近日，美国消费者价格指数同比增速居高不下，已连续6个月高于8%。    Brain-reading AI model reveals how different brain regions are linked to cognitive functions. Photo: Courtesy of Lu Han
Chinese scientists have developed a “brain-reading” AI model that could help predict the risk of depression among adolescents up to four years in advance by analyzing how humans respond to facial expressions, a technology expected to inspire future development of embodied intelligent humanoids capable of perceiving human emotion and thoughts through nuanced facial cues. 
WHO data show that around 332 million people worldwide have depression, about one-third of whom have treatment-resistant forms of the condition. In China, an estimated 95 million people suffer from depression, National Business Daily reported, citing statistics from the China Mental Health Survey. 
Using data from a population-based longitudinal adolescent cohort recruited across several European countries, the research team led by Lu Han, assistant professor at the School of Artificial Intelligence, Shenzhen University, has built an AI model that predicted which 19-year-olds were more likely to develop depression at the age of 23. The predictions were backed up by an independent clinical cohort of individuals with depression. The team’s paper was published in the journal Science Advances this month.
According to Lu, the study used brain scans taken at age 19 to predict depression-related symptoms at age 23. The study focuses on adolescence because the transition from adolescence to early adulthood is a key developmental period when depressive symptoms can increase rapidly. The earlier risks are identified, the greater the opportunity for prevention, Lu told the Global Times on Monday, adding that the findings need to be further validated in middle-aged and older adults and across different ethnic groups in future research. 
In this study, the researchers analyzed data from adolescents in the IMAGEN, a population-based longitudinal cohort recruited across several European countries. At age 19, participants underwent an fMRI emotional-face task, and their emotional symptoms were assessed using standardized questionnaires. Genetic data obtained from blood samples were also analyzed, and participants were followed up at age 23. The researchers examined whether neural representations of angry faces at age 19 were associated with emotional symptoms and could predict elevated emotional symptoms four years later.
According to Lu, people without depression can more easily distinguish emotional changes based on others’ facial expressions and respond accordingly – for example, responding with friendliness to a smiling expression. But people with depression cannot do this, and are more likely to assume people are angry with them. 
A brain-aligned deep-learning model developed by Lu’s team suggested that those participants whose brains were less able to distinguish between different facial emotions and tended to perceive others as angry were more likely to develop symptoms of depression and anxiety in adulthood. 
The hypothesis that adolescents at risk of depression may respond differently to other people’s facial expressions than those without such risk based on the negative information processing bias long observed in depression research: people at risk of depression are more likely to notice, interpret, or remember negative social information, Lu said. 
The researchers focused on angry facial expressions because they signal social threat and rejection, which are closely linked to interpersonal difficulties and negativity bias associated with depression. They hope to further understand how this bias develops within the visual system. 
Building on this, they created a deep learning model, which mimics how the brain processes visual information, to predict how the brain encodes abstract emotional concepts such as anger.
They found that 19-year-olds whose response to facial expressions was skewed in favour of negative emotions or memories were the most likely to develop some form of depression.
Based on these findings, Lu’s team then developed a marker that can identify possible warning signs. 
According to Lu, the study found that the computational biomarker was linked to the depression-related variant rs11123030 and polygenic risk for depression, suggesting that genetic susceptibility may affect emotional perception. It also provided predictive information beyond family stress and socioeconomic factors, complementing rather than replacing environmental risk factors. Therefore, depression is neither purely genetic nor purely psychological, but a complex mental disorder arising from the interplay of genetic susceptibility, brain development, emotional and cognitive processes, and life experiences. 
According to Lu, the study is also expected to advance AI by aligning deep neural networks with human brain activity and using parameter perturbations to probe neural mechanisms, allowing models to both predict and explain how biases may arise. 
The findings suggest that future affective computing and embodied AI should go beyond simply labeling facial expressions, incorporating visual details while preventing prior assumptions from overriding real-time sensory input, Lu said, adding that the findings could provide valuable insights for developing more interpretable robotic perception systems that more closely emulate the way humans process emotions.
。

二 | 分析认为，美国持续面临通胀压力，而美联储为控制物价连续激进加息，将对美国股市乃至整体经济造成进一步冲击。

三 | 美国住房和食品价格持续上行，分析认为这不仅将对消费者心理产生影响，也将影响美联储加息路径。目前市场普遍预期，美联储将不惜以经济增长放缓为代价，通过激进加息抑制需求，从而达到控制物价的目的。

四 | 密苏里大学助理教授 费利佩·席尔瓦：截至目前，市场基本已经更新了关于下次加息的预期，我认为这已经明确了，市场预期下次将会加息75个基点，这是到目前为止最有可能出现的情况。美联储主席鲍威尔此前在全球央行年会上表示，美联储当前的首要任务是将通胀降至2%的目标水平，历史经验表明不应过早放松货币政策。

五 | 这一强硬表态，被市场解读为只要看不到通胀数据明显好转，美联储就会坚持激进加息的货币政策，而这必将对美国经济造成冲击。密苏里大学助理教授 费利佩·席尔瓦：如果利率上升，你的资金成本就会随着时间的推移上升，你的消费决定（也会受到影响），因此消费者信心就会下滑，这是你能想到的最直接的影响。

六 | 当然企业也会调整它们的投资和支出决定，它们将减少投资，减少工作岗位，而这对经济有类似的整体影响。简而言之，这就是货币政策的传导方式。《华尔街日报》日前分析称，尽管美国目前的就业数据尚可，但如果有人认为美联储遏制通胀的举措不会导致利率上升到足以妨碍就业的程度，那将是错误的。而包括美国前财长萨默斯在内的一些经济学家甚至预计，降低通胀的代价，将是美国失业率超过6%。

Current article:http://r391.xiongpeiduanlouduoennongleng.cyou/wpug/p0lrnv.html

Published on:11:31:17