Dataset Information

Resting state EEG oscillatory power differences in ADHD college students and their peers.

ABSTRACT:

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.

SUBMITTER: Woltering S

PROVIDER: S-EPMC3537601 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Publications

Resting state EEG oscillatory power differences in ADHD college students and their peers.

Woltering Steven S Jung Jessica J Liu Zhongxu Z Tannock Rosemary R

Behavioral and brain functions : BBF 20121218

<h4>Background</h4>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.<h4>Methods</h4>EEG profiles, recorded during ...[more]

PMID: 23249444

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Project description:BackgroundPrescription drug misuse is an escalating public health problem that is prevalent among college students. Various factors, such as sex, age, race, mental health conditions, and fraternity and sorority membership, increase the risk for prescription drug misuse. Peer influences may also pose significant risks for starting prescription drug misuse, because peers are the primary source of misused prescription drugs among college students.ObjectivesTo examine college students' beliefs about prescription drug misuse among their peers and to understand their beliefs about the available and potential intervention efforts to reduce prescription drug misuse.MethodWe conducted a cross-sectional survey of 312 college students aged 18 to 30 years who were enrolled in a large midwestern US university. The survey included questions about the responders' demographic characteristics, beliefs, and perceptions regarding prescription drug misuse among their peers and potential reduction programs for prescription drug misuse.ResultsThe respondents' mean age was 21 years, and most (65%) respondents were women. A majority (90%) of the respondents were enrolled in the university as full-time students. More than 60% of the respondents reported knowing someone with prescription drug misuse in the past 12 months. Fraternity or sorority members were more likely to report peer prescription drug misuse than nonmembers (83% vs 60%, respectively; P = .017). Stimulants were the most misused drug class, accounting for 75% of all reported peer prescription drug misuse. Approximately 60% of the misused prescription drugs were sourced from peers, which included friends, roommates, and classmates. A total of 73% of respondents said that prescription drug misuse was a problem among college students and 67% agreed that interventions were needed to reduce prescription drug misuse. The perceived need for interventions varied by peer prescription drug misuse. A total of 90% of students whose peers did not misuse prescription drugs said that interventions were needed, but only 67% of those with peer prescription drug misuse thought that interventions were needed.ConclusionOur findings indicate that peer prescription drug misuse is very common among college students, and the most misused drug class is stimulants. Survey respondents perceived that the current interventions used to address prescription drug misuse are not very effective or may not be targeting the right population. Peer prescription drug misuse influences college students' perceptions about the risks and harms of prescription drug misuse, as well as the need for interventions.

Project description:IntroductionThe differentiation of Lewy body dementia from other common dementia types clinically is difficult, with a considerable number of cases only being found post-mortem. Consequently, there is a clear need for inexpensive and accurate diagnostic approaches for clinical use. Electroencephalography (EEG) is one potential candidate due to its relatively low cost and non-invasive nature. Previous studies examining the use of EEG as a dementia diagnostic have focussed on the eyes closed (EC) resting state; however, eyes open (EO) EEG may also be a useful adjunct to quantitative analysis due to clinical availability.MethodsWe extracted spectral properties from EEG signals recorded under research study protocols (1024 Hz sampling rate, 10:5 EEG layout). The data stems from a total of 40 dementia patients with an average age of 74.42, 75.81 and 73.88 years for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), respectively, and 15 healthy controls (HC) with an average age of 76.93 years. We utilised k-nearest neighbour, support vector machine and logistic regression machine learning to differentiate between groups utilising spectral data from the delta, theta, high theta, alpha and beta EEG bands.ResultsWe found that the combination of EC and EO resting state EEG data significantly increased inter-group classification accuracy compared to methods not using EO data. Secondly, we observed a distinct increase in the dominant frequency variance for HC between the EO and EC state, which was not observed within any dementia subgroup. For inter-group classification, we achieved a specificity of 0.87 and sensitivity of 0.92 for HC vs dementia classification and 0.75 specificity and 0.91 sensitivity for AD vs DLB classification, with a k-nearest neighbour machine learning model which outperformed other machine learning methods.ConclusionsThe findings of our study indicate that the combination of both EC and EO quantitative EEG features improves overall classification accuracy when classifying dementia types in older age adults. In addition, we demonstrate that healthy controls display a definite change in dominant frequency variance between the EC and EO state. In future, a validation cohort should be utilised to further solidify these findings.

Dataset Information

Resting state EEG oscillatory power differences in ADHD college students and their peers.

Background

Methods

Results

Conclusions

Publications

Resting state EEG oscillatory power differences in ADHD college students and their peers.

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