Variability in objective sleep is associated with Alzheimer's pathology and cognition.

Both sleep duration and sleep efficiency have been associated with risk of Alzheimer's disease, suggesting that interventions to promote optimal sleep may be a way to reduce Alzheimer's disease risk. However, studies often focus on average sleep measures, usually from self-report questionnaires, ignoring the role of intra-individual variability in sleep across nights quantified from objective sleep measures. The current cross-sectional study sought to investigate the role of intra-individual variability in accelerometer-based objective sleep duration and sleep efficiency in relation to in vivo Alzheimer's disease pathology (ß-amyloid and tau) using positron emission tomography imaging and cognition (working memory, inhibitory control, verbal memory, visual memory and global cognition). To examine these relationships, we evaluated 52 older adults (age = 66.4 ± 6.89, 67% female, 27% apolipoprotein E4 carriers) with objective early mild cognitive impairment. Modifying effects of apolipoprotein E4 status were also explored. Less intra-individual variability in sleep duration was associated with lower ß-amyloid burden, higher global cognition and better inhibitory control, with a trend for lower tau burden. Less intra-individual variability in sleep efficiency was associated with lower ß-amyloid burden, higher global cognition and better inhibitory control, but not with tau burden. Longer sleep duration was associated with better visual memory and inhibitory control. Apolipoprotein E4 status significantly modified the association between intra-individual variability in sleep efficiency and ß-amyloid burden, such that less sleep efficiency variability was associated with lower ß-amyloid burden in apolipoprotein E4 carriers only. There was a significant interaction between sleep duration and apolipoprotein E4 status, suggesting that longer sleep duration is more strongly associated with lower ß-amyloid burden in apolipoprotein E4 carriers relative to non-carriers. These results provide evidence that lower intra-individual variability in both sleep duration and sleep efficiency and longer mean sleep duration are associated with lower levels of ß-amyloid pathology and better cognition. The relationships between sleep duration and intra-individual variability in sleep efficiency with ß-amyloid burden differ by apolipoprotein E4 status, indicating that longer sleep duration and more consistent sleep efficiency may be protective against ß-amyloid burden in apolipoprotein E4 carriers. Longitudinal and causal studies are needed to better understand these relationships. Future work should investigate factors contributing to intra-individual variability in sleep duration and sleep efficiency in order to inform intervention studies.

Higher baseline levels of CSF inflammation increase risk of incident mild cognitive impairment and Alzheimer's disease dementia.

Introduction: Few studies have investigated how neuroinflammation early in the disease course may affect Alzheimer's disease (AD) progression over time despite evidence that neuroinflammation is associated with AD. Methods: Research participants with cerebrospinal fluid (CSF) biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included in this study. Cox models were used to investigate whether baseline CSF neuroinflammation was associated with incident mild cognitive impairment (MCI) or AD. Moderating effects of sex and apolipoprotein E (APOE) ε4 were also examined. Results: Elevated levels of tumor necrosis factor α (TNF-α), interleukin (IL)-9, and IL-12p40 at baseline were associated with higher rates of conversion to MCI/AD. Interactions with sex and APOE ε4 were observed, such that women with elevated TNF-α and all APOE ε4 carriers with elevated IL-9 levels had shorter times to conversion. In addition, TNF-α mediated the relationship between elevated IL-12p40 and IL-9. Discussion: Elevated neuroinflammation markers are associated with incident MCI/AD, and the factors of sex and APOE ε4 status modify the time to conversion.

Resting state network modularity along the prodromal late onset Alzheimer's disease continuum.

Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.

Brain explorer for connectomic analysis.

Visualization plays a vital role in the analysis of multimodal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional, and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomical structure. In this paper, new surface texture techniques are developed to map non-spatial attributes onto both 3D brain surfaces and a planar volume map which is generated by the proposed volume rendering technique, spherical volume rendering. Two types of non-spatial information are represented: (1) time series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image-based phenotypic biomarkers for brain diseases.

Cognitive complaints in older adults at risk for Alzheimer's disease are associated with altered resting-state networks.

INTRODUCTION: Pathophysiological changes that accompany early clinical symptoms in prodromal Alzheimer's disease (AD) may have a disruptive influence on brain networks. We investigated resting-state functional magnetic resonance imaging (rsfMRI), combined with brain connectomics, to assess changes in whole-brain functional connectivity (FC) in relation to neurocognitive variables. METHODS: Participants included 58 older adults who underwent rsfMRI. Individual FC matrices were computed based on a 278-region parcellation. FastICA decomposition was performed on a matrix combining all subjects' FC. Each FC pattern was then used as a response in a multilinear regression model including neurocognitive variables associated with AD (cognitive complaint index [CCI] scores from self and informant, an episodic memory score, and an executive function score). RESULTS: Three connectivity independent component analysis (connICA) components (RSN, VIS, and FP-DMN FC patterns) associated with neurocognitive variables were identified based on prespecified criteria. RSN-pattern, characterized by increased FC within all resting-state networks, was negatively associated with self CCI. VIS-pattern, characterized by an increase in visual resting-state network, was negatively associated with CCI self or informant scores. FP-DMN-pattern, characterized by an increased interaction of frontoparietal and default mode networks (DMN), was positively associated with verbal episodic memory. DISCUSSION: Specific patterns of FC were differently associated with neurocognitive variables thought to change early in the course of AD. An integrative connectomics approach relating cognition to changes in FC may help identify preclinical and early prodromal stages of AD and help elucidate the complex relationship between subjective and objective indices of cognitive decline and differences in brain functional organization.

The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults.

The human connectome refers to a comprehensive description of the brain's structural and functional connections in terms of brain networks. As the field of brain connectomics has developed, data acquisition, subsequent processing and modeling, and ultimately the representation of the connectome have become better defined and integrated with network science approaches. In this way, the human connectome has provided a way to elucidate key features of not only the healthy brain but also diseased brains. The field has quickly evolved, offering insights into network disruptions that are characteristic for specific neurodegenerative disorders. In this paper, we provide a brief review of the field of brain connectomics, as well as a more in-depth survey of recent studies that have provided new insights into brain network pathologies, including those found in Alzheimer's disease (AD), patients with mild cognitive impairment (MCI), and finally in people classified as being "at risk". Until the emergence of brain connectomics, most previous studies had assessed neurodegenerative diseases mainly by focusing on specific and dispersed locales in the brain. Connectomics-based approaches allow us to model the brain as a network, which allows for inferences about how dynamic changes in brain function would be affected in relation to structural changes. In fact, looking at diseases using network theory gives rise to new hypotheses on mechanisms of pathophysiology and clinical symptoms. Finally, we discuss the future of this field and how understanding both the functional and structural connectome can aid in gaining sharper insight into changes in biological brain networks associated with cognitive impairment and dementia.

Integrated Visualization of Human Brain Connectome Data.

Visualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases.