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High grade gliomas (HGG) are aggressive primary brain malignancies typified by diffuse invasion, genetic heterogeneity, and a universally fatal outcome. MRI-defined contrast-enhancing (CE) tumor burden serves as the clinical standard that guides maximal surgical resection and post-therapy response assessment. However, HGGs also comprise an invasive non-enhancing (NE) tumor margin that extends beyond the CE core and harbors the cells that contribute to recurrence. Sampling restrictions have hindered the comprehensive study of these NE HGG cell populations driving tumor progression. Herein, we present an integrated multi-omic analysis of 313 spatially matched multi-regional CE and NE tumor biopsies from 68 HGG patients, performing whole exome and RNA sequencing of both IDH wild-type and IDH mutant HGGs. We report spatially restricted molecular profiles in IDH-mutant HGG, highlighting a concern for sampling bias given the importance of molecular diagnosis and prognostication in IDH-mutant HGG. Regardless of IDH status, we found that NE tumor regions harbored the highest proportion of private mutations, which reflects an increased development of regional genomic complexity in infiltrative tumor. The multiregional genomic profiling of our IDH wild-type HGG cohort reveals that EGFR and NF1 somatic alterations occur as mutually exclusive events in 98.7% of tumors. However, we resolved rare low allele frequency co-alterations of EGFR and NF1 within the NE region. We find this co-occurrence enriched in recurrent tumors, pointing to the early emergence of NF1 inactivation in the NE regions. We constructed genomic models predictive of recurrent disease from both NE and CE regions, which highlight the occurrence of clonal EGFR copy number alterations and NF1 loss as clonal or subclonal events, respectively, emphasizing the regional and temporal complexity of well-studied canonical driver alterations. We detailed the spatially unique acquisition of multiple distinct EGFR alterations giving rise to intra-tumoral EGFR mosaicism, a challenge in the implementation of EGFR directed therapies. Our study also identified two transcriptomic clusters delineated by the significant overrepresentation of neuronal (NEU) and glycolytic/plurimetabolic (GPM) pathway-based functional states in the NE region. NE regions of the NEU subtype harbor the greatest proportion of private mutations, suggesting these infiltrative tumor cells accumulate alterations without clonal expansion. GPM populations conversely displayed a less branched phylogeny and were transcriptionally enriched in immune cell signatures. This phenotypic dichotomy between GPM and NEU populations supports the growing body of evidence that invasive GBM cells either take on a neuronal phenotype for active invasion or a more metabolic phenotype involving interaction with astrocytes, other glial cells, and infiltrating immune cells. Citation Format: Mylan R. Blomquist, Leland S. Hu, Fulvio D'Angelo, Taylor M. Weiskittel, Francesca P. Caruso, Shannon P. Fortin Ensign, Christopher Sereduk, Gustavo De Leon, Lee Curtin, Javier Urcuyo, Ashlynn Gonzalez, Ashley Nespodzany, Teresa Noviello, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Bernard R. Bendok, Richard S. Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Kliment Donev, Maciej Mrugala, Alyx Porter, Anna Lasorella, Kristin R. Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan L. Tran. Multiregional sampling of high grade glioma identifies regional biologic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1507.

High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.

BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disease that affects aging populations. Current MRI techniques are often limited in their sensitivity to underlying neuropathological changes. PURPOSE To characterize differences in voxel-based morphometry (VBM), apparent diffusion coefficient (ADC), and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) metrics in aging populations. Additionally, to investigate the connection between cognitive assessments and neuroimaging metrics. STUDY TYPE Prospective/cross-sectional. POPULATION In all, 49 subjects, including 13 with AD dementia, 12 with mild cognitive impairment (MCI), and 24 healthy controls (HC). FIELD STRENGTH/SEQUENCE 3T/magnetization-prepared rapid acquisition gradient echo (MP-RAGE) and IVIM-DWI ASSESSMENT: All participants completed a cognitive screening battery prior to MRI. IVIM-DWI maps (pure diffusion coefficient [D], pseudodiffusion coefficient [D*], and perfusion fraction [f]) were generated from a biexponential fit of diffusion MRI data. VBM was performed on the standard T1 -weighted MP-RAGE structural images. Group-wise templates were used to compare across groups. STATISTICAL TESTS Analysis of covariance (ANCOVA) with gender and age as covariates (familywise error [FWE] corrected, post-hoc comparisons using Bonferroni correction) for group comparisons. Partial-η2 and Hedges' g were used for effect-size analysis. Spearman's correlations (false discovery rate [FDR]-corrected) for the relationship between cognitive scores and imaging. RESULTS Clusters of significant group-wise differences were found mainly in the temporal lobe, hippocampus, and amygdala using all VBM and IVIM methods (P

