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 +

Colby-Milley, J., Cavanagh, C., Jego, S., Breitner, J.C.S., Quirion, R., and Adamantidis, A. (2015). Sleep-Wake Cycle Dysfunction in the TgCRND8 Mouse Model of Alzheimer’s Disease: From Early to Advanced Pathological Stages. PLoS One 10, e0130177. https://doi.org/10.1371/JOURNAL. PONE.0130177.

Hahn, E.A., Wang, H.-X., Andel, R., and Fratiglioni, L. (2014). A Change in Sleep Pattern May Predict Alzheimer Disease. Am. J. Geriatr. Psychiatry 22, 1262–1271. https://doi.org/10.1016/j.jagp.2013.04.015.

Jack, C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., and Trojanowski, J.Q. (2010). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9, 119–128. https://doi.org/10.1016/S1474-4422(09) 70299-6.

Kang, J.-E., Lim, M.M., Bateman, R.J., Lee, J.J., Smyth, L.P., Cirrito, J.R., Fujiki, N., Nishino, S., and Holtzman, D.M. (2009). Amyloid-b Dynamics Are Regulated by Orexin and the Sleep-Wake Cycle. Science 326, 1005–1007. https://doi.org/10.1126/science.1180962.

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.

Landolt, H.P., Dijk, D.J., Achermann, P., and Borbe ́ ly, A.A. (1996). Effect of age on the sleep EEG: slow-wave activity and spindle frequency activity in young and middle-aged men. Brain Res. 738, 205–212. https://doi. org/10.1016/S0006-8993(96)00770-6.

Lim, A.S.P., Yu, L., Kowgier, M., Schneider, J.A., Buchman, A.S., and Bennett, D.A. (2013). Modification of the Relationship of the Apolipoprotein E ε4 Allele to the Risk of Alzheimer Disease and Neurofibrillary Tangle Density by Sleep. JAMA Neurol. 70, 1544–1551. https://doi.org/10.1001/ jamaneurol.2013.4215

Ma, C., Li, B., Silverman, D., Ding, X., Li, A., Xiao, C., Huang, G., Worden, K., Muroy, S., Chen, W., et al. (2024). Microglia regulate sleep through calcium-dependent modulation of norepinephrine transmission. Nat. Neurosci. 27, 249–258. https://doi.org/10.1038/s41593-023-01548-5.

Mander, B.A., Rao, V., Lu, B., Saletin, J.M., Lindquist, J.R., Ancoli-Israel, S., Jagust, W., and Walker, M.P. (2013). Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging. Nat. Neurosci. 16, 357–364. https://doi.org/10.1038/nn.3324.

Mander, B.A., Marks, S.M., Vogel, J.W., Rao, V., Lu, B., Saletin, J.M., Ancoli-Israel, S., Jagust, W.J., and Walker, M.P. (2015). b-amyloid disrupts human NREM slow waves and related hippocampus-dependent memory consolidation. Nat. Neurosci. 18, 1051–1057. https://doi.org/10. 1038/nn.4035.

Oh, J., Eser, R.A., Ehrenberg, A.J., Morales, D., Petersen, C., Kudlacek, J., Dunlop, S.R., Theofilas, P., Resende, E.D.P.F., Cosme, C., et al. (2019). Profound degeneration of wake-promoting neurons in Alzheimer’s disease. Alzheimers Dement. 15, 1253–1263. https://doi.org/ 10.1016/j.jalz.2019.06.3916.

Parhizkar, S., & Holtzman, D. M. (2025). The night’s watch: Exploring how sleep protects against neurodegeneration. Neuron.

Roh, J.H., Jiang, H., Finn, M.B., Stewart, F.R., Mahan, T.E., Cirrito, J.R., Heda, A., Snider, B.J., Li, M., Yanagisawa, M., et al. (2014). Potential role of orexin and sleep modulation in the pathogenesis of Alzheimer’s disease. J. Exp. Med. 211, 2487–2496. https://doi.org/10.1084/jem. 20141788.

Simons, J.S., and Spiers, H.J. (2003). Prefrontal and medial temporal lobe interactions in long-term memory. Nat. Rev. Neurosci. 4, 637–648.

Zavecz, Z., Shah, V.D., Murillo, O.G., Vallat, R., Mander, B.A., Winer, J.R., Jagust, W.J., and Walker, M.P. (2023). NREM sleep as a novel protective cognitive reserve factor in the face of Alzheimer’s disease pathology. BMC Med. 21, 156. https://doi.org/10.1186/s12916-023-02811-z.

