Biology subjects

Kiiski, H.

Publications and source records attributed to Kiiski, H..

3 recordsLinked to original sources

bioRxiv2019

Hyperactive/impulsive and inattention symptoms are associated with reduced ERP activity during different reward processing stages: Evidence from the electrophysiological Monetary Incentive Delay Task in adult ADHD

Hyperactivity/impulsivity and inattention are core symptoms dimensions in attention-deficit/hyperactivity disorder. Some approaches suggest that these symptoms arise from deficits in the ability to anticipate and process rewards. However, evidence is equivocal with regard to ADHD-related differences in brain activity during reward processing. The aim of this study was to investigate when, and how, reward-related ERP activity was associated with hyperactive/impulsive symptoms and inattention symptoms. Adults with ADHD (n=34) and matched comparison participants (n=36) completed an electrophysiological version of the Monetary Incentive Delay task. This task separates reward processing into two stages-namely, an anticipation stage and a delivery stage. During the anticipation stage, visual cues signalled a possible monetary incentive (i.e. a reward or loss). After a brief delay, the delivery stage began, and incentives were delivered contingent on a speeded button-press. Electroencephalogram activity was simultaneously sampled and incentive-related event relate potentials (ERPs) calculated. These data were then analysed by calculating multiple regression models, at each sample point, wherein the correlation between incentive-related ERPs and ADHD symptoms was estimated. Linear and curvilinear associations between ERP activity and ADHD symptoms were tested in each regression mode. Findings suggest that ADHD symptoms were associated ERP activity at different reward processing stages. Hyperactive/impulsive symptoms were associated with reduced ERP activity during the initial anticipation of rewards from 224-329 ms post-reward cue. Inattention symptoms were associated with reduced ERP activity during the initial delivery of rewards from 251-280 ms post-reward onset. Finally, extreme ends of hyperactive/impulsive and inattention symptoms were associated with reduced ERP activity towards the end of the anticipation stage from 500 ms post-reward cue onwards. These results support the idea that reward processing is disrupted in ADHD while also shedding new light on the dynamic relationship between ADHD symptom dimensions and the neurological mechanisms of reward processing.

neuroscience↗

bioRxiv2019

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Alzheimers disease (AD) is a neurodegenerative disorder characterised by severe cognitive decline and loss of autonomy. AD is the leading cause of dementia. AD is preceded by mild cognitive impairment (MCI). By 2050, 68% of new dementia cases will occur in low- and middle-income countries. In the absence of objective biomarkers, psychological assessments are typically used to diagnose MCI and AD. However, these require specialist training and rely on subjective judgements. The need for low-cost, accessible and objective tools to aid AD and MCI diagnosis is therefore crucial. Electroencephalography (EEG) has potential as one such tool: it is relatively inexpensive (cf. magnetic resonance imaging; MRI) and is portable. In this study, we collected resting state EEG, structural MRI and rich neuropsychological data from older adults (55+ years) with AD, with MCI and from healthy controls (n~60 per group). Our goal was to evaluate the utility of EEG, relative to MRI, for the classification of MCI and AD. We also assessed the performance of combined EEG and behavioural (Mini-Mental State Examination; MMSE) and structural MRI classification models. Resting state EEG classified AD and HC participants with moderate accuracy (AROC=0.76), with lower accuracy when distinguishing MCI from HC participants (AROC=0.67). The addition of EEG data to MMSE scores had no additional value compared to MMSE alone. Structural MRI out-performed EEG (AD vs HC, AD vs MCI: AROCs=1.00; HC vs MCI: AROC=0.73). Resting state EEG does not appear to be a suitable tool for classifying AD. However, EEG classification accuracy was comparable to structural MRI when distinguishing MCI from healthy aging, although neither were sufficiently accurate to have clinical utility. This is the first direct comparison of EEG and MRI as classification tools in AD and MCI participants.

neuroscience↗

bioRxiv2019

EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD

BackgroundAdults with attention-deficit/hyperactivity disorder (ADHD) have been described as having altered resting-state electroencephalographic (EEG) spectral power and theta/beta ratio (TBR). However, a recent review (Pulini et al. 2018) identified methodological errors in neuroimaging, including EEG, ADHD classification studies. Therefore, the specific EEG neuromarkers of adult ADHD remain to be identified, as do the EEG characteristics that mediate between genes and behavior (mediational endophenotypes).\n\nMethodsResting-state eyes-open and eyes-closed EEG were measured from 38 adults with ADHD, 45 first-degree relatives of people with ADHD and 51 unrelated controls. A machine learning classification analysis using penalized logistic regression (Elastic Net) examined if EEG spectral power (1-45 Hz) and TBR could classify participants into ADHD, first-degree relatives and/or control groups. Random-label permutation was used to quantify any bias in the analysis.\n\nResultsEyes-open absolute and relative EEG power distinguished ADHD from control participants (area under receiver operating characteristic = .71-.77). The best predictors of ADHD status were increased power in delta, theta and low-alpha over centro-parietal regions, and in frontal low-beta and parietal mid-beta. TBR did not classify ADHD status. Elevated eyes-open power in delta, theta, low-alpha and low-beta distinguished first-degree relatives from controls (area under receiver operating characteristic = .68-.72), suggesting that these features may be a mediational endophenotype for adult ADHD.\n\nConclusionsResting-state EEG spectral power may be a neuromarker and mediational endophenotype of adult ADHD. These results did not support TBR as a diagnostic neuromarker for ADHD. It is possible that TBR is a characteristic of childhood ADHD.

neuroscience↗