Juan Antonio Kim Hoo Chong Chie

Emerging clinical and neuropathological evidence suggests a direct link between traumatic brain injury (TBI) and neurodegenerative diseases (ND), but the mechanisms by which TBI contributes to ND onset and progression remain unclear. To bridge this gap, the NINDS TBI-ADRD consortium developed a diagnostic framework to guide the design and evaluation of TBI-ADRD experiments using translational neuroimaging (DCE-MRI, ¹⁸ F-FDG-PET) and advanced image-processing pipelines. Neuro-metabolic and vascular dysregulation (MVD) and metabolic functional connectomics (MFC) were evaluated in 8-month C57BL/6J mice at 30- and 60-days post-injury (DPI). Both MVD and MFC revealed significant changes at 30- and 60-DPI that were sex- and injury side-dependent. Female mice showed greater regional MVD alterations, while MFC identified sex-dependent changes at 30-DPI that diminished by 60-DPI. These findings suggest that a single mid-life closed-head injury induces sexually dimorphic MVD and brain network remodeling, supporting the potential of MVD as a sensitive biomarker for improved clinical monitoring post-TBI.

Background: Advancements in medical imaging have enabled non-invasive diagnosis and staging of cystic fibrosis (CF) using CT scans, revealing dilated airways, an increased number of visible airways, and airway generation splits in these patients. However, manual characterization of airways remains time-consuming and challenging due to the numerous structural changes, thereby limiting clinical feasibility. This study aims to develop an automated algorithm to characterize airways from segmented lung CT scans and apply this to a retrospective population. This approach reduces the time required to analyze images and obtain disease-staging results. Methods: This framework consists of two stages. The first stage extracts and skeletonizes the airway tree from lung CTs, while the second stage measures lung features, including airway volumes, branch counts, generation splits, diameters, and cross-sectional areas. This permits comprehensive characterization for use in clinical assessment. Results: The airways analysis was performed on 169 CT volumes ranging in age from 6 to 18 years of age, revealing substantial differences in detected airway branches, generation splits, and normalized airway volume between the control and CF groups. The framework also measures airway diameters and cross-sectional areas, revealing an increase in the number of small airways in cystic fibrosis patients, due to early bronchiectasis. These findings align with previous research and demonstrate the framework’s ability to accurately quantify airway changes in patients with CF. Discussion: The framework extracts entire airway trees, facilitating measurements of volume, branch count, diameters, and cross-sectional areas, which change with CF severity and/or treatment. However, partial lung atelectasis can limit the accuracy of airway detection in moderate-to-severe cases. Funding: NIA U54 AG054345 and NIA R21 AG07857501

Historically, imaging diagnostics in Alzheimer's disease (AD) have focused primarily on amyloid and tau accumulation; however, recent work suggests that neurometabolic and vascular dysregulation (MVD) may precede protein deposition and persist throughout the disease spectrum, preclinically and clinically. Translating these findings between human patients and preclinical mouse models remains challenging due to cross‐species differences. To address this, regional MVD phenotypes were identified using cerebral metabolism and blood flow, and region‐set enrichment analysis (RSEA) was conducted to assess brain functional category (BFC) changes based on metabolic variations, facilitating systematic cross‐species comparisons. Clinically, MVD showed progressive alterations across the AD spectrum, while mouse models demonstrated similar genotype‐ and age‐dependent changes. Although direct one‐to‐one regional correspondence is limited, RSEA revealed changes in comparable BFCs. Our findings suggest that imaging‐based MVD mapping and RSEAs can bridge species differences, offering a translational framework to support early diagnostics of AD, enhance disease stratification, and enable therapeutic testing.

INTRODUCTION: Functional connectomics studies leverage the power of interregional brain relationships using graph theory of glycolytic metabolism to establish neural connections and their roles in cognition and disease and to monitor therapeutic responses. METHODS: Using a retrospective clinical population ( N = 431) from ADNI, we evaluated disease changes using metabolic covariance analysis. In addition, we developed a novel region set enrichment analysis (RSEA) to detect brain functional changes based on metabolic variations. Results were aligned with transcriptomic signatures and clinical cognitive assessments (CCAs). RESULTS: Our findings highlight sexual dimorphic changes across the disease spectrum, which suggest brain network reorganization occurs as compensatory mechanisms due to pathological disruptions. RSEA indicated functional changes in motor, memory, language, and cognitive functions related to disease progression, and these changes were supported by transcriptomic signatures. DISCUSSION: Together, metabolic covariance analysis, regional connectomics, and RSEA allow for AD progression tracking and functional alteration identification based on metabolic readouts, consistent with CCA.

