scholarly journals Implication of Electrophysiological Biomarkers in Psychosis: Focusing on Diagnosis and Treatment Response

2022 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Ho Sung Lee ◽  
Ji Sun Kim
Keyword(s):  
Precision Medicine ◽  
Disease Progression ◽  
Treatment Response ◽  
Event Related Potentials ◽  
Diagnosis And Treatment ◽  
Current Status ◽  
Neural Activation ◽  
Learning Approaches ◽  
Related Potentials ◽  
The Individual

Precision medicine has been considered a promising approach to diagnosis, treatment, and various interventions, considering the individual clinical and biological characteristics. Recent advances in biomarker development hold promise for guiding a new era of precision medicine style trials for psychiatric illnesses, including psychosis. Electroencephalography (EEG) can directly measure the full spatiotemporal dynamics of neural activation associated with a wide variety of cognitive processes. This manuscript reviews three aspects: prediction of diagnosis, prognostic aspects of disease progression and outcome, and prediction of treatment response that might be helpful in understanding the current status of electrophysiological biomarkers in precision medicine for patients with psychosis. Although previous EEG analysis could not be a powerful method for the diagnosis of psychiatric illness, recent methodological advances have shown the possibility of classifying and detecting mental illness. Some event-related potentials, such as mismatch negativity, have been associated with neurocognition, functioning, and illness progression in schizophrenia. Resting state studies, sophisticated ERP measures, and machine-learning approaches could make technical progress and provide important knowledge regarding neurophysiology, disease progression, and treatment response in patients with schizophrenia. Identifying potential biomarkers for the diagnosis and treatment response in schizophrenia is the first step towards precision medicine.

Late Positive Event-Related Potentials after Commissural Section in Humans

1990 ◽  
Vol 2 (3) ◽  
pp. 258-271 ◽  
Author(s):  
Marta Kutas ◽  
Steven A. Hillyard ◽  
Bruce T. Volpe ◽  
Michael S. Gazzaniga
Keyword(s):  
Corpus Callosum ◽  
Left Hemisphere ◽  
Visual Fields ◽  
Event Related Potentials ◽  
Auditory Oddball ◽  
Oddball Task ◽  
Related Potentials ◽  
The Individual ◽  
The Right ◽  
Auditory Oddball Task

The lateral distribution of the P300 component of the event-related brain potential (ERP) was studied in five epileptic patients whose corpus callosum had been surgically sectioned and in seven neurologically intact controls. The P300 was elicited in an auditory “oddball” task using high- and low-pitched tones and in a visual oddball task in which target words were presented either to the left or right visual fields, or to both fields simultaneously. Commissurotomy altered the normal pattern of bilaterally symmetrical P300 waves over the left and right hemispheres, but in a different manner for auditory and visual stimuli. The auditory P3 to binaural tones was larger in amplitude over the right than the left hemisphere for the patients. In the visual task, the laterality of the P300 varied with the visual field of the target presentation. Left field targets elicited much larger P300 amplitudes over the right than the left hemisphere, as did bilateral targets. In contrast, right field targets triggered P300 waves of about the same amplitude over the two hemispheres. The overall amplitude of the P300 to simultaneous bilateral targets was less than the sum of the individual P300 amplitudes produced in response to the unilateral right and left field targets. These shifts in P300 laterality argue against the view that the P300 is an index of diffuse arousal or activation that is triggered in both hemispheres simultaneously irrespective of which hemisphere processes the target information. The results further demonstrate that the P300 does not depend for its production on interhemispheric comparisons of information mediated by the corpus callosum, as suggested recently by Knight et al. (1989).

scholarly journals Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Caroline Fussing Bruun ◽  
Caroline Juhl Arnbjerg ◽  
Lars Vedel Kessing
Keyword(s):  
Systematic Review ◽  
Event Related Potentials ◽  
Unipolar Depression ◽  
Control Group ◽  
Extensive Literature ◽  
Healthy Controls ◽  
Melancholic Depression ◽  
Related Potentials ◽  
The Individual ◽  
Almost All

Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults.Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist.Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups.Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population.Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.

scholarly journals Looking for effects of qualia on event-related brain potentials of close others in search for a cause of the similarity of qualia assumed across individuals

F1000Research ◽  
2018 ◽  
Vol 3 ◽  
pp. 316
Author(s):  
Sheila Bouten ◽  
Hugo Pantecouteau ◽  
J. Bruno Debruille
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Conscious Experience ◽  
Event Related Potentials ◽  
The Other ◽  
Brain Potentials ◽  
Frontal Electrode ◽  
Related Potentials ◽  
The Individual ◽  
The Brain

