The Brain Dynamically Changes Size Throughout Life

Post by D. Chloe Chung

The takeaway

Researchers have created a growth chart of the human brain, reflecting how the brain changes size throughout the lifespan. This chart can be used as a reference tool for the neuroimaging and clinical communities.

What's the science?

The advancement of neuroimaging techniques such as magnetic resonance imaging (MRI) has helped many clinicians, patients, and researchers over the past decades. However, unlike how we understand the change of height and weight throughout our lives, there has been no standard reference of what the brain looks like at a certain age. An inclusive map that describes developmental milestones and aging-related changes of the brain will benefit both researchers and clinicians. This week in Nature, Bethlehem and colleagues comprehensively examined how our brain dynamically changes its size throughout our lifespan by analyzing more than 100,000 MRI scans of the brain.

How did they do it?

The authors collected 123,984 MRI scans from 101,457 human participants (with or without medical conditions), aged from 16 weeks after conception to 100 years old. These brain scans were obtained from both primary studies and publicly available open databases. To create the brain size chart over the lifespan, the scans were quantified for structural changes in the brain and how fast these changes occur through aging. For this analysis, the authors adapted a modeling approach recommended by the World Health Organization that can help neutralize differences in measurement derived from diverse techniques and machines used across studies. The final brain chart was made into an interactive tool that can be used to analyze additional MRI datasets generated by tool users in the future.

What did they find?

The authors found that, up to 6 years of age, grey matter rapidly increases in its volume and thickness. The white matter volume also showed strong growth during early childhood but in a more delayed fashion than grey matter, peaking in size at around 30 years of age. The authors noted that these changes early in brain development highlight grey/white matter volume differentiation. After their respective peaks, both grey and white matter volume began to decrease over the rest of the lifespan. In contrast to these early developmental milestones, the authors found that the amount of cerebrospinal fluid in the brain ventricles that maintains its plateau throughout life starts to exponentially increase from around 60 years of age. In addition to defining developmental milestones using the brain chart, the authors demonstrated the utility of their brain chart in studying brain-related conditions. For example, the authors observed a faster decrease of the grey matter volume in Alzheimer’s disease patients, especially those who are biologically female, compared to non-patients of the same age.

What’s the impact?

This study generated a comprehensive growth chart of the human brain by examining the largest collection of MRI brain scans to date covering a 100-year age range. This brain chart will serve as a highly useful, standardized reference for neuroimaging in the future. The authors pointed out that even this brain chart is not inclusive enough as it covers mostly European and North American populations because neuroimaging tools are not as readily available to all global communities. Future studies will hopefully improve demographic and socioeconomic diversity in MRI research.

Neurons Detect Cognitive Boundaries to Separate Memories

Post by Andrew Vo

The takeaway

We experience our lives as a continuous stream that is organized and stored in our memories as discrete events separated by cognitive boundaries. A neural mechanism in the medial temporal lobe (MTL) detects such boundaries as we experience them and allows us to remember the ‘what’ and ‘when’ of our memories.

What's the science?

How is our continuous experience of the world transformed into discrete events separated by boundaries in our memories? Whereas we have a clear understanding of how the brain encodes our spatial environments with physical boundaries, the neural mechanism by which nonspatial memories are shaped by abstract event boundaries remains unknown. This week in Nature Neuroscience, Zheng et al. recorded neuronal activity within the MTL of human epilepsy patients and tested their memories for video clips separated by different types of event boundaries.

How did they do it?

The authors recorded single-neuron activity within different regions of the MTL (including the hippocampus, amygdala, and parahippocampal gyrus) of 20 epilepsy patients as they performed a task. During an encoding phase, individuals watched 90 distinct and novel video clips that contained either no boundaries (i.e., a continuous clip), soft boundaries (i.e., cuts to different scenes within the same clip), or hard boundaries (i.e., cuts to different scenes from different clips). During a scene recognition phase, individuals were presented with single static frames (either previously presented ‘target’ clips or never-before-seen ‘foil’ clips) and asked to identify the frames as either ‘old’ or ‘new’ along with a confidence rating. During a time discrimination phase, individuals were shown two old frames side by side and asked to indicate the order they had previously appeared along with a confidence rating.

What did they find?

Scene recognition accuracy did not differ between boundary types. In contrast, time discrimination accuracy was significantly worse when discerning the order of frames separated by hard boundaries compared to soft boundaries. These findings suggest a tradeoff effect in which hard boundaries improve recognition but impair temporal order memory. The authors identified ‘boundary cells’ as those neurons in the MTL that showed firing rate increases following both soft and hard boundaries whereas ‘event cells’ were those neurons that responded only to hard but not soft boundaries. The level of boundary cell firing rate during encoding predicted later scene recognition accuracy, while the coordination of event cell activity with ongoing oscillations in the brain predicted later time discrimination performance. When examining neural state shifts (i.e., changes in the population activity across boundary-responsive neurons), larger shifts were positively related to improved recognition accuracy but negatively related to time discrimination—revealing a neural mechanism for the tradeoff between recognition and temporal order memories. 

