Brian Avants’s research while affiliated with InviCRO, LLC and other places

Background: Positron Emission Tomography (PET) scans are a crucial tool in the diagnosing and monitoring of a number of complex conditions, including cancer, heart health, and especially cognitive brain function. However, they are also often much more expensive than comparable imaging modalities such as X-Ray and magnetic resonance imaging (MRI), which can limit their availability and the impact of their use in both medical and machine learning settings. We propose to address this problem by using generative models to simulate the PET scan results based on prior MRI. Methods: While recent work has yielded impressive realism in image generation, this PET synthesis task presents a series of technical challenges based on the scarcity of paired data as well as the complexity and nuance of the 3D images. So, we propose MRI2PET to generate AV45-PET scans from T1-weighted MRI images. MRI2PET is a 3D diffusion-based method which makes use of style transferred pre-training and a Laplacian pyramid loss to address these challenges by utilizing larger available unpaired MRI datasets and structural similarities between the MRI and PET images while simultaneously emphasizing the crucial details. Findings: We evaluate MRI2PET through a series of studies on the ADNI dataset where we show that it both generates realistic images and improves clinically-based disease classification. When compared to training on only the original AV45-PET data, MRI2PET augmentation increases AUROC of brain scan classification to 0.780 ± 0.005 from 0.688 ± 0.014 when classifying brain scans into one of three clinically defined groups: cognitively normal, mild cognitive impairment, and Alzheimer's Disease. Interpretation The capability to generate high quality, clinically relevant PET scans from MRI has the potential to expand the utility of cost-effective and accessible imaging workflows and improve both image-based machine learning capabilities and patient care.

Among LRRK2-associated parkinsonism cases with nigral degeneration, over two-thirds demonstrate evidence of pathologic alpha-synuclein, but many do not. Understanding the clinical phenotype and underlying biology in such individuals is critical for therapeutic development. Our objective was to compare clinical and biomarker features, and rate of progression over 4 years of follow-up, among LRRK2-associated parkinsonism cases with and without in vivo evidence of alpha-synuclein aggregates. Data were from the Parkinson’s Progression Markers Initiative, a multicentre prospective cohort study. The sample included individuals diagnosed with Parkinson disease with pathogenic variants in LRRK2. Presence of CSF alpha-synuclein aggregation was assessed with seed amplification assay. A range of clinician- and patient-reported outcome assessments were administered. Biomarkers included dopamine transporter scan, CSF amyloid-beta1-42, total tau, phospho-tau181, urine bis(monoacylglycerol)phosphate levels and serum neurofilament light chain. Linear mixed-effects (LMMs) models examined differences in trajectory in CSF-negative and CSF-positive groups. A total of 148 LRRK2 parkinsonism cases (86% with G2019S variant), 46 negative and 102 positive for CSF alpha-synuclein seed amplification assay, were included. At baseline, the negative group was older than the positive group [median (inter-quartile range) 69.1 (65.2–72.3) versus 61.5 (55.6–66.9) years, P < 0.001] and a greater proportion were female [28 (61%) versus 43 (42%), P = 0.035]. Despite being older, the negative group had similar duration since diagnosis and similar motor rating scale [16 (11–23) versus 16 (10–22), P = 0.480] though lower levodopa equivalents. Only 13 (29%) of the negative group were hyposmic, compared with 75 (77%) of the positive group. The negative group, compared with the positive group, had higher per cent-expected putamenal dopamine transporter binding for their age and sex [0.36 (0.29–0.45) versus 0.26 (0.22–0.37), P < 0.001]. Serum neurofilament light chain was higher in the negative group compared with the positive group [17.10 (13.60–22.10) versus 10.50 (8.43–14.70) pg/mL; age-adjusted P-value = 0.013]. In terms of longitudinal change, the negative group remained stable in functional rating scale score in contrast to the positive group who had a significant increase (worsening) of 0.729 per year (P = 0.037), but no other differences in trajectory were found. Among individuals diagnosed with Parkinson disease with pathogenic variants in the LRRK2 gene, we found clinical and biomarker differences in cases without versus with in vivo evidence of CSF alpha-synuclein aggregates. LRRK2 parkinsonism cases without evidence of alpha-synuclein aggregates as a group exhibit less severe motor manifestations and decline. The underlying biology in LRRK2 parkinsonism cases without evidence of alpha-synuclein aggregates requires further investigation.