Zielinski, M.R., McKenna, J.T., and McCarley, R.W. (2016). Functions and mechanisms of sleep. AIMS Neurosci. 3, 67–104. https://doi.org/ 10.3934/Neuroscience.2016.1.67.

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.

Neurons Driving Sugar Consumption

Post by Lila Metko 

The takeaway

A population of neurons in the hypothalamus with a well-established function in satiety, the sensation of being full, may have another important role. This research suggests that pro-opiomelanocortin (POMC) neurons in the hypothalamus signal to another brain region to drive sugar consumption in states of fullness. 

What's the science?

There is a drive present in both humans and many animal species to consume high amounts of sugar even after a substantial meal. Understanding the neurobiological mechanism behind this drive could assist with the production of effective obesity therapeutics. It is well understood that the activation of POMC-projecting neurons in the hypothalamus promotes satiety in a fed state. However, POMC is also a precursor for the neuropeptide b-endorphin that acts on a specific receptor, the mu opioid receptor, to stimulate appetite. This winter in Science, Minère and colleagues measure and manipulate activity in hypothalamic POMC neurons during both standard and high sugar consumption after a meal to investigate their role in the drive to consume sugar. 

How did they do it?

The authors first investigated which regions in the brain had both high amounts of mu opioid receptors and POMC. They used fluorescence in-situ hybridization, a technique that reveals the number of nucleic acid sequences coding for a protein of interest, for the receptor and immunohistochemistry, a detection technique for visualizing cellular components, for POMC. They found that a region with both was the paraventricular nucleus of the thalamus (PVT), a brain region important for feeding and motivated behavior. They then optogenetically activated POMC neurons from the hypothalamus and recorded activity in the PVT under control and different receptor blocker conditions to determine how POMC neurons affect PVT activity and which receptors may be involved. Next, they recorded activity in this circuit (hypothalamic POMC neurons to thalamic PVT neurons) during post-meal high-sugar food consumption or post-meal standard chow consumption to determine if specifically sweet foods were associated with changes in circuit activity. Additionally, the researchers tested if activation of the circuit under control and/or opioid receptor blocker conditions affected general flavor preference to control for potential confounds of sweet taste and post-ingestive sugar sensing. Next, they tested if circuit activation affected conditioned place preference, a preference test that is not associated with food consumption. They then investigated how chemogenetic inhibition of the circuit affected flavor preference (high-sugar food vs standard chow). Next, they used fiber photometry to record circuit activity in response to a high-sugar diet and high-fat diet cues, to determine the circuit's role in different fed-state macronutrient preferences. Finally, they used functional magnetic resonance imaging (fMRI) to examine PVT activity in humans during the consumption of sugar to see if a similar circuit may exist in humans. 

What did they find?

Activation of POMC neurons decreased the firing rate of neurons in the PVT when being exposed to blockers of other neuromodulators but not blockade of the mu opioid receptor. This suggests that signaling from POMC neurons to the PVT is via the mu opioid receptor and that this results in inhibition. Post-meal consumption of high-sugar food brought about an increase in the activity of POMC neuron terminals in the PVT while consumption of a standard chow diet post-meal did not, which suggests that the high-sugar diet brings about an increase in the activity of POMC neurons that project to the PVT. Activation of the circuit did affect general flavor preference conditions but not when mu-opioid receptor blockers were present. However, the circuit’s activation did not affect conditioned place preference, suggesting that the circuit is dietary preference specific. Inhibition of the circuit changed the length of time for a mouse to start showing a preference for a high-sugar diet. Fiber photometry data showed that, while both brought about an increase, high-sugar diet cues increased POMC to PVT activity more than high-fat diet cues. Additionally, fMRI data showed that activity level in the human PVT is decreased by sugar consumption. This suggests that a similar circuit may exist in humans. 

What's the impact?

This study found that hypothalamic POMC neurons projecting via opioid signaling to the PVT are involved in sugar consumption in fed states. Importantly, it sheds light on a brain circuit that may be involved in compulsive or binge eating. According to the World Health Organization obesity is a global epidemic that is a risk factor for many health conditions such as diabetes mellitus, cardiovascular disease, and stroke. These findings could help researchers develop potential therapeutics for obesity. 

Access the original publication here