Qualia, the individual instances of subjective conscious experience, are private events. However, in everyday life, we assume qualia of others and their perceptual worlds, to be similar to ours. One way this similarity is possible is if qualia of others somehow contribute to the production of qualia by our own brain and vice versa. To test this hypothesis, we focused on the mean voltages of event-related potentials (ERPs) in the time-window of the P600 component, whose amplitude correlates positively with conscious awareness. These ERPs were elicited by images of the international affective picture system in 16 pairs of friends, siblings or couples going side by side through hyperscanning without having to interact. Each of the 32 members of these 16 pairs faced one half of the screen and could not see what the other member was presented with on the other half. One stimulus occurred on each half simultaneously. The sameness of these stimulus pairs was manipulated as well as the participants’ belief in that sameness by telling subjects’ pairs that they were going to be presented with the same stimuli in two blocks and with different ones in the two others. ERPs were more positive at all electrode subsets for stimulus pairs that were inconsistent with the belief than for those that were consistent. In the N400 time window, at frontal electrode sites, ERPs were again more positive for inconsistent than for consistent stimuli. As participants had no way to see the stimulus their partner was presented with and thus no way to detect inconsistence, these data might reveal an impact of the qualia of a person on the brain activity of another. Such impact could provide a research avenue when trying to explain the similarity of qualia across individuals.

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

2021 ◽  
Author(s):  
Robin Vlieger ◽  
Elena Daskalaki ◽  
Deborah Apthorp ◽  
Christian J Lueck ◽  
Hanna Suominen
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Objective Assessment ◽  
Diagnostic Testing ◽  
Event Related Potentials ◽  
Disease Status ◽  
Related Potentials ◽  
Machine Learning Models

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.

Prediction of diagnosis and treatment response in adolescents with depression using smartphone application and machine learning approaches: a pilot study (Preprint)

2020 ◽  
Author(s):  
Jae Sung Kim ◽  
Bohyun Wang ◽  
Meelim Kim ◽  
Jung Lee ◽  
Hyungjun Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Mobile Phone ◽  
Treatment Response ◽  
Antidepressant Treatment ◽  
Smartphone Application ◽  
Text Messages ◽  
Diagnosis And Treatment ◽  
Learning Approaches ◽  
Smart Healthcare

BACKGROUND Lack of quantifiable biomarkers is a major obstacle in making diagnosis and predicting treatment response in depression. In adolescents, increasing suicidality during antidepressant treatment further complicate the problems. Emerging healthcare systems based on digital technology are beginning to show promising results in dealing with mental health issues. OBJECTIVE Using Smart Healthcare System for Teens At Risk for Depression and Suicide (STAR-DS) smartphone application and machine learning, we sought to evaluate digital phenotypes which represent the diagnosis and treatment response of depression in adolescents. METHODS Our study included 24 adolescents (15.4±1.4 years, 17 girls) with major depressive disorder (MDD) diagnosed with K-SADS-PL and 10 healthy controls (13.8±0.6 years, 5 girls). Their depression status was evaluated using the Children’s Depression Rating Scale–Revised (CDRS-R) and CGI-S every week during the study period. After collecting the baseline data for 1 week, MDD adolescents were treated with escitalopram in an 8 week, open-label trial. Both MDD and control groups were monitored for another 4 weeks after the baseline week. We applied deep learning approach for the analysis of data. Deep Neural Network (DNN) was employed for classification and NEural network with Weighted Fuzzy Membership functions (NEWFM) for feature selection. We extracted features from directly collected data via the mobile phone (the number and total time of calls and text messages sent or received, mobile phone usage time, movement distance, amount of activity measured by gyroscope) on a daily basis. The distance from the mean value and standard deviation of each features per week were also extracted. RESULTS We could predict the diagnosis of depression with training accuracy of 96.3% and 3-fold validation accuracy of 77%. Of 24 depressed adolescents, 10 responded to antidepressant treatment. Including data on medications taken by the MDD group, we predicted the treatment response of depressed adolescents with training accuracy of 94.2% and 3-fold validation accuracy of 76%. CONCLUSIONS The STAR-DS smartphone application demonstrated preliminary evidence of predicting diagnosis and treatment response in depressed adolescents. This is the first study to predict treatment response of MDD in adolescents, examining smartphone based objective data with machine learning approaches.

scholarly journals Toward Precision Medicine for Smoking Cessation: Developing a Neuroimaging-Based Classification Algorithm to Identify Smokers at Higher Risk for Relapse

