Browsing by Subject "Multimodal"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Classification of Alzheimer’s Disease Leveraging Multi-task Machine Learning Analysis of Speech and Eye-Movement Data
    (Frontiers Media, 2021-09-20) Jang, Hyeju; Soroski, Thomas; Rizzo, Matteo; Barral, Oswald; Harisinghani, Anuj; Newton-Mason, Sally; Granby, Saffrin; da Cunha Vasco, Thiago Monnerat Stutz; Lewis, Caitlin; Tutt, Pavan; Carenini, Giuseppe; Conati, Cristina; Field, Thalia S.; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Alzheimer’s disease (AD) is a progressive neurodegenerative condition that results in impaired performance in multiple cognitive domains. Preclinical changes in eye movements and language can occur with the disease, and progress alongside worsening cognition. In this article, we present the results from a machine learning analysis of a novel multimodal dataset for AD classification. The cohort includes data from two novel tasks not previously assessed in classification models for AD (pupil fixation and description of a pleasant past experience), as well as two established tasks (picture description and paragraph reading). Our dataset includes language and eye movement data from 79 memory clinic patients with diagnoses of mild-moderate AD, mild cognitive impairment (MCI), or subjective memory complaints (SMC), and 83 older adult controls. The analysis of the individual novel tasks showed similar classification accuracy when compared to established tasks, demonstrating their discriminative ability for memory clinic patients. Fusing the multimodal data across tasks yielded the highest overall AUC of 0.83 ± 0.01, indicating that the data from novel tasks are complementary to established tasks.
  • Thumbnail Image
    Item
    Multimodal Atlas of the Murine Inner Ear: From Embryo to Adult
    (Frontiers Media, 2021-07-15) Bryant, Jean-Paul; Chandrashekhar, Vikram; Cappadona, Anthony J.; Lookian, Pashayar P.; Chandrashekhar, Vibhu; Donahue, Danielle R.; Munasinghe, Jeeva B.; Kim, H. Jeffrey; Vortmeyer, Alexander O.; Heiss, John D.; Zhuang, Zhengping; Rosenblum, Jared S.; Pathology and Laboratory Medicine, School of Medicine
    The inner ear is a complex organ housed within the petrous bone of the skull. Its intimate relationship with the brain enables the transmission of auditory and vestibular signals via cranial nerves. Development of this structure from neural crest begins in utero and continues into early adulthood. However, the anatomy of the murine inner ear has only been well-characterized from early embryogenesis to post-natal day 6. Inner ear and skull base development continue into the post-natal period in mice and early adulthood in humans. Traditional methods used to evaluate the inner ear in animal models, such as histologic sectioning or paint-fill and corrosion, cannot visualize this complex anatomy in situ. Further, as the petrous bone ossifies in the postnatal period, these traditional techniques become increasingly difficult. Advances in modern imaging, including high resolution Micro-CT and MRI, now allow for 3D visualization of the in situ anatomy of organs such as the inner ear. Here, we present a longitudinal atlas of the murine inner ear using high resolution ex vivo Micro-CT and MRI.
  • Thumbnail Image
    Item
    Perioperative Multimodal General Anesthesia Focusing on Specific CNS Targets in Patients Undergoing Cardiac Surgeries: The Pathfinder Feasibility Trial
    (Frontiers Media, 2021-10-14) Shanker, Akshay; Abel, John H.; Narayanan, Shilpa; Mathur, Pooja; Work, Erin; Schamberg, Gabriel; Sharkey, Aidan; Bose, Ruma; Rangasamy, Valluvan; Senthilnathan, Venkatachalam; Brown, Emery N.; Subramaniam, Balachundhar; Anesthesia, School of Medicine
    Multimodal general anesthesia (MMGA) is a strategy that utilizes the well-known neuroanatomy and neurophysiology of nociception and arousal control in designing a rational and clinical practical paradigm to regulate the levels of unconsciousness and antinociception during general anesthesia while mitigating side effects of any individual anesthetic. We sought to test the feasibility of implementing MMGA for seniors undergoing cardiac surgery, a high-risk cohort for hemodynamic instability, delirium, and post-operative cognitive dysfunction. Twenty patients aged 60 or older undergoing on-pump coronary artery bypass graft (CABG) surgery or combined CABG/valve surgeries were enrolled in this non-randomized prospective observational feasibility trial, wherein we developed MMGA specifically for cardiac surgeries. Antinociception was achieved by a combination of intravenous remifentanil, ketamine, dexmedetomidine, and magnesium together with bupivacaine administered as a pecto-intercostal fascial block. Unconsciousness was achieved by using electroencephalogram (EEG)-guided administration of propofol along with the sedative effects of the antinociceptive agents. EEG-guided MMGA anesthesia was safe and feasible for cardiac surgeries, and exploratory analyses found hemodynamic stability and vasopressor usage comparable to a previously collected cohort. Intraoperative EEG suppression events and postoperative delirium were found to be rare. We report successful use of a total intravenous anesthesia (TIVA)-based MMGA strategy for cardiac surgery and establish safety and feasibility for studying MMGA in a full clinical trial.