The Role of Cerebellar Perineuronal Nets in Synaptic Plasticity and Associative Learning

Post by Shireen Parimoo

What's the science?

The cerebellum is a brain structure that contains nearly half of all the neurons in the brain and is involved in a wide variety of functions, ranging from motor control to learning. Cerebellar neurons are surrounded by perineuronal nets (PNNs), which are specialized extracellular matrix structures, made up of carbohydrate and protein molecules like chondroitin sulfate proteoglycans (CSPGs). Eye-blink conditioning is a type of associative learning that is dependent on deep cerebellar neurons. Normally, delivering puffs of air to the eye elicits a reflexive blinking response. In eye-blink conditioning, air puffs are paired with a neutral stimulus, such as light, so that after repeated exposures, simply presenting the light elicits the blinking response. Studies have found that disrupting PNNs can enhance structural plasticity and alter memory formation, but the precise role of cerebellar PNNs in learning and neuroplasticity is not known. This week in PNAS, Carulli, and colleagues investigated the molecular mechanisms underlying PNN-mediated synaptic and structural plasticity during associative learning in mice.

How did they do it?

The authors assigned mice to eye-blink conditioning, pseudo-conditioning, or a control group. Mice in the conditioning group were repeatedly exposed to air puffs paired with light, whereas the pseudo-conditioning group was exposed to air puffs and light separately, but these were never presented together. Learning and memory were assessed by the percentage of eye-blinks after eye-blink conditioning and the fraction of eyelid closure when the air puff was delivered (e.g. 1 = eyelid was fully closed, 0 = eyes were fully open).

First, the authors examined the effect of eye-blink conditioning on PNN expression during learning (after five days of conditioning) and following memory formation (after ten days of conditioning). To do this, they stained the deep cerebellar nuclei for CSPGs and further classified the PNNs based on staining intensity (e.g. low intensity = weak PNNs). They then investigated the effect of PNN disruption on plasticity by overexpressing the enzyme “chondroitinase”, which degrades PNNs, in the deep cerebellar nuclei. Using a combination of staining, immunocytochemistry, and single-unit recordings, the authors assessed how PNN degradation altered learning, as well as the structural (e.g. number and size of axon terminals) and functional plasticity (e.g. spiking activity) of cerebellar neurons. Finally, they explored the long-term effects of PNN digestion on memory and structural plasticity of cerebellar neurons, 21 days after eye-blink conditioning.

What did they find?

After five days of training, mice in the conditioning group learned the association between the air puff and light, showing an increase in eye-blinks and a fraction of eyelid closure in response to the light. This was accompanied by a reduction in the proportion of strong PNNs but an increase in medium and weak PNNs in the deep cerebellar nuclei. In contrast, pseudo-conditioned and control mice did not show changes in PNN expression over time. After training for ten days, there was no difference in PNN expression between conditioned and pseudo-conditioned mice. These findings suggest that associative learning in the cerebellum is related to a reduction in strong PNNs, which then return to normal levels after the associative memories are formed.

Animals with overexpressed chondroitinase showed better learning than the control mice. Long-term memory initially declined for both groups, but while it stabilized in the control group, memory retention continued to decline over time among the chondroitinase mice. Thus, although PNN degradation facilitated initial learning, disrupting PNNs was detrimental for long-term retention of associative memories. Disrupting PNNs also altered the structural plasticity of cerebellar neurons, with an increase in the number of inhibitory, GABAergic axon terminals, but a reduction in the number of excitatory, glutamatergic terminals. These structural changes were further accompanied by reduced baseline spiking activity of cerebellar neurons in the chondroitinase mice.

What's the impact?

This is the first study to demonstrate the importance of cerebellar PNNs in associative learning, particularly the finding that PNNs modulate synaptic and functional plasticity at different phases of memory acquisition (learning vs retention). These findings pave the way for future research to elucidate the role of PNNs in other cerebellum-dependent cognitive processes like emotional and motor learning.

Carulli et al. Cerebellar plasticity and associative memories are controlled by perineuronal nets. Proceedings of the National Academy of Sciences (2020). Access the original scientific publication here.

Altered Hierarchical Interactions in Visual Processing in Autism Spectrum Disorder

Post by Flora Moujaes

What's the science?

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties in social communication and interaction, restricted interests and repetitive behaviours. We still don’t fully understand the changes in brain function underlying ASD. There are two basic types of neurotransmission in the brain: excitation and inhibition. One theory that has gained traction in recent years is that ASD involves a higher neural excitation to inhibition ratio, resulting in an increase in neural response. However, findings in the literature have been inconsistent. We know that the brain is organized in a hierarchy of various regions and networks that interact: later processing stages may inherit and build upon earlier (input level) stages, and also influence and shape of earlier stages by top-down modulation. This means that the perceived increase or decrease in neural response in ASD could depend on the stage being measured. This week in The Journal of Neuroscience, Kolodny, and colleagues leverage the well-known hierarchical structure of the visual motion pathway in the brain to examine how fMRI response changes in adjacent stages of processing in ASD.

How did they do it?

The researchers measured sensory-driven fMRI responses in the visual cortex of 24 adults with ASD and 24 demographically matched neurotypical control adults. In the main task, simple visual stimuli were presented to participants during fMRI scanning. Moving stimuli (drift grating stimuli) were presented to the participants in blocks of either low (3%) or high (98%) contrast. Response to the stimuli was measured at two places in the visual cortex: (1) the visual cortex’s primary input region, V1 and (2) the higher-order motion-processing region, the middle temporal area (MT). These two regions were chosen as it is well established that the visual system is hierarchically organized, with information traveling forward from V1 to MT (feedforward projections) as well as backward (feedback projections). Two measures were used to ensure that participants were focusing on the center of the visual stimuli: (1) participants were asked to press a button when a green circle appeared at the center of the visual stimuli, and (2) eye-tracking data was collected so that eye movements could be monitored. A control task involving static non-moving stimuli was also conducted.

