Erin L. Boespflug

The Emerging Relationship Between Interstitial Fluid-Cerebrospinal Fluid Exchange, Amyloid-β, and Sleep

Amyloid-β (Aβ) plaques are a key histopathological hallmark of Alzheimers disease (AD), and soluble Aβ species are believed to play an important role in the clinical development of this disease. Emerging biomarker data demonstrate that Aβ plaque deposition begins decades before the onset of clinical symptoms, suggesting that understanding the biological determinants of the earliest steps in the development of AD pathology may provide key opportunities for AD treatment and prevention. Although a clinical association between sleep disruption and AD has long been appreciated, emerging clinical studies and insights from the basic neurosciences have shed important new light on how sleep and Aβ homeostasis may be connected in the setting of AD. Aβ, like many interstitial solutes, is cleared in part through the exchange of brain interstitial fluid and cerebrospinal fluid along a brain-wide network of perivascular pathways recently termed the glymphatic system. Glymphatic function is primarily a feature of the sleeping brain, rather than the waking brain, and is slowed in the aging and posttraumatic brain. These changes may underlie the diurnal fluctuations in interstitial and cerebrospinal fluid Aβ levels observed in both the rodent and the human. These and other emerging studies suggest that age-related sleep disruption may be one key factor that renders the aging brain vulnerable to Aβ deposition and the development of AD. If this is true, sleep may represent a key modifiable risk factor or therapeutic target in the preclinical phases of AD.

Transcriptional network analysis of human astrocytic endfoot genes reveals region-specific associations with dementia status and tau pathology

The deposition of misfolded proteins, including amyloid beta plaques and neurofibrillary tangles is the histopathological hallmark of Alzheimer’s disease (AD). The glymphatic system, a brain-wide network of perivascular pathways that supports interstitial solute clearance, is dependent upon expression of the perivascular astroglial water channel aquaporin-4 (AQP4). Impairment of glymphatic function in the aging rodent brain is associated with reduced perivascular AQP4 localization, and in human subjects, reduced perivascular AQP4 localization is associated with AD diagnosis and pathology. Using human transcriptomic data, we demonstrate that expression of perivascular astroglial gene products dystroglycan (DAG1), dystrobrevin (DTNA) and alpha-syntrophin (SNTA1), are associated with dementia status and phosphorylated tau (P-tau) levels in temporal cortex. Gene correlation analysis reveals altered expression of a cluster of potential astrocytic endfoot components in human subjects with dementia, with increased expression associated with temporal cortical P-tau levels. The association between perivascular astroglial gene products, including DTNA and megalencephalic leukoencephalopathy with subcortical cysts 1 (MLC1) with AD status was confirmed in a second human transcriptomic dataset and in human autopsy tissue by Western blot. This suggests changes in the astroglial endfoot domain may underlie vulnerability to protein aggregation in AD.

Baseline NAWM structural integrity and CBF predict periventricular WMH expansion over time

Objective We aimed to describe and compare baseline cerebral blood flow (CBF) and microstructural characteristics of normal-appearing white matter (NAWM) within the vulnerable periventricular white matter hyperintensity (PVWMH) penumbra region in predicting white matter hyperintensity (WMH) growth over time. Methods Fifty-two patients, aged 82.8 years, underwent serial brain MRI, including pulsed arterial spin labeling and diffusion tensor imaging (DTI). New WMH and persistent NAWM voxels in relation to WMH penumbra at follow-up were identified. Mean baseline CBF and DTI variables of the new WMH and persistent NAWM voxels were computed. Univariate analyses with paired t tests were performed. Generalized estimating equation analyses were used to compare the relationships of baseline CBF, and structural penumbras with WMH growth, controlling for confounders. Results Low baseline CBF and fractional anisotropy, and high mean diffusivity (MD), were independently associated with new PVWMH voxels, with MD being the best predictor of WMH growth. A separate model demonstrated that radial diffusivity had the strongest relationship with WMH growth compared with CBF and axial diffusivity. Conclusion CBF and DTI measures independently predict WMH growth over time. DTI is a more sensitive predictor of WMH growth than CBF, with WMH progression likely due to demyelinating injury secondary to low perfusion. Findings support the use of MD as a sensitive marker of NAWM vulnerability in future trials aimed at preserving WM integrity.

