Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task

Objectives: The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task. Methods: MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks’ complexities are measured by Graph Index Complexity (Cr) and Efficiency Complexity (Ce). Results: The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere. Conclusions: Ce would be a better measurement for showing the global differences between normal and MCI brains compared with Cr. Significance: The high accuracy of the classification shows Ce at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MC

Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states

Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus

Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images

Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature

The impact of PICALM genetic variations on reserve capacity of posterior cingulate in AD continuum

Phosphatidylinositolbinding clathrin assembly protein (PICALM) gene is one novel genetic player associated with late-onset Alzheimer’s disease (LOAD), based on recent genome wide association studies (GWAS). However, how it affects AD occurrence is still unknown. Brain reserve hypothesis highlights the tolerant capacities of brain as a passive means to fight against neurodegenerations. Here, we took the baseline volume and/or thickness of LOAD-associated brain regions as proxies of brain reserve capacities and investigated whether PICALM genetic variations can influence the baseline reserve capacities and the longitudinal atrophy rate of these specific regions using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In mixed population, we found that brain region significantly affected by PICALM genetic variations was majorly restricted to posterior cingulate. In sub-population analysis, we found that one PICALM variation (C allele of rs642949) was associated with larger baseline thickness of posterior cingulate in health. We found seven variations in health and two variations (rs543293 and rs592297) in individuals with mild cognitive impairment were associated with slower atrophy rate of posterior cingulate. Our study provided preliminary evidences supporting that PICALM variations render protections by facilitating reserve capacities of posterior cingulate in non-demented elderly

Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions

P2‐203: Longitudinal changes in self‐administered gerocognitive examination (SAGE) and mini‐mental state exam (MMSE) scores for subjective cognitive impairment (SCI), mild cognitive impairment (MCI), dementia converters, and Alzheimer’s disease (AD) patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152564/1/alzjjalz201506743.pd

Sense of coherence and coping strategies: How they influence quality of life in Iranian women with breast cancer

Abstract Aim To investigate the mediation/moderation effect between Coping Behaviors (CBs) and Sense of Coherence (SOC) in the prediction of health‐related quality of life (HRQoL) in breast cancer patients. Design Cross‐sectional. Methods A total of 221 patients were included in this study. The 13‐item Orientation to Life Questionnaire, Brief COPE and Functional Assessment of Cancer Therapy—Breast were investigated. Pearson's correlation coefficient and mediation/moderation analysis were performed. Results Significant correlations were observed for SOC, active coping, acceptance, positive reframing (PR), planning, use of emotional support (UES), use of instrumental support, behaviour disengagement and self‐blame with HRQoL. Except for planning and acceptance, SOC partially mediated the CBs' effect on HRQoL. The UES and PR's effects on HRQoL were significant at lower SOC levels and diminished at higher SOC levels. Conclusion Practitioners can incorporate SOC and adaptive CBs, including PR and UES, into the rehabilitation programmes to improve HRQoL in patients