Predicting Impulsivity in Young Adults

Post by Anastasia Sares

What's the science?

A failure to control impulsivity is common to many psychiatric conditions, which often emerge in young adulthood as the frontal lobe completes its development. Finding the specific neural mechanisms behind impulsivity could help us diagnose and treat this aspect of mental health. This week in Molecular Psychiatry, Steele and colleagues looked at the brain areas involved in impulsivity, using a large and diverse group of young adults with different mental health profiles.

How did they do it?

The authors collected functional MRI scans to measure the brain activity of a number of young adults (18-25 years old) who were seeking treatment for a variety of psychiatric conditions (as well as young adults who had no diagnosis and no treatment). Psychiatric conditions were evaluated in a structured interview, and some of those with a diagnosis also came back for a follow-up session.

During the MRI scan, participants looked at a series of faces with different emotions at varying intensities, as well as some gray ovals that had no facial information. This way, the authors could see which brain areas tracked emotional intensity.

The participants also completed an impulsivity questionnaire with 5 subcategories:

1. Negative urgency (urgency to act on negative emotions)

2. Positive urgency (urgency to act on positive emotions)

3. Lack of premeditation (not thinking ahead)

4. Lack of perseverance (giving up on things)

5. Sensation-seeking

Based on previous literature, the authors thought that impulsivity would predict:

1. Higher activity in the amygdala (a structure known for its response to fear)

2. Altered activity in the prefrontal cortex (known for inhibition and emotional control)

3. Lower connectivity between the amygdala and the prefrontal cortex (less regulation of emotional responses)

What did they find?

The authors found that the amygdala significantly responded to the emotional faces. As predicted, the strength of the left amygdala’s response to fearful faces was correlated with a person’s impulsivity, specifically negative urgency and lack of perseverance. The connectivity between the amygdala and prefrontal cortex was also related to impulsivity, with less connectivity in more impulsive people. This confirmed the authors’ second and third predictions. Finally, for the 30 participants who came for a follow-up evaluation 6 months later, the amygdala’s response to sad faces in the first session predicted overall impulsivity at follow-up.

What's the impact?

This work is a step forward in our understanding of the brain areas involved in impulsivity and may provide targets for diagnosis and treatment of psychiatric conditions. However, the authors are quick to point out that the study requires replication. Testing so many people with different psychiatric conditions is good for the generalizability of the results to the general population, but it also means that other factors related to these disorders could confound the results. Although this study attempts to remove the influence of other factors, the best confirmation is to repeat the experiment in an independent sample.

Steele et al. A specific neural substrate predicting current and future impulsivity in young adults. Molecular Psychiatry (2021). Access the original scientific publication here.

Memory Performance is Linked to Neural Repetition Effects Across the Lifespan

Post by Amanda McFarlan

What's the science?

Researchers have long studied neural representations of memories to understand how memories are formed and stored in the brain. One way to do this is by investigating whether neural activity in the brain changes in response to repeated exposure to the same stimuli, known as the repetition effect. Repetition effects can be observed in two different forms: a repetition suppression effect, which occurs when neural activity is lower upon the second presentation of a stimulus, or a repetition enhancement effect, which occurs when neural activity is higher upon the second presentation of a stimulus. This week in Developmental Cognitive Neuroscience, Sommer and colleagues investigated whether memory formation, as measured by neural repetition effects, was associated with memory performance across the lifespan. 

How did they do it?

The authors recruited children (7-9 years), young adults (18-30 years) and older adults (65–76 years) to participate in their study. EEG recordings were acquired for each age group while participants performed an encoding task followed by a recognition task. During the encoding task, participants were shown pictures of objects belonging to different categories (e.g. hats, trees, guitars). The entire encoding task consisted of 720 trials for the adult groups and 360 trials for the children. After completing the encoding task, the authors surprised participants with a recognition task. Participants were not told to memorize the pictures during the encoding task and did not know that they would be tested on their memory. During the recognition task, participants were shown pictures of objects and were asked to identify whether that object was old (an image they had previously seen in the encoding task), similar (an imaging belonging to a category they had previously seen in the encoding task) or new (an image belonging to a novel category). The adult groups completed 480 trials for the recognition task, while the children completed 240.

What did they find?

