Summary: In a first-of-its-kind study, researchers developed an electronic implant that collected information about brain activity from a single neuron for over one year.

Source: Harvard

When a person experiences a happy or sad mood, which brain cells are active?

To answer that question, scientists need to understand how individual brain cells contribute to a larger network of brain activity and what role each cell plays in shaping behavior and overall health. Until now, its been difficult to get a clear view of howbrain cellsin living animals behave over extended periods of time.

But Jia Lius group at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has developed an electronic implant that collected detailed information about brain activity from a single cell of interest for more than a year.

Their findings, based on research in mice, are reported inNature Neuroscience.

This research solves a fundamental issuethe challenge of creating a brain-electronic interface that does not disturbbrain functionor degrade over time, says Liu, who is an assistant professor of bioengineering at SEAS, where he leads a lab dedicated to bioelectronics.

Neuroscientists have long sought better tools to study different cells in the brain, including neurons (which transmit electrical and chemical messages) and microglia (immune cells responsible for maintaining brain health).

A single neuron is very smallonly 10 to 100 micrometersand when it fires, its action potential (the spike inelectrical activity) only lasts about two milliseconds, Liu says.

Certain techniques can detect brain activity from specific cells of interest for short-lived experiments in small areas of the brain, either in tissue recently removed from animals or by using probes or optogenetic techniques to capture activity in situ.

But these conditions are not true to life and they dont provide detailed enough information about electrical activity in individual cells to understand how activity changes with age and otherlife experiences, Liu says. Behaviors, memories, and disease all build up over the course of days, weeks, months, and years.

Much of the difficulty to date, he says, has been due to a mismatch in mechanical properties between livingbrain tissueand electronic recording devices. This has prevented long-term, precise recording of how neurons and microglia behave over time.

The brain is very soft, like the texture of tofu or pudding. In contrast, electronics are rigid. Any small movement of the brain can cause conventional sensors to drift and move in living brain tissue. That mismatch in structure can cause cells around the implantation site to degrade.

To circumvent the problem, Lius team, which specializes in engineering nanoelectronics or cyborgs to bridge the gap between living tissue and electronics, developed an implantable device and minimally invasive technique for delivering it safely into the brain.

The mesh-like, flexible nanoelectronic sensor is designed to be inserted into brain tissue using a water-soluble polymer shuttle. Prior to implantation, the device and its delivery shuttle are connected lithographically. Once the implant is in the brain, a simple saline solution is applied to dissolve the shuttle, leaving only the mesh electronic sensor behind.

In mouse studies, when Lius team implanted their nanoelectronic sensors into multiple areas of the brain, the implantation process and presence of the sensors resulted in minimal disturbance to brain tissue. Then, targeting single neurons for analysis, they used the devices to record the electrical activity of those same cells over the course of the mices adult lives.

Even after one year, we didnt see any degradation of the individual neurons or proliferation of the microglia we were interested in recording with the devices, Liu says. Theres no other technology out there that can track single-cellaction potentialfrom the same cells in active animals over the course of a few months and a year.

Looking ahead, Liu plans to further develop the technique so thatbrain activitycan be transmitted in real time from the biological neural network to an artificial neural network in a computer for analysis. And, he wants to explore how the mesh nanoelectronic sensors can be used to study phenomena such as neural representation.

When you watch a movie or see a car drive down the road, your brain generates electrical activity to represent those images, he says. During that process of neural representation, the brain encodes sensory information and thoughts into a model of external stimuli.

Liu says that, for example, moods are influenced byneural representation, and hes especially interested in studying how changes of neural representations and brain states impact mood fluctuations over time.

Maybe one day its cold and gray outside, and you feel unhappy and in a bad mood. Another day, its sunny and youre on the beach and youre in a great mood. How those representations change in the brain cannot be studied by current technology because we havent been able to stably track activity from the same neuron, he says. This research completely overcomes that limitation. Its the beginning of a new era of neuroscience.

An ultimate goal of Lius research is to develop diagnostic and therapeutic methods for neurological, cardiovascular and developmental diseases.

Author: Kat J. McAlpineSource: HarvardContact: Kat J. McAlpine HarvardImage: The image is credited to Liu Lab, Harvard SEAS

Original Research: Closed access.Tracking neural activity from the same cells during the entire adult life of mice by Siyuan Zhao et al. Nature Neuroscience

Abstract

Tracking neural activity from the same cells during the entire adult life of mice

Stably recording the electrical activity of the same neurons over the adult life of an animal is important to neuroscience research and biomedical applications. Current implantable devices cannot provide stable recording on this timescale.

Here, we introduce a method to precisely implant electronics with an open, unfolded mesh structure across multiple brain regions in the mouse.

The open mesh structure forms a stable interwoven structure with the neural network, preventing probe drifting and showing no immune response and neuron loss during the year-long implantation.

Rigorous statistical analysis, visual stimulus-dependent measurement and unbiased, machine-learning-based analysis demonstrated that single-unit action potentials have been recorded from the same neurons of behaving mice in a very long-term stable manner.

Leveraging this stable structure, we demonstrated that the same neurons can be recorded over the entire adult life of the mouse, revealing the aging-associated evolution of single-neuron activities.

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