What did they find?

Does fMRI response magnitude in participants with ASD vary between adjacent stages of processing in the visual pathway? The researchers found that individuals with ASD showed reduced responses in V1 and increased responses in the MT area. Thus, ASD responses were reduced in a primary visual area but amplified in a subsequent higher-order area. This pattern was stimulus-specific, as V1 responses were reduced for moving stimuli but not for static stimuli.

Is there a relationship between early (V1) and higher order (MT) visual areas? There was a significant negative correlation between V1 and MT response to high contrast stimuli among individuals with ASD. This means that the same participants with lower V1 responses also showed higher MT responses. This could suggest a causal link between the decrease in V1 and the increase MT, and that the observed dissociation in neural response is driven by amplified suppressive feedback from MT to V1.  Is there a relationship between ASD symptom severity and visual cortex response? The researchers found that response magnitudes in both V1 and MT were differentially correlated with autism symptom severity, which suggests that response magnitude in the visual cortex in ASD may be clinically relevant.

What's the impact?

This is the first study to show a neural response dissociation between the adjacent stages of visual processing in ASD, as fMRI responses were reduced in a primary visual area but amplified in a subsequent higher-order area. This study uncovers a previously unknown cortical network alteration in autism. It also highlights that while neural response magnitude is altered in ASD, this altered response may be increased or decreased depending on the stage of the visual motion pathway. More research is needed to understand how these interactions between regions in the visual motion pathway may relate to the basic excitation/inhibition model of the brain.

Kolodny, et al. Response Dissociation in Hierarchical Cortical Circuits: a Unique Feature of Autism Spectrum Disorder. Journal of Neuroscience (2020). Access the original scientific publication here.

Anterior Cingulate Cortex Is Involved in Changing Plans

Post by Deborah Joye

What's the science?

Imagine that you are driving a car, stopped at a red light. We know that when the light turns green, we are supposed to begin driving again. But if a pedestrian steps into the path unexpectedly, we must quickly change our plan of action to avoid hitting them. This processing of conflicting cues in our environment happens extremely fast and is thought to be helped by a brain region called the anterior cingulate cortex. In general, we think that the anterior cingulate cortex processes the conflicting signals, then sends information to other brain regions that control motor planning, such as the dorsal medial striatum, so that our actions reflect the new information from the environment. While we have inferred from research in humans that the anterior cingulate cortex is involved in the ability to abruptly change plans, no one has demonstrated it experimentally. This week in PNAS, Brockett, and colleagues developed a rodent model to demonstrate that the anterior cingulate cortex is necessary for deciding what to do when signals in the environment change suddenly.

How did they do it?

The authors first modified a STOP-signal task for use in rats. The STOP-signal task is commonly used to test cognitive control in human clinical populations and generally involves asking participants to respond to a cue, such as pressing a button when a light comes on the screen. In some of the trials, another cue will flash which tells the participants to not press the button or to do something else entirely. This model tests how well our brains can respond to cues which conflict with one another. The authors designed this test for use in rats by showing a light that indicates which direction a rat must go to in order to receive a sugar reward. In some of the trials, a second light is shown after the first, indicating that the rat must go the opposite direction instead. The authors then tested the necessity of the anterior cingulate cortex in this task by damaging one side of it in both male and female rats. After recovery from the procedure, rats were trained on the STOP-signal task. The authors also recorded electrical activity from neurons within the dorsal medial striatum to investigate how information from the anterior cingulate cortex is processed in this region before a behavior is produced.

What did they find?

The authors found that damage to the anterior cingulate cortex made the STOP-signal task harder. Specifically, lesioned rats had slower response times and were less accurate when they were forced to change their response direction suddenly (STOP trials) but had similar response times to control rats when they did not have to change their response selection suddenly (GO trials). Interestingly, after the presentation of one STOP trial, rats without an anterior cingulate cortex still performed better on the next consecutive trial if it was also a STOP trial. This shows that lesioned rats were still able to learn about conflicting signals. 

In control rats, neuronal activity in the dorsal medial striatum increased in response to the first light cue, then decreased in response to the second light cue. This means that the majority of recorded dorsal medial striatum neurons changed their firing in response to the conflicting cue. During STOP trials where the rat was able to successfully cancel the initial response, neural firing was delayed in rats with a damaged anterior cingulate cortex. However, when control rats made an error on STOP trials, firing for the initial — but incorrect — movement resembled firing on GO trials in lesioned rats, suggesting that anterior cingulate cortex is necessary for applying a brake on neuronal firing to cancel the first response. This effect was not seen in rats with a lesioned anterior cingulate, again suggesting that this region normally acts as a brake on activity in other brain regions.

What's the impact?

While the function of the anterior cingulate has been inferred based on previous research it has remained controversial. This study is the first to experimentally demonstrate that the anterior cingulate cortex is necessary for processing and correctly responding to conflicting environmental cues. It also provides useful experimental tools for testing this type of higher-level cognition while simultaneously monitoring neuronal activity in rodent models. Overall, these findings present an important contribution to figuring out exactly what the anterior cingulate cortex does and how it sends information to other brain regions that ultimately affect our behavior.

Brockett et al., Anterior cingulate cortex is necessary for adaptation of action plans, PNAS (2020). Access the original scientific publication here.