The Brain Represents Others as a Summation of Their Mental States

Post by Flora Moujaes

What's the science?

To be successful in life it helps to take people’s individual dispositions into account. For example, a good teacher will tailor their teaching strategy to the temperament of the student, encouraging shy students to participant more while challenging the assumptions of overconfident students. However, we still don’t have a clear picture of how people tailor their actions to the idiosyncrasies of specific individuals. Traditionally, it has been assumed that people may do this by representing others using traits: the unchanging characteristics that define someone, such as trustworthiness or intelligence. More recently, it has been suggested that people represent others using summed states: feelings people experience on a moment-to-moment basis, such as happiness or shame. There are three main reasons that representing others using states may be advantageous (1) states are easier to observe than traits, as they can be seen in the moment rather than having to get to know someone over a long period of time, (2) states, even if unrelated to a person’s general disposition, are an independently useful for predicting behaviour, and (3) by summing someone’s mental states over time, one can infer long-term characteristics or traits.

This week in Nature Communications, Thornton and colleagues demonstrate in an fMRI study that we represent other people as the sum of their moment-to-moment states, as our neural representations of other people are composed of combinations of representations of the mental states those people are perceived to frequently experience.

How did they do it?

To explore the hypothesis that the brain represents others according to a sum of their states, the authors began by establishing the pattern of brain activity associated with 60 different celebrities, from Shakespeare to Snoop Dog, based on the states people associated with each celebrity. To do this they first conducted an fMRI experiment to establish what patterns of activity are elicited when the brain thinks about each mental state (e.g. patience). They then conducted an experiment to determine which mental states were associated with each celebrity (e.g. is Snoop Dog patient?). Finally, they combined the data from both studies to come up with a pattern of brain activity for each celebrity based on the set of states associated with them.

They then tested whether the pattern of brain activity they created for each celebrity based on the states associated with them, reflected the brain activity seen when people thought about the celebrities. In order to do this, they conducted an additional fMRI experiment where they asked people questions about each celebrity (e.g., how much would Snoop Dog like to learn karate?) to get them to think about the celebrity. They then correlated the artificial (based on the states associated with them) and actual patterns of brain activity elicited by thinking about these famous people.

What did they find?

Testing the Summed State Account: They found evidence that people did represent others as a sum of their states, as there was a correlation between the artificial patterns of brain activity created for each celebrity based on the states associated with them, and the actual brain activity of each participant when thinking about the celebrity. Summed State vs. Trait Accounts: They also compared whether participants’ neural representations of celebrities were better explained by considering the states or traits associated with a celebrity. They found that the summed states account consistently outperformed the trait alternative in explaining person representation.

For more details see Thornton’s summary on Twitter.

What's the impact?

This is the first study to show that when you think about a person, your brain may represent them as the sum of the mental states you think that they frequently experience. This suggests that people tailor their actions to the unique characteristics of individuals by observing differences in their momentary thoughts and feelings. These findings also help us understand the relationship between how we might think about people's momentary thoughts and feelings to infer their long-term traits. Thus, the trait–state divide may be narrower than commonly thought. Overall, the summed state hypothesis provides a compelling model of how the mind and brain may learn about, represent, and predict other people.

Thornton et al. The brain represents people as the mental states they habitually experience. Nature Communications (2019). Access the original scientific publication here.

Fast Learners Have High-Dimensional Representations of Neural Activity

Post by Shireen Parimoo

What's the science?

People naturally group new pieces of information into categories to differentiate between them, eliciting distinct patterns of brain activity. One measure of quantifying how information is coded in the brain is by estimating the geometric representation or dimensionality of neural responses. For example, stimuli can be coded in a psychological dimension and represented based on their size and color. Patterns of brain activity can be represented in a high dimension, meaning that the information is coded along many dimensions and that neural responses to each stimulus are likely more distinct from one another. Alternatively, low-dimensionality of neural representations would suggest that there are fewer dimensions along which new information can be coded in the brain, resulting in less distinction between stimuli. Is there a relationship between the dimensionality of neural responses and how fast people can learn new information? This week in Nature Neuroscience, Tang and colleagues used functional magnetic resonance imaging (fMRI) to examine how the dimensionality of neural representations is related to the speed with which people learn new information.

