Biology subjects

Johnson, K. G.

Publications and source records attributed to Johnson, K. G..

4 recordsLinked to original sources

bioRxiv2026

Calibration-Aware and Interpretable Graph Learning for Multi-Cohort Diffusion Connectome Brain-Age Modeling

Brain-age models derived from diffusion MRI-based structural connectomes may provide imaging biomarkers of accelerated brain aging, but their biological interpretation and transportability across heterogeneous populations remain uncertain. We developed a calibration-aware and hierarchically interpretable graph-learning framework and evaluated it across four independent aging and Alzheimer's disease-related cohorts: ADNI, Duke/UNC ADRC, HABS-HD, and AD-DECODE. The analysis included 1,093 connectome sessions from 789 participants. Cohort-specific graph neural networks were trained using participant-grouped cross-validation across five imaging and multimodal feature configurations. Prediction performance varied more strongly across cohorts than across feature sets, with imaging-only out-of-fold mean absolute error ranging from 4.72 years in ADNI to 9.75 years in AD-DECODE. The imaging-only graph neural network was competitive with ridge, elastic-net, and gradient-boosted regression models trained on matched vectorized connectome features, but was not uniformly superior. Age-bias-corrected brain-age gap was most consistently associated with reduced diffusion-derived microstructural integrity and structural-network organization across cohorts. In longitudinal analyses, corrected brain-age gap showed moderate-to-good within-person preservation in ADNI and HABS-HD, with intraclass correlation coefficients of 0.67 and 0.81, respectively; higher baseline values also predicted subsequent microstructural and network deterioration in ADNI. Multiscale SHAP analysis identified distributed contributions from global graph topology, regional imaging features, edge-derived regional summaries, and individual structural connections involving thalamic, striatal, frontal, parietal, cerebellar, hippocampal, and entorhinal circuitry. External transfer was highly sensitive to cohort shift: across 12 off-diagonal train-test evaluations, median mean absolute error decreased from 17.39 to 8.22 years after target-cohort linear recalibration, whereas median Pearson correlation remained 0.17. Because recalibration used target-cohort chronological age, it was interpreted as a diagnostic sensitivity analysis rather than deployable external validation. Together, these findings support calibration-aware diffusion-connectome brain age as an interpretable imaging biomarker of structural brain aging and prospective microstructural and network vulnerability, while emphasizing the need for cohort-specific calibration before external application.

neuroscience↗

bioRxiv2025

Olfactory biopsy analysis of Alzheimer's pathobiology across disease stages

Alzheimers Disease (AD) is a neurodegenerative condition affecting millions worldwide. Defining early pathobiological events remains challenging, in part due to inaccessibility of neural tissue. Because olfactory neurons are accessible, and olfactory loss is prevalent in AD, we evaluated olfactory brush biopsies from controls, individuals with cerebrospinal fluid (CSF) biomarker-confirmed AD, and cognitively typical individuals whose positive biomarkers signal a pre-clinical AD stage. We define via single cell RNA-sequencing (n=22 subjects) conserved neuroinflammatory T cell, myeloid, and olfactory neuron changes detectable even in pre-clinical AD subjects. Activated memory T cell states were a hallmark of pre-clinical AD, paralleling CSF T cell phenotypes seen in advanced disease, accompanied by both microglia-like inflammatory programs and olfactory neuron inflammatory injury. Together, our findings establish a novel platform permitting analysis of neural tissue in AD at its earliest stages.

neuroscience↗

bioRxiv2024

Mapping the impact of age and APOE risk factors for late onset Alzheimer disease on long range brain connections through multiscale bundle analysis

Alzheimers disease currently has no cure and is usually detected too late for interventions to be effective. In this study we have focused on cognitively normal subjects to study the impact of risk factors on their long-range brain connections. To detect vulnerable connections, we devised a multiscale, hierarchical method for spatial clustering of the whole brain tractogram and examined the impact of age and APOE allelic variation on cognitive abilities and bundle properties including texture e.g., mean fractional anisotropy, variability, and geometric properties including streamline length, volume, and shape, as well as asymmetry. We found that the third level subdivision in the bundle hierarchy provided the most sensitive ability to detect age and genotype differences associated with risk factors. Our results indicate that frontal bundles were a major age predictor, while the occipital cortex and cerebellar connections were important risk predictors that were heavily genotype dependent, and showed accelerated decline in fractional anisotropy, shape similarity, and increased asymmetry. Cognitive metrics related to olfactory memory were mapped to bundles, providing possible early markers of neurodegeneration. In addition, physiological metrics such as diastolic blood pressure were associated with changes in white matter tracts. Our novel method for a data driven analysis of sensitive changes in tractography may differentiate populations at risk for AD and isolate specific vulnerable networks.

neuroscience↗

bioRxiv2022

Vulnerable Brain Networks Associated with Risk for Alzheimer's Disease

Brain connectomes provide untapped potential for identifying individuals at risk for Alzheimers disease (AD), and can help provide novel targets based on selective circuit vulnerability. Age, APOE4 genotype, and female sex are thought to contribute to the selective vulnerability of brain networks in Alzheimers disease, in a manner that differentiates pathological versus normal aging. These brain networks may predict pathology otherwise hard to detect, decades before overt disease manifestation and cognitive decline. Uncovering network based biomarkers at prodromal, asymptomatic stages may offer new windows of opportunity for interventions, either therapeutic or preventive. We used a sample of 72 people across the age span to model the relationship between Alzheimers disease risk and vulnerable brain networks. Sparse Canonical Correlation analysis (SCCA) revealed relationships between brain subgraphs and AD risk, with bootstrap based confidence intervals. When constructing a composite AD risk factor based on sex, age, genotype, the highest weight was associated with genotype. Next, we mapped networks associated with auditory, visual, and olfactory memory, and identified networks extending beyond the main nodes known to be involved in these functions. The inclusion of cognitive metrics in a composite risk factor pointed to vulnerable networks, and associated with the specific memory tests. These regions with the highest cumulative degree of connectivity in our studies were the pericalcarine, insula, banks of the superior sulcus and cerebellum. To help scale up our approach, we extended Tensor Network Principal Component Analysis (TNPCA) to evaluate AD risk related subgraphs, introducing CCA components and sparsity. When constructing a composite AD risk factor based on sex, age, and genotype, and family risk factor the most significant risk was associated with age. Our sparse regression based predictive models revealed vulnerable networks associated with known risk factors. The prediction error was 17% for genotype, 24% for family risk factor, and 5 years for age. Age prediction in groups including MCI and AD subjects involved several regions that were not prominent for age prediction otherwise. These regions included the middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. The joint estimation of AD risk and connectome based mappings involved the cuneus, temporal, and cingulate cortices known to be associated with AD, and add new candidates, such as the cerebellum, whose role in AD is to be understood. Our predictive modeling approaches for AD risk factors represent a stepping stone towards single subject prediction, based on distances from normative graphs.

neuroscience↗