The PREVENT-AD Research Group,

bioRxiv2026

CSF complement proteins are associated with early tau pathology and synaptic damage in an asymptomatic population at risk of Alzheimer's disease

Complement-mediated neuroinflammation has been implicated in Alzheimer's disease (AD), but its role during the pre-symptomatic phase of the disease remains unclear. In the PREVENT-AD cohort of cognitively unimpaired individuals at increased familial risk of AD, we investigated whether CSF complement proteins relate to early AD pathology and synaptic dysfunction, then assessed our results' reproducibility across the clinical AD spectrum. Baseline CSF C1q, C3, C3b, and Factor H were measured in relation to CSF AD biomarkers, synaptic proteins, cognition, MRI volumetry, and amyloid and tau PET. Key findings were then examined in 708 participants from ADNI spanning cognitively normal, mild cognitive impairment (MCI), and dementia stages of AD. In PREVENT-AD, C1q was positively associated with CSF P-tau181, T-tau, and multiple synaptic markers including ADAM23, GAP43, SNAP25, and SYT1. Factor H showed similarly strong positive associations with P-tau181, T-tau, ADAM22, ADAM23, GAP43, and SYT1. By contrast, C3 showed minimal associations, while C3b displayed weaker positive relationships with P-tau181, T-tau, ADAM22, and ADAM23. Complement proteins were not robustly associated with amyloid or tau PET, and only C1q related to lower global cognitive performance. In ADNI, C1q emerged as the most consistent analyte, showing positive associations with tau, neurofilament light, and synaptic markers across all diagnostic groups. C3 exhibited predominantly negative associations, whereas C3b and Factor H showed stage-dependent relationships, particularly with evident neurodegeneration and synaptic injury in symptomatic individuals. These findings identify complement dysregulation, especially involving C1q, as an early correlate of tau-linked synaptic pathology, and support a role for complement activation in the AD molecular cascade.

neuroscience↗

bioRxiv2023

Ventromedial Frontoinsular Connectivity is Associated with Long-term Smoking Behavior Change in Aging

A central question in the field of cognitive aging and behavioral neuroscience is what enables some individuals to successfully change their behavior more than others? Smoking is a significant risk factor for cognitive decline, particularly in vulnerable populations, including those who are at an elevated risk for Alzheimers disease (AD). Developing effective smoking reduction strategies is therefore a public health priority. The goal of the current study is to better understand the brain mechanisms underlying long-term smoking behavior change in cognitively normal, but at-risk, older adults. Neuroimaging and human lesional studies have implicated the insula and its functional network in subjective interoceptive awareness of cigarette craving and smoking-cue reactivity. We sought to characterize the extent to which anterior insular resting-state functional connectivity MRI predicted long-term smoking reduction (mean: 2.7 years, range 8 months - 4 years) using a seed-to-voxel approach. Twenty-three (18 women; 26% APOE4 carriers; 61.5 years, SD = 3.7) cognitively unimpaired older individuals who smoked cigarettes at their baseline visit and have a first-degree family history of AD (at least one parent or multiple siblings affected) were included from a prospective longitudinal cohort, PREVENT-AD (Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease) in the current study. We found that reduced long-term smoking behavior was associated with diminished functional connectivity between bilateral anterior insula and ventromedial prefrontal cortex (vmPFC). In a second pre-registered replication study within a larger, independent sample of one hundred and eighteen cognitively normal older adults who smoked cigarettes at baseline from the UK Biobank (73 women; 27.9 % APOE4 carriers; 60.3 years, SD = 2.7), we found that baseline diminished resting-state functional connectivity between anterior insula and vmPFC predicted long-term smoking reduction (mean 5.2 years; ranging from 3 years to 7 years). To our knowledge, this is the largest study to examine the neural substrates of long-term smoking cessation in human aging. Our results suggest that frontoinsular circuits may be a therapeutic target for smoking reduction and disease prevention in older adults at risk for AD.

neuroscience↗

bioRxiv2023

Study design features that improve effect sizes in cross-sectional and longitudinal brain-wide association studies

Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimers Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between-versus within-subject associations of the brain and non-brain measures. Explicitly modeling between-versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.

neuroscience↗

bioRxiv2021

DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89%accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

neuroscience↗

bioRxiv2018

Deep learning of quality control for stereotaxic registration of human brain MRI

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the following image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none of them has a 100% success rate. Manual assessment of the registration is commonly used as part of quality control.\n\nWe propose a completely automatic quality control method based on deep learning that replaces human rater and accurately performs quality control assessment for stereotaxic registration of T1w brain scans.\n\nIn a recently published study from our group comparing linear registration methods, we used a database of 9693 MRI scans from several publically available datasets and applied five linear registration tools. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed.\n\nOur method was able to achieve 88% accuracy and 11% false positive rate in detecting scans that should pass quality control, better than a manual QC rater.

neuroscience↗