Synaptic Plasticity in CA1 Pyramidal Dendrites Depends on Specific Input Patterns

Post by Amanda McFarlan

What's the science?

Pyramidal neurons in the brain receive many complex inputs, and these electrical signals are then integrated and propagated by dendrites to the cell body of the neuron. Although dendritic activity is known to be involved in long term potentiation (LTP), the classical Hebbian view of plasticity still considers the backpropagating action potential (propagation back towards the dendrites) to be critical for inducing LTP. However, recent studies have shown that local dendritic activity may play an important role in mediating synaptic plasticity. This week in the Journal of Neuroscience, Magό and colleagues used 2-photon glutamate uncaging to investigate the plasticity rules at proximal (near the neuronal cell body) and distal (far from the neuronal cell body) dendritic spines.

How did they do it?

The authors used 2-photon glutamate uncaging to activate dendritic spines in CA1 pyramidal neurons that were targeted for whole-cell recording in acute hippocampal slices from adult male rats. To investigate the plasticity rules at these dendritic spines, the authors first recorded baseline excitatory post-synaptic potentials (EPSPs) in response to asynchronous activation of either proximal or distal dendritic spines. Then, they applied an LTP induction protocol whereby specific clusters of proximal or distal dendritic spines were synchronously activated and recorded proximal and distal dendritic spine EPSPs to measure LTP-induced changes in synaptic function.  

Next, the authors investigated whether dendritic spiking plays a role in inducing plasticity at proximal and distal dendritic spines. Dendritic spiking allows for non-linear amplification of electric inputs that are spatially and temporally correlated. To study this, they activated small clusters of either proximal or distal dendritic spines during the LTP induction while simultaneously inducing dendritic spiking. Then, they explored the spatial rules of plasticity at distal dendrites by applying the LTP induction protocol to distal dendritic spines that were spread out along the dendrite (rather than clustered together) with and without dendritic spiking. Finally, the authors investigated whether changes in plasticity in targeted clusters of dendritic spines induced heterosynaptic plasticity (when a change in synaptic strength in one neuron occurs following the activation of another neuron or pathway) in nearby spines. 

What did they find?

The authors found that the activation of proximal dendritic spines resulted in robust and long-lasting LTP only when it was coupled with dendritic spiking, suggesting that inducing LTP at synapses located on proximal dendrites requires a large depolarization from a local or backpropagating action potential. Next, the authors revealed that unlike proximal dendritic spines, the coactivation of a few distal dendritic spines alone was sufficient to induce LTP when the spines were located close in proximity to one another. They also showed that LTP was induced in distal dendritic spines that were spread out along the dendrite when their activation was coupled with dendritic spiking. Additionally, they found that LTP was induced using a much lower stimulus number in distal dendritic spines when the activation of these spines was coupled with dendritic spiking. Together, these results suggest that dendritic spiking is not required for the induction of LTP in synapses located in the distal dendrite but can be beneficial for reducing the number of coincident activity events required for LTP induction and for allowing cooperativity between spatially distant dendritic spines. Finally, the authors determined that following the LTP induction and dendritic spiking, dendritic spines that were not directly targeted for activation, but that were in close proximity to activated spines, showed evidence of LTP. Wash-in experiments with blockers revealed that this effect was abolished when the NMDA receptor, as well as the MEK/ERK pathway (important for mediating local plasticity of GTPases), were inhibited. Together, these results suggest that the activation of nearby dendritic spines by dendritic spiking induces heterosynaptic plasticity that is mediated by NMDA receptor and MEK/ERK signaling. 

What’s the impact?

This is the first study to show that several mechanisms are involved in facilitating LTP at dendritic spines. The authors found that the LTP is induced when distal dendritic spines close in proximity to one another are synchronously activated. Additionally, they revealed that dendritic spiking coupled with dendritic spine activation enables cooperativity between dendritic spines that are spatially distant as well as induces heterosynaptic plasticity at nearby synapses. Together, these findings provide insight into the many forms of plasticity that are occurring locally at the dendrite and are allowing neurons to store new information in the absence of somatic firing. 

