Browsing by Author "Svaldi, Diana O."

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    Accumulation of high magnitude acceleration events predicts cerebrovascular reactivity changes in female high school soccer athletes
    (Springer, 2018) Svaldi, Diana O.; Joshi, Chetas; McCuen, Emily C.; Music, Jacob P.; Hannemann, Robert; Leverenz, Larry J.; Nauman, Eric A.; Talavage, Thomas M.; Neurology, School of Medicine
    Mitigating the effects of repetitive exposure to head trauma has become a major concern for the general population, given the growing body of evidence that even asymptomatic exposure to head accelerations is linked with increased risk for negative life outcomes and that risk increases as exposure is prolonged over many years. Among women's sports, soccer currently exhibits the highest growth in participation and reports the largest number of mild traumatic brain injuries annually, making female soccer athletes a relevant population in assessing the effects of repetitive exposure to head trauma. Cerebrovascular biomarkers may be useful in assessing the effects of repetitive head trauma, as these are thought to contribute directly to neurocognitive symptoms associated with mild traumatic brain injury. Here we use fMRI paired with a hypercapnic breath hold task along with monitoring of head acceleration events, to assess the relationship between cerebrovascular brain changes and exposure to repetitive head trauma over a season of play in female high school soccer athletes. We identified longitudinal changes in cerebrovascular reactivity that were significantly associated with prolonged accumulation to high magnitude (> 75th percentile) head acceleration events. Findings argue for active monitoring of athletes during periods of exposure to head acceleration events, illustrate the importance of collecting baseline (i.e., pre-exposure) measurements, and suggest modeling as a means of guiding policy to mitigate the effects of repetitive head trauma.
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    Every hit matters: White matter diffusivity changes in high school football athletes are correlated with repetitive head acceleration event exposure
    (Elsevier, 2019-07-16) Jang, Ikbeom; Chun, Il Yong; Brosch, Jared R.; Bari, Sumra; Zou, Yukai; Cummiskey, Brian R.; Lee, Taylor A.; Lycke, Roy J.; Poole, Victoria N.; Shenk, Trey E.; Svaldi, Diana O.; Tamer, Gregory G., Jr.; Dydak, Ulrike; Leverenz, Larry J.; Nauman, Eric A.; Talavage, Thomas M.; Neurology, School of Medicine
    Recent evidence of short-term alterations in brain physiology associated with repeated exposure to moderate intensity subconcussive head acceleration events (HAEs), prompts the question whether these alterations represent an underlying neural injury. A retrospective analysis combining counts of experienced HAEs and longitudinal diffusion-weighted imaging explored whether greater exposure to incident mechanical forces was associated with traditional diffusion-based measures of neural injury-reduced fractional anisotropy (FA) and increased mean diffusivity (MD). Brains of high school athletes (N = 61) participating in American football exhibited greater spatial extents (or volumes) experiencing substantial changes (increases and decreases) in both FA and MD than brains of peers who do not participate in collision-based sports (N = 15). Further, the spatial extents of the football athlete brain exhibiting traditional diffusion-based markers of neural injury were found to be significantly correlated with the cumulative exposure to HAEs having peak translational acceleration exceeding 20 g. This finding demonstrates that subconcussive HAEs induce low-level neurotrauma, with prolonged exposure producing greater accumulation of neural damage. The duration and extent of recovery associated with periods in which athletes do not experience subconcussive HAEs now represents a priority for future study, such that appropriate participation and training schedules may be developed to minimize the risk of long-term neurological dysfunction.
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    Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease
    (Wiley, 2021-08) Svaldi, Diana O.; Goñi, Joaquín; Abbas, Kausar; Amico, Enrico; Clark, David G.; Muralidharan, Charanya; Dzemidzic, Mario; West, John D.; Risacher, Shannon L.; Saykin, Andrew J.; Apostolova, Liana G.; Medicine, School of Medicine
    Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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    Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes
    (Springer, 2018-01) Svaldi, Diana O.; Goñi, Joaquín; Sanjay, Apoorva Bharthur; Amico, Enrico; Risacher, Shannon L.; West, John D.; Dzemidzic, Mario; Saykin, Andrew; Apostolova, Liana; Neurology, School of Medicine
    Alzheimer’s disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer’s spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.