In researching neurological injury and disease, it is imperative that we measure the right theoretical constructs in the right way. For instance, in a genome-wide association study of “recovery” following stroke, the hits we get across the genome will depend on how that recovery phenotype is defined. The NIH Stroke Scale is one of the most widely used instruments for measuring stroke severity and recovery, but when aggregated to a total score it loses specificity (i.e., two people can have the same total score for very different reasons). And when taken at the individual item level it loses sensitivity (i.e., the 5-level ordinal motor score for the arm has strong ceiling/floor effects and lacks resolution compared to continuous measures). Thus, we often face constraints between the informational value of a measure relative to the cost of its collection. To that end, I have worked on many different projects exploring the costs/benefits of measuring behavioral phenotypes in neurology. 

Example Publications:

•  Lang CE, Holleran CL, Strube MJ, Ellis TD, Newman CA, Fahey M, DeAngelis TR, Nordahl TJ, Reisman DS, Earhart GM, Lohse KR, Bland, MD. Improvement in the capacity for activity versus improvement in performance of activity in daily life during outpatient rehabilitation. Journal of Neurologic Physical Therapy. 2023 Jan 1;47(1):16-25.

• Krishnagopal S, Lohse KR, Braun R. Stroke recovery phenotyping through network trajectory approaches and graph neural networks. Brain Inform. 2022;9(1):1–11.

•  Braun RG, Heitsch L, Cole JW, Lindgren A, de Havenon A, Dude JA, Lohse KR, Cramer SC, Worrall BB, Core P, GPAS Collaboration. What the modified Rankin isn’t ranking: Domain-specific outcomes for stroke clinical trials. Neurology. 2021 May 11.

•  Barth, J., Lohse, K.R., Konrad, J., Bland, M., & Lang, C.E. (2021). Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits. Frontiers in Rehabilitation, 2:741393. doi: 10.3389/fresc.2021.741393

Rehabilitation is fundamentally about change within a person over time. Statistically speaking, however, these temporally dependent data violate the assumptions of many statistical tests and require specialized tools for analysis. People often use the term “longitudinal data” to refer to few data points (e.g., <10) that are collected over a long timescale (e.g., days or months apart). In contrast, people use the term “time-series data” to refer to large numbers of data points (e.g., hundreds to millions) that are sampled at a very high density (e.g., milliseconds or microseconds apart). Although these different data types actually exist on a continuum, it is still useful to talk about them separately as differences in the sampling rate and number of observations make them amenable to different types of analyses. For instance, time-series data can be transformed into the frequency domain with Fourier analysis and future data can be predicted with various autoregressive models. In contrast, longitudinal data might be analyzed with linear or non-linear mixed-effect regression models, with the specifics of the model depending on the nature of the outcome (e.g., binary, ordinal, or interval/ratio) and the “shape” of the trajectory (e.g., linear, exponential, or sigmoidal). Although we do not create these mathematical tools, we apply them to rehabilitation problems and create instructional materials for rehabilitation researchers to use them. 

Example Publications:

• Lohse KR, Kozlowski AJ, Strube MJ. Model Specification in Mixed-Effects Models: A Focus on Random Effects. Communications in Kinesiology. 2023. https://doi.org/10.51224/cik.2023.52

• de Havenon A, Heitsch L, Sunmonu A, Braun R, Lohse KR, Cole JW, Mistry E, Lindgren A, Worrall BB, Cramer SC. Accurate Prediction of Persistent Upper Extremity Impairment in Patients with Ischemic Stroke. Archives of Physical Medicine and Rehabilitation. 2022 May 1;103(5):964-9.

• Krishnagopal S, Lohse KR, Braun R. Stroke recovery phenotyping through network trajectory approaches and graph neural networks. Brain Inform. 2022;9(1):1–11.

• Lohse KR, Shen J, Kozlowski AJ. Modeling longitudinal outcomes: A contrast of two methods. Journal of Motor Learning and Development. 2020 Jan 27;8(1):145-65.

As with many fields, rehabilitation has seen astronomical growth in the amount and complexity of the data we produce. For instance, physiological data from EEG or accelerometry data from inertial sensors contain highly structured data (e.g., voltages or forces in discrete intervals of time) in very dense samples (e.g., 250-1,000Hz for minutes or hours of recording). In contrast, electronic health records contain loosely structured data from millions of individuals all with complex data types that all have unique relationships to each other and may or may not be recorded over time. This means that researchers and their students are facing increasingly large and complex data sets. In my research group, we want to give researchers the tools and training to work with their own data effectively. More than any one project, we want to make sure that data are Findable, Accessible, Interoperable, and Reusable (FAIR) in rehabilitation science. As part of that effort, I am part of the educational leadership team for the Reproducible Rehabilitation (“ReproRehab”) program funded by NCMRR and I collaborate with other researchers at WUSTL to harmonize and archive large research datasets. 

Example publications:

•  de Havenon A, Bangad A, Skolarus L, Aldridge C, Braun R, Cole J, Cramer S, Lindgren AG, Sunmonu N, Worrall BB, Lohse KR. Understanding patterns of missingness in acute ischemic stroke trials: A secondary analysis of pooled patient-level follow-up data. Stroke. 2023; 54:e201–e202.

• Liew SL, Zavaliangos‐Petropulu A, Jahanshad N, Lang CE, Hayward KS, Lohse KR, Juliano JM, Assogna F, Baugh LA, Bhattacharya AK, Bigjahan B. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke. Human brain mapping. 2022 Jan;43(1):129-48.

• Mohabbati-Kalejahi N, Yazdi MAA, Megahed FM, Schaefer SY, Boyd LA, Lang CE, Lohse KR. Streamlining science with structured data archives: insights from stroke rehabilitation. Scientometrics. 2017 Nov;113(2):969–983

•  Lohse KR, Schaefer SY, Raikes AC, Boyd LA, Lang CE. Asking new questions with old data: the centralized open-access rehabilitation database for stroke. Frontiers in Neurology. 2016 Sep 20;7:153.

  • The interactive SCOAR website: https://keithlohse.github.io/SCOAR_data_viz/.