Hope Center for Neurological Disorders

Cerebrospinal fluid (CSF) biomarkers are central to Alzheimer's disease (AD) diagnosis and research. However, CSF composition is shaped not only by neurodegeneration, but also by underlying physiological and pathological processes that remain poorly characterized. By integrating multi-omics data from the deeply characterized memory-clinic ACE CSF cohort (N=1,372), the Global Neurodegeneration Proteomics Consortium (N=1,863), and publicly available quantitative trait loci data, we reveal that 73.2-85.9% of the molecular variance in CSF omics data is driven by two main factors: one reflecting CSF turnover rate, and another representing blood-brain barrier (BBB) integrity. CSF turnover mainly determines brain-derived molecules, while BBB damage leads to increased blood-derived protein abundance. CSF turnover/clearance severely impacted core AD biomarker levels, affecting the classification of subjects in the A/T framework. Adjusting biomarker levels for OPCML, a novel reference marker, improved biomarker-based prediction of AD progression and removed confounded associations, revealing a proteomic signature of sporadic AD pathology that closely resembles that of autosomal dominant AD. Finally, using the ACE CSF cohort as discovery (N=1,221) and Knight ADRC as replication (N=1,073), we report a curated AD signature comprising 446 unique proteins. Our findings identify CSF dynamics as a major source of molecular variation, reshaping the interpretation of CSF biomarkers.

BackgroundThere is increasing evidence for an association between white matter hyperintensities (WMH) and brain beta‐amyloid deposition. WMH are not exclusive correlates of a single etiology, and the spatial topography can associate with different pathologic markers of vascular or neurodegenerative disease. How WMH are longitudinally associated with brain beta‐amyloid burden requires further investigation, particularly with respect to co‐existent vascular risk factors and differences across brain regions.MethodWe retrospectively measured WMH on MRI and vascular risk factors in a combined neuroimaging data set comprised of the ADNI, AIBL and OASIS3 studies, which included harmonized centiloid estimates of beta‐amyloid burden from PET imaging. WMH were measured using the TrUE‐Net algorithm. Vascular risk factors were extracted from provided clinical data and used to calculate individual revised Framingham Stroke Risk Profile (FSRP) scores. Five established data‐driven WM regions (juxtacortical, deep frontal, periventricular, parietal, posterior) were used for regional relationships with WMH volume and growth. Linear mixed effects modelling was used to determine the relationship between the growth rate of normalized regional WMH volumes and baseline beta‐amyloid burden, controlling for age, sex, APOE4 status, and vascular risk factors.Result1245 participants [48.7% female, mean age 71.7 y (SD 7.6 y)] had at least 3 brain MRIs suitable for WMH volume measurement. Linear mixed models demonstrate robust independent cross‐sectional relationships between WMH and baseline beta‐amyloid burden (p <0.001), age (p <0.001) and FSRP (p <0.001). Growth rates of WMH increased with baseline beta‐amyloid burden (p <0.05) and decreased with vascular risk (p <0.001), above and beyond age, sex, and APOE4 status. Regional analyses revealed both baseline beta‐amyloid burden and FSRP associated with the juxtacortical deep frontal and periventricular regions, but the parietal region was unique to beta‐amyloid (p <0.05). Longitudinally, the association for beta‐amyloid burden (p <0.005) persisted in only the parietal WMH and no normalized regional values associated longitudinally with FSRP.ConclusionOur study suggests that in Alzheimer disease research cohorts, WMH progression is associated with beta‐amyloid burden, particularly in parietal white matter. Vascular risk associated WMH were influential for WMH volume but did not associate with WMH progression in a regionally specific manner.

