Browsing by Author "Schmid, William"

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    A biophysically constrained brain connectivity model based on stimulation-evoked potentials.
    (Elsevier, 2024) Schmid, William; Danstrom, Isabel A.; Crespo Echevarria, Maria; Adkinson, Joshua; Mattar, Layth; Banks, Garrett P.; Sheth, Sameer A.; Watrous, Andrew J.; Heilbronner, Sarah R.; Bijanki, Kelly R.; Alabastri, Alessandro; Bartoli, Eleonora
    Background Single-pulse electrical stimulation (SPES) is an established technique used to map functional effective connectivity networks in treatment-refractory epilepsy patients undergoing intracranial-electroencephalography monitoring. While the connectivity path between stimulation and recording sites has been explored through the integration of structural connectivity, there are substantial gaps, such that new modeling approaches may advance our understanding of connectivity derived from SPES studies. New method Using intracranial electrophysiology data recorded from a single patient undergoing stereo-electroencephalography (sEEG) evaluation, we employ an automated detection method to identify early response components, C1, from pulse-evoked potentials (PEPs) induced by SPES. C1 components were utilized for a novel topology optimization method, modeling 3D electrical conductivity to infer neural pathways from stimulation sites. Additionally, PEP features were compared with tractography metrics, and model results were analyzed with respect to anatomical features. Results The proposed optimization model resolved conductivity paths with low error. Specific electrode contacts displaying high error correlated with anatomical complexities. The C1 component strongly correlated with additional PEP features and displayed stable, weak correlations with tractography measures. Comparison with existing method Existing methods for estimating neural signal pathways are imaging-based and thus rely on anatomical inferences. Conclusions These results demonstrate that informing topology optimization methods with human intracranial SPES data is a feasible method for generating 3D conductivity maps linking electrical pathways with functional neural ensembles. PEP-estimated effective connectivity is correlated with but distinguished from structural connectivity. Modeled conductivity resolves connectivity pathways in the absence of anatomical priors.
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    Large-Scale Decentralized Solar Desalination: A Blueprint to Make Efficient Day-Long Technologies a Reality
    (2023-08-08) Schmid, William; Alabastri, Alessandro
    Among potential solutions to global water scarcity, thermal desalination is a flexible choice for water treatment, given its key advantages in robustness and limited salinity dependence. Light can power thermal desalination by dissipating electromagnetic energy into heat. Solar-driven photothermal desalination (SDPD) can lead to decentralized water purification, improving accessibility and reducing the environmental impact over conventional, heavy infrastructure-based desalination practices like reverse osmosis. Unfortunately, today’s best decentralizable SDPD technologies barely surpass 10% of the thermodynamic limit for thermal desalination. Furthermore, there is limited consensus on how to best evaluate and compare the efficiency of diverse solar-driven systems, particularly with respect to their day-long performance under natural, time-varying intensity. Recently, we proposed a generalized approach for achieving efficient, scalable, and day-long SDPD. Instead of optimizing individual components like solar absorbers and evaporators, our approach emphasizes the critical transfers of power underpinning the entire thermal desalination process. In particular, the minimization of environmental losses and maximization of heat recovery depend on each other, and their combination is paramount in bolstering the performance of real-world practical systems. Guided by our approach, we have determined that SDPD systems of a specific size and input salinity operate most efficiently at a specific optimal input power, a previously understudied fact with major implications on the day-long operation of modular networks of individual systems. By focusing on optimizing system-wide thermal energy recovery, loss mitigation and exploiting dynamic energy recovery schemes that can be tuned adaptively for time-varying input power, highly efficient, cost-effective systems can be designed to best take advantage of available light.
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    Review of wearable technologies and machine learning methodologies for systematic detection of mild traumatic brain injuries
    (IOP Publishing, 2021) Schmid, William; Fan, Yingying; Chi, Taiyun; Golanov, Eugene; Regnier-Golanov, Angelique S.; Austerman, Ryan J.; Podell, Kenneth; Cherukuri, Paul; Bentley, Timothy; Steele, Christopher T.; Schodrof, Sarah; Aazhang, Behnaam; Britz, Gavin W.; Neuroengineering Initiative (NEI)
    Mild traumatic brain injuries (mTBIs) are the most common type of brain injury. Timely diagnosis of mTBI is crucial in making ‘go/no-go’ decision in order to prevent repeated injury, avoid strenuous activities which may prolong recovery, and assure capabilities of high-level performance of the subject. If undiagnosed, mTBI may lead to various short- and long-term abnormalities, which include, but are not limited to impaired cognitive function, fatigue, depression, irritability, and headaches. Existing screening and diagnostic tools to detect acute and early-stage mTBIs have insufficient sensitivity and specificity. This results in uncertainty in clinical decision-making regarding diagnosis and returning to activity or requiring further medical treatment. Therefore, it is important to identify relevant physiological biomarkers that can be integrated into a mutually complementary set and provide a combination of data modalities for improved on-site diagnostic sensitivity of mTBI. In recent years, the processing power, signal fidelity, and the number of recording channels and modalities of wearable healthcare devices have improved tremendously and generated an enormous amount of data. During the same period, there have been incredible advances in machine learning tools and data processing methodologies. These achievements are enabling clinicians and engineers to develop and implement multiparametric high-precision diagnostic tools for mTBI. In this review, we first assess clinical challenges in the diagnosis of acute mTBI, and then consider recording modalities and hardware implementation of various sensing technologies used to assess physiological biomarkers that may be related to mTBI. Finally, we discuss the state of the art in machine learning-based detection of mTBI and consider how a more diverse list of quantitative physiological biomarker features may improve current data-driven approaches in providing mTBI patients timely diagnosis and treatment.