At What Age Does Memory Formation Come “Online?”

Post by Anastasia Sares

The takeaway

This study shows that the hippocampus, a crucial brain structure involved in forming memories, is active as young as 1 year of age. This may indicate that memory formation is present early on, but the ability to maintain or retrieve memories is slower to develop.

What's the science?

Humans don’t remember what happened in the first three years of their lives. This is referred to as infantile amnesia. Why does infantile amnesia exist? It’s still a mystery. One idea that has been suggested is that the hippocampus, a brain region involved in forming memories, might still be developing during these early years, and it might not be fully functional. On the other hand, studies of infant behavior show that infants can react to familiar objects and situations as early as 3 months of age.

This week in Science, Yates and colleagues used functional MRI to examine infant brain activity during a memory task to understand how infant memory works.

How did they do it?

The authors examined infants’ brain activity using functional MRI while they completed a memory task. The infants ranged from 4 to 25 months in age. Infants, of course, can’t respond in the usual way during an fMRI task, since they are unable to press buttons in response to instructions. Instead, the researchers use a preferential looking task, where an infant is shown two objects while their gaze is tracked to determine which object they are looking at. It is assumed that infants will gaze longer at an image they have recently seen compared to a completely new image (however, if the old image becomes too familiar, they may instead switch to looking at the new image, something the authors avoided in this study).

In this study, infants were familiarized with single images appearing in the center of the screen while inside the MRI. Periodically throughout the experiment, there would be a test trial with two images – an image that the infant had seen before, and an image they hadn’t seen before. The amount of time they spent looking at each image was tracked. If the infant looked at the familiar image more than the new image, this counted as “remembering” the old image. Then, the researchers went back and looked at the brain activity in the hippocampus when the infant had first seen each image, comparing remembered images to non-remembered images. They expected more brain activity in the hippocampus for remembered images.

What did they find?

Activity in the hippocampus (specifically, the posterior portion) was significantly greater for remembered images compared to non-remembered images only for older infants, from around 1 year of age onwards. This shows that even though infantile amnesia occurs for approximately the first three years of life, our ability to encode memories is present within the first year. What, then, accounts for the gap in memory for years two and three? It is important to recognize that remembering something involves more than just the initial commitment to memory. We have to maintain that memory and then retrieve it when needed. The authors suggest that infantile amnesia past age 1 may be related to these maintenance and retrieval components, but more research is needed to understand this fully. 

What's the impact?

This study improves our understanding of the timeline of human memory development, helping us identify the first 12 months as crucial for encoding in the hippocampus. Longitudinal studies focused on this developmental period, and studies investigating other brain areas will help us to further crack the code of infantile amnesia.

Good Night, Sleep Tight, Don’t Let the Neurodegenerative Diseases Bite!

Post by Rebecca Glisson

The takeaway

Sleep is often considered a restorative process that enhances the brain’s defenses and facilitates memory processing. Recent studies also highlight its protective properties against neurodegenerative diseases such as Alzheimer’s disease.

What happens in our brains when we sleep?

Proper sleep helps with maintaining memory, stable mood, and disposing of waste from brain activity. As we sleep, the cells in our brains interact with each other to form networks that are required for normal brain function. Memory processing happens during slow-wave sleep, the deepest stage of sleep before we begin to dream. Unfortunately, our slow-wave sleep stage deteriorates every year of adulthood, especially between young and middle-aged adulthood. This may lead to brain deterioration in areas of the brain that coordinate our thoughts and actions, such as the prefrontal cortex. This week in Neuron, Parhizkar and Holtzman highlighted recent research focused on the link between disrupted sleep and neurodegenerative diseases like Alzheimer’s disease.

How is Alzheimer’s disease linked to sleep?

Amyloid-b plaques form in a person’s brain before they develop Alzheimer’s disease. These plaques directly disrupt the brain networks involved in the sleep/wake cycle and memory processing during sleep. In studies in both animal models and in patients with Alzheimer’s disease, as these plaques build up, slow-wave sleep gets worse. Also, people with excessive daytime sleepiness were more likely to have plaque buildup in the brain. Studies in mice have found that sleep loss can lead to higher plaque and abnormally phosphorylated tau concentrations in the brain, while more sleep can lower the amount of plaque and pathological tau.

How does sleep protect the brain?