BackgroundAmyloid‐Related Imaging Abnormalities (ARIA), characterized by vasogenic edema or sulcal effusions (ARIA‐E) or hemorrhagic lesions (microhemorrhages, superficial siderosis, or, rarely, microhemorrhages) (ARIA‐H), remain a significant challenge in monoclonal antibody (mAb) therapy for AD (1). Despite their clinical importance, the mechanisms underlying ARIA, particularly neuroinflammation, remain poorly understood. Advanced imaging techniques are crucial for elucidating these processes. Generalized diffusion basis spectrum imaging (g‐DBSI), an advanced diffusion MRI method evolved from DBSI (2,3), can reveal microstructural changes linked to neuroinflammation and vasogenic edema in white and gray matter. This study aims to use g‐DBSI to investigate the microstructural alterations linked to neuroinflammation and vasogenic edema during the development and recovery of ARIA‐E in the context of mAb treatment.MethodThis pilot study included three participants from the DIAN‐TU‐001 trial who received Gantenerumab (4) and underwent longitudinal g‐DBSI scans. ARIA assessment was conducted by a panel of trained neuroradiologists using an FDA grading scale and standardized structured reporting templates, as recommended by the American Society for Neuroradiology (5). Regions of interest (ROIs) were delineated on the FLAIR sequence in areas exhibiting ARIA‐E. Within these ROIs, we quantified the g‐DBSI‐derived restricted ratio (RR, a putative marker of inflammation) and hindered ratio (HR, a putative marker of vasogenic edema).ResultFor the first participant, an elevated g‐DBSI‐derived RR and HR were observed one month before the onset of ARIA‐E in the third participant compared to the 2‐year follow‐up g‐DBSI (Figure 1A). For the second participant, the initial g‐DBSI scan during ARIA‐E onset showed elevated RR and HR, which decreased significantly in the follow‐up scan two years later (Figure 1B). For the third participant, g‐DBSI‐derived RR and HR remained stable between 1‐ and 2‐year follow‐ups after the resolution of ARIA‐E (Figure 1C).ConclusionOur findings suggest neuroinflammation's role in ARIA‐E progression and demonstrate the potential of g‐DBSI to detect microstructural changes before the onset of ARIA‐E. This pilot study supports g‐DBSI as a noninvasive imaging tool to monitor ARIA progression, providing insights into neuroinflammation and vasogenic edema. Further research with larger cohorts is necessary to further delineate and validate the potential clinical utility of g‐DBSI.

BackgroundEarly identification of neuropathological changes in Alzheimer's disease (AD) is essential for the development and application of disease‐modifying therapies. Currently, brain accumulation of β‐amyloid (Aβ) and/or CSF amyloid level reduction are considered among the earliest AD biomarkers, and both are indirectly associated with brain neurodegeneration. Direct methods identifying neurodegeneration are usually based on MRI measurements of brain volume reduction (atrophy). Herein we use a quantitative Gradient‐Recalled‐Echo (qGRE) MRI method, providing a sensitive biomarker (Neuronal Density Index, NDI) for quantifying the neuronal cell damage (pre‐atrophic neurodegeneration) before it can be detected by atrophy measurements. While atrophy appears at late stages of amyloidosis, our results show that NDI can serve as a biomarker of pre‐atrophic neurodegeneration coincident with early amyloidosis.Methods143 participants, ages between 48 and 88 (69.5±7.1; 85 females; 58 males), recruited through the Knight ADRC and SEABIRD study, provided informed consent. qGRE and PET amyloid 11C‐PiB images were acquired concurrently. For each participant, amyloidosis level was identified by mean cortical standard uptake ratio (mcSUVr), while NDI was quantitated in all cortical FreeSurfer‐defined brain regions. Group differences between participants’ NDIs were established for a range of mcSUVR cutoff levels.ResultsA standard mcSUVR cutoff of 1.42 is usually used to separate participants into Aβ(‐) and Aβ(+) groups. Results in Figure 1 show dramatic pre‐atrophic neurodegeneration in Aβ(+) group with NDI significantly decreased in multiple brain regions, while significant volume changes were observed only in frontal and insular regions. Furthermore, the NDI exhibited significant group differences in multiple brain regions even when mcSUVR cut‐off threshold was reduced to 1.2. Importantly, NDI exhibited significant changes in medial temporal lobe even when comparing groups with mcSUVr < 1.2 and mcSUVR between 1.2 and 1.6 (Figure 2). In contrast, volume measurements did not show significant differences until participants with mcSUVR greater than 3.4 were included in the analysis.ConclusionqGRE‐based NDI provides in‐vivo microstructural brain neurodegeneration distribution in early amyloid pathology where atrophy is not detectable. Although the NDI parameter reflects neuronal cell density, presence of inflammation can also reduce the NDI value. Thus, further investigations are required to understand the microstructural nature of pre‐atrophic neurodegeneration.

