Browsing by Author "Cifre, Ignacio"
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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.
- Increased functional connectivity patterns in mild Alzheimer’s disease: a rsfMRI studyPublication . Penalba-Sánchez, Lucía; Oliveira-Silva, Patrícia; Sumich, Alexander Luke; Cifre, IgnacioBackground: Alzheimer’s disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer’s disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques. Methods: In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer’s disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer’s disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson’s correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed. Results: Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition. Conclusion: The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.