Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution.

Preliminary Assessment of Intravoxel Incoherent Motion Diffusion-Weighted MRI (IVIM-DWI) Metrics in Alzheimer's Disease.

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.

The influence of age and ASD on verbal fluency networks.

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.

Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object.

Purpose: Brain tumor dynamic susceptibility contrast (DSC) MRI is adversely impacted by T and contrast agent leakage effects that result in inaccurate hemodynamic metrics. While multi-echo acquisitions remove T 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.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI.

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.

Visuospatial working memory, auditory discrimination, and attention.

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).

The contribution of disengagement to temporal discriminability.

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.