Browsing by Subject "P300"
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Item Open Access Associations between psychosis endophenotypes across brain functional, structural, and cognitive domains(Cambridge University Press, 2018) Blakey, R.; Ranlund, S.; Zartaloudi, E.; Cahn, W.; Calafato, S.; Colizzi, M.; Crespo-Facorro, B.; Daniel, C.; Díez-Revuelta, A.; Forti, M. D.; Iyegbe, C.; Jablensky, A.; Jones, R.; Hall, M. -H.; Kahn, R.; Kalaydjieva, L.; Kravariti, E.; Lin, K.; McDonald, C.; McIntosh, A. M.; Picchioni, M.; Powell, J.; Presman, A.; Rujescu, D.; Schulze, K.; Shaikh, M.; Thygesen, J. H.; Toulopoulou, Timothea; Haren, N. V.; Os, J. V.; Walshe, M.; Murray, R. M.; Bramon, E.Background A range of endophenotypes characterise psychosis, however there has been limited work understanding if and how they are inter-related.Methods This multi-centre study includes 8754 participants: 2212 people with a psychotic disorder, 1487 unaffected relatives of probands, and 5055 healthy controls. We investigated cognition [digit span (N = 3127), block design (N = 5491), and the Rey Auditory Verbal Learning Test (N = 3543)], electrophysiology [P300 amplitude and latency (N = 1102)], and neuroanatomy [lateral ventricular volume (N = 1721)]. We used linear regression to assess the interrelationships between endophenotypes.Results The P300 amplitude and latency were not associated (regression coef.-0.06, 95% CI-0.12 to 0.01, p = 0.060), and P300 amplitude was positively associated with block design (coef. 0.19, 95% CI 0.10-0.28, p < 0.001). There was no evidence of associations between lateral ventricular volume and the other measures (all p > 0.38). All the cognitive endophenotypes were associated with each other in the expected directions (all p < 0.001). Lastly, the relationships between pairs of endophenotypes were consistent in all three participant groups, differing for some of the cognitive pairings only in the strengths of the relationships.Conclusions The P300 amplitude and latency are independent endophenotypes; the former indexing spatial visualisation and working memory, and the latter is hypothesised to index basic processing speed. Individuals with psychotic illnesses, their unaffected relatives, and healthy controls all show similar patterns of associations between endophenotypes, endorsing the theory of a continuum of psychosis liability across the population.Item Open Access Hybrid and model based approaches for new BCI spellers(Bilkent University, 2019-07) Memon, Suleman AijazElectroencephalography (EEG) based brain-computer interfaces (BCIs), due to their non-invasive, portable and temporal resolution properties, are widely used in the field of neural engineering. In order to make BCI paradigms more practical and feasible for real life applications, new approaches are being tested such as hybrid BCIs and model based BCIs. In the first phase of this study, a novel hybrid speller BCI is proposed, incorporating P300 and code-modulated visual evoked potential (c-VEP) paradigms, with the objective of improving the spelling accuracy and information transfer rate (ITR), compared to individual P300 and c-VEP paradigms. Moreover, fusion techniques have been applied in order to effectively combine the information of P300 and c-VEP at the score level. We have implemented and compared two different approaches, linear discriminant analysis (LDA) and maximum probability estimation (MPE), in order to identify which one works best for this hybrid BCI. The proposed BCI consists of 36 targets presented as 6x6 matrix on screen with a refresh rate of 120 Hz. Seven healthy subjects participated in experiments where each subject performed a training session followed by five test sessions. The P300 and c-VEP signals are obtained by using bandpass filters of 0.5-6 Hz and 6-41 Hz respectively, on the raw hybrid data. For P300, stepwise linear discriminant analysis (SWLDA) is performed on training data from all the 10 EEG channels to obtain the feature vector. For c-VEP, canonical correlation analysis (CCA) is performed on training data to obtain the reference templates for all 36 symbols. In comparison with the accuracy and ITR values of c-VEP alone, that is without simultaneously making use of the P300 data obtained during the