How Neurons Store Memory Content and Context

Post by Amanda Engstrom 

The takeaway

Recalling memories often requires linking what happened with the context in which it happened. In humans, information about content and context is largely encoded by separate neuronal populations that are coordinated over time, allowing the brain to form context-dependent memories while preserving stable independent representations. 

What's the science?

The medial temporal lobe (MTL) plays a crucial role in forming and retrieving memories. In particular, the hippocampus has been implicated in encoding items-in-context memory, which involves combining what happened (the item) with the context in which it occurred (a task or environment). Rodents rely on conjunctive encoding where hippocampal representations are context dependent, however in humans, concept cells often fire independently of context. Therefore, it remains unclear how item and context memories are formed and combined at the single neuron level. This week, in Nature, Bausch and colleagues recorded activity from thousands of individual neurons during a memory and decision-making task to investigate how human MTL neurons combine information about item and context.

How did they do it?

The authors recorded activity from over 3,000 neurons in 16 neurosurgical patients implanted with depth electrodes for clinical monitoring. Individual neurons were recorded while participants performed a task-dependent picture comparison test. Each trial began with a question that defined the context (Bigger?”, “Older?”, “Last seen in real life?”, “Like better?”, or “More expensive?”). Participants were then shown pairs of pictures for which to apply the question. This task required participants to remember items (each picture) within a specific context (answering the question), while keeping the item and context information independent of each other. The electrodes recorded neuronal spiking activity across multiple regions of the MTL, including the hippocampus, entorhinal cortex, parahippocampal cortex, and amygdala. Spike timing was precisely aligned to stimulus and/or task events. Using these recordings, the authors applied statistical modeling to determine whether firing was linked to the item (picture) alone, the context (task question) alone, or specific item – context interactions. 

What did they find?

Only a small fraction of neurons fired selectively for specific item-context combinations. Instead, the majority of neurons across the MTL regions were primarily selective to stimulus (item/ picture) or context (task question). Item and context coding were largely independent, supporting a flexible and generalized memory formation pattern. 

After experimental pairing and statistical modeling, the authors observed coordinated activity across MTL regions. Firing of stimulus-selective neurons in the entorhinal cortex predicted later firing of context-specific neurons in the hippocampus, suggesting that although item and context are encoded independently, they interact dynamically during memory processing. These findings support a model in which humans rely on distinct but coordinated neuronal streams to form flexible, context-dependent memories.

What's the impact?

This study found that rather than combining item and context information within single neurons, the human brain encodes them separately and then integrates them through coordinated activity between distinct neuronal groups, enabling both generalized and contextually specific recall. These findings bridge the gap between rodent models of hippocampal-dependent memory formation and human concept-cell research and provide a mechanistic explanation for how the human brain balances memory specificity with generalization, a fundamental feature of adaptive cognition. 

Access the original scientific publication here.

Neural Signature of Recovery Following Traumatic Brain Injury

Post by Shalana Atwell

The takeaway

The extent of functional recovery from moderate to severe traumatic brain injury (TBI) in individual patients can be challenging to predict accurately. This research found that individuals with normal ‘on-off’ toggling (anticorrelation) between key brain networks following TBI are more likely to have a favorable functional recovery.

What's the science?

Current clinical models used to predict functional outcomes after TBI rely on age, extent of injury, motor scores, and the presence of other illnesses. Still, these predictive models do not account for neuroanatomical location or functional integrity of brain networks. Prior studies have used resting state functional magnetic resonance imaging (rs-fMRI) to measure functional connectivity (how brain regions communicate) in TBI studies. However, connectivity measures were inconsistent, and no model derived from fMRI data has been able to predict functional outcomes following TBI across independent datasets. Recently, in PNAS, Snider and colleagues investigated whether rs-fMRI can predict functional outcomes 6 months following injury across independent studies, as well as interrogated whether the predictive capacity of these measures is on par with or exceeds current prognostic tools for TBI.

How did they do it?

The authors pooled data from three TBI studies where each participant had completed multiple brain scans [MRI, diffusion tensor imaging, and rs-fMRI] and a 6-month follow-up after injury that assessed functional dependence [Glasgow Outcome Scale–Extended]. Brain scans measured structure (MRI), as well as the strength of connections between different brain regions by analyzing correlations in their activity over time (rs-fMRI) and organization and structural integrity of white matter tracts (diffusion tensor imaging). They cleaned and standardized MRI [CONN toolbox] and diffusion tensor imaging data to correct for excessive motion, technical differences across studies, and poor scan quality. 