2019 ◽  
Vol 22 (8) ◽  
pp. 1277-1284 ◽  
Author(s):  
David W Frank ◽  
Paul M Cinciripini ◽  
Menton M Deweese ◽  
Maher Karam-Hage ◽  
George Kypriotakis ◽  
...  
Keyword(s):  
Smoking Cessation ◽  
Precision Medicine ◽  
Function Analysis ◽  
Predictive Ability ◽  
Event Related Potentials ◽  
Classification Algorithm ◽  
Independent Data ◽  
Data Set ◽  
Electrophysiological Responses ◽  
Related Potentials

Abstract Introduction By improving our understanding of the neurobiological mechanisms underlying addiction, neuroimaging research is helping to identify new targets for personalized treatment interventions. When trying to quit, smokers with larger electrophysiological responses to cigarette-related, compared with pleasant, stimuli (“C > P”) are more likely to relapse than smokers with the opposite brain reactivity profile (“P > C”). Aim and Method The goal was to (1) build a classification algorithm to identify smokers characterized by P > C or C > P neuroaffective profiles and (2) validate the algorithm’s classification outcomes in an independent data set where we assessed both smokers’ electrophysiological responses at baseline and smoking abstinence during a quit attempt. We built the classification algorithm applying discriminant function analysis on the event-related potentials evoked by emotional images in 180 smokers. Results The predictive validity of the classifier showed promise in an independent data set that included new data from 177 smokers interested in quitting; the algorithm classified 111 smokers as P > C and 66 as C > P. The overall abstinence rate was low; 15 individuals (8.5% of the sample) achieved CO-verified 12-month abstinence. Although individuals classified as P > C were nearly 2.5 times more likely to be abstinent than smokers classified as C > P (12 vs. 3, or 11% vs. 4.5%), this result was nonsignificant, preliminary, and in need of confirmation in larger trials. Conclusion These results suggest that psychophysiological techniques have the potential to advance our knowledge of the neurobiological underpinnings of nicotine addiction and improve clinical applications. However, larger sample sizes are necessary to reliably assess the predictive ability of our algorithm. Implications We assessed the clinical relevance of a neuroimaging-based classification algorithm on an independent sample of smokers enrolled in a smoking cessation trial and found those with the tendency to attribute more relevance to rewards than cues were nearly 2.5 times more likely to be abstinent than smokers with the opposite brain reactivity profile (11% vs. 4.5%). Although this result was not statistically significant, it suggests our neuroimaging-based classification algorithm can potentially contribute to the development of new precision medicine interventions aimed at treating substance use disorders. Regardless, these findings are still preliminary and in need of confirmation in larger trials.

scholarly journals Recollection is more than retrieving context or source memory: evidence from ERPs of Recognition and Source Memory combinations

2020 ◽  
Author(s):  
Richard J. Addante ◽  
Alana Muller ◽  
Lindsey A. Sirianni
Keyword(s):  
Source Memory ◽  
Response Times ◽  
Event Related Potentials ◽  
Individual Variability ◽  
List Type ◽  
Opposite Pattern ◽  
Physiological Characterization ◽  
Related Potentials ◽  
The Individual

AbstractThe goal of this study was to investigate a relatively unstudied memory condition for paradoxical combinations of item + source memory confidence responses, which challenged the conventional views of the memory processes supporting item and source memory judgments. We studied instances in which people provided accurate source memory judgments (conventionally ascribed as representing recollection) after having first produced low- confidence item recognition hits for the same items (conventionally thought to reflect familiarity-based processing). This paradoxical combination does not fit traditional accounts of being recollection (because it had low-confidence recognition) nor accounts of familiarity (since it had accurate source memory), and event-related potentials (ERPs) were used to adjudicate which processes support these kinds of memories. ERP results were unlike the conventional ERP effects of memory, lacking both an FN400 and the parietal old-new effect (LPC), and instead exhibited a significant negative-going ERP effect occurring later in time (800-1200ms) in central-parietal sites. Behavioral measures of response times revealed a crossover interaction: low confident recognition hits were slower during recognition but faster during source memory when compared to the opposite pattern seen for instances of high confident hits. Results provide a comprehensive characterization of the individual variability of the FN400 and LPC effects of memory, while adding the behavioral and physiological characterization of a late negative-going ERP effect for accurate source memory without recollection. Conclusions indicated that episodic context could be retrieved independently from recollection, while suggesting a role for a process of context familiarity that is independent from item-familiarity.HighlightsRecollection is often defined as remembering the source or context of informationPrior work used ERPs to identify times when source memory did not have recollectionCurrent work replicated ERPs with added response times and measures of varianceRecollection was not evident in certain source memories, which had a negative ERPRecollection is independent of context and is more than just remembering sourcesGraphical Abstract

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