Magό et al. Synaptic Plasticity Depends on the Fine-Scale Input Pattern in Thin Dendrites of CA1 Pyramidal Neurons. Journal of Neuroscience (2020). Access the original scientific publication here.

Modeling the Propagation of Neurodegeneration in Amyotrophic Lateral Sclerosis

Post by Elisa Guma

What's the science?

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes muscle weakness, paralysis, and ultimately, respiratory failure. It has been hypothesized that a misfolded protein (phosphorylated 43 kDa TAR DNA-binding protein) spreads through the brain of patients in a ‘prion-like’ manner, causing neurodegeneration as it spreads. Impairment is thought to begin in the motor regions of the brain, but it’s unclear if and how the spreading occurs. Further, there is a lot of heterogeneity in survival times following symptom onset. It is difficult to assess this network spreading hypothesis in vivo or in post-mortem tissue of ALS patients, and therefore computational approaches are appealing. This week in the Annals of Neurology, Meier, and colleagues examine the propagation model for disease progression in ALS and aim to predict disease progression by using network analyses of brain imaging data from patients.

How did they do it?

The authors leveraged a longitudinal dataset of 60 participants with ALS who underwent four magnetic resonance image (MRI) scans (comprising of a structural and a diffusion-weighted scan) over the course of their illness. They also used a different dataset with 120 controls matched at each timepoint for age and sex. To construct the connectome, the authors first parcellated the brain (using the structural MRI scan) into 68 cortical and 14 subcortical regions. Next, white matter connections between parcellated brain regions were estimated based on the diffusion-weighted scans. The authors applied network-based statistics to find the largest grouping of impaired brain regions in ALS patients. Brain region impairment was defined by the number of  impaired white matter connections/streamlines (with lower fractional anisotropy than normal) attached to that brain region

A random walker model was used to model disease progression. They started the model in regions shown to be impaired first in ALS and tested all the possible connections coming out of that region. Regions with a higher number of connections had a greater likelihood of being traversed by the random walker in the model. Upper motor neuron burden was also calculated per brain region. Their models were compared to regions known to be affected at four stages of disease progression. Finally, they used a deep learning model to predict patient survival. They used a previous model they had built but included the random walker aggregation levels to assess whether they could improve their prediction accuracy. They examined 30 ALS patients and 30 controls from an external dataset who were scanned three times longitudinally for validation.

What did they find?

Based on their network-based statistics analysis, as well as the random walker model, the authors found that ALS patients had an impaired connectome that seemed to spread in a spatiotemporal manner, in line with the hypothesis they were testing. Impairment based on the network-based statistics analysis and the random walker was correlated. More importantly, impairment overlapped with known stages of disease progression, suggesting that the computational model is reflective of disease progression. When their computational model was tested on a dataset, they found a similar correlation between simulated disease spread and known stages of disease progression.  They also found that the level of upper motor neuron burden for each region correlated with their model of disease spread, suggesting that patients with higher upper motor neuron burden had more widespread aggregation, and patients with lower burden had higher variance and aggregation. Finally, the authors found that including the random walker model in their prediction algorithm improved their ability to accurately predict survival time for ALS patients from 79.6% to 83.3%.

What's the impact?

This study provides compelling evidence for the utility of computational network models to predict neurodegeneration in ALS. The authors show that a random walker model based on MRI scans could predict degeneration over time in ALS patients, with overlap between known stages of disease progression and upper motor neuron burden. These results support the hypothesis that ALS impairment begins in the motor cortex and spreads along white matter tracts in a spatiotemporal manner. Future work is needed to investigate the impact of various genetic mutations associated with ALS on network degeneration.

Jil M. Meier et al. Connectome-based propagation model in amyotrophic lateral sclerosis. Annals of Neurology (2020). Access the original scientific publication here.

Distinct Patterns of Cortico-Limbic Connectivity Underlie Social Concern for Others

Post by Shireen Parimoo

What's the science?

