The use of event-related potentials in the investigation of cognitive performance in people with Multiple Sclerosis: Systematic review.

A biomarker of cognition in Multiple Sclerosis (MS) that is independent from the response of people with MS (PwMS) to test questions would provide a more holistic assessment of cognitive decline. One suggested method involves event-related potentials (ERPs). This systematic review tried to answer five questions about the use of ERPs in distinguishing PwMS from controls: which stimulus modality, which experimental paradigm, which electrodes, and which ERP components are most discriminatory, and whether amplitude or latency is a better measure. Our results show larger pooled effect sizes for visual stimuli than auditory stimuli, and larger pooled effect sizes for latency measurements than amplitude measurements. We observed great heterogeneity in methods and suggest that future research would benefit from more uniformity in methods and that results should be reported for the individual subtypes of PwMS. With more standardised methods, ERPs have the potential to be developed into a clinical tool in MS.

Evaluating Effects of Resting-State Electroencephalography Data Pre-Processing on a Machine Learning Task for Parkinson's Disease.

Resting-state electroencephalography pre-processing methods in machine learning studies into Parkinson's disease classification vary widely. Here three separate data sets were pre-processed to four different stages to investigate the effects on evaluation metrics, using power features from six regions-of-interest, Random Forest Classifiers for feature selection, and Support Vector Machines for classification. This showed muscle artefact inflated evaluation metrics, and alpha and theta band features produced the best results when fully pre-processing data.

Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson's disease.

Collecting resting-state electroencephalography (RSEEG) data is time-consuming and data sets are therefore often small. Because many machine learning (ML) algorithms work better with ample data, researchers looking to use RSEEG and ML to develop diagnostic models have used oversampling methods that may seem to contradict averaging methods used in conventional electroencephalography (EEG) research to improve the signal-to-noise ratio. Using eyes open (EO) and eyes closed (EC) recordings from 3 different research groups, we investigated the effect of different averaging and oversampling methods on classification metrics when classifying people with Parkinson's disease (PD) and controls. Both EC and EO recordings were used due to differences found between these methods. Our results indicated that grouping 58 electrodes into regions-of-interest (ROI) based on anatomical location is preferable to using single electrodes. Furthermore, although recording EO data led to slightly better classification, the number of data points for each participant was reduced and recordings for three participants entirely lost during pre-processing due to a higher level of artefacts than in the EC data.Clinical relevance- RSEEG is a potential biomarker for the diagnosis and prognostication of PD, but for RSEEG to have clinical relevance, it is necessary to establish which averaging and oversampling of data most reliably segregates the classes for people with PD and controls. We found that using of ROIs and EC data performed the best, as EO data was often contaminated with artefacts.

Autistic traits are associated with individual differences in finger tapping: an online study.

In a novel online study, we explored whether finger tapping differences are evident in people with autistic traits in the general population. We hypothesised that those with higher autistic traits would show more impairment in finger tapping, and that age would moderate tapping output. The study included a non-diagnosed population of 159 participants aged 18-78 who completed an online measure of autistic traits (the AQ-10) and a measure of finger tapping (the FTT). Results showed those with higher AQ-10 scores recorded lower tapping scores in both hands. Moderation analysis showed younger participants with more autistic traits recorded lower tapping scores for the dominant hand. This suggests motor differences seen in autism studies are evident in the general population.

Has machine learning over-promised in healthcare?: A critical analysis and a proposal for improved evaluation, with evidence from Parkinson's disease.

Adoption of artificial intelligence (AI) by the medical community has long been anticipated, endorsed by a stream of machine learning literature showcasing AI systems that yield extraordinary performance. However, many of these systems are likely over-promising and will under-deliver in practice. One key reason is the community's failure to acknowledge and address the presence of inflationary effects in the data. These simultaneously inflate evaluation performance and prevent a model from learning the underlying task, thus severely misrepresenting how that model would perform in the real world. This paper investigated the impact of these inflationary effects on healthcare tasks, as well as how these effects can be addressed. Specifically, we defined three inflationary effects that occur in medical data sets and allow models to easily reach small training losses and prevent skillful learning. We investigated two data sets of sustained vowel phonation from participants with and without Parkinson's disease, and revealed that published models which have achieved high classification performances on these were artificially enhanced due to the inflationary effects. Our experiments showed that removing each inflationary effect corresponded with a decrease in classification accuracy, and that removing all inflationary effects reduced the evaluated performance by up to 30%. Additionally, the performance on a more realistic test set increased, suggesting that the removal of these inflationary effects enabled the model to better learn the underlying task and generalize. Source code is available at https://github.com/Wenbo-G/pd-phonation-analysis under the MIT license.

