Publications by authors named "T Jason Druzgal"

28 Publications

Olfaction, cholinergic basal forebrain degeneration, and cognition in early Parkinson disease.

Parkinsonism Relat Disord 2021 09 27;90:27-32. Epub 2021 Jul 27.

Department of Radiology and Medical Imaging, Division of Neuroradiology, University of Virginia Health System, Charlottesville, VA, USA.

Introduction: Impaired olfaction and reduced cholinergic nucleus 4 (Ch4) volume both predict greater cognitive decline in Parkinson's disease (PD). We examined the relationship between olfaction, longitudinal change in cholinergic basal forebrain nuclei and their target regions, and cognition in early PD.

Methods: We analyzed a cohort of 97 PD participants from the Parkinson's Progression Markers Initiative with brain MRIs at baseline, 1 year, 2 years, and 4 years. Using probabilistic maps, regional grey matter density (GMD) was calculated for Ch4, cholinergic nuclei 1, 2, and 3 (Ch123), and their target regions.

Results: Baseline University of Pennsylvania Smell Identification Test score correlated with change in GMD of all regions of interest (all p < 0.05). Rate of change of Ch4 GMD was correlated with rate of change of Ch123 (p = 0.034), cortex (p = 0.001), and amygdala GMD (p < 0.001), but not hippocampus GMD (p = 0.38). Rate of change of Ch123 GMD was correlated with rate of change of cortex (p = 0.001) and hippocampus (p < 0.001), but not amygdala GMD (p = 0.133). In a linear regression model including change in GMD of all regions of interest and age as predictors, change in cortex GMD (βˆ= 38.2; 95 % CI: [0.47, 75.9]) and change in hippocampus GMD (βˆ= 24.8; 95 % CI: [0.80, 48.8]) were significant predictors of Montreal Cognitive Assessment score change over time.

Conclusion: Impaired olfaction is associated with degeneration of the cholinergic basal forebrain and bilateral cortex, amygdala, and hippocampus in PD. The relationship between impaired olfaction and cognitive decline may be mediated by greater atrophy of the cortex and hippocampus.
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September 2021

The Effect of Muscle Activation on Head Kinematics During Non-injurious Head Impacts in Human Subjects.

Ann Biomed Eng 2020 Dec 14;48(12):2751-2762. Epub 2020 Sep 14.

Center for Applied Biomechanics, University of Virginia, Charlottesville, VA, USA.

In this study, twenty volunteers were subjected to three, non-injurious lateral head impacts delivered by a 3.7 kg padded impactor at 2 m/s at varying levels of muscle activation (passive, co-contraction, and unilateral contraction). Electromyography was used to quantify muscle activation conditions, and resulting head kinematics were recorded using a custom-fit instrumented mouthpiece. A multi-modal battery of diagnostic tests (evaluated using neurocognitive, balance, symptomatic, and neuroimaging based assessments) was performed on each subject pre- and post-impact. The passive muscle condition resulted in the largest resultant head linear acceleration (12.1 ± 1.8 g) and angular velocity (7.3 ± 0.5 rad/s). Compared to the passive activation, increasing muscle activation decreased both peak resultant linear acceleration and angular velocity in the co-contracted (12.1 ± 1.5 g, 6.8 ± 0.7 rad/s) case and significantly decreased in the unilateral contraction (10.7 ± 1.7 g, 6.5 ± 0.7 rad/s) case. The duration of angular velocity was decreased with an increase in neck muscle activation. No diagnostic metric showed a statistically or clinically significant alteration between baseline and post-impact assessments, confirming these impacts were non-injurious. This study demonstrated that isometric neck muscle activation prior to impact can reduce resulting head kinematics. This study also provides the data necessary to validate computational models of head impact.
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December 2020

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.

J Neurooncol 2020 Sep 9;149(2):325-335. Epub 2020 Sep 9.

Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, 22908, USA.

Purpose: The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG.

Methods: Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10mm/s (V), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach.

Results: V classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with V ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited V ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign.

Conclusion: Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with V achieving > 90% classification specificity in both internal and validation cohorts. V exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.
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September 2020

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

Ann Biomed Eng 2020 Oct 28;48(10):2412-2424. Epub 2020 Jul 28.

Department of Mechanical and Aerospace Engineering, Center for Applied Biomechanics, University of Virginia, 4040 Lewis and Clark Dr., Charlottesville, VA, 229011, USA.

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.
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October 2020

Cholinergic nucleus 4 atrophy and gait impairment in Parkinson's disease.

J Neurol 2021 Jan 28;268(1):95-101. Epub 2020 Jul 28.

Department of Neurology, University of Virginia, 1221 Lee St 4th Floor, Charlottesville, VA, 22908, USA.

Background: There is evidence that cortical cholinergic denervation contributes to gait and balance impairment in Parkinson's Disease (PD), especially reduced gait speed.

Objectives: The objective of this study was to determine the relationship between cholinergic basal forebrain gray matter density (GMD) and gait in PD patients.

Methods: We investigated 66 PD patients who underwent a pre-surgical evaluation for a neurosurgical procedure to treat motor symptoms of PD. As part of this evaluation patients had a brain MRI and formal gait assessments. By applying probabilistic maps of the cholinergic basal forebrain to voxel-based morphometry of brain MRI, we calculated gray matter density (GMD) for cholinergic nucleus 4 (Ch4), cholinergic nucleus 1, 2, and 3 (Ch123), and the entire cortex.

Results: Reduced Ch4 GMD was associated with reduced Fast Walking Speed in the "on" medication state (FWSON, p = 0.004). Bilateral cortical GMD was also associated with FWSON (p = 0.009), but Ch123 GMD was not (p = 0.1). Bilateral cortical GMD was not associated with FWSON after adjusting for Ch4 GMD (p = 0.44). While Ch4 GMD was not associated with improvement in Timed Up and Go (TUG) or Cognitive TUG in the "on" medication state, reduced Ch4 GMD was associated with greater percent worsening based on dual tasks (p = 0.021).

Conclusions: Reduced Ch4 GMD is associated with slower gait speed in PD and greater percent worsening in TUG during dual tasks in patients with PD. These findings have implications for planning of future clinical trials investigating cholinergic therapies to improve gait impairment in PD.
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January 2021