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Chinese scientists develop ‘brain-reading’ AI model to help predict depression risk, may inspire future emotional-perception humanoids_我的网站

一 |     今年第13号台风“白海豚”于昨天（9日）17时30分前后在玉环坎门街道登陆（强台风，42米/秒，945百帕），9日18时40分前后在乐清翁垟街道二次登陆（台风级，38米/秒，950百帕）。

二 |     

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.
。9日22时减弱为强热带风暴，今天（10日）凌晨01时减弱为热带风暴。浙江省气象服务中心说，“白海豚”二次登陆后受地形影响向西偏南移动，穿过温州城区、瑞安、平阳、泰顺，今天02时进入福建宁德寿宁，05时再进入浙江庆元境内，08时位于龙泉（热带风暴，23米/秒，985百帕）。

三 | “白海豚”风雨强度强、影响范围广、局地降水大风极端。

四 | 受“白海豚”影响，我省沿海和浙北地区出现大范围大暴雨，台州、宁波中南部、温州北部、丽水东部特大暴雨。7日20时至10日07时，全省面雨量173毫米，已超越“利奇马”、“巴威”等台风；地市较大的有台州市356毫米、宁波市272毫米、温州市208毫米、舟山市201毫米、嘉兴市173毫米；有78个县（市、区）超过100毫米，其中24个超过200毫米，15个超过300毫米，最大三门县417毫米。单站累计雨量最大为三门县亭旁镇枧头村815毫米，单站最大小时雨强为乐清乐成城北景北103毫米。沿海最大风力普遍达12～14级部分15～17级，内陆地区部分10～11级，最大为玉环鲜迭60.8米/秒（17级）。8月7日20时至10日07时实况雨量预计“白海豚”将以每小时20公里左右的速度向西北方向移动，强度逐渐减弱，于今天中午前后移出我省。

五 | 降雨预报：今天沿海和浙北地区大部有大雨暴雨局部大暴雨，其他地区中到大雨局部暴雨。明天浙北地区部分中到大雨，局部暴雨，个别大暴雨，其他地区局部大到暴雨。

六 | 大风预报：预计浙中北沿海海面今天最大阵风仍有10～12级，夜里逐渐减弱至9～11级，明天下午继续减弱至8级； 浙南沿海海面今天8～10级，夜里起逐渐减弱至7～8级；内陆部分地区今天最大阵风7～9级，夜里逐渐减弱至6～8级局部9级。重点关注：“白海豚”已给我省带来严重风雨影响，今天明天我省部分地区仍有强降水，暴雨致灾风险极高，需继续加强对山洪、地质灾害、中小河流洪水和城市积涝等次生灾害的防范。另外，今天沿海和部分内陆地区仍有大风，请继续加强防范。“白海豚”未来24小时路径概率预报10日08时-12日08时累计雨量预报图杭州市气象台预计，今天白天全市阴有中阵雨，淳安西部南部、临安西部、建德南部局部大到暴雨，今天夜里到明天白天多云到阴有阵雨，局部大雨，明天夜里全市多云到阴有分散性阵雨；后天阴有阵雨，局部大雨。今天白天最大偏东风6~7级，高山区和江湖水面7~9级，傍晚起逐渐减弱到5~6级，高山区和江湖水面6~8级。目前，临安区发布台风黄色预警信号，其他区县均维持台风蓝色及相关预警信号。上铁集团：杭深铁路、杭台高铁、杭温高铁、沪昆高铁等多条线路列车陆续恢复开行据上铁集团消息，根据台风“白海豚”最新路径变化和影响情况，铁路部门扩大旅客列车恢复开行范围，计划8月10日8时起对途经杭深铁路杭州东至苍南段、杭衢高铁、合福高铁，8时30分起对途经金温铁路，9时起对途经杭台高铁（含温玉段）、杭温高铁、杭昌高铁、宣绩高铁、池黄高铁，10时起对途经沪昆高铁杭州东至江山段等线路旅客列车陆续恢复开行，更好满足旅客出行需求。下一步，铁路部门将密切关注台风路径变化，根据风速、雨量和灾害影响程度等实际，适时采取限速运行、加开列车、恢复开行等措施，动态调整列车开行方案，确保旅客安全出行需要。广大旅客可关注车站公告或登录12306网站（含APP），及时了解列车开行信息，妥善安排行程。国家防减救灾委、应急管理部对浙江启动国家四级救灾应急响应8月10日，国家防减救灾委、应急管理部针对第13号台风“白海豚”对浙江省造成的严重影响，启动国家四级救灾应急响应，派出救灾工作组赴灾区实地查看灾情，指导协助地方做好灾情统计核查、群众安置救助、基本生活保障等救灾救助工作。

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Published on:06:27:05