How did they do it?

Nineteen young adults completed various tasks for four days while undergoing fMRI scanning. On each day, they first completed a value-learning task, in which they learned the monetary value of twelve stimuli that ranged from $1 and $12. They were then tested on the stimulus-value associations in a value-judgment task and their response accuracy was used as a measure of learning effectiveness. In the value-judgment task, participants were shown the stimuli and had to indicate if their monetary value was in the top 50% ($7-$12) or the bottom 50% ($1-$6) of the range. In the fMRI data, dimensionality was estimated from label assortativity and separability values of the neural responses. Label assortativity provides a measure of how easily neural responses can be distinguished from each other. In general, higher assortativity is associated with greater dimensionality, but it is also possible for high dimensional representations to have low assortativity. Assortativity was calculated using a linear support vector machine, which is a machine learning technique used to classify information into categories. On the other hand, separability values provide a more direct measure of dimensionality; this was calculated by assigning binary labels to neural activity for each stimulus and using a support vector machine to classify the neural responses according to these labels. High separability values indicate that neural responses are organized in a larger number of dimensions, whereas low values indicate lower dimensionality of representations in neural responses.

The authors examined the relationship between dimensionality and how effectively participants learned the stimulus-value associations. They also conducted a virtual lesioning analysis, in which neural activity in specific brain regions was systematically excluded from the analysis to see which regions contributed to the association between separability and learning effectiveness. Finally, as high dimensionality of neural responses is associated with greater reliance on neural resources, the authors investigated whether resources are allocated efficiently through a balance between high- and low-dimensionality of representations during learning. Participants’ response accuracy was correlated with the dimensionality of representations to specific stimuli (stimulus dimension) during learning and to neural responses independent of the stimulus (embedding dimension), which represents the overall dimensionality of participants’ neural representations.

What did they find?

Response accuracy on the value-judgment task improved over the course of four days, and fast learners – participants who performed above average on the first day – had high accuracy on subsequent days. Assortativity of brain activity was higher among participants who learned fast. That is, stimulus-specific neural responses of fast learners could be distinguished from each other more easily than that of slower learners. Fast learners also had higher dimensionality of neural representations, which is another indication that their representations of the different stimuli were more distinct from one another. In fact, high performance on the first day of the experiment was correlated with high dimensionality on the fourth day, suggesting that effective learning facilitates more precise coding of information in the brain. Interestingly, there was a positive correlation between response accuracy and dimensionality in the stimulus dimension, but a negative correlation in the embedding dimension. This means that fast learners allocate neural resources efficiently by representing all stimuli in an overall low dimensional space, but within that low-dimensional embedding space, specific stimulus-value associations are represented by higher dimensional, and thus more distinct patterns of activity.

What's the impact?

This is the first study to demonstrate how the efficiency of neural responses is optimized during learning, depending on whether it is associated with learning stimulus associations or independently of specific stimuli. The approach of quantifying the dimensionality of representations in neural responses and the finding that dimensionality is correlated with behavioral performance both have important implications for our understanding of how information is represented in the brain and how that affects behavior.

Tang et al. Effective learning is accompanied by high-dimensional and efficient representations of neural activity. Nature Neuroscience (2019). Access the original scientific publication here.

BIN1 Interacts with Tau Protein and Rescues Memory deficits in a Mouse Model of Tauopathy

Post by Amanda McFarlan

What's the science?

Alzheimer’s disease (AD) is a neurodegenerative disorder known to cause deficits in short-term memory, long-term memory and spatial memory. Neurofibrillary tangles, that arise due to the aggregation of hyperphosphorylated Tau proteins, are one of the main biomarkers of AD. Recent studies have shown that the bridging integrator 1 gene (BIN1) is associated with late-onset forms of AD and interacts directly with the Tau protein. This week in the Acta Neuropathologica, Sartori and colleagues investigated the role of overexpressed BIN1 in a mouse model of Tauopathy as well as the underlying molecular mechanisms regulating BIN1-Tau interactions.

