EEG), Adults, Power, Attention-deficit/hyperactivity disorder (ADHD), Resting state, Alpha, Beta, Theta, Intra-individual variability, Eyes open, Eyes closed Background psychopathology (e.g., depression, anxiety), and substance Attention-deficit/hyperactivity disorder (ADHD) is a per- abuse [5,8-10].These problems present unique challenges vasive mental health condition that is characterized by to individuals in post-secondary educational settings that symptoms of inattention and hyperactivity. About 5% of demand self-discipline and higher order executive func- children are estimated to meet the diagnosis of ADHD tioning such as attentional control. worldwide . Adults with ADHD, particularly the subset In the last few years, research investigating ADHD popu- that pursue post-secondary education, are an understudied lations has used neurophysiological measures such as EEG population despite research showing that over 50% of chil- oscillatory power to determine whether ADHD can be dren with ADHD continue to show symptoms in adult- distinguished by specific neural abnormalities . Neu- hood [2-4]. ADHD adults often show a decrease in their ronal oscillations are an important mechanism enabling hyperactivity symptoms, but symptoms relating to cog- coordinated communication in a neural network . Dif- nitive impairments, although less marked, remain [5-7]. ferent neural oscillations observed during a resting state Despite an apparent age-related decline in symptoms, pro- represent brain activity at different spatial and temporal blems with inattention, working memory, and an in- scales, and these cortical oscillation profiles may underlie creased mental restlessness continue to undermine their particular ADHD symptomatology. For example, childhood occupational/academic functioning and raise their risk for ADHD has been characterized by higher power in slow oscillations (e.g., delta and theta frequencies), and lower * Correspondence: email@example.com power in fast oscillations (e.g., alpha and beta frequencies) Applied Psychology and Human Development, Ontario Institute for Studies relative to normative control groups (see, [13,14], for in Education, University of Toronto, Toronto, Canada Full list of author information is available at the end of the article © 2012 Woltering et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 2 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 reviews). A ratio measure, dividing slow-oscillatory by ADHD and their normal healthy peers. To the best of fast-oscillatory power, has shown to be one of the most re- ourknowledge,thisisthe first study to investigate liable neurophysiological indices of ADHD , although oscillatory power in college students with ADHD. This its reliability for diagnoses remains uncertain . relatively successful subset, accepted into post-secondary However, the specificity of this EEG power profile to education, continues to manifest cognitive and other adult ADHD, and its meaning, are under debate [16,17]. functional impairments [9,11]. In addition to comput- Similar abnormalities in EEG power have been observed ing measures of absolute power and relative power, in patients with head injuries, dementia, and schizophre- we also examined eyes-open and eyes-closed conditions nia [16,18,19], indicating a more general atypical neural as differences in these measures might explain discrep- functioning or organization. The EEG profile seen with ancies seen in the literature. Furthermore, we also ex- ADHD has been interpreted in various ways: the rela- plored measures of intra-individual variability in brain tively low power in fast oscillations is consistent with the activation. cortical hypo-arousal theory giving rise to reduced ex- ecutive functioning and self-control , whereas the Methods high power in slow oscillations could reflect diminished Participants control of strong subcortical drives and impulses . Eighteen participants with ADHD (8 male; 1 left-handed; Another perspective builds on work showing that, com- mean age = 25.8, sd = 4.27) were recruited from University pared to normally developing children, children with Student Services and 17 normal healthy controls were ADHD display a neural oscillatory pattern that resem- recruited through campus advertisements (10 male; 2 left- bles younger children , providing support for the handed; mean age = 24.4, sd = 4.39). Inclusion criteria were maturational lag model of ADHD which explains their 1) current enrollment in a post-secondary program, 2) a symptoms as being developmentally inappropriate . previous diagnosis of ADHD, and 3) registration with re- Compared to studies of children with ADHD, research spective university or college Student Disability Services, examining oscillatory power