Other-regarding preference (ORP) is a social phenomenon referring to concern for others’ well-being in addition to one’s own. Social decision-making requires complex cognitive operations and relies on communication between a distributed network of brain regions. This includes the basolateral amygdala (BLA), which is sensitive to reward-related decisions about oneself and others, and the gyrus of the rostral anterior cingulate cortex (ACCg), which is involved in encoding reward outcomes for others. It is not known how the BLA and ACCg interact with each other during social decision-making in primates. This week in Nature Neuroscience, Dal Monte and colleagues investigated the neural mechanism of ORPs during a social-interaction task in monkeys.

How did they do it?

Two male “actor” monkeys and two female “recipient” monkeys participated in a social-interaction task while the authors recorded neural activity from the BLA and ACCg. The monkeys completed the task in pairs, with one actor and one recipient participating in each interaction. The task was to decide how to allocate a juice reward. On half of the trials, the actors could keep it for themselves or they could receive the reward as well as deliver it to the recipient (Self/Both condition). On the other half of the trials, the actor had to choose between delivering it to the recipient or to an empty bottle (Other/Bottle condition). Therefore, the actor always received juice in the Self/Both condition but never received juice in the Other/Bottle condition. In a control condition, the actors were delivered juice by a computer, and did not need to make a decision.. The authors computed a preference index for the actors that indicated how often they chose the recipient over the bottle (positive ORP) and themselves over the recipient (negative ORP). To examine neuronal communication, the authors computed spike-field coherence between the BLA and the ACCg. Spike-field coherence represents the relationship between spiking activity (action potentials) in one region and local field potentials (rhythmic neuronal activity) in another region. They first identified neurons in the ACCg and the BLA that were sensitive to the actor’s decisions during the task and then computed spike-field coherence in the beta (15-25 Hz) and gamma (45-75 Hz) frequency bands. They also calculated partial directed coherence, which is a statistical technique used to determine the direction of communication between two brain regions (i.e. which region drives activity in the other region). Finally, they performed linear discriminant analysis to determine whether neural activity could be used to decode the actor’s decision. To do this, they trained a linear classifier (a type of statistical model used for classification) on spike-field coherence from 75% of the trials and tested the classifier’s accuracy at identifying the actor’s decisions on the remaining trials. 

What did they find?

In the Other/Bottle condition, when their own interests were not involved, the actors looked at and gave the reward to the recipient more than the bottle, reflecting a positive ORP. On the other hand, a negative ORP was observed in the Self/Both condition, with the actors choosing to receive the reward themselves rather than giving it to the recipient at the same time.

Spike-field coherence following decisions was enhanced for positive ORPs and suppressed for negative ORPs. Specifically, positive ORPs were associated with greater BLAspike–ACCgfield coherence in the beta band and greater ACCgspike–BLAfield coherence in the gamma band, whereas this was suppressed for negative ORPs. Importantly, this was not observed during the control condition, which means that spike-field coherence was specific to the decision rather than the reward outcome itself. Moreover, the direction of information flow in the beta and gamma bands differed for positive and negative ORPs. For positive ORPs, BLA activity led ACCg activity, whereas ACCg activity led BLA activity for negative ORPs. Finally, the classifier successfully distinguished between the actor’s decisions based on spike-field coherence values. For positive ORPs, classifier performance was greater for beta band BLAspike–ACCgfield coherence occurring around the decision point and gamma band ACCgspike–BLAfield coherence later in the post-decision period. Classifier accuracy for negative ORPs showed a similar pattern but was much lower overall and did not vary as much over the time course of the trial. Thus, spike-field coherence between the ACCg and BLA reliably distinguished between reward decisions related to the self and to others, particularly for positive ORPs. 

What's the impact?

This study is the first to highlight differential patterns of inter-regional communication and transfer of information during social decision-making in primates, particularly between the BLA and the ACCg. These findings point to an important role of cortico-limbic connectivity in social decision-making processes that promote social cohesion.  

Dal Monte et al. Specialized medial prefrontal-amygdala coordination in other-regarding decision preference. Nature Neuroscience. (2020). Access the original scientific publication here.