Background Individual clinical cognitive assessments (CCA) for Alzheimer's disease (AD) provide broad disease stratification but are limited in sensitivity and specificity, requiring integration of multiple CCA for optimal disease staging. Recent work from our lab suggests that neuro-metabolic and vascular dysregulation (MVD) occurs early in AD, prior to clinical symptoms, and may provide higher sensitivity and specificity than CCA alone. In this study, we combined three widely accepted CCA with MVD readouts and developed a multimodal ensemble machine learning approach across the AD spectrum to predict disease stage and grade. Methods AD subjects (N=372) across the disease spectrum with imaging (PET:18F-FDG, MRI:T1w, T2 FLAIR, ASL) and CCAs (ADAS-Cog, CDR, MoCA) data were analyzed from ADNI. Imaging data were registered to MNI152+, z-scored relative to cognitively normal controls, and processed for MVD. A clinical-set-enrichment analysis (CSEA) was developed to link regional brain changes with CCA scores, map changes to functional categories, project them into a 3D Cartesian space, and model trajectories, thus revealing at-risk and resilient regions. In addition, an ensemble machine-learning approach was utilized for disease stage classification, and a disease grading scheme across the AD spectrum was developed to further stratify within disease stages. Findings Regional data followed an MVD pattern across AD stages stratified by CSEA scores. Females showed greater stage separation along the CCA axis within each region, indicating faster disease progression. Moreover, progression in at-risk brain regions (e.g., mid- and inf-temporal gyri, amygdala) was associated with longer disease path lengths, whereas progression in resilient brain regions (supramarginal gyrus) was not. Moreover, our classification and grading approach can predict AD stage and grade independent of amyloid-beta and tau with high precision and accuracy. Interpretation A framework was developed to evaluate MVD and CCA variations across the AD spectrum, thereby distinguishing at-risk and resilient brain regions. Distinct disease trajectories were identified, and a new data-driven grading scheme was proposed to highlight the potential for precision medicine and therapeutic evaluation. Funding NIH T32AG071444

Background: Historically, clinical diagnosis of ADRD via medical imaging has largely focused on singular modalities late in the disease process to identify structural and/or functional changes; however, by this point, irreversible brain damage has occurred, thus resulting in a lack of sensitivity and specificity of the measure. Since treatment planning is often based on these readouts, outcomes are often compromised, leading to ineffective treatment responses. Recent preclinical data suggests that regional metabolic and vascular dysfunction (MVD) persist across the disease spectrum; however, few studies have explored these combined measures in ADRD. Therefore, we hypothesized that MVD may serve as a sensitive and early noninvasive diagnostic tool, and tested this in a retrospective clinical population spanning from CN to AD. Method: Subjects from ADNI who were scanned with ¹⁸ F‐FDG PET and ASL MRI within 180 days were included ( N = 290; CN(91), MCI(116), AD(83), M/F(51.2/48.8%)). Cerebral blood flood (CBF) and images were generated from ASL images using ExploreASL. PET and CBF images were registered to the augmented MNI152+ Atlas. Mean intensities for unilateral 59 brain regions were ratioed to whole brain values, z ‐scores computed for each region (relative to CN), and were projected into Cartesian space. Statistical analysis of at‐risk regions was evaluated using Student's t ‐test ( p 18 F‐FDG,↑CBF) and Type 2 (↑ ¹⁸ F‐FDG,↓CBF) uncoupling MVD (Figure 1B); where AD subjects showed prodromal (↑ ¹⁸ F‐FDG,↑CBF) and neuro‐metabolic‐vascular failure (↓ ¹⁸ F‐FDG,↓CBF) MVD phenotype (Figure 1C). Importantly, these changes aligned with transcriptomic and cognitive signatures. Conclusion: Our findings show that MVD of the at‐risk brain regions depends on sex, APOE status, age, and disease stage. Moreover, our data reveals that MVD patterns can provide a sensitive diagnostic tool for early diagnosis of ADRD, which may improve patient monitoring, stratification, and therapeutic testing.

Background: MODEL‐AD has developed and characterized novel mouse models that aim to better phenocopy human LOAD for preclinical testing. More than 40 different models carrying combinations of LOAD risk factors have been created and assessed for relevance to LOAD. In this study, we aim to determine the mechanisms by which variations in the phospholipase C gamma 2 ( PLCG2 ) gene drives the increase risk for LOAD with or without high‐fat diet (HFD). Method: The M28L variant in PLCG2 ( PLCG2*M28L ) was prioritized as a LOAD risk factor. Mice were generated by CRISPR/CAS9 introduction of Plcg2*M28L variant on to LOAD2 (B6. APOE4 . Trem2*R47H.hAβ) and transcriptional profiled at 18 months. To evaluate the contribution of LOAD risk, LOAD2 and LOAD2. Plcg2*M28L mice were fed control or HFD as a driver of neuroinflammation. Female and male mice were characterized at 18 months via the MODEL‐AD phenotyping pipeline. Uncoupling and connectomics analysis of in vivo brain perfusion and glycolytic metabolism via PET/CT were performed. Result: Uncoupling analysis of LOAD2 on HFD and LOAD2. Plcg2*M28L mice both showed a prodromal (↑18F‐FDG,↑CBF) phenotype relative to LOAD2 mice. However, when LOAD2. Plcg2*M28L were fed HFD, they showed Type 2 (↑18F‐FDG,↓CBF) uncoupling phenotype relative to LOAD2. Plcg2*M28L mice. This suggests that the combination of risk factors is not simply additive. Multi‐resolution connectomics revealed a difference in clusters numbers and organization between male and females. LOAD2. Plcg2*M28L on HFD relative to LOAD2. Plcg2*M28L showed fewer clusters compared to LOAD2 on HFD and LOAD2. Plcg2*M28L mice. In addition, significant changes in clustering coefficient, positive nodal strength, and negative nodal strength were found. Region set enrichment analysis in LOAD2 on HFD and LOAD2. Plcg2*M28L mice showed similar phenotypic changes; however, LOAD2. Plcg2*M28L on HFD relative to LOAD2. Plcg2*M28L showed a reduction in modules and different endophenotypes. Conclusion: Data collected to date revealed that Plcg2 and HFD have a similar effect on the end biology of the brain. Moreover, this suggests Plcg2 is involved in an inflammatory process in a similar manner to HFD; however, Plcg2 in the presence of HFD showed to be responsive to the diet effect in a regulatory manner which reduced disease stage.

