Conducting Longitudinal Studies of Brain Health: Data Measurements and Analysis

Longitudinal studies of brain health provide unique insights into the mechanisms and hallmarks of neurological diseases. They are critical for advancing research into neurological diseases and disorders and informing future drug discoveries. Whether conducting clinical trials to quantitatively assess drug efficacy or collecting and analyzing longitudinal data as part of a diagnosis, it is important to understand the tools available to measure and analyze neurocognitive function as well as their limitations.

Below, we take a look at accessible tools for conducting longitudinal studies of brain health as well as advancements towards robust, digital methods for measuring and analyzing neurocognitive function.

Traditional Tools for Longitudinal Brain Health Measurement and Analysis

Conventional tools utilized for longitudinal studies of brain health typically include pencil and paper memory tests, such as the Mini-Mental State Exam (MMSE) and the Montreal Cognitive Assessment (MoCA). While the MMSE is intended to be utilized as a diagnostic tool, it is regularly used in research studies as a measurement tool.

Limitations of Traditional Tools for Research and Clinical Trials

While it is often important to implement well-recognized neurocognitive assessment tools like the MMSE and MoCA for research purposes, these tools have definite limitations and may not provide the level of data granularity necessary to fully understand drug efficacy or detect small changes in neurocognitive function that may not yet be clinically visible.

While the MMSE and MoCA can be great screening tools, they fall short in providing the data quantity and specificity needed to provide new and meaningful insights into neurological diseases and a comprehensive understanding of their development and progression. The MMSE, MoCA, and similar memory assessments are limited by the following factors:

• The MMSE and MoCA only assess a few neurocognitive abilities, such as memory, attention, and language, and thus only scratch the surface for understanding brain function across many neurocognitive domains.
• The MMSE and MoCA require a healthcare professional to administer the tests and interpret the results. This not only introduces the risk of bias or human-to-human variability but is also quite time-consuming and thus inefficient for studies analyzing large populations.
• The MMSE and MoCA are scored on simple 30-point scales that do not provide a granular level of insight into neurocognitive health.
• Traditional neurocognitive assessments are highly subject to an individual’s “good” and “bad” days, which may lead to variable results.
• By nature, simple memory tests do not place the research subject’s brain under a neurocognitive load representative of activities of daily living and thus may not be ecologically valid. 

The Future of Neurocognitive Assessments is Digital

In recent years, research studies and clinical trials have begun embracing digital neurocognitive assessment tools. However, the majority of these assessments are digitized versions of traditional memory tests like the MMSE and MoCA, meaning they still carry the same challenges and limitations. While they do simplify the administration of the assessments and tracking of data across a population, there is a strong need for more robust and comprehensive methods for longitudinal studies of brain health.

Advancements in measurement and monitoring tools and the adoption of wearables and portables in healthcare provide new and promising opportunities to better understand neurological diseases through digital biomarkers. Digital biomarkers can provide longitudinal data collection on both an individual and population level. Utilizing portables or wearables to collect and analyze digital data presents the opportunity to determine and collect objective and clinically significant data in an efficient and scalable manner.

Collecting digital biomarkers while placing the subject’s brain under a higher neurocognitive load may enable detection of subtle changes in cognition, sensory, and motor function that may be clinically invisible in longitudinal studies of brain health that utilize traditional neurocognitive assessments.