in adults with ADHD has which requires supporting documentation of a confirmed been scarce and findings have been inconsistent. For diagnosis of ADHD. All participants completed the Adult example, in keeping with the child ADHD literature, a ADHD Self Report Scale (ASRS) to assess current symp- higher power in slow oscillations has been found for toms of ADHD. Exclusion criteria were 1) uncorrected ADHD adults compared to healthy comparison groups sensory impairment, 2) major neurological dysfunction in some studies [24,25], but this finding has not been and psychosis, and 3) current use of sedating or mood replicated in another . The discrepancies become altering medication other than stimulants prescribed for more complex when examining fast oscillations. Consist- ADHD. Among the clinical sample, 10 subjects (56%) ent with the child ADHD literature, Bresnahan et al., were being treated with medication. Of those 10 subjects,  found ADHD adults to have lower beta power. 6 subjects were using stimulants only, 2 subjects were However, this finding was only valid for relative beta using a combination of stimulants and antidepressants, power (Indicates the power of a specific band relative to and 2 subjects were using a combination of stimulants, power in all bands) because no group differences were antidepressants, and other non-prescriptive medications. found in the beta and alpha bands for absolute power. In Participants were asked not to change their medication contrast, Clarke et al.,  found higher absolute and treatment when visiting the lab for assessment. Three par- relative beta power for ADHD groups, and Koehler ticipants had a comorbid learning disability, and one et al.,  found that ADHD adults have higher absolute participant was diagnosed with anxiety and depression. alpha power. It is possible methodological differences might account for some of the inconsistencies. For ex- Procedure ample, some studies measured the resting state EEG The present study was approved by the University of during an eyes-closed condition [25,26], whereas others Toronto Research Ethics Board (protocol reference recorded during an eyes-open condition [24,27]. No #23977) and all participants provided informed written strong theoretical framework for interpreting differences consent prior to the start of the study. for both eye-conditions exists in the literature, partly be- Participants were seated in a comfortable chair and fit- cause very few studies have explicitly examined differ- ted with a 129-channel EEG net (Electrical Geodesic ences between both conditions in ADHD. The most Inc., EGI). Acquisition started after impedances for all consistent finding remains an elevated theta/beta ratio, channels were reduced to below 50 kΩ in accordance however, only a few studies so far have examined this in with standard data collection procedures [29,30]. Data adults [24,25,27,28]. were collected using a . 1 – 1000 Hz bandpass hardware The present pilot study investigated neural oscillatory filter and a 500 Hz sampling rate. Data were referenced power during a resting state in college students with to electrode Cz. After becoming familiar with the Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 3 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 environment, instructions on a screen explained the task 46 segments (sd = 9.5) during eyes-open. Groups did not to the participants. A sound signaled when they were to significantly differ in segment count (p’s > .25). No sub- alternate closing or opening their eyes. Participants did jects were discarded for meeting our cutoff criterion of this for six 40 second intervals (i.e., 120 seconds for each less than 11 segments. condition). Participants were encouraged to relax, prevent Next, data were exported to MATLAB 7.5 (The Math- excessive blinking, and to keep the eyes fixated on a cen- works, Inc.) for further analysis. tral cross to prevent eye-movements during eyes-open. In MATLAB, the data were average referenced, and a Fast Fourier Transform was run using the pwelch algo- Behavioural measures rithm, with a 128 sample triangular window, to obtain Each participant completed a number of standard question- time-frequency domain measures. Mean spectral esti- naires and tasks to assess current symptom impairment: mates in various power bands (theta, 4–7; alpha, 8–12; The Adult ADHD Self-Report Scale (ASRS v1.1) is a beta, 13–25) were computed. As a measure of intra- reliable and valid scale for evaluating current ADHD individual variability in neural activity, the standard devi- symptoms in adults . The ASRS v1.1 consists of ation of the power in each 2-second trial was calculated eighteen questions based on the criteria used for diag- for each band for each