Li Yumeng, Zhang Xinyue, Sun Jiaqing, Zhang Junying, Zhu Aiqin, Li Xin, Zhang Zhanjun
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No.19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China, 86 15810127521.
Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, China.
J Med Internet Res. 2025 Jul 22;27:e73360. doi: 10.2196/73360.
The rapid integration of information technology into daily life has exacerbated the digital divide (DD), particularly among older adults, who often face barriers to technology adoption. Although prior research has linked technology use to cognitive benefits, the long-term neurostructural and cognitive consequences of the DD remain poorly understood.
The aim of this study is to use large-scale neuroimaging data to examine how the DD affects long-term brain structure and cognitive aging in older adults. It specifically investigates (1) structural and cognitive differences between older adults with and without DD engagement, (2) predictive relationships between group-distinctive brain regions and cognitive outcomes, and (3) longitudinal impacts of DD exposure on accelerated aging trajectories of neural substrates and cognitive functions.
The study included 1280 community-dwelling older adults (aged 65-90 y) who completed comprehensive cognitive assessments and structural magnetic resonance imaging scans at baseline. Longitudinal data were available for 689 participants (mean follow-up 3.2 y). Participants were classified into the DD (n=640) and overcoming DD (n=640) groups using rigorous propensity score matching to control for age, education, gender, and baseline health conditions. A computational framework using the searchlight technique and cross-validation classification model investigated group differences in structural features and cognitive representation. The aging rate of each voxel's structural feature was calculated to explore the long-term influence of the DD.
The DD group showed significant deficits in executive function (t=4.75; P<.001; Cohen d=0.38) and processing speed (t=4.62; P<.001; Cohen d=0.37) compared to the overcoming DD group. Reduced gray matter volume in the DD group spanned the fusiform gyrus, hippocampus, parahippocampal gyrus, and superior temporal sulcus (false discovery rate-corrected P<.05). The computational framework identified the key structural substrates related to executive function and processing speed, excluding the ventro-orbitofrontal lobe (classification accuracy <0.6). Longitudinal findings highlighted the long-term impact of the DD. The DD group exhibited faster gray matter volume decline in the middle frontal gyrus (t=3.95 for the peak voxel in this cluster, false discovery rate-corrected P<.05), which mediated 17% of episodic memory decline (P=.02).
Older adults who overcome the DD demonstrate preserved gray matter structure and slower cognitive decline, particularly in frontotemporal regions critical for executive function. Our findings underscore that mobile digital interventions should be explored as potential cognitive decline prevention strategies.
信息技术迅速融入日常生活加剧了数字鸿沟（DD），尤其是在老年人中，他们在采用技术方面常常面临障碍。尽管先前的研究已将技术使用与认知益处联系起来，但数字鸿沟对长期神经结构和认知的影响仍知之甚少。
本研究旨在使用大规模神经影像数据来研究数字鸿沟如何影响老年人的长期脑结构和认知衰老。具体调查内容包括：（1）有和没有数字鸿沟困扰的老年人在结构和认知方面的差异；（2）不同组别的独特脑区与认知结果之间的预测关系；（3）数字鸿沟暴露对神经基质和认知功能加速衰老轨迹的纵向影响。
该研究纳入了1280名社区居住的老年人（年龄在65 - 90岁之间），他们在基线时完成了全面的认知评估和结构磁共振成像扫描。689名参与者有纵向数据（平均随访3.2年）。通过严格的倾向得分匹配，将参与者分为数字鸿沟组（n = 640）和克服数字鸿沟组（n = 640），以控制年龄、教育程度、性别和基线健康状况。使用探照灯技术和交叉验证分类模型的计算框架研究结构特征和认知表征方面的组间差异。计算每个体素结构特征的衰老率，以探索数字鸿沟的长期影响。
与克服数字鸿沟组相比，数字鸿沟组在执行功能（t = 4.75；P <.001；Cohen d = 0.38）和处理速度（t = 4.62；P <.001；Cohen d = 0.37）方面存在显著缺陷。数字鸿沟组灰质体积减少的区域包括梭状回、海马体、海马旁回和颞上沟（错误发现率校正后P <.05）。计算框架确定了与执行功能和处理速度相关的关键结构基质，不包括腹侧眶额叶（分类准确率<0.6）。纵向研究结果突出了数字鸿沟的长期影响。数字鸿沟组在额中回的灰质体积下降更快（该簇中的峰值体素t = 3.95，错误发现率校正后P <.05），这介导了情景记忆下降的17%（P =.02）。
克服数字鸿沟的老年人表现出保留的灰质结构和较慢的认知衰退，特别是在对执行功能至关重要的额颞区域。我们的研究结果强调，应探索移动数字干预措施作为预防认知衰退的潜在策略。
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