Intrinsic functional brain connectivity changes following aerobic exercise, computerized cognitive training, and their combination in physically inactive healthy late-middle-aged adults: the Projecte Moviment

Lifestyle interventions have positive neuroprotective effects in aging. However, there are still open questions about how changes in resting-state functional connectivity (rsFC) contribute to cognitive improvements. The Projecte Moviment is a 12-week randomized controlled trial of a multimodal data acquisition protocol that investigated the effects of aerobic exercise (AE), computerized cognitive training (CCT), and their combination (COMB). An initial list of 109 participants was recruited from which a total of 82 participants (62% female; age¿=¿58.38¿±¿5.47) finished the intervention with a level of adherence¿>¿80%. Only in the COMB group, we revealed an extended network of 33 connections that involved an increased and decreased rsFC within and between the aDMN/pDMN and a reduced rsFC between the bilateral supplementary motor areas and the right thalamus. No global and especially local rsFC changes due to any intervention mediated the cognitive benefits detected in the AE and COMB groups. Projecte Moviment provides evidence of the clinical relevance of lifestyle interventions and the potential benefits when combining them.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Projecte Moviment is a project funded by the Spanish Ministry of Economy and Competitiveness under two grants: Neuroplasticity in the adulthood: physical exercise and cognitive training (PSI2013-47724-P) and Integrative omics study on the neurobiological effects of physical activity and cognitive stimulation (PSI2016-77475-R). It has also been rewarded with three pre-doctoral fellowships (FPU014/01460, FI-2016, and FI-2018). It was supported by the ICREA Academia Program to MM.SID is supported by a Beatriu de Pinós fellowship (2020 BP 00116). This work was supported by the Spanish Ministry of Economy and Competitiveness (www. mineco.gob.es) PID2021-122952OB-I00, Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), initiatives of Instituto de Investigación Carlos III (ISCIII), and Share4Rare project (Grant Agreement 780262).Peer ReviewedPostprint (published version

COVID-19 severity is related to poor executive function in people with post-COVID conditions

Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NaturePatients with post-coronavirus disease 2019 (COVID-19) conditions typically experience cognitive problems. Some studies have linked COVID-19 severity with long-term cognitive damage, while others did not observe such associations. This discrepancy can be attributed to methodological and sample variations. We aimed to clarify the relationship between COVID-19 severity and long-term cognitive outcomes and determine whether the initial symptomatology can predict long-term cognitive problems. Cognitive evaluations were performed on 109 healthy controls and 319 post-COVID individuals categorized into three groups according to the WHO clinical progression scale: severe-critical (n¿=¿77), moderate-hospitalized (n¿=¿73), and outpatients (n¿=¿169). Principal component analysis was used to identify factors associated with symptoms in the acute-phase and cognitive domains. Analyses of variance and regression linear models were used to study intergroup differences and the relationship between initial symptomatology and long-term cognitive problems. The severe-critical group performed significantly worse than the control group in general cognition (Montreal Cognitive Assessment), executive function (Digit symbol, Trail Making Test B, phonetic fluency), and social cognition (Reading the Mind in the Eyes test). Five components of symptoms emerged from the principal component analysis: the “Neurologic/Pain/Dermatologic” “Digestive/Headache”, “Respiratory/Fever/Fatigue/Psychiatric” and “Smell/ Taste” components were predictors of Montreal Cognitive Assessment scores; the “Neurologic/Pain/Dermatologic” component predicted attention and working memory; the “Neurologic/Pain/Dermatologic” and “Respiratory/Fever/Fatigue/Psychiatric” components predicted verbal memory, and the “Respiratory/Fever/Fatigue/Psychiatric,” “Neurologic/Pain/Dermatologic,” and “Digestive/Headache” components predicted executive function. Patients with severe COVID-19 exhibited persistent deficits in executive function. Several initial symptoms were predictors of long-term sequelae, indicating the role of systemic inflammation and neuroinflammation in the acute-phase symptoms of COVID-19.” Study Registration: www.ClinicalTrials.gov, identifier NCT05307549 and NCT05307575.This research was supported by the Agency for Management of University and Research Grants (AGAUR) from the Generalitat de Catalunya (Pandemies, 202PANDE00053) and La Marató de TV3 Foundation (202111-30-31-32).Peer ReviewedArticle signat per 16 autors/es: Mar Ariza, Neus Cano, Bàrbara Segura, Ana Adan, Núria Bargalló, Xavier Caldú, Anna Campabadal, Maria Angeles Jurado, Maria Mataró, Roser Pueyo, Roser Sala‑Llonch, Cristian Barrué, Javier Bejar, Claudio Ulises Cortés on behalf of NAUTILUS Project Collaborative Group, Maite Garolera Carme JunquéPostprint (published version

Early prediction of Alzheimer's disease using null longitudinal model-based classifiers

Incipient Alzheimer’s Disease (AD) is characterized by a slow onset of clinical symptoms, with pathological brain changes starting several years earlier. Consequently, it is necessary to first understand and differentiate age-related changes in brain regions in the absence of disease, and then to support early and accurate AD diagnosis. However, there is poor understanding of the initial stage of AD; seemingly healthy elderly brains lose matter in regions related to AD, but similar changes can also be found in non-demented subjects having mild cognitive impairment (MCI). By using a Linear Mixed Effects approach, we modelled the change of 166 Magnetic Resonance Imaging (MRI)-based biomarkers available at a 5-year follow up on healthy elderly control (HC, n = 46) subjects. We hypothesized that, by identifying their significant variant (vr) and quasi-variant (qvr) brain regions over time, it would be possible to obtain an age-based null model, which would characterize their normal atrophy and growth patterns as well as the correlation between these two regions. By using the null model on those subjects who had been clinically diagnosed as HC (n = 161), MCI (n = 209) and AD (n = 331), normal age-related changes were estimated and deviation scores (residuals) from the observed MRI-based biomarkers were computed. Subject classification, as well as the early prediction of conversion to MCI and AD, were addressed through residual-based Support Vector Machines (SVM) modelling. We found reductions in most cortical volumes and thicknesses (with evident gender differences) as well as in sub-cortical regions, including greater atrophy in the hippocampus. The average accuracies (ACC) recorded for men and women were: AD-HC: 94.11%, MCI-HC: 83.77% and MCI converted to AD (cAD)-MCI non-converter (sMCI): 76.72%. Likewise, as compared to standard clinical diagnosis methods, SVM classifiers predicted the conversion of cAD to be 1.9 years earlier for females (ACC:72.5%) and 1.4 years earlier for males (ACC:69.0%).Peer Reviewe