participant. To reduce unneces- nosing ADHD in the DSM-IV-TR. Scores for each item sary computation and multiple tests, we chose to extract were added to calculate a total score. Subtypes were not data for 66 channels based on the standard EGI template. investigated considering recent conclusions drawn in the literature questioning their validity . Statistical analysis The Cognitive Failures Questionnaire (CFQ) measures To examine potential outliers, the modified thompson- self-reported failures in perception, memory, and motor tau method was used. In addition to absolute power, the function in everyday life. Twenty-five questions ask sub- relative power of each frequency band (the amount of jects to rank how often these mistakes occur . power in one band divided by the power in all other The Reading Fluency subscale of the Woodcock bands) was also computed for the eyes-open as well as Johnson-III Tests of Achievement  was administered eyes-closed conditions to permit comparison with other to determine automaticity of identifying words, to provide studies. Independent sample t-tests were used to assess a confirmatory index of Specific Learning Disabilities. The differences at each electrode between the ADHD and dependent variable was the number of correctly com- control groups. Data were presented in difference plots pleted items in three minutes. All subtest raw scores were showing the t-value test statistic between groups at each converted to standard scores. electrode. Significant effects are indicated on the plots The Digit Span subtest from the Wechsler Adult using dots at a .05 level, and using crosses when signifi- Intelligence Scale- Fourth Edition (WAIS-IV) was used to cance was reached using the conservative Bonferroni assess auditory-verbal working memory, as a crude index correction for multiple comparisons. This manner of of executive function . The Digit Span raw score was presenting data instantly shows the direction as well as converted to an age-adjusted scaled score. the significance of the differences found between groups, and provides an overview of the spatial variability of the EEG data processing data across the scalp. Netstation (Electrical Geodesic Inc, EGI) was used to fil- ter (FIR, .1-100 Hz, excluding 60 Hz notch) and segment Results the data into 2-second segments (e.g., 60 segments per Group characteristics condition). Segments containing artifacts were removed The ASRS confirmed that the ADHD group, relative to using standard, automatic algorithms for the detection the control group, exhibited more ADHD symptoms of eye blinks, eye movements, as well as large drifts, and (p’s < .001). Furthermore, the CFQ showed that the ADHD spikes in the data. Segments containing more than 20% group reported significantly more general cognitive fail- bad channels were automatically removed. In addition, ures in their everyday life (all p’s < .001). all segments were visually inspected by a trained re- Both groups had a comparable number of years of search assistant blind to the hypotheses. Bad channels education, and performed similarly on tests of reading were replaced by values interpolated from neighboring fluency and working memory, and also didn’t differ in channel data. Across all subjects, an average of 1 chan- age. Table 1 shows the means and standard deviations nel needed to be replaced. The ADHD group had an for the ADHD and control group for each of the ques- average of 38 useful segments (sd = 13.6) in the eyes- tionnaires and tasks. closed condition and 43 segments (sd = 12.7) during Participants on medication (m = 55.80, sd = 12.56) eyes-open, whereas the control group had an average of reported more ADHD symptoms on the ASRS, at a 43 segments (sd = 14.2) in the eyes-closed condition and trend level, than those who were not using medication Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 4 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 (m = 45.88, sd = 8.06), t(16) = 1.93, p = .07. It is possible particular for the central electrode for alpha and beta, and that those subjects who were not using medication had the occipital electrodes for alpha (p’s < .0008, corrected for less severe symptoms to begin with. multiple comparisons). Mean, sample standard deviation, and intra-individual standard deviation values are pre- Absolute power sented in additional file 1 for each band and for each elec- For the eyes-closed condition, significant differences trode site for eyes-closed (Additional file 1: Table A1) as using t-tests (p’s < .05) between groups were found for well as eyes-open (Additional file 1: Table A2). slow as well as fast oscillations. Slow oscillations (theta) showed increased power for ADHD compared to the Relative power control group for anterior