A fast intracortical brain–machine interface with patterned optogenetic feedback — Paper Summary

Summary #07. A fast intracortical brain–machine interface with patterned optogenetic feedback

📄 [ paper ]

⚡Briefly

The neuroengineers provided a proof-of-concept of a fast closed-loop brain-computer interface (BCI) in mice. They used a control signal from the motor cortex to control a virtual bar and stimulated sensory cortex if the bar “touched” a mouse. The mouse successfully learnt the behavioral task relying solely on artificial inputs and outputs.

⌛️ Prerequisites

The experimental setup included neurostimulation is done with optogenetics. If you are not yet familiar with what it is, check out our summary first: optogenetics

🚀 Motivation

Our body is a closed-loop system: we make actions via motor systems and acquire feedback using sensory signals. In neuroprosthetics one of the current issues is to provide appropriate sensory feedback to the brain to have a sense of touch, temperature and pain of a lost extremity while being able to move a prosthesis. Medical application are mostly concerned with interpreting pathological brain signals and providing appropriate stimulation — a closed-loop design (adaptive DBS, epilepsy closed-loop system, depression closed-loop link). Thus, closed-loop solutions, especially with low latencies, are essential to be developed further.

🔍 Research pipeline

Useful notions:

• vS1 — analogue of a sensory cortex in mice
• vM1 — analogue of a motor cortex
• Mouse whiskers — things like cats moustache, allowing a mouse to sense its environment. Mice rely heavily on touch information (Figure 1)

Figure 1. Mouse whiskers

1) Prepare genetically modified mice

The special strain of mice was used, for which only pyramidal cortical neurons were genetically targeted to express opsins (ion-channels activated by light). As opposed to electrical stimulation, optogenetic stimulation is capable of targeting only a specific subset of neurons, what the researchers took advantage of.

For a deeper explanation please refer to the optogenetics summary.

2) Identify brain regions responsible for each whisker

The researchers did this by touching each whisker separately and measuring brain response via intrinsic imaging (Figure 2).

Figure 2. Intrinsic imaging setup. Piezo-electric bender stimulates whiskers of the anaesthetized mouse. Camera was recording a deflected laser light, which changes its intensity due to different optical properties of active and inactive brain tissue.

At the result, specific brain areas responsive for sensing the touch of a whisker were identified (Figure 3). This mapping can later be used to provide sensory input to the brain directly.

Figure 3. Mapping of whiskers to brain areas obtained using intrinsic imaging.

3) Implant electrodes into motor area

32 channel multisite electrode (Neuronexus, 8 tetrodes across 4 shanks) was implanted into vM1 to record brain output (Figure 4, left).

Figure 4. Recording and stimulation closed-loop setup. Left part is constituted by the recording silicon electrodes over vM1.

4) Link input and output into a closed-loop device

Now, having set up both recording and stimulation, it is possible to set up an autonomous device, reading activity from motor area and projecting to the sensory area (Figure 4, 5).

Figure 5. Closed-loop schematic setup.

In a closed-loop output signals are converted into a control variable by an interpreter. It could be machine-learning based solution, simple threshold algorithm or a different means of extracting a meaningful signal. A feedback is generated on the basis of the control signal and fed to an agent. In turn, the agent could adjust its output on the basis of received input.

5) Use this design in the behavioral experiment 🔥

In the case of this study, control signal was an aggregated activity of hundreds of recorded neurons in motor cortex. Mean firing rate interpreted by a computer controlled a location of virtual bar. A bar was chosen, because it can move only by one 1D-axis, touching different columns of whiskers. If a virtual bar “touched” a virtual whisker, sensory feedback was provided to the mouse sensory cortex with optogenetic stimulation (Figure 6).

Figure 6. Virtual environment created by experimenters. Virtual bar can move along one axis controlled by the motor cortex output. If it touches the virtual whisker input is provided to the mouse sensory area via optostimulation.

To test whether mouse could actually navigate in this environment with artificial inputs and outputs, researchers trained a mouse to receive reward only when a virtual bar touched certain whiskers (green range). The only way mouse could achieve it is by somehow increasing or decreasing firing rate of motor neurons. Note, that no actual bar touched the actual mouse whiskers! Nevertheless, mouse has successfully learned this task to receive reward (Figure 7).

Figure 7. An example of a trial with and without optogenetic feedback. Note that mouse can control the firing rate to control the bar to touch target whisker column. With feedback provided mouse successfully accomplishes this task.

It can be seen that without feedback (sense of touch), mouse can not longer perform a task, because it “didn’t know” when virtual bar touched its moustache, even thought the control variable was high enough.

📈 Experimental insights / Key takeaways

1. With appropriate training it is possible to train an animal to operate a closed-loop system — if a system provides relevant feedback. The researchers provided a proof-of-concept that optogenetics can be used to deliver targeted stimuli meaningful to the brain.
2. This setup operates at very low latencies, enhancing its ability to be interpretable by the brain (Figure 8).
3. Motor and sensory cortices of the mouse (and humans) are adjacent, meaning only one compact skull chunk can be surgically removed to access both input and outputs areas of the brain

Figure 8. Latency distribution for all trials in a closed-loop experiment (millisecond range).

✏️ My Notes / Future prospects

Limitations & Drawbacks

• Mean firing rate of a neurons is a very simple control signal. With contemporary deep learning solutions more meaningful features can be extracted. Even conventional connectivity, clustering and pattern analysis methods can be applied. In 1D-paradigm, mean firing rate is enough, though.
• Also, several surgical procedures are required to get this setup to work. In some mice, electrophysiological recordings could not be established to have long-term stability. This can be overcomed with optogenetic imaging (Ca++) or using a bio-compatible electrodes, which are yet to be developed.
• The mouse was in fixed position during the experiment. It is not yet clear how to make this solution mobile.
• Lastly, for all of the closed-loop solutions, it is not yet clearly understandable, how exactly to stimulate to convey meaningful information into the brain. In case of mice, large brain areas are responsible for receiving inputs from the whiskers. However, for motor areas there is no strict topographical mapping. Neural code and neural representations must be cracked to further increase complexity of closed-loop BCIs.

Final thoughts

Overall, however, combining optogenetic stimulation, which is precise, fast and accurate, with electrophysiological readout (e.g. Neuropixel, Neuralink), could substantially enhance intracortical BCI abilities, if applied to humans.

For example, readout can be done from language production area and stimulation — to language comprehension zone. I personally believe reinforcement learning algorithms could even overcome the issue of neural code cracking. Not knowing the exact neural code underlying speech production and comprehension processes (or movement/sensing process), the task could still be accomplished by simultaneous collaborative brain and AI training.

😉 Stay tuned and see you in our medium publication

Author: Alexey Timchenko

Collaborator: Alexander Kovalev

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