What's in a Badge? A Computational Reproducibility Investigation of the Open Data Badge Policy in One Issue of Psychological Science.

In April 2019, Psychological Science published its first issue in which all Research Articles received the Open Data badge. We used that issue to investigate the effectiveness of this badge, focusing on the adherence to its aim at Psychological Science: sharing both data and code to ensure reproducibility of results. Twelve researchers of varying experience levels attempted to reproduce the results of the empirical articles in the target issue (at least three researchers per article). We found that all 14 articles provided at least some data and six provided analysis code, but only one article was rated to be exactly reproducible, and three were rated as essentially reproducible with minor deviations. We suggest that researchers should be encouraged to adhere to the higher standard in force at Psychological Science. Moreover, a check of reproducibility during peer review may be preferable to the disclosure method of awarding badges.

Why take the risk? Exploring the psychosocial determinants of floodwater driving.

As anthropogenic climate change progresses, there is an increasing need for individuals to make appropriate decisions regarding their approach to extreme weather events. Natural hazards are involuntary risk environments (e.g., flooded roads); interaction with them cannot be avoided (i.e., a decision must be made about how to engage). While the psychological and sociocultural predictors of engagement with voluntary risks (i.e., risk situations that are sought out) are well-documented, less is known about the factors that predict engagement with involuntary risk environments. This exploratory study assessed whether mental health (depression, anxiety, and stress symptoms), personality traits, and cultural worldviews combine to predict engagement with involuntary risk, using the situation of floodwater driving. An Australian sample (N = 235) was assessed via questionnaire and scenario measures. Results were analyzed in a binomial logistic regression assessing which individual factors predicted decision-making in a proxy floodwater driving scenario. Agreeableness and gender were individually significant predictors of floodwater driving intention, and four factors (named "affect," "progressiveness," "insightfulness," and "purposefulness") were derived from an exploratory factor analysis using the variables of interest, though only two ("progressiveness" and "insightfulness") predicted floodwater driving intention in an exploratory binomial logistic regression. The findings highlight the need for further research into the differences between voluntary and involuntary risk. The implication of cultural worldviews and personality traits in interaction with mental health indicators on risk situations is discussed.

The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson's Disease.

Current tests of disease status in Parkinson's disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson's disease.

Finger Tapping Measures for Parkinson's Disease: Preliminary Evaluation of an Android Application for Data Collection in Australia.

Parkinson's Disease (PD) is a progressive chronic disorder with a high misdiagnosis rate. Because finger-tapping tasks correlate with its fine-motor symptoms, they could be used to help diagnose and assess PD. We first designed and developed an Android application to perform finger-tapping tasks without trained supervision, which is not always feasible for patients. Then, we conducted a preliminary user evaluation in Australia with six patients clinically diagnosed with PD and sixteen controls without PD. The application could be used in research and healthcare for regular symptom and progression assessment and feedback.

Which features of postural sway are effective in distinguishing Parkinson's disease from controls? A systematic review.

