Modeling Brain Development in Neonates

Post by Elisa Guma

What's the science?

During the perinatal period, the brain is rapidly developing, resulting in changes in size, gyrification, and contrast between tissues (as seen during brain imaging). To further complicate the situation, these changes may occur at different rates in different brain regions. This complexity makes it very challenging to accurately and reliably interpret clinical magnetic resonance imaging (MRI) data for those who may have experienced premature birth or perinatal brain injury. It is difficult to know what is abnormal for a neonatal brain given the age and clinical history of a patient. This week in Brain, O’Muircheartaigh and colleagues leveraged a large multi-contrast MRI dataset acquired during the perinatal period to model trajectories of normal brain development, and to accurately identify focal brain injury.  

How did they do it?

The authors used cross-sectionally acquired structural MRI data (T1- and T2- weighted) for 408 neonates, 189 of which were female, from the developing Human Connectome Project database, a publicly available dataset. The participants were all scanned postnatally, with post-menstrual ages ranging from 26 to 44 weeks (and gestational age at birth ranging from 23-42 weeks). A template was created from scans for 20 neonates over a wide age range based on two imaging features: T2-weighted image volume intensity and the cortical mantle. This was used to represent the midpoint for the sample’s age range. Next, the 408 scans were registered to the template, which means that the brain images were warped to match the template brain (using linear and nonlinear registration). The degree to which each voxel had to be expanded or shrunk to match the template gives us a measure of volume difference. The authors used a Gaussian process regression, which is a non-parametric approach, which they argue is a superior way to model growth curves for tissue intensity and shape at each voxel in the brain accounting for age (or degree of prematurity) and sex.

Next, the authors wanted to determine whether their model was longitudinally valid. Thus, for a subset of 46 neonates that had a second scan (excluded from the model construction), they quantified the deviation from the predicted image intensity. They then tested to see whether their model was able to detect deviations in tissue contrast that would predict the presence of punctate white matter lesions. A common brain injury associated with premature birth is a punctate white matter lesion, which is detectable using MRI. Their presence was identified in 40 neonates and manually labeled on each of the scans. Since focal abnormalities such as these lesions are reflected by deviations from typical development, the authors wanted to see if their model could accurately detect these deviations.

What did they find?

The authors were able to identify anatomically informed growth curves at each voxel in the brain, based on the MRI image intensity, with corresponding measures of variability, accounting for the degree of prematurity and sex of the infant. They observed differences in variability and shape in the growth curve based on structure; for example, the subventricular and intermediate zones were observed to have a rapid growth around term-equivalent age as they transition into white matter, whereas other white matter structures had a more linear growth, such as the sensory cortex. Interestingly, the frontal cortex had a flat curve in the perinatal period, as its development typically occurs later in life.

Longitudinal accuracy was also found to be high - 83% of the subset of neonates who had longitudinal scans had growth curves that matched those of the model. Accuracy, however, was not as good for younger neonates who had more intermediate structures present in their brains. The model also proved to have clinical utility as it was able to detect signal deviations due to punctate white matter lesions with high accuracy. 

What's the impact?

This study provides accurate estimates of non-linear changes in brain tissue intensity by modeling ex utero brain development over a wide age range (26 to 45 post menstrual weeks). Further, this work provides continuous growth charts for brain development based on shape and image intensity, similar to those used for height in clinical practice, providing an index that accounts for age and clinical history (i.e. prematurity). Future work may incorporate in utero MRI, and perhaps extend the postnatal period further.

Jonathan O’Muircheartaigh et al. Modelling brain development to detect white matter injury in term and preterm born neonates. Brain (2020). Access the original scientific publication here

Surprise and Decision Making in the Anterior Cingulate Cortex

Post by Anastasia Sares

What's the science?

The anterior cingulate cortex, situated in the frontal lobe at the midline where the two hemispheres meet, is an important region of the human brain. Previous studies have connected it to error detection, cognitive control, and decision making. However, there are a few different hypotheses about what drives activity in this area. The “choice difficulty” hypothesis (CD) says that the anterior cingulate tracks difficulty—tougher decisions generate more activity. The “expected value of control” hypothesis (EVC) tracks net gain—how much reward will I get for the effort I put in? EVC predicts more activity for high reward, especially if the brain has to gear up and make an effort in order to get the reward. Finally, the “predicted response outcome” hypothesis (PRO) tracks surprise—events that violate expectations generate more activity, regardless of whether the results are perceived as “good” or “bad.” This week in Nature Human Behaviour, Vassena and colleagues pitted these three hypotheses of anterior cingulate function against each other in a speeded decision-making task.

How did they do it?

Before they started the experiment, participants learned to associate random fractal images with a certain number of points (picture 1 = 30 points, picture 2 = 80 points, etc.). They were told that the points they accumulated in the task would translate into extra money at the end of the experiment. In each trial of the task, four of the fractal images were shown on the screen. The participants had three-quarters of a second to choose either the set of images on the left or the set of images on the right. Of the images they chose, one would be randomly selected, and they would receive the equivalent amount of points. To complete this task, therefore, the participants had to be good at quickly estimating the value of the options on the right versus the options on the left. The task was completed in MRI so the response of the anterior cingulate could be measured.