and lateral electrodes. How- For the eyes-closed condition, the ADHD group showed ever, lower power in slow oscillations was found in more significantly higher relative power in the slow oscillatory central and posterior electrodes. As expected, the ADHD bands compared to the control group. This effect was group exhibited lower power in the fast oscillations for present across the entire scalp. Fast oscillations showed all electrodes. This pattern of results also held when in- significantly smaller power for the ADHD compared to vestigating those subjects with ADHD who used medica- the control group. These effects were most pronounced tion. A similar pattern of differences was found for the in the alpha band. We note that a similar pattern of eyes-open condition, however, the increases in slow results was present for just those ADHD on medication. oscillations seemed less pronounced. Figure 1 shows the The eyes-open condition also showed a similar pattern, scalp difference plots between the groups for each power however, the effects were not as strong. Figure 2 shows band for the eyes-closed as well as eyes-open condition. the differences between the groups in scalp plots for To test whether the two Eye conditions (eyes-open, each condition and each power band (values of Fz, Cz, eyes-closed) differed between ADHD and controls, a 2 Pz, and Oz for each eye condition are presented in (Group) x 2 (Eye Condition) Mixed Model ANOVA Additional file 1, Table A1 and A2). looking at electrode sites Fz, Cz, Pz, and Oz was con- To illustrate the contributions of various bands under- ducted with medication as a covariate. A main effect of lying the relative power differences, a power-by-frequency Eye condition was found in the alpha band for all elec- plot (see Figure 3) was computed at electrode site Cz only trodes (p’s < .05) with, as expected, the eyes-closed con- for the eyes-closed and eyes-open condition. The ADHD dition showing increased alpha compared to eyes-open. group showed decreased power compared to the control A significant main effect of Eye Condition was also group in the alpha (p <.0001) and beta (p <.001) band. found for the Beta band, but only for electrode site Pz We note that the strong decrease in alpha power for the (p < .05). Significant Group by Eye Condition interac- ADHD group is indirectly driving the relatively larger con- tions were found for power in the alpha band at elec- tribution of slow wave oscillations shown in computations trode site Fz, F(1, 32) = 7.83, p < .01, as well as Oz, F of relative power. (1,32) = 4.63, p < .05. Post-hoc analyses showed that the Figure 4 shows the data points for each group for ab- comparison group had a stronger decrease in alpha power solute power in the alpha band at electrode Cz. compared to the ADHD group from eyes-closed to eyes- open. This is partly due to the comparison group having higher alpha power during the eyes-closed condition. Ratio power values Group differences in intra-individual variability in neural For the eyes-closed condition, the theta/beta ratio was oscillatory power across the different frequency bands significantly higher in the ADHD group compared to the were tested for electrode sites Fz, Cz, Pz, and Oz. The control group at anterior and lateral electrode sites. A ADHD group showed significantly less variability in the theta/alpha ratio yielded a similar but stronger pattern, fast oscillatory band as shown by multiple t-tests, in for the entire scalp. Results for the eyes-open condition Table 1 Questionnaire and task results for the ADHD and control group Clinical (n = 18) (mean/sd) Control (n = 17) (mean/sd) Years of Education 15.7 (1.2) 15.9 (1.7) *** ASRS 51.4 (11.7) 20.0 (12.7) *** CFQ 57.3 (11.2) 26.8 (10.1) WJ-III Reading fluency 97.9 (14.5) 104.7 (18.4) WAIS - Digit Span 8.3 (3.4) 9.4 (2.7) Independent sample t-tests between clinical and control group, * p < .05, ** p < .01, *** p < .001. ASRS, Adult ADHD Self Report Scale; CFQ, Cognitive Failures Questionnaire; WJ-III, Woodcock Johnson III Tests of Achievement; WAIS, Wechsler Adult Intelligence Scale. Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 5 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 Figure 1 t-scores for differences in absolute power between ADHD and control group across the scalp for the eyes-closed (top) and eyes-open (bottom) condition for the theta (θ), alpha (α), and beta (β) bands. Greater values (red) represent higher power in the ADHD group. Squares (▪) indicate significance at p < .05, and plus signs (+) indicate significance p < .0008 (corrected for multiple comparisons - Bonferroni). showed a similar pattern, however, effects were generally associated with ADHD, even in adulthood. Generally, weaker (see, Figure 5). these results replicate the findings found in childhood ADHD that show increased slow oscillatory power and Discussion decreased fast oscillatory power [13,14]. Because vari- In the present study, young adults with ADHD showed ables such as age, sex, and estimates of executive func- decreased oscillatory power in fast frequencies, particu- tion and years of education were similar between larly within the alpha band, relative to normal healthy groups, different oscillatory activation patterns likely controls. Increased power for slow frequencies was also represent differences in neural communication related to found for individuals with ADHD, although these results ADHD symptomatology. were more specific to measures of relative power. The Our findings are consistent with some studies done on ADHD group showed a higher theta/beta ratio com- adults pertaining to higher power in slow oscillations pared to the control group in fronto-central and lateral [27,36], lower power in fast oscillations  and an higher electrode sites, confirming the ratio measure to be ratio values [24,25,27,28]. But there are discrepancies with Figure 2 t-scores for differences in relative power between ADHD and control group for the eyes-closed (top) and eyes-open (bottom) condition for the theta (θ), alpha (α), and beta (β) bands. Greater values (red) represent higher power in the ADHD group. Squares (▪) indicate significance at p < .05, and plus signs (+) indicate significance p < .0008 (corrected for multiple comparisons - Bonferroni). Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 6 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 Figure 3 Power (mV ) by frequency (Hz) plot at Electrode Cz for the ADHD (red) and the control (blue) groups in the eyes-closed (A) and eyes-open condition (B). Theta, 4–7 Hz; alpha, 8–12 Hz; beta, 13–25 Hz. Shaded area represents standard error. other studies, in particular results reporting increases in of young adults. During resting state, different frequencies fast oscillatory power bands [25,26]. More studies are of neural oscillations coexist and may interact with each required to investigate the nature of these discrepancies in other to maintain a physiological and functional balance in the high frequency oscillations. One possible explanation the brain. Low frequency oscillations (e.g., theta) can coord- for the discrepancies might be the nature of our sample inate long-distance brain regions and function in larger that consists out of relatively high-functioning college temporal scales . Furthermore, low frequencies oscilla- students. tions are more prominent in deep cortical laminar regions, The differences found in the current study may reflect a different neurophysiology in this specific ADHD population Figure 5 t-scores for differences in ratio power between ADHD and control group for the eyes-closed (top) and eyes-open (bottom) condition for the Theta/beta Ratio (θ/β, left) and Theta/alpha ratio (θ/α, right). Greater values (red) represent higher Figure 4 Individual data points for Absolute power (mV )at power in the ADHD group. Squares (▪) indicate significance at p electrode site Cz for the ADHD and control groups for the < .05, and plus signs (+) indicate significance p < .0008 (corrected for eyes-closed and eyes-open conditions. multiple comparisons - Bonferroni). Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 7 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 which are more easily influenced by cortical-subcortical cleaner, reflection of intrinsic alpha oscillation because interaction . Instead, high frequency oscillations (e.g., visual input from the thalamus during the eyes-open alpha, beta) measured by scalp EEG may reflect more local condition may 'disturb' alpha rhythms mediated cortical computation for executive, memory, and motor by cortico-thalamic loops [42,43]. Concerning intra- functions [39,40]. The increased power in slow oscillations, individual variability, interestingly, the ADHD group and especially the reduced fast oscillatory power, may re- showed less inter-trial variability in power, particularly in flect an unbalanced or non-optimal interaction among local fast oscillations such as alpha. These results agree with cortical neural activities and long-range corticocortical/cor- emerging notions in the field suggesting low variability tico-subcortical neural activities, which may be related to of metastable brain states are associated with less behav- their ADHD symptomatology. ioural stability [44,45]. Indeed, one characteristic of Although the child ADHD literature mostly focuses on people with ADHD is a high variability in task perform- power in theta and beta bands [13,14], it seems the ance . We shall further explore this phenomenon