Our staff editors continue to share brilliant, thoughtful, and meaningful topics and articles in this recommended series.
This week, we would like to share the latest articles about the effects of diet and sleep on the nervous system.
Title: A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition
Authors: MeenakshiKhosla, N. Apurva Ratan Murty, Nancy Kanwisher
Type: Research article
Prior work has identified cortical regions selectively responsive to specific categories of visual stimuli. However, this hypothesis-driven work cannot reveal how prominent these category selectivities are in the overall functional organization of the visual cortex, or what others might exist that scientists have not thought to look for. Furthermore, standard voxel-wise tests cannot detect distinct neural selectivities that coexist within voxels. To overcome these limitations, we used data-driven voxel decomposition methods to identify the main components underlying fMRI responses to thousands of complex photographic images. Our hypothesis-neutral analysis rediscovered components selective for faces, places, bodies, and words, validating our method and showing that these selectivities are dominant features of the ventral visual pathway. The analysis also revealed an unexpected component with a distinct anatomical distribution that responded highly selectively to images of food. Alternative accounts based on low- to mid-level visual features, such as color, shape, or texture, failed to account for the food selectivity of this component. High-throughput testing and control experiments with matched stimuli on a highly accurate computational model of this component confirm its selectivity for food. We registered our methods and hypotheses before replicating them on held-out participants and in a novel dataset. These findings demonstrate the power of data-driven methods and show that the dominant neural responses of the ventral visual pathway include not only selectivities for faces, scenes, bodies, and words but also the visually heterogeneous category of food, thus constraining accounts of when and why functional specialization arises in the cortex.
Access this article: https://doi.org/10.1016/j.cub.2022.08.009
Title: Exploring the causal relationship between dietary macronutrients and neurodegenerative diseases: a bi-directional two-sample Mendelian randomization study
Authors: Tao Wei1, Zheng Guo, Zhibin Wang, Xingang Li, Yulu Zheng, Haifeng Hou, Yi Tang
Type: Original Article
The associations between dietary macronutrient intake and neurodegenerative diseases (NDDs) have been widely reported; however, the causal effect remains unclear. The current study aimed to estimate the causal relationship between dietary macronutrient intake (i.e., carbohydrate, fat, and protein) and NDDs [e.g., Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS)].
Mendelian randomization (MR) was applied to evaluate the causal relationship between dietary macronutrient intake and NDDs. We used the single-nucleotide polymorphisms strongly associated (P < 5 × 10-8) with the exposures from the genome-wide association studies as instrumental variables. Inverse-variance weighted, MR-Egger, weighted median, and the MR pleiotropy residual sum and outlier were used to verify the MR assumptions.
Genetically predicted higher carbohydrate intake was associated with an increased risk of ALS [odds ratio (OR), 2.741, 95% confidence interval (CI): 1.419-5.293, P = 0.003). Vulnerability to PD was negatively associated with the relative intake of fat (OR, 0.976, 95%CI: 0.959-0.994, P = 0.012) and protein (OR, 0.987, 95%CI: 0.975-1.000, P = 0.042). The study also identified the causal influence of AD on dietary carbohydrate intake (OR, 1.022, 95%CI: 1.011-1.034, P = 0.001).
We found solid evidence supporting the idea that a higher carbohydrate proportion causally increases ALS risk. Genetically predicted higher AD risk is causally associated with increased dietary carbohydrate intake. Vulnerability to PD may have a causal relationship with a decrease in the dietary intake of protein and fat.
Access this article: http://dx.doi.org/10.20517/and.2022.12
Title: A circuit from lateral septum neurotensin neurons to tuberal nucleus controls hedonic feeding
Authors: Zijun Chen, Gaowei Chen, Jiafeng Zhong, Shaolei Jiang, Shishi Lai, Hua Xu, Xiaofei Deng, Fengling Li, Shanshan Lu, Kuikui Zhou, Changlin Li, Zhongdong Liu, Xu Zhang, Yingjie Zhu
Feeding behavior is regulated by both the homeostatic needs of the body and hedonic values of the food. Easy access to palatable energy-dense foods and the consequent obesity epidemic stress the urgent need for a better understanding of neural circuits that regulate hedonic feeding. Here, we report that neurotensin-positive neurons in the lateral septum (LSNts) play a crucial role in regulating hedonic feeding. Silencing LSNts specifically promotes feeding of palatable food, whereas activation of LSNts suppresses overall feeding. LSNts neurons project to the tuberal nucleus (TU) via GABA signaling to regulate hedonic feeding, while the neurotensin signal from LSNts→the supramammillary nucleus (SUM) is sufficient to suppress overall feeding. In vivo calcium imaging and optogenetic manipulation reveal two populations of LSNts neurons that are activated and inhibited during feeding, which contribute to food seeking and consumption, respectively. Chronic activation of LSNts or LSNts→TU is sufficient to reduce high-fat diet-induced obesity. Our findings suggest that LSNts→TU is a key pathway in regulating hedonic feeding.
Access this article: https://doi.org/10.1038/s41380-022-01742-0
Title: Memory-enhancing properties of sleep depend on the oscillatory amplitude of norepinephrine
Authors: Celia Kjaerby, Mie Andersen, Natalie Hauglund, Verena Untiet, Camilla Dall, Björn Sigurdsson, Fengfei Ding, Jiesi Feng, Yulong Li, Pia Weikop, Hajime Hirase, Maiken Nedergaard
Sleep has a complex micro-architecture, encompassing micro-arousals, sleep spindles and transitions between sleep stages. Fragmented sleep impairs memory consolidation, whereas spindle-rich and delta-rich non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep promote it. However, the relationship between micro-arousals and memory-promoting aspects of sleep remains unclear. In this study, we used fiber photometry in mice to examine how release of the arousal mediator norepinephrine (NE) shapes sleep micro-architecture. Here we show that micro-arousals are generated in a periodic pattern during NREM sleep, riding on the peak of locus-coeruleus-generated infraslow oscillations of extracellular NE, whereas descending phases of NE oscillations drive spindles. The amplitude of NE oscillations is crucial for shaping sleep micro-architecture related to memory performance: prolonged descent of NE promotes spindle-enriched intermediate state and REM sleep but also associates with awakenings, whereas shorter NE descents uphold NREM sleep and micro-arousals. Thus, the NE oscillatory amplitude may be a target for improving sleep in sleep disorders.
Access this article: https://doi.org/10.1038/s41593-022-01102-9
Title: Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals
Authors: Yuzhe Yang, Yuan Yuan, Guo Zhang, Hao Wang, Ying-Cong Chen, Yingcheng Liu, Christopher G. Tarolli, Daniel Crepeau, Jan Bukartyk, Mithri R. Junna, Aleksandar Videnovic, Terry D. Ellis, Melissa C. Lipford, Ray Dorsey & Dina Katabi
There are currently no effective biomarkers for diagnosing Parkinson’s disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (R = 0.94, P = 3.6 × 10–25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person’s body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.
Access this article: https://doi.org/10.1038/s41591-022-01932-x