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Buldu J.M.,Rey Juan Carlos University | Buldu J.M.,Center for Biomedical Technology | Bajo R.,Complutense University of Madrid | Maestu F.,Complutense University of Madrid | And 12 more authors.
PLoS ONE | Year: 2011

Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI) subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG) time series obtained during a memory task were evaluated by synchronization likelihood (SL), to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD), these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD. © 2011 Buldú et al. Source


Gil-Gregorio P.,Memory Unit | Yubero-Pancorbo R.,Memory Unit
Reviews in Clinical Gerontology | Year: 2014

Summary Recently, diagnostic criteria for preclinical Alzheimer's disease have been proposed. These describe and define three stages of disease. Stage I is focused on asymptomatic cerebral amyloidosis. Stage II includes evidence of synaptic dysfunction and/or early degeneration. Finally, stage III of the disease is characterized by the beginning of cognitive decline. © Cambridge University Press 2014. Source


Bullock J.M.,University of Nottingham | Medway C.,University of Nottingham | Cortina-Borja M.,University College London | Turton J.C.,University of Nottingham | And 26 more authors.
Neurobiology of Aging | Year: 2013

Despite recent discoveries in the genetics of sporadic Alzheimer's disease, there remains substantial " hidden heritability." It is thought that some of this missing heritability may be because of gene-gene, i.e., epistatic, interactions. We examined potential epistasis between 110 candidate polymorphisms in 1757 cases of Alzheimer's disease and 6294 control subjects of the Epistasis Project, divided between a discovery and a replication dataset. We found an epistatic interaction, between rs7483 in GSTM3 and rs1111875 in the HHEX/IDE/KIF11 gene cluster, with a closely similar, significant result in both datasets. The synergy factor (SF) in the combined dataset was 1.79, 95% confidence interval [CI], 1.35-2.36; p = 0.00004. Consistent interaction was also found in 7 out of the 8 additional subsets that we examined post hoc: i.e., it was shown in both North Europe and North Spain, in both men and women, in both those with and without the ε4 allele of apolipoprotein E, and in people older than 75 years (SF, 2.27; 95% CI, 1.60-3.20; p < 0.00001), but not in those younger than 75 years (SF, 1.06; 95% CI, 0.59-1.91; p = 0.84). The association with Alzheimer's disease was purely epistatic with neither polymorphism showing an independent effect: odds ratio, 1.0; p ≥ 0.7. Indeed, each factor was associated with protection in the absence of the other factor, but with risk in its presence. In conclusion, this epistatic interaction showed a high degree of consistency when stratifying by sex, the ε4 allele of apolipoprotein E genotype, and geographic region. © 2013 Elsevier Inc. Source


Somme J.,Memory Unit | Somme J.,Alava University Hospital | Fernandez-Martinez M.,Memory Unit | Molano A.,Memory Unit | Zarranz J.J.,Memory Unit
Current Alzheimer Research | Year: 2013

Introduction. Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) but its role as a predictive factor for the progression to dementia is still not clear. The objective of this study is to identify NPS that predict the progression from amnestic MCI (a-MCI) to dementia using an easy to administer screening tool for NPS. Material and Methods. 132 patients with a-MCI were assessed for NPS by the Neuropsychiatric Inventory (NPI) and followed to detect progression to dementia. Results. The mean follow-up time was 3.5±2.9 years and rate of progression to dementia 28.8%. Two items of NPI were found to be independent risk factors for progression, nighttime behavioural disturbance (hazard ratio(HR)=2.2, 95%CI=1.10-4.43), anxiety (HR=2.5, 95%CI=1.01-6.20) and apathy (HR=2.2, 95%CI=1.003-4.820). The risk of progression increased with higher score on NPI (HR=1.046 per point, 95%CI=1.019-1.073), and with a higher number of items of NPI affected (HR=3.6 per item, 95%CI=2.0-6.4). Faster progression to dementia was observed in patients with either nighttime behavioural disturbance, apathy or anxiety (4.6 vs. 8.3 years, 5.3 vs. 8.4 years and 3.0 vs. 7.7 years respectively, p<0.01) as well as in those with a higher number of items affected (no items = 9.2 years, 1-3 items = 6.6 years and >3 items = 2.9 years, p<0.001). Conclusions. Assessing a broad spectrum of NPS can help identify patients with a-MCI presenting a higher risk for progression to dementia. This can be useful to select patients for closer follow-up, clinical trials and future therapeutic interventions. © 2013 Bentham Science Publishers. Source


Llufriu S.,Center for Neuroimmunology | Llufriu S.,University of Barcelona | Martinez-Heras E.,Center for Neuroimmunology | Martinez-Heras E.,University of Barcelona | And 21 more authors.
Multiple Sclerosis Journal | Year: 2014

Objectives: Our aim was to investigate the impact of gray matter (GM) integrity on cognitive performance in multiple sclerosis (MS), and its relationship with white matter (WM) integrity and presence of lesions. Methods: Sixty-seven patients with MS and 26 healthy controls underwent voxel-based analysis of diffusion tensor images (DTI) in GM and tract-based spatial statistics (TBSS) from WM to identify the regional correlations between cognitive functions and integrity. Lesion probability mapping (LPM) was generated for correlation analysis with cognition. Multiple linear regression analyses were used to identify the imaging measures associated with cognitive scores. Results: Compared with controls, patients showed abnormal DTI indices in several GM regions and in most WM tracts. Impairment in DTI indices in specific GM regions was associated with worse performance of distinct cognitive functions. Those regions showed anatomical correspondence with cognitively relevant tracts in TBSS and LPM. The combination of regional GM and WM DTI and lesion volume accounted for 36-51% of the variance of memory and attention scores. Regional GM DTI explained less than 5% of that variance. Conclusion: GM and WM integrity of specific networks influences cognitive performance in MS. However, GM damage assessed by DTI only adds a small increment to the explained variance by WM in predicting cognitive functioning. © The Author(s) 2013. Source

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