Background The integrity and connectivity of the frontal lobe, which subserves fluency, may be compromised by both ASD and aging. Alternate networks often integrate to help compensate for compromised functions during aging. We used network analyses to study how compensation may overcome age-related compromised in individuals with ASD. Method Participants consisted of middle-aged (40–60; n = 24) or young (18–25; n = 18) right-handed males who have a diagnosis of ASD, and age- and IQ-matched control participants (n = 20, 14, respectively). All performed tests of language and executive functioning and a fluency functional MRI task. We first used group individual component analysis (ICA) for each of the 4 groups to determine whether different networks were engaged. An SPM analysis was used to compare activity detected in the network nodes from the ICA analyses. Results The individuals with ASD performed more slowly on two cognitive tasks (Stroop word reading and Trailmaking Part A). The 4 groups engaged different networks during the fluency fMRI task despite equivalent performance. Comparisons of specific regions within these networks indicated younger individuals had greater engagement of the thalamus and supplementary speech area, while older adults engaged the superior temporal gyrus. Individuals with ASD did not disengage from the Default Mode Network during word generation. Conclusion Interactions between diagnosis and aging were not found in this study of young and middle-aged men, but evidence for differential engagement of compensatory networks was observed.

INTRODUCTION The quantification of intratumoral heterogeneity – through radiomics-based approaches - can help resolve the regionally distinct genetic drug targets that may co-exist within a single Glioblastoma (GBM) tumor. While this offers potential diagnostic value under the paradigm of individualized oncology, clinical decision-making must also consider the degree of uncertainty associated with each model. In this study, we evaluate the performance of a novel machine-learning (ML) algorithm, called Gaussian Process (GP) modeling, that can quantify the impact of multiple sources of uncertainty in ML model development and prediction accuracy, including variabilities in the copy number measurement, radiomics features, training sample characteristics, and training sample size. METHOD We collected 95 image-localized biopsies from 25 primary GBM patients. We coregistered stereotactic locations with preoperative multi-parametric MRI features (conventional MRI, DSC perfusion, Diffusion Tensor Imaging) to generate spatially matched pairs of MRI and copy number variants (CNV) for for each biopsy. We developed a Gaussian Process (GP) model to predict CNV for Epidermal Growth Factor Receptor (EGFR) based on MRI radiomic features in each patient. We used leave-one-patient-out cross validation to quantify prediction accuracy and model uncertainty. Spatial prediction and uncertainty (p-value) maps were overlaid to help visualize regional genetic variation of EGFR and uncertainty of the radiomic predictions. RESULT: The initial GP radiomics model for EGFR amplification (CNV > 3.5) produced a sensitivity of 0.8 and specificity of 0.8. Samples/regions associated with high uncertainty (p-value >0.05) correlated with either 1) extrapolation of radiomic features from the training set-defined feature space or 2) insufficient training samples in the feature space. CONCLUSION We present a ML-based model that quantifies spatial genetic heterogeneity in GBM, while also estimating model uncertainties that result from multi-source data variabilities. This approach lays the groundwork for prospective clinical integration of modeling-based diagnostic approaches in the paradigm of individualized medicine.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p

BACKGROUND: MRI-based modeling of tumor cell density (TCD) can significantly improve targeted treatment of Glioblastoma (GBM). Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a Transfer Learning (TL) method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient’s own histologic data. METHODS: We recruited primary GBM patients undergoing image-guided biopsies and preoperative imaging including contrast-enhanced MRI (CE-MRI), Dynamic-Susceptibility-Contrast (DSC)-MRI, and Diffusion Tensor Imaging (DTI). We calculated relative cerebral blood volume (rCBV) from DSC-MRI and mean diffusivity (MD) and fractional anisotropy (FA) from DTI. Following image coregistration, we assessed TCD for each biopsy and identified corresponding localized MRI measurements. We then explored a range of univariate and multivariate predictive models of TCD based on MRI measurements in a generalized one-model-fits-all (OMFA) approach. We then implemented both univariate and multivariate individualized TL predictive models, which harness the available population level data but allow for individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized TL versus generalized OMFA models. RESULTS: TCD significantly correlated with rCBV (r=0.33,p

