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Resting-state functional connectivity (FC) is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. For instance, studies in pre-clinical AD subjects have shown increases of cerebral spinal fluid soluble platelet-derived growth factor receptor-β (CSF sPDGFRβ, a marker of BBB breakdown) but have not demonstrated if this vascular impairment affects neuronal dysfunction. It’s possible that increased levels of sPDGFRβ in the CSF may correlate with impaired FC in metabolically demanding brain regions (i.e. Default Mode Network, DMN). Our study aimed to investigate the relationship between these two markers in older individuals that were cognitively normal and had cognitive impairment. Eighty-nine older adults without dementia from the University of Southern California were selected from a larger cohort. Region of interest (ROI) to ROI analyses were conducted using DMN seed regions. Linear regression models measured significant associations between BOLD FC strength among seed-target regions and sPDGFRβ values, while covarying for age and sex. Comparison of a composite ROI created by averaging FC values between seed and all target regions among cognitively normal and impaired individuals was also examined. Using CSF sPDGFRβ as a biomarker of BBB breakdown, we report that increased breakdown correlated with decreased functional connectivity in DMN areas, specifically the PCC while the hippocampus exhibited an interaction effect using CDR score. We conclude that BBB breakdown as measured by CSF sPDGFRβ affects neural networks resulting in decreased functional connections that leads to cognitive dysfunction.

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 (β-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 β-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 β-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 β-amyloid burden, such that less sleep efficiency variability was associated with lower β-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 β-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 β-amyloid pathology and better cognition. The relationships between sleep duration and intra-individual variability in sleep efficiency with β-amyloid burden differ by apolipoprotein E4 status, indicating that longer sleep duration and more consistent sleep efficiency may be protective against β-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.

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.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 (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 andElevated neuroinflammation markers are associated with incident MCI/AD, and the factors of sex and

Background Few studies have investigated how inflammation early in the disease course may affect AD progression over time despite converging evidence that elevated levels of inflammation are associated with AD in cross-sectional studies. Methods Two-hundred ninety-two research participants with CSF biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were included in this study. Cox proportional hazards models were used to investigate whether baseline levels of inflammatory CSF markers were associated with incident mild cognitive impairment or AD and time to conversion. Potential moderating effects of sex and APOE4 were also examined. Results Elevated levels of pro-inflammatory markers TNF-α, IL-9, and IL-12p40 at baseline were associated with higher rates of conversion to MCI/AD. Interactions with sex and APOE4 were observed, such that women with elevated TNF- α and all APOE4 carriers with elevated IL-9 levels had shorter times to conversion. Additionally, TNF-α mediated the relationship between elevated IL-12p40 and IL-9. Conclusion Elevated inflammation markers are associated with incident MCI or AD, and the factors of sex and APOE4 status modify the time to conversion.

It is now recognized that understanding how neuroinflammation affects brain function may provide new insights into Alzheimer’s pathophysiology. Tumor necrosis factor (TNF)-α, an inflammatory cytokine marker, has been implicated in Alzheimer’s disease (AD), as it can impair neuronal function through suppression of long-term potentiation. Our study investigated the relationship between cerebrospinal fluid TNF-α and functional connectivity (FC) in a cohort of 64 older adults (μ age = 69.76 years; 30 cognitively normal, 34 mild AD). Higher cerebrospinal fluid TNF-α levels were associated with lower FC among brain regions important for high-level decision-making, inhibitory control, and memory. This effect was moderated by apolipoprotein E-ε4 (APOE4) status. Graph theory metrics revealed there were significant differences between APOE4 carriers at the node level, and by diagnosis at the network level suggesting global brain network dysfunction in participants with AD. These findings suggest proinflammatory mechanisms may contribute to reduced FC in regions important for high-level cognition. Future studies are needed to understand the role of inflammation on brain function and clinical progression, especially in APOE4 carriers., Graphical Abstract

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.

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, and examining 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 test for statistical association with average memory and executive function cognitive scores. Across both analysis approaches 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 holds potential as a new biomarker predictive of clinical progression along the Alzheimer's disease spectrum.

Age-related changes in cognition are partially mediated by the presence of neuropathology and neurodegeneration. This manuscript evaluates the degree to which biomarkers of Alzheimer's disease, (AD) neuropathology and longitudinal changes in brain structure, account for age-related differences in cognition. Data from the AD Neuroimaging Initiative (n = 1012) were analyzed, including individuals with normal cognition and mild cognitive impairment. Parallel process mixed effects regression models characterized longitudinal trajectories of cognitive variables and time-varying changes in brain volumes. Baseline age was associated with both memory and executive function at baseline (p's < 0.001) and change in memory and executive function performances over time (p's < 0.05). After adjusting for clinical diagnosis, baseline, and longitudinal changes in brain volume, and baseline levels of cerebrospinal fluid biomarkers, age effects on change in episodic memory and executive function were fully attenuated, age effects on baseline memory were substantially attenuated, but an association remained between age and baseline executive function. Results support previous studies that show that age effects on cognitive decline are fully mediated by disease and neurodegeneration variables but also show domain-specific age effects on baseline cognition, specifically an age pathway to executive function that is independent of brain and disease pathways.

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.

OBJECTIVES/SPECIFIC AIMS: Recent evidence from resting-state fMRI studies have shown that brain network connectivity is altered in patients with neurodegenerative disorders. However, few studies have examined the complete connectivity patterns of these well-reported RSNs using a whole brain approach and how they compare between dementias. Here, we used advanced connectomic approaches to examine the connectivity of RSNs in Alzheimer disease (AD), Frontotemporal dementia (FTD), and age-matched control participants. METHODS/STUDY POPULATION: In total, 44 participants [27 controls (66.4±7.6 years), 13 AD (68.5.63±13.9 years), 4 FTD (59.575±12.2 years)] from an ongoing study at Indiana University School of Medicine were used. Resting-state fMRI data was processed using an in-house pipeline modeled after Power et al. (2014). Images were parcellated into 278 regions of interest (ROI) based on Shen et al. (2013). Connectivity between each ROI pair was described by Pearson correlation coefficient. Brain regions were grouped into 7 canonical RSNs as described by Yeo et al. (2015). Pearson correlation values were then averaged across pairs of ROIs in each network and averaged across individuals in each group. These values were used to determine relative expression of FC in each RSN (intranetwork) and create RSN profiles for each group. RESULTS/ANTICIPATED RESULTS: Our findings support previous literature which shows that limbic networks are disrupted in FTLD participants compared with AD and age-matched controls. In addition, interactions between different RSNs was also examined and a significant difference between controls and AD subjects was found between FP and DMN RSNs. Similarly, previous literature has reported a disruption between executive (frontoparietal) network and default mode network in AD compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT: Our approach allows us to create profiles that could help compare intranetwork FC in different neurodegenerative diseases. Future work with expanded samples will help us to draw more substantial conclusions regarding differences, if any, in the connectivity patterns between RSNs in various neurodegenerative diseases.