BACKGROUND: Postural sway may be useful as an objective measure of Parkinson's disease (PD). Existing studies have analyzed many different features of sway using different experimental paradigms. We aimed to determine what features have been used to measure sway and then to assess which feature(s) best differentiate PD patients from controls. We also aimed to determine whether any refinements might improve discriminative power and so assist in standardizing experimental conditions and analysis of data. METHODS: In this systematic review of the literature, effect size (ES) was calculated for every feature reported by each article and then collapsed across articles where appropriate. The influence of clinical medication status, visual state, and sampling rate on ES was also assessed. RESULTS: Four hundred and forty-three papers were retrieved. 25 contained enough information for further analysis. The most commonly used features were not the most effective (e.g., PathLength, used 14 times, had ES of 0.47, while TotalEnergy, used only once, had ES of 1.78). Increased sampling rate was associated with increased ES (PathLength ES increased to 1.12 at 100 Hz from 0.40 at 10 Hz). Measurement during "OFF" clinical status was associated with increased ES (PathLength ES was 0.83 OFF compared to 0.21 ON). CONCLUSIONS: This review identified promising features for analysis of postural sway in PD, recommending a sampling rate of 100 Hz and studying patients when OFF to maximize ES. ES complements statistical significance as it is clinically relevant and is easily compared across experiments. We suggest that machine learning is a promising tool for the future analysis of postural sway in PD.

The role of Weber's law in human time perception.

Weber's law predicts that stimulus sensitivity will increase proportionally with increases in stimulus intensity. Does this hold for the stimulus of time - specifically, duration in the milliseconds to seconds range? There is conflicting evidence on the relationship between temporal sensitivity and duration. Weber's law predicts a linear relationship between sensitivity and duration on interval timing tasks, while two alternative models predict a reverse J-shaped and a U-shaped relationship. Based on previous research, we hypothesised that temporal sensitivity in humans would follow a U-shaped function, increasing and then decreasing with increases in duration, and that this model would provide a better statistical fit to the data than the reverse-J or the simple Weber's Law model. In a two-alternative forced-choice interval comparison task, 24 participants made duration judgements about six groups of auditory intervals between 100 and 3,200 ms. Weber fractions were generated for each group of intervals and plotted against time to generate a function describing sensitivity to the stimulus of duration. Although the sensitivity function was slightly concave, and the model describing a U-shaped function gave the best fit to the data, the increase in the model fit was not sufficient to warrant the extra free parameter in the chosen model. Further analysis demonstrated that Weber's law itself provided a better description of sensitivity to changes in duration than either of the two models tested.

The impact of sleep loss on sustained and transient attention: an EEG study.

Sleep is one of our most important physiological functions that maintains physical and mental health. Two studies examined whether discrete areas of attention are equally affected by sleep loss. This was achieved using a repeated-measures within-subjects design, with two contrasting conditions: normal sleep and partial sleep restriction of 5-h. Study 1 compared performance on a sustained attention task (Psychomotor Vigilance task; PVT) with performance on a transient attention task (Attentional Blink; AB). PVT performance, but not performance on the AB task, was impaired after sleep restriction. Study 2 sought to determine the neural underpinnings of the phenomenon, using electroencephalogram (EEG) frequency analysis, which measured activity during the brief eyes-closed resting state before the tasks. AB performance was unaffected by sleep restriction, despite clearly observable changes in brain activity. EEG results showed a significant reduction in resting state alpha oscillations that was most prominent centrally in the right hemisphere. Changes in individual alpha and delta power were also found to be related to changes in subjective sleepiness and PVT performance. Results likely reflect different levels of impairment in specific forms of attention following sleep loss.

Cerebellar-cortical dysconnectivity in resting-state associated with sensorimotor tasks in schizophrenia.

Abnormalities of cerebellar function have been implicated in the pathophysiology of schizophrenia. Since the cerebellum has afferent and efferent projections to diverse brain regions, abnormalities in cerebellar lobules could affect functional connectivity with multiple functional systems in the brain. Prior studies, however, have not examined the relationship of individual cerebellar lobules with motor and nonmotor resting-state functional networks. We evaluated these relationships using resting-state fMRI in 30 patients with a schizophrenia-spectrum disorder and 37 healthy comparison participants. For connectivity analyses, the cerebellum was parcellated into 18 lobular and vermal regions, and functional connectivity of each lobule to 10 major functional networks in the cerebrum was evaluated. The relationship between functional connectivity measures and behavioral performance on sensorimotor tasks (i.e., finger-tapping and postural sway) was also examined. We found cerebellar-cortical hyperconnectivity in schizophrenia, which was predominantly associated with Crus I, Crus II, lobule IX, and lobule X. Specifically, abnormal cerebellar connectivity was found to the cerebral ventral attention, motor, and auditory networks. This cerebellar-cortical connectivity in the resting-state was differentially associated with sensorimotor task-based behavioral measures in schizophrenia and healthy comparison participants-that is, dissociation with motor network and association with nonmotor network in schizophrenia. These findings suggest that functional association between individual cerebellar lobules and the ventral attentional, motor, and auditory networks is particularly affected in schizophrenia. They are also consistent with dysconnectivity models of schizophrenia suggesting cerebellar contributions to a broad range of sensorimotor and cognitive operations.

Temporal predictability does not impact attentional blink performance: effects of fixed vs. random inter-trial intervals.

BACKGROUND: Does the inclusion of a randomized inter-trial interval (ITI) impact performance on an Attentional Blink (AB) task? The AB phenomenon is often used as a test of transient attention (Dux & Marois, 2009); however, it is unclear whether incorporating aspects of sustained attention, by implementing a randomized ITI, would impact task performance. The current research sought to investigate this, by contrasting a standard version of the AB task with a random ITI version to determine whether performance changed, reflecting a change in difficulty, engagement, or motivation. METHOD: Thirty university students (21 female; age range 18-57, M age= 21.5, SD = 7.4) completed both versions of the task, in counterbalanced order. RESULTS: No significant difference in performance was found between the standard AB task and the AB task with the random ITI. Bayesian analyses suggested moderate evidence for the null. CONCLUSION: Temporal unpredictability did not appear to impact task performance. This suggests that the standard AB task has cognitive properties with regards to task difficulty, engagement, and motivation, that are inherently similar to tasks that employ a randomized ITI to measure sustained attention (e.g., the Psychomotor Vigilance Task; PVT; Dinges & Powell, 1985). This finding provides important support for future research which may seek to obtain a more detailed understanding of attention through the comparison of performance on transient and sustained attention tasks.

Postural sway correlates with cognition and quality of life in Parkinson's disease.

BACKGROUND: The severity of Parkinson's disease (PD) is difficult to assess objectively owing to the lack of a robust biological marker of underlying disease status, with consequent implications for diagnosis, treatment and prognosis. The current standard tool is the Unified Parkinson's Disease Rating Scale (MDS-UPDRS), but this is hampered by variability between observers and within subjects. Postural sway has been shown to correlate with complex brain functioning in other conditions. This study aimed to investigate the relationship between postural sway, MDS-UPDRS and other non-motor measures of disease severity in patients with PD. METHOD: 25 patients with PD and 18 age-matched controls participated in the study. All participants underwent assessment of postural sway using a force plate, with eyes open and closed. In addition, participants underwent tests of cognition and quality of life: Montreal Cognitive Assessment (MoCA), Neuropsychiatry Unit Cognitive Assessment (NUCOG) and, for the patients, the Parkinson's Disease Questionnaire (PDQ-39-1), and assessment of clinical status using the motor component of the MDS-UPDRS. RESULTS: Patients swayed significantly more than controls. This was most obvious in the eyes-closed condition. Sway path length showed strong correlations with PDQ-39-1, MoCA and the verbal fluency component of the NUCOG, and, to a lesser degree, with the UPDRS-III in patients with PD. CONCLUSION: These results suggest that motor and non-motor symptoms of PD are associated in patients, and, in particular, that postural sway shows potential as a possible measure of underlying disease status in PD, either alone or in combination with other measures.

Postural Sway Abnormalities in Schizotypal Personality Disorder.

