In order to understand and characterize preclinical models of diseases of the central nervous system (CNS), it is critical to be able to conduct continuous neuroimaging of multiple physiological variables—neuronal activity, blood flow, blood volume, oxygenation, and cellular dynamics—within the CNS microenvironment. This continuous multimodality neuroimaging capability is known as neurosurveillance.

However, a long-standing challenge in neuroscience and neuropathology has been how to continuously observe biological processes that unfold over hours, days, and even weeks in the brain.

Now, researchers at Johns Hopkins Medicine have demonstrated a new approach to brain imaging that enables continuous monitoring of brain activity and the physiologic changes associated with neurological disease progression for more than 24 hours in freely moving mouse models. The cloud-based miniaturized microscope, CloudScope, operates autonomously and allows scientists to access live imaging data remotely from anywhere in the world, creating new opportunities to study diseases as they develop over time.

The study demonstrates the ability to remotely capture and analyze changes in brain activity, blood flow, blood vessel remodeling, oxygenation and cellular behavior over extended periods, providing a more holistic picture of brain disease progression than conventional imaging approaches.

This work is published in Nature Methods in the paper, “A cloud-based miniscope for neurosurveillance of brain health and disease in freely behaving animals.”

“We started with a fundamental question: If we wanted to image a seizure or brain tumor formation continuously in a preclinical or animal model over 24 hours or longer, how would we do that?” says Arvind Pathak, PhD, professor of radiology, oncology, and biomedical and electrical engineering at Johns Hopkins. “The consequence of us working through this question and its associated challenges is what resulted in this innovation.”

Using this approach, the team captured spontaneous seizures occurring several hours after a drug-induced seizure in mice, events that would have been missed using conventional short-term imaging methods. In separate studies of brain cancer, researchers were able to characterize the behavior of individual cancer cells and observe dynamic changes in the brain’s microenvironment as the disease progressed. These findings suggest that continuous monitoring may reveal critical biological events that occur outside the limited observation windows typically used in laboratory research.

“Most central nervous system diseases develop over hours, days or even weeks. Yet modern imaging tools are designed to continuously probe only a small fraction of this time window,” says Janaka Senarathna, PhD, assistant professor of radiology at Johns Hopkins. “We developed a device to break this time barrier.”

In addition to advancing neuroimaging research, the investigators have also demonstrated a promising application involving artificial intelligence. By combining the first-ever 24-hour brain imaging dataset with video recordings of the lab animals’ behavior, the team successfully trained an AI framework to predict whether an animal was minimally mobile, moderately active, or running based solely on neuronal activity measurements made with the device. The researchers believe this approach could help scientists better understand the neurological effects of conditions such as stroke or Parkinson’s disease and potentially reveal new insights into the relationship between brain activity and behavior. Additionally, researchers say the device enables time-shared imaging from anywhere in the world, and it creates a pathway to reduce animal use while enabling neuroscientific and neuropathological insights. Lastly, CloudScope’s architecture enables “time-shared” imaging, which potentially reduces animal use.

To explore the effect of disease on different brain regions, the team plans to continue expanding the platform’s capabilities, such as imaging larger regions of the animals’ brains and leveraging AI to accelerate brain imaging and cancer cell tracking.

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Newly solidified in their roles as the heads of CBER and CDER, Karim Mikahil and Michael Davis have been working for months to right the ship that had begun to capsize under former Commissioner Marty Makary. Meanwhile, Jared Seehafer will become the first-ever deputy commissioner focused on artificial intelligence.

Artificial intelligence is advancing quickly into laboratory operations, which is why thinking about the quality and integrity of the supporting data early on is important. In regulated environments, that means understanding where the data came from, how it changes, and how AI-generated insights or recommendations can be traced, reviewed, trusted, and ultimately defended.

In this GEN podcast, LabVantage’s Gary Stimson digs into what it takes to build a data foundation for trustworthy AI. The discussion covers concepts like data lineage, traceability, explainability and governance, and the importance of audit-ready laboratory operations. It also looks at the role of intended use, ongoing monitoring, and human review in deploying AI responsibly.

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