in results in our study of adult students are mostly driven future studies. by differences in alpha power. First, the strongest, most A limitation of this study is its relatively low sample size, and widespread, differences between the ADHD and which constrained our ability to investigate the effects of control group are seen in the alpha band. Second, the comorbidities. Furthermore, this study included subjects effects of alpha were the most reliable, as they held for who were on medication. Questionnaire data indicated computations of absolute as well as relative power, and that ADHD symptomatology was stronger among those for the eyes-closed as well as the eyes-open condition. taking medication compared to those in the ADHD group, Third, the group differences found for relative power in however, analyses demonstrated that subjects on medica- the slow frequency bands can be attributed to decreases tion showed a similar pattern of differences with the in alpha. Last, the theta/alpha ratio showed even stron- control group for absolute as well as relative power. Fur- ger group differences than the theta/beta ratio. thermore, we point out that previous studies have found We suggest that the lower alpha seen in the ADHD that stimulants tend to normalize EEG oscillatory power group may be related to problems in attentional self- on ADHD adults , suggesting effects would have been control. Recently, alpha power has been associated with stronger had we excluded those subjects on medication. active inhibition of external stimuli in a variety of tasks . This framework would suggest that more alpha Conclusions desynchronization may reflect an increased focus on the These data suggest that the neurophysiological differ- processing of external stimuli. However, subjects in a ences found between individuals with ADHD and their non-task related, relaxing state generally do not actively peers in childhood are also present in adulthood. The process external stimuli to great extent. It is possible findings may help document the behavioral and neural that the neural circuitry of people with ADHD is wired nature of adult ADHD, which may eventually lead to a such that they are more attuned to process external better understanding and treatment. stimuli, and that the decreased alpha power is a reflec- tion of this propensity. Such increased vigilance to exter- Additional file nal stimuli could be beneficial in certain contexts, however, when attention needs to be consistently directed to internal Additional file 1: Table A1. Mean (sample standard deviation, inter-trial goals, it may become problematic. Though speculative, this 2 standard deviation) in absolute (mV ) and relative power for theta, alpha, interpretation could complement Rowe et al.’s  account and beta bands between the ADHD and control group for electrodes Fz, Cz, Pz, and Oz in the eyes-closed condition. Table A2. Mean (sample that individuals with ADHD suffer from a lack of inhibition standard deviation) in absolute (mV ) and relative power for theta, alpha, over sensory input, and might explain the distractibility and and beta bands between the ADHD and control group for electrodes Fz, concentration problems adults with ADHD experience, and Cz, Pz, and Oz in the eyes-open condition. Figure A3. Variability in absolute Alpha power (in mV ) for electrode 40 in the eyes closed specifically the student population. condition for ADHD subjects on, or off, medication. Figure A4. Power The current study also investigated differences in 2 (in mV ) x Frequency plot for eyes-closed for Controls, ADHD participants oscillatory power between the eyes-open and eyes-closed who were on medication, and those ADHD participants who were not. conditions as well as the intra-individual variability of that power. Concerning eyes-open and eyes-closed con- Abbreviations ditions, based on our data we conclude that both condi- ADHD: Attention-Deficit/Hyperactivity Disorder; EEG: Electroencephalography; ASRS: Adult ADHD Self Report Scale; CFQ: Cognitive Failures Questionnaire; tions are relatively similar between groups, however, the WJ-III: Woodcock Johnson-III Tests of Achievement; WAIS-IV: Wechsler Adult effects of higher slow oscillatory power and lower fast Intelligence Scale- Fourth Edition. oscillatory power seemed more pronounced during the eyes-closed condition for alpha in the ADHD group. It is Competing interests possible that the eyes-closed condition is a better, or The authors declare that they have no competing interests. Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 8 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 Authors' contributions 17. Bresnahan SM, Anderson JW, Barry RJ: Age-related changes in quantitative SW, ZL and RT contributed to the conception and design of the study. SW EEG in attention- deficit/hyperactivity disorder. Biol Psychiatry 1999, and JJ were responsible for data acquisition, performed the data processing 46:1690–1697. and analyses, and drafted the manuscript. ZL assisted with the power 18. Coutin-Churchman P, Añez Y, Uzcátegui M, Alvarez L, Vergara F, Mendez L, analyses. RT and ZL helped critically revise the manuscript. All authors read Fleitas R: Quantitative spectral analysis of EEG in psychiatry revisited: and approved the final manuscript. drawing signs out of numbers in a clinical setting. Neurophysiol Clin 2003, 114:2294–2306. 19. Prichep LS, John ER: QEEG profiles of psychiatric disorders. Brain Topogr Acknowledgements 1992, 4:249–257. We would like to thank Dr. Marc Lewis for the use of his Canadian 20. Rowe DL: Biophysical modeling of tonic cortical electrical activity in Foundation for Innovation (CFI #482246) - funded EEG lab. This research was attention deficit hyperactivity disorder. Int J Neurosci 2005, 115:1273–1305. financially supported by Dr. Rosemary Tannock’s Canada Research Chair 21. Schutter DJLG, Leitner C, Kenemans JL, Honk Jv: Electrophysiological program and the Canadian Institutes of Health Research operating Grant correlates of cortico-subcortical interaction: a cross-frequency spectral (CIHR # 245899, Tannock & Lewis). EEG analysis. Neurophysiol Clin 2006, 117:381–387. 22. John ER, Ahn H, Prichep L, Trepetin M, Brown D, Kaye H: Developmental Author details equations for the electroencephalogram. Science 1980, 210:1255–1258. Applied Psychology and Human Development, Ontario Institute for Studies 23. Kinsbourne M: Minimal brain dysfunction as a neurodevelopmental lag. in Education, University of Toronto, Toronto, Canada. Neuroscience and Ann N Y Acad Sci 1973, 205:268–273. Mental Health Research Program, Hospital for Sick Children, Toronto, Ontario, 24. Bresnahan SM, Barry RJ, Clarke AR, Johnstone SJ: Quantitative EEG analysis Canada. in dexamphetamine-responsive adults with attention-deficit/ hyperactivity disorder. Psychiatry Res 2006, 141:151–159. Received: 10 July 2012 Accepted: 13 December 2012 25. Koehler S, Lauer P, Schreppel T, Jacob C, Heine M, Boreatti-Hümmer A, Published: 18 December 2012 Fallgatter A, Herrmann M: Increased EEG power density in alpha and theta bands in adult ADHD patients. J Neural Transm 2009, 116:97–104. 26. Clarke AR, Barry RJ, Heaven PCL, McCarthy R, Selikowitz M, Byrne MK: EEG in References adults with attention-deficit/hyperactivity disorder. Int J Psychophysiol 1. Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA: The worldwide 2008, 70:176–183. prevalence of ADHD: a systematic review and metaregression analysis. 27. Bresnahan SM, Barry RJ: Specificity of quantitative EEG analysis in adults Am J Psychiatry 2007, 164:942–948. with attention deficit hyperactivity disorder. Psychiatry Res 2002, 2. Mannuzza S, Klein RG, Bessler A, Malloy P, LaPadula M: Adult psychiatric 112:133–144. status of hyperactive boys grown Up. Am J Psychiatry 1998, 155:493–498. 28. van Dongen-Boomsma M, Lansbergen MM, Bekker EM, Sandra Kooij JJ, van 3. Kessler RC, Green JG, Adler LA, Barkley RA, Chatterji S, Faraone SV, Finkelman der Molen M, Kenemans JL, Buitelaar JK: Relation between resting EEG to M, Greenhill LL, Gruber MJ, Jewell M, Russo LJ, Sampson NA, Van Brunt DL: cognitive performance and clinical symptoms in adults with attention- The structure and diagnosis of adult ADHD: an analysis of expanded deficit/hyperactivity disorder. Neurosci Lett 2010, 469:102–106. symptom criteria from the adult ADHD clinical diagnostic scale (ACDS). 29. Ferree TC, Luu P, Russell GS, Tucker DM: Scalp electrode impedance, Arch Gen Psychiatry 2010, 67:1168–1178. infection risk, and EEG data quality. Neurophysiol Clin 2001, 112:536–544. 4. Spencer TJ, Biederman J, Mick E: Attention-deficit/hyperactivity disorder: 30. Kappenman ES, Luck SJ: The effects of electrode impedance on data diagnosis, lifespan, comorbidities, and neurobiology. J Pediatr Psychol quality and statistical significance in ERP recordings. Psychophysiology 2007, 32:631–642. 2010, 47:888–904. 5. Wilens TE, Biederman J, Faraone SV, Martelon M, Westerberg D, Spencer TJ: 31. Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, Howes MJ, Jin R, Presenting ADHD symptoms, subtypes, and comorbid disorders in Secnik K, Spencer T, Ustun TB, Walters EE: The world health organization clinically referred adults with ADHD. J Clin Psychiatry 2009, 70:1557–1562. adult ADHD self-report scale (ASRS): a short screening scale for use in 6. Larsson H, Lichtenstein P, Larsson J-O: Genetic contributions to the the general population. Psychol Med 2005, 35:245–256. development of ADHD subtypes from childhood to adolescence. JAm 32. Willcutt EG, Nigg JT, Pennington BF, Solanto MV, Rohde LA, Tannock R, Loo Acad Child Adolesc Psychiatry 2006, 45:973–981. SK, Carlson CL, McBurnett K, Lahey BB: Validity of DSM–IV attention deficit/ 7. Halperin JM, Trampush JW, Miller CJ, Marks DJ, Newcorn JH: hyperactivity disorder symptom dimensions and subtypes. J Abnorm Neuropsychological outcome in adolescents/young adults with Psychol 2012, In Press. childhood ADHD: profiles of persisters, remitters and controls. J Child 33. Broadbent DE, Cooper PF, FitzGerald P, Parkes KR: The cognitive failures Psychol Psychiatry 2008, 49:958–966. questionnaire (CFQ) and its correlates. Br J Clin Psychol 1982, 21(Pt 1):1–16. 8. Gropper R, Tannock R: A pilot study of working memory and academic 34. Woodcock R, McGrew K, Mather N, Schrank F: Woodcock-Johnson III NU achievement in college students with ADHD. J Atten Disord 2009, tests of achievement. Riverside 2001. 12:574–581. 35. Wechsler D, Kaplan E, Fein D, Kramer J, Morris R, Delis D, Maerlander A: 9. Wolf L, Simkowitz P, Carlson H: College students with attention-deficit/ Wechsler Intelligence Scale for Children - Fourth Edition. Integrated: The hyperactivity disorder. Curr Psychiatry Rep 2009, 11:415–421. Psychological Corporation; 2004. 10. DuPaul G: College students with ADHD. J Atten Disord 2009, 13:234–250. 36. Hermens DF, Williams LM, Lazzaro I, Whitmont S, Melkonian D, Gordon E: 11. Monastra VJ, Lubar JF, Linden M: The development of a quantitative Sex differences in adult ADHD: a double dissociation in brain activity electroencephalographic scanning process for attention deficit- and autonomic arousal. Biol Psychol 2004, 66:221–233. hyperactivity disorder : reliability and validity studies. Neuropsychology, 37. Loo SK, Hale TS, Macion J, Hanada G, McGough JJ, McCracken JT, Smalley 15:136–144. SL: Cortical activity patterns in ADHD during arousal, activation and 12. Buzsáki G, Draguhn A: Neuronal oscillations in cortical networks. Science sustained attention. Neuropsychologia 2009, 47:2114–2119. 2004, 304:1926–1929. 38. Maier A, Adams GK, Aura C, Leopold DA: Distinct superficial and deep 13. Barry RJ, Clarke AR, Johnstone SJ: A review of electrophysiology in laminar domains of activity in the visual cortex during rest and attention-deficit/hyperactivity disorder: I. Qualitative and quantitative stimulation. Front Syst Neurosci 2010, 4:1–11. electroencephalography. Neurophysiol Clin 2003, 114:171–183. 39. Haegens S, Händel BF, Jensen O: Top-down controlled alpha band activity 14. Snyder SM, Hall JR: A meta-analysis of quantitative EEG power associated in somatosensory areas determines behavioral performance in a with attention-deficit hyperactivity disorder. J Clin Neurophysiol 2006, discrimination task. J Neurosci 2011, 31:5197–5204. 23:441–456. 40. Knyazev GG, Slobodskaya HR: Personality trait of behavioral inhibition is 15. Arns M, Conners CK, Kraemer HC: A decade of EEG theta/beta ratio associated with oscillatory systems reciprocal relationships. Int J research in ADHD: a meta-analysis. J Atten Disord 2012, . Psychophysiol 2003, 48:247–261. 16. Chabot RJ, di Michele F, Prichep L: The role of quantitative electroencephalography in child and adolescent psychiatric disorders. 41. Jensen O, Mazaheri A: Shaping functional architecture by oscillatory alpha Child Adolesc Psychiatr Clin N Am 2005, 14:21–53. activity: gating by inhibition. Front Hum Neurosci 2010, 4:1–8. Woltering et al. Behavioral and Brain Functions 2012, 8:60 Page 9 of 9 http://www.behavioralandbrainfunctions.com/content/8/1/60 42. Bollimunta A, Mo J, Schroeder CE, Ding M: Neuronal mechanisms and attentional modulation of corticothalamic alpha oscillations. J Neurosci 2011, 31:4935–4943. 43. Saalmann YB, Pinsk MA, Wang L, Li X, Kastner S: The pulvinar regulates information transmission between cortical areas based on attention demands. Science 2012, 337:753–756. 44. Deco G, Jirsa VK, McIntosh AR: Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 2011, 12:43–56. 45. McIntosh AR, Kovacevic N, Itier RJ: Increased brain signal variability accompanies lower behavioral variability in development. PLoS Comput Biol 2008, 4:e1000106. 46. Klein C, Wendling K, Huettner P, Ruder H, Peper M: Intra-subject variability in attention-deficit hyperactivity disorder. Biol Psychiatry 2006, 60:1088–1097. doi:10.1186/1744-9081-8-60 Cite this article as: Woltering et al.: Resting state EEG oscillatory power differences in ADHD college students and their peers. Behavioral and Brain Functions 2012 8:60. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color ﬁgure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit
Behavioral and Brain Functions – Springer Journals
Published: Dec 18, 2012
Access the full text.
Sign up today, get DeepDyve free for 14 days.