Importance Blast-related mild traumatic brain injuries (TBIs), the “signature injury” of post-9/11 conflicts, are associated with clinically relevant, long-term cognitive, psychological, and behavioral dysfunction and disability; however, the underlying neural mechanisms remain unclear. Objective To investigate associations between a history of remote blast-related mild TBI and regional brain volume in a sample of US veterans and active duty service members. Design, Setting, and Participants Prospective cohort study of US veterans and active duty service members from the Long-Term Impact of Military-Relevant Brain Injury Consortium–Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC), which enrolled more than 1500 participants at 5 sites used in this analysis between 2014 and 2023. Participants were recruited from Veterans Affairs medical centers across the US; 774 veterans and active duty service members of the US military met eligibility criteria for this secondary analysis. Assessment dates were from January 6, 2015, to March 31, 2023; processing and analysis dates were from August 1, 2023, to January 15, 2024. Exposure All participants had combat exposure, and 82% had 1 or more lifetime mild TBIs with variable injury mechanisms. Main Outcomes and Measures Regional brain volume was calculated using tensor-based morphometry on 3-dimensional, T1-weighted magnetic resonance imaging scans; history of TBI, including history of blast-related mild TBI, was assessed by structured clinical interview. Cognitive performance and psychiatric symptoms were assessed with a battery of validated instruments. We hypothesized that regional volume would be smaller in the blast-related mild TBI group and that this would be associated with cognitive performance. Results A total of 774 veterans (670 [87%] male; mean [SD] age, 40.1 [9.8] years; 260 [34%] with blast-related TBI) were included in the sample. Individuals with a history of blast-related mild TBI had smaller brain volumes than individuals without a history of blast-related mild TBI (which includes uninjured individuals and those with non–blast-related mild TBI) in several clusters, with the largest centered bilaterally in the superior corona radiata and subcortical gray and white matter (cluster peak Cohen d range, −0.23 to −0.38; mean [SD] Cohen d , 0.28 [0.03]). Additionally, causal mediation analysis revealed that these volume differences significantly mediated the association between blast-related mild TBI and performance on measures of working memory and processing speed. Conclusions and Relevance In this cohort study of 774 veterans and active duty service members, robust volume differences associated with blast-related TBI were identified. Furthermore, these volume differences significantly mediated the association between blast-related mild TBI and cognitive function, indicating that this pattern of brain differences may have implications for daily functioning.

Background Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD) are the most common neurodegenerative conditions. These central nervous system disorders impact both the structure and function of the brain and may lead to imaging changes that precede symptoms. Patients with ADRD or PD have long asymptomatic phases that exhibit significant heterogeneity. Hence, quantitative measures that can provide early disease indicators are necessary to improve patient stratification, clinical care, and clinical trial design. This work uses machine learning techniques to derive such a quantitative marker from T1-weighted (T1w) brain Magnetic resonance imaging (MRI). Methods In this retrospective study, we developed machine learning (ML) based disease-specific scores based on T1w brain MRI utilizing Parkinson's Disease Progression Marker Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. We evaluated the potential of ML-based scores for early diagnosis, prognosis, and monitoring of ADRD and PD in an independent large-scale population-based longitudinal cohort, UK Biobank. Findings 1,826 dementia images from 731 participants, 3,161 healthy control images from 925 participants from the ADNI cohort, 684 PD images from 319 participants, and 232 healthy control images from 145 participants from the PPMI cohort were used to train machine learning models. The classification performance is 0.94 [95% CI: 0.93-0.96] area under the ROC Curve (AUC) for ADRD detection and 0.63 [95% CI: 0.57-0.71] for PD detection using 790 extracted structural brain features. The most predictive regions include the hippocampus and temporal brain regions in ADRD and the substantia nigra in PD. The normalized ML model's probabilistic output (ADRD and PD imaging scores) was evaluated on 42,835 participants with imaging data from the UK Biobank. There are 66 cases for ADRD and 40 PD cases whose T1 brain MRI is available during pre-diagnostic phases. For diagnosis occurrence events within 5 years, the integrated survival model achieves a time-dependent AUC of 0.86 [95% CI: 0.80-0.92] for dementia and 0.89 [95% CI: 0.85-0.94] for PD. ADRD imaging score is strongly associated with dementia-free survival (hazard ratio (HR) 1.76 [95% CI: 1.50-2.05] per S.D. of imaging score), and PD imaging score shows association with PD-free survival (hazard ratio 2.33 [95% CI: 1.55-3.50]) in our integrated model. HR and prevalence increased stepwise over imaging score quartiles for PD, demonstrating heterogeneity. As a proxy for diagnosis, we validated AD/PD polygenic risk scores of 42,835 subjects against the imaging scores, showing a highly significant association after adjusting for covariates. In both the PPMI and ADNI cohorts, the scores are associated with clinical assessments, including the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog), and pathological markers, which include amyloid and tau. Finally, imaging scores are associated with polygenic risk scores for multiple diseases. Our results suggest that we can use imaging scores to assess the genetic architecture of such disorders in the future. Interpretation Our study demonstrates the use of quantitative markers generated using machine learning techniques for ADRD and PD. We show that disease probability scores obtained from brain structural features are useful for early detection, prognosis prediction, and monitoring disease progression. To facilitate community engagement and external tests of model utility, an interactive app to explore summary level data from this study and dive into external data can be found here https://ndds-brainimaging-ml.streamlit.app. As far as we know, this is the first publicly available cloud-based MRI prediction application. Funding US National Institute on Aging, and US National Institutes of Health.

