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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

A |     在全球航空兵器竞争愈发激烈的当下，印度空军的未来规划引起广泛关注。然而，最近一次关于印度战斗机发展进程的访谈节目，却让这个曾经雄心壮志的军事梦显得有些不堪。    

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.
。刚刚退休的印度空军副参谋长纳格什·卡普尔在新德里电视台（NDTV）的节目中痛批印度斯坦航空公司（HAL），称其研发的“光辉”MK2战斗机毫无用处，不仅真相直击，也引发了关于中国和印度空军力量差距的深思。    卡普尔在节目中指出，预计到2035年，“光辉”MK2才会开始量产，而此时中国可能已经装备了第六代战斗机。根据更为乐观的估算，未来几年内，中国空军的歼-20战斗机数量将从现在的500架飙升至1000架。而对比之下，印度空军所面临的压力及中印空中力量的差距，似乎远没有得到应有的重视。        这种差距不仅体现在数量上，更在于技术的领先。以“光辉”系列战斗机的研发为例，从上世纪80年代启动的LCA计划就是个惨痛的教训。到如今“光辉”MK1战斗机的交付问题频出，正是对这一历史教训的生动映射。    虽然“光辉”MK2是被寄予厚望的一款战斗机，但卡普尔的直言无疑揭示了它的短板。

B | 这款被视为“光辉”系列最终改进版的战斗机，其研发与试飞周期依然漫长且不确定。即使在2026年首飞，形成战斗力也要到2038年，整个过程充满了变量。此外，MK2的核心技术，包括F414发动机的采购以及相关系统的集成，一直徘徊在延迟与不确定性之中。    HAL屡次延迟交付的背后，反映的是印度国防工业的低效与无能。即便经历了多次的计划调整和改进，最终的成果却依旧让人失望。印度空军希望通过“光辉”MK1A来提升自身实力，但迄今为止，HAL的交付时间已经一再推迟，甚至被迫接收“半成品”，这在任何军队中都几乎是不可想象的噩梦。        万一“光辉”MK2真能按期研发完成，然而随着美国GE公司宣布将F414发动机大幅涨价，这一切似乎又添变数。当前，印度正遭遇来自美国的技术与价格的双重“痛宰”。如果说“光辉”MK2只是面临资金问题，那么未来的项目如TEDBF和AMCA则更是苦不堪言。由于在发动机选项上的局限，印度空军不得不开始寻找替代方案，然而这些方案大多在技术上没有保障，甚至连研发合同都未能落实。    例如，印度国内的“卡佛里”发动机开发延迟，以至于这一计划几乎成为了笑柄；而与法国和英国企业的合作，则面临着研发技术转让的博弈，恐怕不久的将来也难以成功落地。    在这种背景下，印度空军要想重新建立自己的强大形象，必须真正面对自身存在的深层次问题。如何打破HAL的研发瓶颈与管理弊端，如何确保战斗机的性价比以及持续的技术更新，将是摆在决策者面前的重要课题。    而对于中国而言，这一情况自然形成了一种隐秘的优势。在这一问题的背后，体现出的是两国之间在国防工业独立自主、技术积累和市场运作等方面的深刻差异。

C |         印度空军的未来，是否会在“光辉”MK2的阴影下步入困境？或许这个问题并没有简单的答案。但是，在全球科技日新月异的今天，任何国家无法在短时期内逆袭他国的技术优势，唯有正视现实，脚踏实地的推进国防建设，才能真正实现“光辉”的理想。    作为世界上最大的发展中国家，印度需要反思的不仅仅是战斗机的数量，更是整体国防工业能力的提升。

D | 只有当技术与战略同频共振，印度空军才能迎来真正的曙光。返回，查看更多。

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Published on:02:08:18