Percorrer por autor "Sumich, Alexander"
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- Classification of sleep quality and aging as a function of brain complexity: a multiband non-linear EEG analysisPublication . Penalba-Sánchez, Lucía; Silva, Gabriel; Crook-Rumsey, Mark; Sumich, Alexander; Rodrigues, Pedro Miguel; Oliveira-Silva, Patrícia; Cifre, IgnacioUnderstanding and classifying brain states as a function of sleep quality and age has important implications for developing lifestyle-based interventions involving sleep hygiene. Current studies use an algorithm that captures non-linear features of brain complexity to differentiate awake electroencephalography (EEG) states, as a function of age and sleep quality. Fifty-eight participants were assessed using the Pittsburgh Sleep Quality Inventory (PSQI) and awake resting state EEG. Groups were formed based on age and sleep quality (younger adults n = 24, mean age = 24.7 years, SD = 3.43, good sleepers n = 11; older adults n = 34, mean age = 72.87; SD = 4.18, good sleepers n = 9). Ten non-linear features were extracted from multiband EEG analysis to feed several classifiers followed by a leave-one-out cross-validation. Brain state complexity accurately predicted (i) age in good sleepers, with 75% mean accuracy (across all channels) for lower frequencies (alpha, theta, and delta) and 95% accuracy at specific channels (temporal, parietal); and (ii) sleep quality in older groups with moderate accuracy (70 and 72%) across sub-bands with some regions showing greater differences. It also differentiated younger good sleepers from older poor sleepers with 85% mean accuracy across all sub-bands, and 92% at specific channels. Lower accuracy levels (<50%) were achieved in predicting sleep quality in younger adults. The algorithm discriminated older vs. younger groups excellently and could be used to explore intragroup differences in older adults to predict sleep intervention efficiency depending on their brain complexity.
- Developing conjoint research work through a joint PhD programmePublication . Cunha, Rosário Serrão; Sánchez, Lucia Penalba; Dias, Pedro; Bowe, Mhairi; Andrés, Ana; Cifre, Ignacio; Silva, Patricia Oliveira; Sumich, AlexanderAs mentioned in the scope of the IFCU 7th International Psychology Congress, “Catholic Universities with Psychology Departments/Academic Units have an opportunity to develop relevant conjoint work”, between each other and with other Universities. The International PhD Programme in Applied Psychology: Adaptation and change in contemporary societies is, undoubtedly, an illustration of the potential of inter-research groups cooperation. It is a Joint PhD Programme between two Catholic Universities (Universitat Ramon Llull (URL), Universidade Católica Portuguesa (UCP) with Nottingham Trent University (NTU). This poster will present a synthesis of this International Programme, its’ advantages for PhD students’ learning and development, and an example of two students being developed through this collaboration, one focused on Mental Health and Wellbeing in Schools, and another one focused on brain connectivity in Mild cognitive impairment and Alzheimer's disease. These studies are being conducted by the two first doctoral students of this Programme, and the poster will present a graphical overview of both.
- EEG low conventional bands non‐linear machine learning‐based analysis for classifying MCI and sleep quality as a function of brain complexityPublication . Penalba‐Sánchez, Lucía; Ribeiro, Pedro Baptista; Crook‐Rumsey, Mark; Sumich, Alexander; Howard, Christina; Sanei, Saeid; Zandbagleh, Ahmad; Azami, Hamed; Düzel, Emrah; Hämmerer, Dorothea; Rodrigues, Pedro MiguelBACKGROUND: Good sleep quality is essential for both physiological and mental health. It helps in clearing TAU and beta-amyloid aggregates and consolidating memory, key processes in delaying dementia. Poor sleep is linked to reduced cognitive flexibility in daily life, likely due to decreased brain complexity, reflecting a reduced range of adaptive spatiotemporal brain dynamics. This study introduces a novel approach using non-linear EEG analysis focused on low conventional bands to classify sleep quality in individuals with mild cognitive impairment (MCI), based on brain complexity. METHOD: Resting-state EEG was collected from 22 participants with MCI aged 60+, grouped by sleep quality (Pittsburgh Sleep Quality Index): 11 MCI with good sleep, and 11 MCI with poor sleep (Table 1). EEG data (128 channels, 5-minute recordings) were normalized and decomposed using the Discrete Wavelet Transform to reach delta (1-4 Hz) and theta (4-8 Hz) bands. Ten non-linear complexity features, namely approximate entropy, correlation dimension, detrended fluctuation analysis, energy, Higuchi fractal dimension, Hurst exponent, Katz fractal dimension, Boltzmann Gibbs entropy, Lyapunov exponent and Shannon entropy, were extracted from 5 second segments. Statistical measures (mean, standard deviation, 95th percentile, variance, median, kurtosis) were computed from these time-distribution features. These statistics were then used for training and testing a set of classic machine learning classifiers, employing leave-one-out cross-validation (Figure 2). RESULTS: Brain complexity successfully classified sleep quality in MCI, achieving an accuracy and area under the curve (AUC) of 1 in channel D13 (delta subband) using Quadratic Discriminant Analysis (QDA), and an accuracy of 0.94 and an AUC of 0.95 in channel B17 (theta subband) using the Extra Trees Classifier (ETC) (Figure 3). CONCLUSION: Specific machine learning classifiers distinguish excellently sleep quality in MCI using spatiotemporal complexity features from slow EEG subbands. The most relevant channels for group discrimination were primarily located in bilateral temporal regions of the neocortex known to be among the first affected in amnestic MCI, as previously shown in neuroimaging studies. Future longitudinal studies could investigate whether changes in brain complexity within these slow-frequency temporal regions, influenced by sleep quality, are associated with an earlier or faster onset of dementia.
- International affect, personality, and embodied brain (APE) networkPublication . Sumich, Alexander; Oliveira-Silva, Patrícia; Heym, NadjaThis special issue comprises a selection of representative studies from the Affect, Personality and Embodied Brain conferences. The included studies span behavioral and brain studies of nonclinical and clinical populations.