Time proxies based on blood biomarkers may facilitate understanding of the timing of biomarker change and symptom onset in Alzheimer's disease (AD) and could potentially enable biological staging of AD. Compared to CSF biomarkers or brain imaging, blood biomarkers are more accessible and less burdensome, making a "plasma clock" a practical and useful tool. Longitudinal plasma %p-tau217 (p-tau217 concentration divided by np-tau217 concentration x 100) measurements from individuals enrolled in the Knight ADRC (n=420) or ADNI (n=392) cohorts were analyzed to develop plasma clocks for each cohort. The %p-tau217 values were transformed into a time scale by integrating the inverse of the modeled rate of change. A %p-tau217 value of 4.06%, which corresponds to amyloid PET Centiloid=20, was considered the threshold for positivity and set as time zero. The estimated age at %p-tau217 positivity was then used to predict symptom onset, defined as a change in Clinical Dementia Rating® (CDR) from CDR=0 to CDR>0. The rate of change for plasma %p-tau217 was relatively consistent between 0.65-8.16% (Figure 1) and was used to construct independent plasma %p-tau217 clocks for the Knight ADRC and ADNI cohorts (Figure 2). For individuals with plasma %p-tau217 measured before and after positivity (4.06%), the estimated age at positivity based on the clock was strongly correlated with the actual age at conversion (Knight ADRC: R2=0.646, r=0.807; ADNI: R2=0.790, r=0.891). The Knight ADRC and ADNI plasma clocks provided highly similar estimates of the age at %p-tau217 positivity for individuals in either study (adjusted R2=0.993, Pearson r=0.997, Figure 2). The estimated age at plasma %p-tau217 positivity predicted the age at symptom onset (Figure 3) in the Knight ADRC (Spearman r=0.421, adjusted R2=0.151) and ADNI cohorts (Spearman r=0.648, adjusted R2=0.397). Two independently constructed %p-tau217 plasma clocks had strong correlations with actual time measures and were consistent across the two cohorts. Additionally, the age at %p-tau217 positivity predicted symptom onset. Overall, these findings suggest that plasma %p-tau217 may be useful in predicting symptom onset and in biological staging of AD.

Alzheimer disease participants with autosomal dominant mutations in PSEN1, PSEN2, and APP (ADAD) exhibit variability in clinical presentation (age at onset and clinical symptoms), even within the same family. We hypothesized that even though ADAD participants carry highly penetrant mutations, additional and more common genetics variants may influence the risk of developing the disease. Therefore, we aimed to identify genetic modifiers of disease risk in ADAD mutation carriers using a case/control approach. We conducted a genome-wide association study assessing the effect of common genetic variants (minor allele frequency <0.005) on AD risk using a logistic regression model. Our cohort included whole genome sequencing data from 101 unrelated symptomatic participants with ADAD mutations and 5,050 asymptomatic unrelated control participants without ADAD mutations of non-Hispanic European ancestry. Participants were recruited through the Charles F. and Joanne Knight Alzheimer Disease Research Center, Alzheimer's Disease Sequencing Project, and Dominantly Inherited Alzheimer Network. To assess the effect of the newly identified loci, we performed age at onset analysis using a linear regression model, and Kaplan-Meier survival analysis. Segregation analyses were conducted within families. Finally, we conducted linear regression analyses on cerebrospinal fluid biomarkers (Aβ42/40, tTau, pTau181) obtained with Lumipulse assay for 91 participants. All analyses were adjusted for sex, 10 first principal components and cohort when appropriate. We identified four genome-wide significant loci associated with AD risk (Table 1, Figure 1). Remarkably, the association signal at the CNIH4 locus was driven by a missense variant (p.Gly54Ser). The rare allele at the CCNG1 locus reduced the age at AD onset (p-value=6.81×10-03, beta=-10.15). This finding was further confirmed using Kaplan-Meier analysis (p-value=9.3×10-04) with a median age at onset was 48 for non-carriers and 40 for carriers. The rare allele was consistently present in participants with an earlier age at onset in two independent families. Finally, at the RHOJ locus, the rare allele was associated with increased tTau (p-value=4.74×10-05) and pTau181 (p-value=4.07×10-07), and decreased Aβ42/40 ratio (p-value=4.32×10-05). We identified 4 genetic modifiers that influence disease risk and affect multiple ADAD mutations, offering insights for future clinical trials and genetic counseling.