PURPOSE Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T1 and T2∗ contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T1 leakage effects, there is no consensus on the optimal set of acquisition parameters. Using a computational approach, we systematically evaluated a wide range of acquisition strategies to determine the optimal multi-echo DSC-MRI perfusion protocol. METHODS Using a population-based DSC-MRI digital reference object (DRO), we assessed the influence of preload dosing (no preload and full dose preload), field strength (1.5 and 3T), pulse sequence parameters (echo time, repetition time, and flip angle), and leakage correction on relative cerebral blood volume (rCBV) and flow (rCBF) accuracy. We also compared multi-echo DSC-MRI protocols with standard single-echo protocols. RESULTS Multi-echo DSC-MRI is highly consistent across all protocols, and multi-echo rCBV (with or without use of a preload dose) had higher accuracy than single-echo rCBV. Regression analysis showed that choice of repetition time and flip angle had minimal impact on multi-echo rCBV and rCBV, indicating the potential for significant flexibility in acquisition parameters. The echo time combination had minimal impact on rCBV, though longer echo times should be avoided, particularly at higher field strengths. Leakage correction improved rCBV accuracy in all cases. Multi-echo rCBF was less biased than single-echo rCBF, although rCBF accuracy was reduced overall relative to rCBV. CONCLUSIONS Multi-echo acquisitions were more robust than single-echo, essentially decoupling both repetition time and flip angle from rCBV accuracy. Multi-echo acquisitions obviate the need for preload dosing, although leakage correction to remove residual T2∗ leakage effects remains compulsory for high rCBV accuracy.

The present study examined the degree to which tests of visuospatial storage capacity tap into domain-general storage and attention processes. This was done by comparing performance of visuospatial memory tasks with performance on sound-based sensory discrimination tasks. We found that memory task- and discrimination task performance both tapped into a cross-modality factor (visual and auditory). We further examined the degree to which this common variance could be explained by attention control and sustained attention. These attention factors accounted for roughly 60% of the variance in memory. This indicates that tests of visuospatial memory capacity reflect more than modality-specific memory.

This chapter provides an overview of methods and considerations that must be made when one studies working memory. Working memory is the mental system in which relevant information is maintained and used in ongoing processing. Critically, working memory represents maintenance that is intentional and selective. A. D. Baddeley's model conceives of working memory as arising from the interaction of three critical components. In practice complex span tasks are probably robust to differences in scoring method. The accuracy with which people complete the processing component of the complex span task is not a traditional dependent variable. The visual arrays task was designed to specifically test the storage capacity of visuo-spatial short-term memory. In the running memory span task, test-takers see a serially presented list of items of an indeterminate length. Confirmatory factor analysis is good for testing theoretical assumptions about the number of factors that influence task performance.

INTRODUCTION Neuroimaging dogma for glioblastoma asserts that hyperintensity on T1Gd MRI reveals the bulk of the tumor, while T2/FLAIR signal indicates edema. However, it is unclear whether this edema results from immune response or increased tumor cells. Further, one significant driver of the known sex differences in glioblastoma may be differences in immune response, due to the X-linkage of many immune genes. Based on this, we hypothesized that assumptions regarding tumor cellularity in T2/FLAIR images should be tailored to the biological sex of the patient. METHODS Using a retrospective cohort of 18 primary glioblastoma patients receiving multiple image-localized biopsies (82 total) and standard MRI, we assessed: distance of biopsy from T1Gd and T2 areas; a pathologist’s score of percent tumor cell density; and an imaging-based invasion metric, D/ρ. This metric is derived from the biomathematical Proliferation-Invasion model of glioma growth, which features two parameters, net growth rate (ρ) and net invasion rate (D). Their ratio D/ρ is related to degree of invasion, and is estimated from volumetric measurements of MRI abnormalities. Additionally, 25 patient-derived xenograft models implanted in females were grown until moribund, at which point brains were excised and stained for DAPI (to show all cells) and Lamin (to highlight tumor cells). Image processing of lamin-stained sections defines contours of intensity correlating with cell density. RESULTS Outside both the T1Gd and T2 region, male patient biopsies had higher tumor cell densities than females. Males also tended to have higher invasion metrics. Although each set derived from different patients, preclinical metrics of invasion were positively correlated with clinical invasion in females but negatively correlated in males. CONCLUSION Our preliminary finding that cell distribution patterns correlate with imaging metrics differently between the sexes supports the hypothesis that the degree of tumor cell density represented on certain MRI sequences may be sex-specific.

