Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

PURPOSE: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION: Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

ß-amyloid PET harmonisation across longitudinal studies: Application to AIBL, ADNI and OASIS3.

INTRODUCTION: The Centiloid scale was developed to harmonise the quantification of ß-amyloid (Aß) PET images across tracers, scanners, and processing pipelines. However, several groups have reported differences across tracers and scanners even after centiloid conversion. In this study, we aim to evaluate the impact of different pre and post-processing harmonisation steps on the robustness of longitudinal Centiloid data across three large international cohort studies. METHODS: All Aß PET data in AIBL (N = 3315), ADNI (N = 3442) and OASIS3 (N = 1398) were quantified using the MRI-based Centiloid standard SPM pipeline and the PET-only pipeline CapAIBL. SUVR were converted into Centiloids using each tracer's respective transform. Global Aß burden from pre-defined target cortical regions in Centiloid units were quantified for both raw PET scans and PET scans smoothed to a uniform 8 mm full width half maximum (FWHM) effective smoothness. For Florbetapir, we assessed the performance of using both the standard Whole Cerebellum (WCb) and a composite white matter (WM)+WCb reference region. Additionally, our recently proposed quantification based on Non-negative Matrix Factorisation (NMF) was applied to all spatially and SUVR normalised images. Correlation with clinical severity measured by the Mini-Mental State Examination (MMSE) and effect size, as well as tracer agreement in 11C-PiB-18F-Florbetapir pairs and longitudinal consistency were evaluated. RESULTS: The smoothing to a uniform resolution partially reduced longitudinal variability, but did not improve inter-tracer agreement, effect size or correlation with MMSE. Using a Composite reference region for 18F-Florbetapir improved inter-tracer agreement, effect size, correlation with MMSE, and longitudinal consistency. The best results were however obtained when using the NMF method which outperformed all other quantification approaches in all metrics used. CONCLUSIONS: FWHM smoothing has limited impact on longitudinal consistency or outliers. A Composite reference region including subcortical WM should be used for computing both cross-sectional and longitudinal Florbetapir Centiloid. NMF improves Centiloid quantification on all metrics examined.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Predicting cognitive decline: Which is more useful, baseline amyloid levels or longitudinal change?

The use of biomarkers for the early detection of Alzheimer's disease (AD) is crucial for developing potential therapeutic treatments. Positron Emission Tomography (PET) is a well-established tool used to detect ß-amyloid (Aß) plaques in the brain. Previous studies have shown that cross-sectional biomarkers can predict cognitive decline (Schindler et al.,2021). However, it is still unclear whether longitudinal Aß-PET may have additional value for predicting time to cognitive impairment in AD. The current study aims to evaluate the ability of baseline- versus longitudinal rate of change in-11C-Pittsburgh compound B (PiB) Aß-PET to predict cognitive decline. A cohort of 153 participants who previously underwent PiB-PET scans and comprehensive clinical assessments were used in this study. Our analyses revealed that baseline Aß is significantly associated with the rate of change in cognitive composite scores, with cognition declining more rapidly when baseline PiB Aß levels were higher. In contrast, no signification association was identified between the rate of change in PiB-PET Aß and cognitive decline. Additionally, the ability of the rate of change in the PiB-PET measures to predict cognitive decline was significantly influenced by APOE ε4 carrier status. These results suggest that a single PiB-PET scan is sufficient to predict cognitive decline and that longitudinal measures of Aß accumulation do not improve the prediction of cognitive decline once someone is amyloid positive.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology.

BACKGROUND: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aß) profiles, and to predicted progression to Alzheimer's disease (AD). METHODS: Functional and structural brain age measures were obtained for resting-state functional MRI and structural MRI, respectively, in 3460 cognitively normal individuals across an age range spanning 42-85 years. Participants were categorised into four groups based on the difference between their chronological and predicted age in each modality: advanced age in both (n = 291), resilient in both (n = 260) or advanced in one/resilient in the other (n = 163/153). With the resilient group as the reference, brain-age groups were compared across neuroimaging features of neuropathology (white matter hyperintensity volume, neuronal loss measured with Neurite Orientation Dispersion and Density Imaging, AD-specific atrophy patterns measured with the Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease index, amyloid burden using amyloid positron emission tomography (PET), progression to mild cognitive impairment and baseline and longitudinal cognitive measures (trail making task, mini mental state examination, digit symbol substitution task). FINDINGS: Individuals with advanced structural and functional brain-ages had more features indicative of neurodegeneration and they had poor cognition. Individuals with a resilient brain-age in both modalities had a genetic variant that has been shown to be associated with age of onset of AD. Mixed brain-age was associated with selective cognitive deficits. INTERPRETATION: The advanced group displayed evidence of increased atrophy across all neuroimaging features that was not found in either of the mixed groups. This is in line with biomarkers of preclinical AD and cerebrovascular disease. These findings suggest that the variation in structural and functional brain ageing across individuals reflects the degree of underlying neuropathological processes and may indicate the propensity to develop dementia in later life. FUNDING: The National Institute on Aging, the National Institutes of Health, the Swiss National Science Foundation, the Kaiser Foundation Research Institute and the National Heart, Lung, and Blood Institute.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum.