Motor abnormalities are among the most robust findings in schizophrenia, and increasing evidence suggests they are a core feature of the disorder. Postural sway during balance tasks is a highly sensitive probe of sensorimotor systems including the cerebellum, basal ganglia, and motor cortices. Postural sway deficits are present in schizophrenia as well as groups at high risk for psychosis, suggesting altered postural control may be sensitive to the pathophysiological processes associated with risk and expression of schizophrenia spectrum disorders. This study examined postural sway performance in schizotypal personality disorder (SPD). Individuals with SPD have attenuated psychotic symptoms and share genetic risk with schizophrenia but are usually free from antipsychotic medication and other illness confounds, making SPD useful for assessing candidate biomarkers. We measured postural sway using force plates in 27 individuals with SPD, 27 carefully matched controls, and 27 matched patients with schizophrenia. It was predicted that postural sway in the SPD group would fall intermediate to schizophrenia and controls. In all conditions (eyes open and closed, with feet together or apart), the SPD group swayed significantly more than the controls, as measured by path length and sway area. Moreover, the magnitude of the sway deficit was comparable in the SPD and schizophrenia groups. These findings suggest that postural sway measures may represent a sensorimotor biomarker of schizophrenia spectrum disorders.

Disturbances of postural sway components in cannabis users.

INTRODUCTION: A prominent effect of acute cannabis use is impaired motor coordination and driving performance. However, few studies have evaluated balance in chronic cannabis users, even though density of the CB1 receptor, which mediates the psychoactive effects of cannabis, is extremely high in brain regions critically involved in this fundamental behavior. The present study measured postural sway in regular cannabis users and used rambling and trembling analysis to quantify the integrity of central and peripheral nervous system contributions to the sway signal. METHODS: Postural sway was measured in 42 regular cannabis users (CB group) and 36 non-cannabis users (N-CB group) by asking participants to stand as still as possible on a force platform in the presence and absence of motor and sensory challenges. Center of pressure (COP) path length was measured, and the COP signal was decomposed into rambling and trembling components. Exploratory correlational analyses were conducted between sway variables, cannabis use history, and neurocognitive function. RESULTS: The CB group had significantly increased path length and increased trembling in the anterior-posterior (AP) direction. Exploratory correlational analyses suggested that AP rambling was significantly inversely associated with visuo-motor processing speed. DISCUSSION: Regular cannabis use is associated with increased postural sway, and this appears to be predominantly due to the trembling component, which is believed to reflect the peripheral nervous system's contribution to the sway signal.

The Shepard-Risset glissando: music that moves you.

Sounds are thought to contribute to the perceptions of self-motion, often via higher-level, cognitive mechanisms. This study examined whether illusory self-motion (i.e. vection) could be induced by auditory metaphorical motion stimulation (without providing any spatialized or low-level sensory information consistent with self-motion). Five different types of auditory stimuli were presented in mono to our 20 blindfolded, stationary participants (via a loud speaker array): (1) an ascending Shepard-Risset glissando; (2) a descending Shepard-Risset glissando; (3) a combined Shepard-Risset glissando; (4) a combined-adjusted (loudness-controlled) Shepard-Risset glissando; and (5) a white-noise control stimulus. We found that auditory vection was consistently induced by all four Shepard-Risset glissandi compared to the white-noise control. This metaphorical auditory vection appeared similar in strength to the vection induced by the visual reference stimulus simulating vertical self-motion. Replicating past visual vection findings, we also found that individual differences in postural instability appeared to significantly predict auditory vection strength ratings. These findings are consistent with the notion that auditory contributions to self-motion perception may be predominantly due to higher-level cognitive factors.

Adaptation-Induced Blindness Is Orientation-Tuned and Monocular.

We examined the recently discovered phenomenon of Adaptation-Induced Blindness (AIB), in which highly visible gratings with gradual onset profiles become invisible after exposure to a rapidly flickering grating, even at very high contrasts. Using very similar stimuli to those in the original AIB experiment, we replicated the original effect across multiple contrast levels, with observers at chance in detecting the gradual onset stimuli at all contrasts. Then, using full-contrast target stimuli with either abrupt or gradual onsets, we tested both the orientation tuning and interocular transfer of AIB. If, as the original authors suggested, AIB were a high-level (perhaps parietally mediated) effect resulting from the 'gating' of awareness, we would not expect the effects of AIB to be tuned to the adapting orientation, and the effect should transfer interocularly. Instead, we find that AIB (which was present only for the gradual onset target stimuli) is both tightly orientation-tuned and shows absolutely no interocular transfer, consistent with a very early cortical locus.