Using one dataset, a model [logistic regression] tested the ability of 435 pairs of connected brain regions to discriminate favorable vs unfavorable functional TBI outcomes. These pairs were validated using two-fold cross-validation and repeated testing to ensure results were not due to chance. The best-performing connections were combined into a predictive model and then tested on a separate dataset, confirming generalizability. They utilized brain connectivity patterns to predict recovery outcomes following TBI. Finally, the authors investigated whether adding MRI-based measures matched or improved predictions beyond the standard clinical scoring systems.

What did they find?

Using their model, they found that the strongest predictor of 6-month recovery was the functional anticorrelation (one network turns off when the other turns on) between the salience network’s left anterior insula and the default mode network’s right parietal region. Patients with more normal anticorrelation between networks following TBI were more likely to have better recovery at 6 months following injury. Adding two more connections—the frontoparietal network’s right lateral prefrontal cortex to the visual network and the language network’s right frontal gyrus to the default mode network’s medial prefrontal cortex—created a three-connection model that improved performance and predicted recovery with high accuracy across independent data sets. Incorporating two additional connections created a more accurate prognostic model. Finally, they combined the three-connections identified from rs-fMRI with standard clinical scoring systems, which yielded a significantly improved outcome prediction, showing higher sensitivity and specificity for identifying patients likely to recover. This highlights the possibility of augmenting predictive TBI scoring models with rs-fMRI data to refine treatment planning.

What's the impact?

This study identifies preservation of specific resting-state anti-correlations between brain networks as a strong marker of functional recovery following moderate to severe TBI. Changes in brain connectivity patterns after injury could be incorporated into clinical prognostic tools and possibly leveraged as a therapeutic target. 

Access the original scientific publication here.

Brain Stages of Aging Across the Lifespan

Post by Lila Metko 

The takeaway

Topology in neuroscience is the study of the motifs that arise when observing how neural connections are interrelated and arranged. This research revealed that there are five major phases of topological development, with a significant topological turning point occurring between each phase at approximately the ages of 9, 32, 66, and 83 years old. 

What's the science?

Many scientists who study the development of the human brain look for linear trends in the developmental trajectory, or differences between age groups. To date, there are few papers that look at development outside of the confines of linear relationships. Recently, in Nature Communications, Mousley and colleagues analyzed brain imaging data from a large sample of individuals aged zero to 90 and found that rather than smooth trends in topographical organization, the human brain undergoes major age-specific turning points in topography. 

How did they do it?

The study included nine separate data sets, so the first step was to harmonize the data to ensure the differences between the data sets, such as the brain scanners and data acquisition protocols used, did not create nuisance variation. A double harmonization method was used to harmonize the data across atlas and study. Next, the authors used UMAP (Uniform Manifold Approximation and Projection), a manifold learning method, to project high-dimensional data onto a lower-dimensional space. Doing this makes highly complex data easier to understand while still maintaining its intrinsic structure. They mathematically analyzed the lower-dimensional manifold to find topological turning points within the data set. Finally, to understand the topological characteristics of these turning points, they did a principal components analysis of the data with eleven different topological metrics. These topological metrics measured how efficient communication was throughout a network, how many non-overlapping, communicating nodes can be found within a network, and to what extent different nodes are particularly important for network function. 

What did they find?

The major topological turning points were characterized by differences in three properties: segregation, integration, and centrality. Segregation is defined as the partitioning of the network into subgroups, integration measures the ease of communication across the network, and centrality is network communication’s dependency on a few nodes that are particularly important for network function. Segregation measures loaded most heavily onto principal component 1 (PC1), and integration measures onto PC2. Both segregation and centrality load most heavily onto PC3. Significant shifts in PC 1 and 2 (network subdivision and network communication ease) are found at roughly the ages of 9 and 32. At roughly the age of 66, there are significant shifts in segregation, integration, and centrality (network subdivision, network communication ease, and dependence on central nodes). At around age 83, there is a significant shift in integration, or network communication. The two epochs with the biggest differences in trajectories were epochs three and four, suggesting that topological change is particularly dissimilar in the years before around age 66 and after around age 66. 

What's the impact?

This study is the first to examine a large dataset with the goal of finding true qualitative transitions in brain topology, not just age group comparisons or linear trends. This paper allows us to see aging as unique stages rather than just progression and decline. Moreover, understanding the impact of therapeutics on different brain aging stages can allow for a more targeted approach.

Access the original scientific publication here.