Dynamic susceptibility contrast (DSC) MRI measures of brain tumor cerebral blood volume (CBV) are able to predict grade, overall survival and response to treatment. Wide-spread acceptance of DSC-MRI has been challenged by the need to balance contrast agent dose and CBV accuracy. The goal of this study was to identify and validate single-dose, BTIP compliant, DSC-MRI protocols. Using a validated, patient-based DRO, we evaluated CBV accuracy across a range of acquisition parameters (field strength, TR, TE, flip angle, multi-echo acquisitions, dosing protocols) and post-processing steps. To validate the optimal protocols, we next collected DSC-MRI data following ASFNR’s recommended “double – dose” approach, where a single-dose preload (to minimize T1 effects) is given prior to a second bolus injection (for DSC-MRI data acquisition). The single-dose DSC-MRI data was collected during the preload bolus injection. Consistency of the derived CBV data, visual agreement and data characteristics (e.g. CNR) was statistically evaluated. When using a single-dose and routine single-echo pulse sequence, the DRO analysis found that a low flip angle (LFA = 30o) and 30ms TE provided the highest CBV accuracy (concordance correlation coefficient (CCC) = 0.92) and precision (coefficient of variation (CV) = 8.2%)). For comparison, the maximum accuracy found with the DRO utilizes a double-dose injection protocol and yielded a CCC of 0.98 and CV of 6.8%. Single-dose, multi-echo acquisitions provided higher accuracy than the LFA data and matched that found with the double-dose approach. In patients (data collection ongoing), the agreement between single-dose LFA (n > 40) or multi-echo (n > 40) based CBV values and the reference double-dose approach was very high (CCC > 0.94) and were statistically equivalent. Optimized single-dose DSC-MRI protocols provide highly accurate CBV data, use lower doses of contrast agent, and simplify scan procedures, indicating their potential for robust use in clinical practice and trials.

BACKGROUND An important challenge in radiomics research is reproducibility. Images are collected on different image scanners and protocols, which introduces significant variability even for the same type of image across institutions. In the present proof-of-concept study, we address the reproducibility issue by using domain adaptation – an algorithm that transforms the radiomic features of each new patient to align with the distribution of features formed by the patient samples in a training set. METHOD Our dataset included 18 patients in training with a total of 82 biopsy sample. The pathological tumor cell density was available for each sample. Radiomic (statistical + texture) features were extracted from the region of six image contrasts locally matched with each biopsy sample. A Gaussian Process (GP) classifier was built to predict tumor cell density using radiomic features. Another 6 patients were used to test the training model. These patients had a total of 31 biopsy samples. The images of each test patient were purposely normalized using a different approach, i.e., using the CSF instead of the whole brain as the reference. This was to mimic the practical scenario of image source discrepancy between different patients. Domain adaptation was applied to each test patient. RESULTS Among the 18 training patients, the leave-one-patient-out cross validation accuracy is 0.81 AUC, 0.78 sensitivity, and 0.83 specificity. When the trained model was applied to the 6 test patients (purposely normalized using a different approach than that of the training data), the accuracy dramatically reduced to 0.39 AUC, 0.08 sensitivity, and 0.61 specificity. After using domain adaption, the accuracy improved to 0.68 AUC, 0.62 sensitivity, and 0.72 specificity. CONCLUSION We provide candidate enabling tools to address reproducibility in radiomics models by using domain adaption algorithms to account for discrepancy of the images between different patients.

The present study examines the idea that time-based forgetting of outdated information can lead to better memory of currently relevant information. This was done using the visual arrays task, along with a between-subjects manipulation of both the retention interval (1 s vs. 4 s) and the time between two trials (1 s vs. 4 s). Consistent with prior work [Shipstead, Z., & Engle, R. W. (2013). Interference within the focus of attention: Working memory tasks reflect more than temporary maintenance. Journal of Experimental Psychology: Learning, Memory, and Cognition, 39, 277–289; Experiment 1], longer retention intervals did not lead to diminished memory of currently relevant information. However, we did find that longer periods of time between two trials improved memory for currently relevant information. This replicates findings that indicate proactive interference affects visual arrays performance and extends previous findings to show that reduction of proactive interference can occur in a time-depen...