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Dissociable neural routes to successful prospective memory.

Identifying the processes by which people remember to execute an intention at an appropriate moment (prospective memory) remains a fundamental theoretical challenge. According to one account, top-down attentional control is required to maintain activation of the intention, initiate intention retrieval, or support monitoring. A diverging account suggests that bottom-up, spontaneous retrieval can be triggered by cues that have been associated with the intention and that sustained attentional processes are not required. We used a specialized experimental design and functional MRI methods to selectively marshal and identify each process. Results revealed a clear dissociation. One prospective-memory task recruited sustained activity in attentional-control areas, such as the anterior prefrontal cortex; the other engaged purely transient activity in parietal and ventral brain regions associated with attentional capture, target detection, and episodic retrieval. These patterns provide critical evidence that there are two neural routes to prospective memory, with each route emerging under different circumstances.

MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network.

Background: IDH mutation and 1p/19q codeletion status are important prognostic markers for glioma that are currently determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to noninvasively determine molecular alterations from MRI. Methods: Pre-operative MRI scans of 2648 glioma patients were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774) datasets. A 2.5D hybrid convolutional neural network was proposed to simultaneously localize glioma and classify its molecular status by leveraging MRI imaging features and prior knowledge features from clinical records and tumor location. The models were trained on 223 and 348 cases for IDH and 1p/19q tasks, respectively, and tested on one internal (TCGA) and two external (WUSM and EGD) test sets. Results: For IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively. Conclusions: The high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform "virtual biopsy" for tailoring treatment planning and overall clinical management of gliomas.

Non-invasive classification of IDH mutation status of gliomas from multi-modal MRI using a 3D convolutional neural network.

Glioma is the most common form of brain tumor with a high degree of heterogeneity in imaging characteristics, treatment-response, and survival rate. An important factor causing this heterogeneity is the mutation of isocitrate dehydrogenase (IDH) enzyme. The current clinical gold-standard for identifying IDH mutation status involves invasive procedures that involve risk, may fail to capture intra-tumoral spatial heterogeneity or can be inaccessible in low-resource settings. In this study, we propose a deep learning-based method to non-invasively and pre-operatively determine IDH status of high- and low-grade gliomas by leveraging their phenotypical characteristics from volumetric MRI scans. For this purpose, we propose a 3D Mask R-CNN-based approach to simultaneously detect and segment glioma as well as classify its IDH status - thus obviating the requirement of any separate tumor segmentation step. The network can operate on routinely acquired MRI sequences and is agnostic to glioma grade. It was trained on patient-cases from publicly available datasets (n = 223) and tested on two hold-out datasets acquired from The Cancer Genome Atlas (TCGA; n = 62) and Washington University School of Medicine (WUSM; n = 261). The model achieved areas under the receiver operating characteristic of 0.83 and 0.87, and areas under the precision-recall curves of 0.78 and 0.79, on the TCGA and WUSM sets, respectively. The model can be used to perform a pre-operative 'virtual biopsy' of gliomas, thus facilitating treatment planning, potentially leading to better overall survival.

The Spatial Patterns and Determinants of Cerebrospinal Fluid Circulation in the Human Brain.

The circulation of cerebrospinal fluid (CSF) is essential for maintaining brain homeostasis and clearance, and impairments in its flow can lead to various brain disorders. Recent studies have shown that CSF circulation can be interrogated using low b-value diffusion magnetic resonance imaging (low-b dMRI). Nevertheless, the spatial organization of intracranial CSF flow dynamics remains largely elusive. Here, we developed a whole-brain voxel-based analysis framework, termed CSF pseudo-diffusion spatial statistics (CΨSS), to examine CSF mean pseudo-diffusivity (MΨ), a measure of CSF flow magnitude derived from low-b dMRI. We showed that intracranial CSF MΨ demonstrates characteristic covariance patterns by employing seed-based correlation analysis. Importantly, we applied non-negative matrix factorization analysis to further elucidate the covariance patterns of CSF MΨ in a hypothesis-free, data-driven way. We identified distinct CSF spaces that consistently displayed unique pseudo-diffusion characteristics across multiple imaging datasets. Our study revealed that age, sex, brain atrophy, ventricular anatomy, and cerebral perfusion differentially influence MΨ across these CSF spaces. Notably, individuals with anomalous CSF flow patterns displayed incidental findings on multimodal neuroradiological examinations. Our work sets forth a new paradigm to study CSF flow, with potential applications in clinical settings.

Deep learning-based end-to-end scan-type classification, pre-processing, and segmentation of clinical neuro-oncology studies.