REGIONAL AND MORPHOLOGICAL CHARACTERISTICS OF ENLARGED PERIVASCULAR SPACES IN RELATIONSHIP TO AMYLOID PET AND CSF Aβ IN ADNI DATASETS

over time. Methods:We used dMRI data from healthy elderly subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI: 23/73 APOE4 carriers/non-carriers; 2-4 time-points) and Singapore Longitudinal Ageing Brain Study (S-LABS: 21/73 APOE4 carriers/non-carriers; 2-3 time-points). We derived subject-specific FW and FAt maps from dMRI data and averaged over the whole-brain WM and 18 major WM fibres traced using the TRACULAmethod (Yendiki et al., 2016). Linear mixed models assessed the contribution of cross-sectional age, time and APOE genotype (E4 carrier/non-carrier) on FW and FAt longitudinal trajectories. Covariates included gender, years of education and estimated total intracranial volume. Results: APOE4 carriers exhibited larger increases in average FW over time than non-carriers in ADNI (APOE4*age*time; p<0.05) and S-LABS (APOE4*time; p<0.05; Fig.1). Specifically, APOE4 carriers had greater FW increases in the right cingulum angular bundle (CAB) for S-LABS (p1⁄40.003; Fig.2) and bilateral CAB for ADNI (p<0.05). APOE4 carriers that were older at baseline showed steeper reduction in left CAB FAt over time in S-LABS (APOE4*age*time; p<0.05; Fig.3). Moreover, APOE4 carriers had faster FAt decline (S-LABS) and greater age-dependent FW increases (ADNI) in the right inferior longitudinal fasciculus (p<0.05; Fig.4). Conclusions:Across the two datasets, we consistently demonstrated that the presence of APOE4 allele in healthy elderly appears to be associated with greater neuroinflammation, mild vascular changes and excessive WM degeneration over time in temporal-parietal and temporal-occipital fibres.

POSTMORTEM 7T MRI FOR GUIDED HISTOLOGY AND TISSUE SEGMENTATION

MK6240 SUVRw [F]AZD4694 SUVR + age + gender + APOE + education. Results:The unique association between amyloidosis and NFTs was present in entorhinal cortex and PCC in CN; precuneus, PCC, and parahippocampal gyrus in MCI; ACC, entorhinal cortex, parahippocampal gyrus, and orbitofrontal cortex in AD. All groups showed the association in lateral temporal and middle frontal gyrus. Conclusions: Our results revealed both similar and different association patterns between amyloidosis andNFTs across AD stage. Most common association pattern was present at left lateral temporal cortex across all stages while the different association pattern moved from PCC, precuneus, to orbitofrontal cortex in CN, MCI, and AD, respectively. This corroborates the two pathologies spread from the posterior to anterior regions of the brain.

MR Imaging–based Multimodal Autoidentification of Perivascular Spaces (mMAPS): Automated Morphologic Segmentation of Enlarged Perivascular Spaces at Clinical Field Strength

Purpose To describe a fully automated segmentation method that yields object-based morphologic estimates of enlarged perivascular spaces (ePVSs) in clinical-field-strength (3.0-T) magnetic resonance (MR) imaging data. Materials and Methods In this HIPAA-compliant study, MR imaging data were obtained with a 3.0-T MR imager in research participants without dementia (mean age, 85.3 years; range, 70.4-101.2 years) who had given written informed consent. This method is built on (a) relative normalized white matter, ventricular and cortical signal intensities within T1-weighted, fluid-attenuated inversion recovery, T2-weighted, and proton density data and (b) morphologic (width, volume, linearity) characterization of each resultant cluster. Visual rating was performed by three raters, including one neuroradiologist, after established single-section guidelines. Correlations between visual counts and automated counts, as well session-to-session correlation of counts within each participant, were assessed with the Pearson correlation coefficient r. Results There was a significant correlation between counts by visual raters and automated detection of ePVSs in the same section (r = 0.65, P < .001; r = 0.69, P < .001; and r = 0.54, P < .01 for raters 1, 2, and 3, respectively). With regard to visual ratings and whole-brain count consistency, average visual rating scores were highly correlated with automated detection of total burden volume (r = 0.58, P < .01) and total ePVS number (r = 0.76, P < .01). Morphology of clusters across 28 data sets was consistent with published radiographic estimates of ePVS; mean width of clusters segmented was 3.12 mm (range, 1.7-13.5 mm). Conclusion This MR imaging-based method for multimodal autoidentification of perivascular spaces yields individual whole-brain morphologic characterization of ePVS in clinical MR imaging data and is an important tool in the detailed assessment of these features.

FULLY AUTOMATED, WHOLE BRAIN MORPHOLOGICAL SEGMENTATION OF ENLARGED PERIVASCULAR SPACES AT CLINICAL FIELD STRENGTH: MRI-BASED MULTIMODAL AUTOIDENTIFICATION OF PERIVASCULAR SPACES (MMAPS)

and global (right) rich club network between EOAD and CTL. Left. Nodes and connections in red indicate rich club components averaged across all non-amnestic and amnestic EOAD subjects; Most affected cortical regions with a decrease in nodal degree are indicated in blue (FDR p-value1⁄40.010); blue large spheres are part of the rich club network. EOAD targets the rich club nodes of the left hemisphere, but the overall organization of the network is preserved as seen in the global normalized rich club coefficient (right) –where only high degree nodes were altered in EOAD (purple curve, red dots) (k>13), relative to controls (blue curve) (FDR p-value1⁄40.016). Low degree nodes were spared (k<13), possibly preserving the global structure of the brain network in EOAD.