The Parkinson's Progression Markers Initiative (PPMI) delivers multiple modality MRI (M3RI) and biomarker data for a comprehensive longitudinal study of Parkinson's Disease (PD). These provide quantitative indices of deep brain and cortical structure (T1-weighted MRI), microstructural integrity of brain tissue (diffusion-weighted imaging) and resting brain function (resting state functional MRI). Integrating and uniformly analyzing M3RI alongside non-imaging biological and clinical data is challenging due to the distinct nature of each modality. This study systematically organizes these complex data into a structured format, provides a PD-focused evaluation of the methodologies and evidence for technical robustness of the approach. The cohort encompasses 841 idiopathic PD, 309 genetic PD, 1364 presymptomatic PD and 240 control subjects at baseline with followup at a mean of 1.83 years.

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

Background: Among LRRK2-associated parkinsonism cases with nigral degeneration, over two-thirds demonstrate evidence of pathologic alpha-synuclein, but many do not. Understanding the clinical phenotype and underlying biology in such individuals is critical for therapeutic development. Our objective was to compare clinical and biomarker features, and rate of progression over 4 years follow-up, among LRRK2-associated parkinsonism cases with and without in vivo evidence of alpha-synuclein aggregates. Methods: Data were from the Parkinson's Progression Markers Initiative, a multicenter prospective cohort study. The sample included individuals diagnosed with Parkinson disease with pathogenic variants in LRRK2. Presence of CSF alpha-synuclein aggregation was assessed with seed amplification assay. A range of clinician- and patient- reported outcome assessments were administered. Biomarkers included dopamine transporter SPECT scan, CSF amyloid-beta1-42, total tau, phospho-tau181, urine bis(monoacylglycerol)phosphate levels, and serum neurofilament light chain. Linear mixed effects models examined differences in trajectory in CSF negative and positive groups. Results: 148 LRRK2-parkinsonism cases (86% with G2019S variant), 46 negative and 102 positive for CSF alpha-synuclein seed amplification assay were included. At baseline, the negative group were older than the positive group (median [interquartile range] 69.1 [65.2-72.3] vs 61.5 [55.6-66.9] years, p<0.001) and a greater proportion were female (28 (61%) vs 43 (42%), p=0.035). Despite being older, the negative group had similar duration since diagnosis, and similar motor rating scale (16 [11-23] vs 16 [10-22], p=0.480) though lower levodopa equivalents. Only 13 (29%) of the negative group were hyposmic, compared to 75 (77%) of the positive group. Lowest putamen dopamine transporter binding expected for age and sex was greater in the negative vs positive groups (0.36 [0.29-0.45] vs 0.26 [0.22-0.37], p<0.001). Serum neurofilament light chain was higher in the negative group compared to the positive group (17.10 [13.60-22.10] vs 10.50 [8.43-14.70]; age-adjusted p-value=0.013). In terms of longitudinal change, the negative group remained stable in functional rating scale score in contrast to the positive group who had a significant increase (worsening) of 0.729 per year (p=0.037), but no other differences in trajectory were found. Conclusion: Among individuals diagnosed with Parkinson disease with pathogenic variants in the LRRK2 gene, we found clinical and biomarker differences in cases without versus with in vivo evidence of CSF alpha-synuclein aggregates. LRRK2 parkinsonism cases without evidence of alpha-synuclein aggregates as a group exhibit less severe motor manifestations and decline may have more significant cognitive dysfunction. The underlying biology in LRRK2-parkinsonism cases without evidence of alpha-synuclein aggregates requires further investigation. Key words: Parkinsonism, LRRK2, alpha-synuclein

UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency analysis, and test-time instance optimization per-case basis as a post-processing step. The top-ranked method attained the MEE at or below that of the inter-rater variability for approximately 60% of the evaluated landmarks, underscoring the scope for further accuracy and robustness improvements, especially relative to human experts. The aim of BraTS-Reg is to continue to serve as an active resource for research, with the data and online evaluation tools accessible at https://bratsreg.github.io/.