The authors found that participants in all age groups were able to identify whether an image was old, similar, or new at levels above chance. They showed that children had higher specific item memory (correctly identifying whether an image was old or similar) compared to both adult groups and both children and young adults had a higher lure discrimination index (the difference between images correctly identified as similar and mistaken as old) compared to older adults. However, this finding did not persist after accounting for the fact that children performed an easier and shorter task. Next, the authors examined the EEG recordings to look for changes in neural activity related to repetition effects. They found repetition suppression effects in the posterior, frontal, and central electrode sites for all age groups. They also found a repetition enhancement effect in the frontal and temporal electrode sites for both adult groups as well as in the centro-parietal electrode sites for older adults. The repetition suppression effect (observed in all age groups) and the repetition enhancement effect (observed in adults) were positively correlated with item-specific memory performance.

What’s the impact?

This study shows that item-specific memory performance for all ages is positively correlated with both repetition suppression effects and repetition enhancement effects. This suggests that memory encoding may have similar neural mechanisms in children and adults. Together, these findings provide evidence that neural repetition effects may be a useful neural indicator of memory encoding across the lifespan.

Sommer et. al. Memory specificity is linked to repetition effects in event-related potentials across the lifespan. Developmental Cognitive Neuroscience (2021). Access the original scientific publication here.

Scalable Representation of Time in the Hippocampus

Post by Andrew Vo

What's the science?

Hippocampal place cells allow us to form map-like spatial representations of our environments, and these maps adaptively rescale themselves when our environments change. Whether hippocampal “time cells” can also form such scalable representations for information about time has yet to be systematically investigated. This week in Science Advances, Shimbo and colleagues examined if hippocampal CA1 activity of rats during encoding of time intervals scaled in response to expansions or contractions in elapsed time.

How did they do it?

The authors trained rats to perform a task during which they ran on a treadmill for either long (e.g. 10 s) or short (e.g. 5 s) time intervals before navigating a Y-maze (shaped like a Y). The left and right arms of the maze were associated with long and short treadmill time intervals, respectively, and so the rats had to discriminate between intervals to select the correct arm and receive a reward. The rats performed three blocks of trials, across which the sets of time intervals were scaled up or down. For example, rats would discriminate between 10 (long) and 5 s (short) intervals in block 1, which were scaled up to 20 (long) and 10 s (short) intervals in block 2, before returning to their original interval durations in block 3.

During the treadmill interval periods, the authors recorded activity from hippocampal CA1 to identify time cells (i.e., neurons whose activity represented information of elapsed time). The firing activity of these cells was compared across experimental blocks using peri-event time histograms (PETHs) that quantify the rise and fall of activity over time in relation to the event. Using this method, scaling factors in response to changes in time intervals could be quantified. To test whether time cell activity was specific to a time-based task,  they trained a different set of rats on a light discrimination task that shared the same structure as their original task, except Y-maze performance was based on a light cue instead of interval times.

Next, the authors recorded theta sequences in the brain, which are patterns of neuron firing among cell assemblies that represent compressed time episodes. They also tested if these theta sequences scaled to changes in time intervals. Finally, they used a statistical method — Bayesian decoding — to decode these theta sequences and see if time cell activity predicted the rats’ Y-maze decisions.

What did they find?

The authors found that rat hippocampal CA1 activity during the treadmill interval period represented information on the elapsed time that scaled up or down depending on the expansion and contraction of the time intervals. This finding appeared to be related to task demands, as the number of time cells was significantly reduced when rats were not required to estimate time during the light discrimination task. This reduced number of time cells continued to display scalable representations of elapsed time, however. Examining the finer temporal structure of time cell ensembles, they noted the presence of theta sequences that were also scalable when time intervals were varied. The temporal information of these theta sequences could be decoded and reflected the rats’ decisions based on time estimation during test trials.

What's the impact?

In summary, this study demonstrated that time cells in the hippocampus can form scalable temporal representations of the environment, similar to how place cells code for spatial information. These findings suggest there is a common mechanism in the hippocampus underlying representations of temporal and spatial information by time and place cells, respectively. The ability to flexibly scale such representations might allow us to better navigate our complex and changing environments.

Shimbo et al. Scalable representation of time in the hippocampus. Science Advances (2021). Access the original scientific publication here.