Background: Historically, clinical diagnosis of ADRD via medical imaging has largely focused on singular modalities late in the disease process to identify structural and/or functional changes; however, by this point, irreversible brain damage has occurred, thus resulting in a lack of sensitivity and specificity of the measure. Since treatment planning is often based on these readouts, outcomes are often compromised, leading to ineffective treatment responses. Recent preclinical data suggests that regional metabolic and vascular dysfunction (MVD) persist across the disease spectrum; however, few studies have explored these combined measures in ADRD. Therefore, we hypothesized that MVD may serve as a sensitive and early noninvasive diagnostic tool, and tested this in a retrospective clinical population spanning from CN to AD. Method: Subjects from ADNI who were scanned with ¹⁸ F‐FDG PET and ASL MRI within 180 days were included ( N = 290; CN(91), MCI(116), AD(83), M/F(51.2/48.8%)). Cerebral blood flood (CBF) and images were generated from ASL images using ExploreASL. PET and CBF images were registered to the augmented MNI152+ Atlas. Mean intensities for unilateral 59 brain regions were ratioed to whole brain values, z ‐scores computed for each region (relative to CN), and were projected into Cartesian space. Statistical analysis of at‐risk regions was evaluated using Student’s t ‐test ( p 18 F‐FDG,↑CBF) and Type 2 (↑ ¹⁸ F‐FDG,↓CBF) uncoupling MVD (Figure 1B); where AD subjects showed prodromal (↑ ¹⁸ F‐FDG,↑CBF) and neuro‐metabolic‐vascular failure (↓ ¹⁸ F‐FDG,↓CBF) MVD phenotype (Figure 1C). Importantly, these changes aligned with transcriptomic and cognitive signatures. Conclusion: Our findings show that MVD of the at‐risk brain regions depends on sex, APOE status, age, and disease stage. Moreover, our data reveals that MVD patterns can provide a sensitive diagnostic tool for early diagnosis of ADRD, which may improve patient monitoring, stratification, and therapeutic testing.

Clinical imaging diagnostics in AD primarily focus on amyloid and tau accumulation; however, recent work suggests that neuro-metabolic and vascular dysregulation (MVD) begins prior to protein deposition. MVD changes persist in preclinical and clinical cases across the disease spectrum; therefore, comparing them could provide valuable insight into disease onset and progression. Regional MVD phenotypes were determined using imaging readouts as surrogates for cerebral metabolism and blood flow, and region-set enrichment analysis (RSEA) was performed to determine brain functional category (BFC) changes based on metabolic variations. Clinically, MVD showed changes across the disease spectrum, while mice showed similar trajectories, though these were genotype- and age-dependent. Although there is no one-to-one relationship between BFC across species, RSEA showed significant changes in comparable categories. Our findings indicate that clinical MVD and RSEA changes can be mapped to preclinical models, aiding the development of early diagnostic techniques, improving disease stratification, and enabling therapeutic testing.

INTRODUCTION: Recent studies suggest that the brain undergoes anatomical and functional restructuring, resulting in neurometabolic and vascular dysregulation (MVD) prior to amyloid beta accumulation, which begins at an early age and leads to the onset of Alzheimer's disease (AD). METHODS: Using a retrospective clinical population ( n = 403) from the Alzheimer's Disease Neuroimaging Initiative, cerebral perfusion and metabolism changes across 59 brain regions were evaluated from clinical studies. Results were verified by transcriptomic signatures and clinical cognitive assessments. RESULTS: Our findings suggest that disease progression follows a stage‐dependent MVD pattern that can identify at‐risk regions. Although each region progresses at a different pace, regions related to memory, cognition, and motor function showed significant early dysregulation. Importantly, these changes aligned with transcriptomic and cognitive signatures. DISCUSSION: This study underscores that MVD in brain regions varies by sex and disease stage, making it a sensitive tool for early AD diagnosis. This approach could improve patient monitoring, stratification, and therapeutic testing. Highlights: The potential of metabolic and vascular dysfunction as an early biomarker was assessed. An analytical method to assess dysregulation via imaging was developed. An analytical and graphical method to visualize the changes across disease spectrum was developed. Brain regions progress at different rates across Alzheimer's disease progression. Results were aligned with transcriptomics and cognitive signatures.