Modern neuro-oncology workflows are driven by large collections of high-dimensional MRI data obtained using varying acquisition protocols. The concomitant heterogeneity of this data makes extensive manual curation and pre-processing imperative prior to algorithmic use. The limited efforts invested towards automating this curation and processing are fragmented, do not encompass the entire workflow, or still require significant manual intervention. In this work, we propose an artificial intelligence-driven solution for transforming multi-modal raw neuro-oncology MRI Digital Imaging and Communications in Medicine (DICOM) data into quantitative tumor measurements. Our end-to-end framework classifies MRI scans into different structural sequence types, preprocesses the data, and uses convolutional neural networks to segment tumor tissue subtypes. Moreover, it adopts an expert-in-the-loop approach, where segmentation results may be manually refined by radiologists. This framework was implemented as Docker Containers (for command line usage and within the eXtensible Neuroimaging Archive Toolkit [XNAT]) and validated on a retrospective glioma dataset (n = 155) collected from the Washington University School of Medicine, comprising preoperative MRI scans from patients with histopathologically confirmed gliomas. Segmentation results were refined by a neuroradiologist, and performance was quantified using Dice Similarity Coefficient to compare predicted and expert-refined tumor masks. The scan-type classifier yielded a 99.71% accuracy across all sequence types. The segmentation model achieved mean Dice scores of 0.894 (± 0.225) for whole tumor segmentation. The proposed framework can automate tumor segmentation and characterization - thus streamlining workflows in a clinical setting as well as expediting standardized curation of large-scale neuro-oncology datasets in a research setting.

Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-Oncology (I3CR-WANO).

PURPOSE: Efforts to use growing volumes of clinical imaging data to generate tumor evaluations continue to require significant manual data wrangling, owing to data heterogeneity. Here, we propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-oncology MRI data to extract quantitative tumor measurements. MATERIALS AND METHODS: Our end-to-end framework (1) classifies MRI sequences using an ensemble classifier, (2) preprocesses the data in a reproducible manner, (3) delineates tumor tissue subtypes using convolutional neural networks, and (4) extracts diverse radiomic features. Moreover, it is robust to missing sequences and adopts an expert-in-the-loop approach in which the segmentation results may be manually refined by radiologists. After the implementation of the framework in Docker containers, it was applied to two retrospective glioma data sets collected from the Washington University School of Medicine (WUSM; n = 384) and The University of Texas MD Anderson Cancer Center (MDA; n = 30), comprising preoperative MRI scans from patients with pathologically confirmed gliomas. RESULTS: The scan-type classifier yielded an accuracy of >99%, correctly identifying sequences from 380 of 384 and 30 of 30 sessions from the WUSM and MDA data sets, respectively. Segmentation performance was quantified using the Dice Similarity Coefficient between the predicted and expert-refined tumor masks. The mean Dice scores were 0.882 (±0.244) and 0.977 (±0.04) for whole-tumor segmentation for WUSM and MDA, respectively. CONCLUSION: This streamlined framework automatically curated, processed, and segmented raw MRI data of patients with varying grades of gliomas, enabling the curation of large-scale neuro-oncology data sets and demonstrating high potential for integration as an assistive tool in clinical practice.

A universal neocortical mask for Centiloid quantification.

INTRODUCTION: The Centiloid (CL) project was developed to harmonize the quantification of amyloid beta (Aß) positron emission tomography (PET) scans to a unified scale. The CL neocortical mask was defined using 11C Pittsburgh compound B (PiB), overlooking potential differences in regional distribution among Aß tracers. We created a universal mask using an independent dataset of five Aß tracers, and investigated its impact on inter-tracer agreement, tracer variability, and group separation. METHODS: Using data from the Alzheimer's Dementia Onset and Progression in International Cohorts (ADOPIC) study (Australian Imaging Biomarkers and Lifestyle + Alzheimer's Disease Neuroimaging Initiative + Open Access Series of Imaging Studies), age-matched pairs of mild Alzheimer's disease (AD) and healthy controls (HC) were selected: 18F-florbetapir (N = 147 pairs), 18F-florbetaben (N = 22), 18F-flutemetamol (N = 10), 18F-NAV (N = 42), 11C-PiB (N = 63). The images were spatially and standardized uptake value ratio normalized. For each tracer, the mean AD-HC difference image was thresholded to maximize the overlap with the standard neocortical mask. The universal mask was defined as the intersection of all five masks. It was evaluated on the Global Alzheimer's Association Interactive Network (GAAIN) head-to-head datasets in terms of inter-tracer agreement and variance in the young controls (YC) and on the ADOPIC dataset comparing separation between HC/AD and HC/mild cognitive impairment (MCI). RESULTS: In the GAAIN dataset, the universal mask led to a small reduction in the variance of the YC, and a small increase in the inter-tracer agreement. In the ADOPIC dataset, it led to a better separation between HC/AD and HC/MCI at baseline. DISCUSSION: The universal CL mask led to an increase in inter-tracer agreement and group separation. Those increases were, however, very small, and do not provide sufficient benefits to support departing from the existing standard CL mask, which is suitable for the quantification of all Aß tracers. HIGHLIGHTS: This study built an amyloid universal mask using a matched cohort for the five most commonly used amyloid positron emission tomography tracers.There was a high overlap between each tracer-specific mask.Differences in quantification and group separation between the standard and universal mask were small.The existing standard Centiloid mask is suitable for the quantification of all amyloid beta tracers.

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.