hybrid experiments, MPE-based hybrid improved only by 1.1% and 2.1 bits/min, on average, respectively, whereas the values worsened by 12.3% and 19.8 bits/min in the case of LDA-based hybrid. Moreover, the statistical tests on the mean accuracy and ITR values of all the subjects showed that the results of MPE-based hybrid and of c-VEP alone are not statistically different (p=0.293). Although the MPE-based hybrid is not statistically better than the c-VEP alone, it can be highly effective if the primary goal is to only increase the accuracy, using a range of improvements in P300 methods as discussed in conclusion. However, it would not be useful if the purpose is to increase the speed of the speller since the individual c-VEP paradigm, when optimized for timing, has the capability of giving an average ITR of 114.9bits/min or higher, on its own. In the second phase of this study, model based c-VEP BCI is implemented, aimed at improving the training time compared to the case where all the targets are assigned arbitrary pseudorandom binary sequences and training is required for all the symbols separately. For this purpose, moving average model has been implemented to simulate the responses for c-VEP visual stimulation patterns, for 60Hz and 120Hz monitor refresh rate respectively. The average of the correlation between measured response and modeled response for 60Hz and 120Hz is 0.357 and 0.396 respectively. The average accuracy and ITR obtained for model based c-VEP BCI is 87.1% and 76.4 bits/min for 60Hz respectively and 82.1% and 72.4 bits/min for 120Hz respectively. Modeling results suggest that it is possible to perform a training on a single visual stimulus pattern and achieve a good fit model.Item Open Access Psychosis endophenotypes: a gene-set-specific polygenic risk score analysis(Oxford University Press, 2023-08-14) Wang, B.; Irizar, H.; Thygesen, J. H.; Zartaloudi, E.; Austin-Zimmerman, I.; Bhat, A.; Harju-Seppänen, J.; Pain, O.; Bass, N.; Gkofa, V.; Alizadeh, B. Z.; Van Amelsvoort, T.; Arranz, M. J.; Bender, S.; Cahn, W.; Stella Calafato, M.; Crespo-Facorro, B.; Di Forti, M.; Giegling, I.; De Haan, L.; Hall, J.; Hall, M.; Van Haren, N.; Iyegbe, C.; Kahn, R. S.; Kravariti, E.; Lawrie, S. M.; Lin, K.; Luykx, J. J.; Mata, I.; McDonald, C.; McIntosh, A. M.; Murray, R. M.; Picchioni, M.; Powell, J.; Prata, D. P.; Rujescu, D.; Rutten, B. P. F.; Shaikh, M.; Simons, C. J. P.; Toulopoulou, Timothea; Weisbrod, M.; Van Winkel, R.; Kuchenbaecker, K.; McQuillin, A.; Bramon, E.Background and Hypothesis: Endophenotypes can help to bridge the gap between psychosis and its genetic predispositions, but their underlying mechanisms remain largely unknown. This study aims to identify biological mechanisms that are relevant to the endophenotypes for psychosis, by partitioning polygenic risk scores into specific gene sets and testing their associations with endophenotypes. Study Design: We computed polygenic risk scores for schizophrenia and bipolar disorder restricted to brain-related gene sets retrieved from public databases and previous publications. Three hundred and seventy-eight gene-set-specific polygenic risk scores were generated for 4506 participants. Seven endophenotypes were also measured in the sample. Linear mixed-effects models were fitted to test associations between each endophenotype and each gene-set-specific polygenic risk score. Study Results: After correction for multiple testing, we found that a reduced P300 amplitude was associated with a higher schizophrenia polygenic risk score of the forebrain regionalization gene set (mean difference per SD increase in the polygenic risk score: -1.15 μV; 95% CI: -1.70 to -0.59 μV; P = 6 × 10-5). The schizophrenia polygenic risk score of forebrain regionalization also explained more variance of the P300 amplitude (R2 = 0.032) than other polygenic risk scores, including the genome-wide polygenic risk scores. Conclusions: Our finding on reduced P300 amplitudes suggests that certain genetic variants alter early brain development thereby increasing schizophrenia risk years later. Gene-set-specific polygenic risk scores are a useful tool to elucidate biological mechanisms of psychosis and endophenotypes, offering leads for experimental validation in cellular and animal models.