By maintaining good sleep habits, you might have a lower likelihood of developing neurodegenerative diseases. A recent study found that adults that had at least 2 hours less than the normal amount of adult sleep had a greater risk of developing dementia and Alzheimer’s disease. Fragmented sleep, with lots of disruptions, may also lead to a higher risk of neurodegenerative diseases. The primary immune cells in the brain that break down and remove waste are called microglia. Studies in animal models have shown that microglia have higher calcium concentrations, used for signaling, during sleep. This suggests that sleep allows calcium to increase in microglia, allowing them to remove waste.

What’s next?

We all spend a large portion of our lives asleep. While it could seem like a waste of time, many processes are happening in the brain during sleep that are required to keep us healthy. Without proper sleep, we become lethargic and unproductive. Long-term poor sleep habits could eventually lead to serious neurological disorders. The more we understand the link between sleep and neurodegenerative diseases, the more likely it is that we might find treatments that could prevent diseases before they develop

References +

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Krueger, J.M., Frank, M.G., Wisor, J.P., and Roy, S. (2016). Sleep Function: Toward Elucidating an Enigma. Sleep Med. Rev. 28, 46–54. https:// doi.org/10.1016/J.SMRV.2015.08.005.

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Predicting the Risk of Childhood Mental Illness with AI

Post by Laura Maile

The takeaway

Adolescent mental illness is an increasingly serious problem in the US and globally. Early risk assessment and intervention are key to the successful treatment of mental health disorders. Artificial intelligence can be trained to accurately predict the risk of future psychiatric illness in children using patient and parent questionnaires of current symptoms. 

What's the science?

Rates of mental illness in adolescents have risen in recent years, presenting an incredible challenge to patients and their mental health providers. Identifying those children with the highest risk for developing psychiatric illness is essential to early intervention and effective mental health treatment. Due to the many complex factors contributing to mental illness and the diversity of psychiatric disorders present in the adolescent population, predicting the risk of disease in individual children remains a challenge. This week in Nature Medicine, Hill and colleagues developed a method to predict the risk of future psychiatric disease by training a neural network model using data from 11,000 children. 

How did they do it?

The authors utilized data from the Adolescent Brain and Cognitive Development (ABCD) study, which included 11,416 participants aged 8-15 years old. In the ABCD study, patient assessments were conducted multiple times a year and included interviews with patients and their parents, fMRI scans, symptom screenings, and questionnaires. Data collected in multiple patient assessments across five years was used to train an artificial intelligence (AI) neural network model, which produced two general approaches to future disease prediction. One approach used assessments of symptoms like the Child Behavior Checklist (CBCL) to predict future disease, while the other focused on potential disease mechanisms, which relied on questionnaires about factors such as adverse childhood events, family history, sleep disturbances, and socioeconomic status. A p-factor was calculated, which represents the general psychopathology score for each individual. The authors set out to determine whether their model could accurately predict future p-factor based on current patient measurements. They also aimed to predict which children were likely to move to a higher-risk group and which factors had the biggest influence on prediction accuracy

What did they find?

The authors found that their neural network model could accurately predict mental illness one year following the initial assessment. It also accurately predicted a shift into the high-risk category after one year for 11% of participants. The model performed similarly well across demographic groups, indicating the broad application of this predictive model. While both symptom-based and mechanism-based approaches performed well as prediction models, the symptom approach was slightly better. Within the mechanism approach, sleep disturbance was the best predictor of future psychiatric illness. Up to a certain point, increased sleep disturbance was correlated with a higher risk of mental illness. This result indicates that moderate sleep disturbance is a positive predictor of psychiatric disease. Parent questionnaires had a stronger influence on positive prediction than patient questionnaires. This highlights the importance of parent perspectives in determining the risk of future mental illness.

What's the impact?

This study created an AI model to accurately predict the risk of future mental illness in adolescents across a broad range of mental health conditions. The success of symptom and disease mechanism analysis as predictors of future mental illness highlights the benefits of incorporating such inexpensive analyses into the clinical setting for individual patients. Strong predictors of future mental illness like sleep disturbance provide an opportunity for successful intervention, with some studies showing that targeted intervention can improve both sleep disturbance and psychiatric symptoms. This model could lead to improved preemptive screening and earlier intervention for children at risk of future mental illness. 

Access the original scientific publication here.