Each of the hypotheses presented earlier (choice difficulty, expected value of control, and predicted response outcome) should show a different pattern of activity in the anterior cingulate during this task. CD predicts the most activity when the choice is difficult; that is when the amount of points on the left is similar to the amount on the right. EVC predicts the opposite: the anterior cingulate should be least active when the options are similar because the difference in potential reward is small compared to the effort required to distinguish between them. PRO predicts two peaks of activity, because with a large difference in value, one option is surprisingly bad, and with a small difference in value it is uncertain which option you will choose (making the choice itself “surprising”).

 What did they find?

The authors compared the activity in the anterior cingulate cortex to the three different predictions and found that the PRO model (predicted response outcome) matched most closely. This is in line with the idea that the anterior cingulate reacts to unlikely or unpredicted events (surprise). During a task like this, people are quickly able to figure out how difficult the average trial is, so when a surprisingly bad option or two very similar options appear, the anterior cingulate’s activity increases. What’s more, an extremely low or high reward at the time of feedback also activated the same area.

What's the impact?

This study tested predictions from three different hypotheses and found one to be the clear winner: the main function of the anterior cingulate is processing surprise. This is a crucial step in the scientific enterprise; after some hypotheses are generated about the natural world, they must be tested against one another in carefully designed experiments that allow us to determine which hypothesis is stronger.

Vassena et al. Surprise, value and control in anterior cingulate cortex during speeded decision-making. Nature Human Behavior (2020). Access the original scientific publication here.

The Time Of Day We Eat Is Associated with Diet-Induced Obesity

Post by Flora Moujaes 

What's the science? 

Worldwide obesity has nearly tripled since 1975: currently over 13% of the world’s adult population is obese. This increase in obesity is correlated with the more widespread availability of highly processed energy-dense rewarding foods that encourage snacking outside of regular meal times. However, it is not just the number of calories consumed that are important for understanding weight gain, but also when they are consumed. Proper maintenance of energy throughout the day requires that meals are synchronized with daily metabolic rhythms. For example, even if two mice consume the same number of calories, eating food at different times (e.g. snacking) could lead to obesity in one mouse but not the other. This issue is particularly prevalent in modern society as the central pacemaker is under constant dysregulation by artificial light. This week in Current Biology, Grippo et al. investigate the mechanisms through which the increased availability of energy-dense food and feed times lead to diet-induced obesity. 

How did they do it? 

To explore the mechanisms underlying diet-induced obesity, mice were either fed a diet comparable to that eaten in the wild, or had unlimited access to a high fat, high sugar diet. To examine the involvement of dopamine in diet-induced obesity, the cre-lox recombinase enzyme (an enzyme that allows you to knock out genes solely in subsets of cells e.g. the brain) was used to knock out the Drd1 gene in the brain. This gene encodes the D1 subtype of the dopamine receptor, which is the most abundant dopamine receptor in the central nervous system. These mice are referred to as the ‘knockout’ mice. Finally, to explore exactly where in the brain dopamine is involved in diet-induced obesity, the researchers selectively restored Drd1 expression in (1) the nucleus accumbens or (2) the suprachiasmatic nucleus (SCN). The nucleus accumbens was chosen as it is the reward processing center of the brain. The SCN was chosen as it is the main biological clock: the SCN receives light cues from the eyes and interprets them as the time of day, as well as cues when the body consumes and metabolizes food.

What did they find?

Researchers first showed that unlimited access to energy-dense food led to obesity. While mice fed a diet akin to that eaten in the wild maintained normal eating and exercise schedules and proper weight, mice with unlimited access to energy-dense food rapidly developed obesity, diabetes, and metabolic diseases. However, knockout mice with impaired dopamine D1 receptor functioning were resistant to weight gain following exposure to unlimited energy-dense food. Researchers also found that unlimited access to energy-dense food led to eating at irregular times. As nocturnal animals, mice usually eat 80% of their food during the night when exposed to a healthy diet, however mice with unlimited access to energy-dense food only ate 60% of their food during the night. In contrast, knockout mice with impaired dopamine D1 receptor functioning did not change their feeding times following exposure to unlimited energy-dense food. Taken together, these data suggest that D1 is important for the overconsumption of energy-dense food, predominantly during rest, leading to obesity.

Mice with restored D1 dopamine receptor functioning in the nucleus accumbens did not gain weight when exposed to unlimited energy-dense food - while they did increase their consumption of food during rest, they did not increase their overall calorie intake and therefore, did not become obese. In contrast, mice with restored D1 dopamine receptor functioning in the central circadian clock (SCN) did gain a substantial amount of weight when exposed to unlimited energy-dense food. Both their consumption of food at rest and overall calorie intake was significantly increased. Overall, this indicates that dopamine D1 receptor functioning in the central circadian clock (SCN) is crucial for diet-induced obesity. 

What's the impact? 

This study uncovered a novel mechanism for understanding how energy-dense diets lead to obesity, defining a connection between the reward and circadian pathways in the regulation of pathological calorie consumption. The authors demonstrate that dopaminergic signalling within the central circadian clock (SCN) disrupts the timing of feeding, resulting in an overconsumption of food, which leads to obesity, diabetes, and metabolic disease. This research not only has significant clinical implications by furthering our understanding of the mechanisms that underlie obesity but also helps to explain the growing popularity and effectiveness of diets that involve time-restricted feeding (e.g. intermittent fasting).

Grippo et al. Dopamine Signaling in the Suprachiasmatic Nucleus Enables Weight Gain Associated with Hedonic Feeding. Current Biology (2020). Access the original scientific publication here.