Nia Therapeutics

How does memory formation work?

Biomarkers of Memory Encoding

Certain patterns of brain activity underlie successful memory encoding. While common patterns emerge across individuals, the specific patterns vary from person to person. Using electrodes placed within the brain, we detect patterns that predict momentary memory lapses in each individual.

Can AI predict memory formation?

Classification of Memory States

Using AI, our Smart Neurostimulation System deciphers when the brain is in a good or poor state for memory formation and retrieval. When the classifier predicts that our brain is in a good memory state, we are more likely to remember the experienced information.

How can memory formation be restored?

Closed-Loop Stimulation Therapy

The electrodes record neural signals and the AI algorithms use those data to decode momentary periods of good and poor memory. When it is determined that the brain is not in a state conducive to successful memory function, our therapy applies gentle electrical stimulation to coax the brain into a more functional state.

Explore the neuroscientific principles of memory established by Nia researchers

01.

Defining the neural network of memory

Research from Dr. Kahana’s lab has identified the network of brain regions critical to remembering items from lists of words. By testing participants with electrodes implanted in their brain, his team determined the specific brain regions that are essential to proper functioning of the memory network (Burke et al., 2014). With these regions established, his team explored not just where in the brain memory resides, but how to directly stimulate these areas to correct lapses in memory.

02.

Modulating memory-related brain states using direct brain stimulation

In 2017, we determined that the brain effectively operates in two different “memory states,” one that allows memories to be effectively stored, and another that does not. By analyzing the brain activity from each patient we could distinguish these two brain states. We then showed that we could flip the switch using direct stimulation of the brain, toggling between the “forgetting” and “remembering” brain states (Ezzyat et al., 2017).

03.

Using feedback from the brain to improve treatment

In 2018, we took the concept one step further by using AI algorithms to predict the brain’s state in real-time, and using this prediction to control stimulation delivery. This is called personalized (or closed-loop) stimulation, because the stimulation therapy is meant to be personalized to each participant’s unique pattern of brain activity. In our research, personalized stimulation to the temporal lobe during periods of poor predicted memory resulted in memory improvements (Ezzyat et al., 2018).

04.

Incorporating AI algorithms to optimize brain stimulation patterns

Finally, we incorporated all of these learnings into a clinical study of participants with a history of TBI. Using a research prototype, we have shown that AI-guided, closed-loop stimulation of the temporal lobe improved verbal memory in participants with TBI (Kahana et al., 2023). Four of the eight participants in this study demonstrated clinically-relevant improvement in memory function, and we are currently studying whether refinement of the surgical procedure may improve functional outcomes (Ezzyat et al., 2024).

Disclaimer

The Smart Neurostimulation System (SNS) is in pre-clinical development to potentially treat the symptom of verbal memory loss in patients with traumatic brain injury. The SNS is not yet cleared by FDA to diagnose or treat any disease. The studies referenced above were all approved by an Institutional Review Board and conducted using an early research prototype.

Our Science