What's the impact?

This study revealed a neural mechanism in the MTL that responded to boundaries separating discrete events and helped to shape the content and temporal order memories for these events. A particular highlight of this paper is the use of single-neuron recordings in human patients, which allows for a more direct study of memory-related brain activity compared to less invasive approaches such as functional MRI or EEG.

Utilizing Artificial Intelligence Improves Planning and Decision Making

Post by Lincoln Tracy

The takeaway

Benjamin Franklin once said, “Failing to plan is planning to fail”. Artificial intelligence can teach people to improve their planning and decision-making strategies by using optimal feedback processes, thereby avoiding sub-optimal outcomes.

What's the science?

Decision-making is an important part of everyday life, but it is often plagued by errors that can have shocking consequences. In many cases, these consequences could be avoided if proper planning strategies were implemented. A crucial part of developing optimal planning strategies is reliable, valid, and timely feedback. However, many real-world settings do not provide enough high-quality feedback to help people discover the optimal strategies on their own. This week in PNAS, Callaway and colleagues developed an artificial intelligence tutor to help people quickly discover the best possible decision-making strategies before testing its effectiveness across several experiments in different settings.

How did they do it?

First, the authors used artificial intelligence to develop a virtual tutor to teach people optimal decision-making processes. The tutor was designed to provide metacognitive feedback (to help participants learn the optimal strategies for themselves) rather than direct feedback (e.g., “you should have gone left”) during an initial training phase before the testing phase began. The authors then recruited over 2500 participants across six different online experiments hosted on Amazon Mechanical Turk or Prolific to test the effectiveness of the intelligent tutor (against direct action feedback or no feedback) in six different settings:

·       Experiment 1 introduced participants to the Web of Cash game, where they were required to navigate a spider through a web from its center to an outer edge. Each space on the web contained a reward (or a loss), and participants were aiming to collect as many rewards as possible. All rewards and losses were hidden initially, meaning participants did not know the optimal path to obtain the most rewards. However, participants could pay a small fee to uncover the reward on each space on the web. Participants undertook the training phase with metacognitive, direct, or no feedback before completing the testing phase. The authors quantified participant’s performance relative to how often they used the optimal strategy to navigate the spider through the web.    

·       Experiment 2 tested whether the metacognitive feedback training was effective in a more complicated alteration of the Web of Cash game than Experiment 1 (routing an airplane through a larger series of airports).

·       Experiment 3 tested whether the metacognitive feedback training could be retained by adding a 24-hour delay between the training and testing phases of the Web of Cash game.

·       Experiment 4 tested whether metacognitive feedback training was effective in a less structured version of Experiment 1.

·       Experiment 5 tested metacognitive feedback in a real-world context—planning an inexpensive road trip (the Road Trip paradigm). Rather than navigating a spider through a web, participants were required to road trip across a country, stopping at several hotels, to end up at a city with an airport. A fourth training condition (watching a video about if-then plans) was added. 

·       Experiment 6 explored which aspect of the metacognitive feedback (i.e., a time-based penalty for selecting a suboptimal move or a message describing what move the participant should have made) made the largest contribution to the improved scores on the Web of Cash game.

What did they find?

In Experiment 1, the authors found participants performed better on the Web of Cash game after receiving metacognitive feedback compared to the other two feedback conditions. This suggests metacognitive feedback increased participants’ ability to make better decisions without having to think harder. Participants who received metacognitive feedback also performed better in a more complicated version of the Web of Cash game (Experiment 2), when there was a 24-hour delay between training and testing (Experiment 3, suggesting training effects were retained over time), and in a less structured version of the game (Experiment 4). Metacognitive feedback resulted in better performance on the more naturalistic Road Trip paradigm compared to the video only and no feedback groups, suggesting metacognitive training can be transferred to new situations (Experiment 5). Metacognitive feedback with both the delay penalties and information about the optimal choice improved performance more than either of the components individually, and neither individual component improved performance more than receiving no training (Experiment 6). This suggests both aspects of metacognitive feedback are critical to the improvements in decision-making and planning.

What's the impact?

This study found metacognitive feedback provided by an artificially intelligent tutor taught people to quickly learn effective decision-making strategies. The novel feedback method performed better than conventional approaches to providing feedback to improve decision-making performance. These findings represent the first steps in using artificial intelligence tutors in increasingly realistic situations to improve decision-making processes and lead to more optimal outcomes.

Access the original scientific publication here.