En bloc spondylectomies are essential for controlling malignant spinal tumors, but existing classification systems are often complex and impractical. This study introduces the Column, Line-of-Sight, Margins, Ring (CLiMR) framework, a novel system simplifying and standardizing en bloc spondylectomy planning and classification. A retrospective review of patients who underwent single- or multistage en bloc spinal tumor resections was conducted at a single academic center (October 2019-December 2024). The CLiMR framework assigns scores to 4 components: Column (osteotomies/diskectomies for tumor release), Line-of-Sight (approach for circumferential access), Margin (adjacent structures sacrificed), and Ring (size of the vertebral ring window for safe delivery of neural tissue). Scores were applied using operative notes and imaging. Column scores ranged from 0.5 to 4.5. Of the patients included, 40.9% underwent sagittal split osteotomies, 22.7% radical diskectomies, 18.2% horizontal osteotomies, and the remaining 18.2% combinations of previous osteotomies . A posterior-only approach (Line-of-Sight score = 1) was used in 45.4% of cases, whereas 45.4% required a multistage approach (score = 2). Margin scores reflected increasing morbidity: 31.8% (n = 7) involving chest wall/pleura resection (grade 3), 40.9% (n = 9) requiring eloquent nerve sacrifice (grade 6), and 9.1% (n = 2) necessitating limb sacrifice (grade 7). The majority (86.4%, n = 19) of ring scores were grade 1, requiring laminectomies/facetectomies. The mean CLiMR score was 9.36 ± 2.43, with higher scores correlating with longer hospital stays (R = 0.64, P = .0012), longer case durations (R = 0.61, P = .0023), and increased blood loss (R = 0.40, P = .067). Patients with local recurrence, positive margins, or complications trended toward greater CLiMR scores. The CLiMR framework provides a novel, user-friendly method for planning and communicating en bloc spondylectomies. By addressing technical complexities and morbidity, CLiMR enables standardized assessment, facilitates surgical decision-making, and supports future research. This study demonstrates feasibility and lays the foundation for future validation studies.

While scientific research has extensively explored how the brain integrates touch and pain signals, the cerebral processing of specific vibrotactile frequencies remains poorly understood. This gap is particularly significant given clinical evidence that vibrotactile stimulation can reduce pain in both chronic pain patients and experimental settings. Our study investigated the cortical electrophysiological correlates of peripheral vibrotactile stimulation across different frequencies in healthy volunteers, with a focus on frequency-dependent patterns of neuronal activation. While electroencephalogram (EEG) was recorded, healthy participants received vibrotactile stimulation (high-frequency burst stimulation with different inter-burst intervals) to the left index fingertip at frequencies corresponding to established neural rhythms: delta (2 Hz), theta (6 Hz), alpha (12 Hz), beta (20 Hz), and gamma (40 Hz). We compared the EEG bandwidth activity between vibrotactile stimulation conditions relative to resting baseline. Our findings demonstrated that vibrotactile stimulation produces distinct frequency-dependent patterns of cortical activation. A key finding was that 6 Hz stimulation selectively enhanced theta power in the left prefrontal cortex - an electrophysiological signature previously linked to successful pain relief. These findings advance the understanding of the “spectrotopic” nature of vibrotactile frequency processing in the cortex and provide a mechanistic foundation for developing novel vibration-based therapies in the future.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-14870-8.