How did they do it?

In the first set of experiments, the authors assessed the role of BIN1 expression levels on cognitive function using male and female mice from three different genetic strains: Tau mice (overexpressed the human MAPT gene to produce a Tauopathy model), Tau/BIN1 mice (overexpressed both human MAPT and human BIN1 genes) and control mice. They performed the novel object recognition and Morris water maze at 3, 6, 9, 12, and 15 months to assess the effect of BIN1 overexpression on short-term, non-spatial memory and long-term spatial memory, respectively. In the second set of experiments, the authors investigated the underlying mechanisms that modulate the interaction of BIN1 and Tau — they performed immunolabelling to quantify the level of Tau phosphorylation in the hippocampus. Next, they used proximity ligation assay and primary neuronal cultures to assess the effect of BIN1 overexpression on the amount and localization of BIN1-Tau complexes. It is known that phosphorylation of Tau prevents its interaction with BIN1. Therefore, the authors developed a semi-automated high-content screening approach to identify specific compounds in signaling pathways that may be involved in Tau phosphorylation. Finally, in the third set of experiments, the authors quantified the levels of total and phosphorylated BIN1 in human brain samples from 28 individuals (10 controls, 18 diagnosed with Alzheimer’s disease) with varying degrees of Tau pathology.

What did they find?

The authors found that short-term memory deficits were induced in male and female Tau mice starting at 9 months, while Tau/BIN1 mice showed short-term memory deficits as early as 3 months. Conversely, they determined that male Tau mice displayed long-term and spatial memory deficits at 12 months, while male Tau/BIN1 mice did not display any long-term or spatial memory deficits at any age. Together, these results suggest that overexpression of BIN1 worsens Tau pathology phenotypes for short-term memory deficits but rescues long-term and spatial memory deficits. Next, they revealed that Tau/BIN1 mice had significantly lower levels of Tau phosphorylation in the hippocampus compared to Tau mice (as determined by fewer cells with intracellular inclusions) and that Tau/BIN1 mice had a strong increase in the proximity ligation assay signal (amount of BIN1-Tau complexes) compared to Tau mice and controls.

Together, these results suggest that overexpression of BIN1 increases the number of BIN1-Tau complexes in the hippocampus which decreases the amount of phosphorylated Tau that can form toxic intracellular inclusions (i.e. protective against neurofibrillary tangles). Next, the authors determined that the signaling pathways regulated by Cyclosporin A (an inhibitor of the serine/threonine protein phosphatase Calcineurin) were important for mediating the interaction of BIN1 and Tau. They showed that dephosphorylation of BIN1 by Calcineurin on a cyclin-dependent kinase phosphorylation site at T348 promoted the open conformation of BIN1. Phosphorylation at this site increased the likelihood of BIN1 and Tau interactions. These findings suggest that Cyclosporin A mediates the interaction of BIN1 and Tau via the dephosphorylation of T348 by Calcineurin. Finally, the authors determined that although global levels of BIN1 were unchanged in AD conditions, a higher proportion of overall BIN1 levels were phosphorylated in individuals with AD compared to controls.

What's the impact?

This is the first study to show that the complex regulation of the interaction between BIN1 and Tau is involved in AD pathology. Mouse models revealed that overexpression of BIN1 had neuroprotective effects for Tau phenotypes including long-term and spatial memory deficits, and that this may be regulated by the interaction between BIN1 and Tau. Altogether, these findings provide important insight into the underlying mechanisms leading to AD pathology.

Sartori et al. BIN1 recovers tauopathy-induced long-term memory deficits in mice and interacts with Tau through Thr348 phosphorylation. Acta Neuropathologica (2019). Access the original scientific publication here.