心理科学进展 ›› 2026, Vol. 34 ›› Issue (9): 1500-1513.doi: 10.3724/SP.J.1042.2026.1500 cstr: 32111.14.2026.1500
侯鑫
收稿日期:2026-04-05
出版日期:2026-09-15
发布日期:2026-07-20
基金资助:HOU Xin
Received:2026-04-05
Online:2026-09-15
Published:2026-07-20
摘要: 大尺度脑网络协同模式的系统性偏离是工作记忆老化的核心神经机制, 工作记忆的重度衰退严重损害老年个体生活, 并给社会带来沉重负担。现有神经调控技术或局限于单点干预, 或简化网络反馈信号, 均难以反映多节点动态协同的网络全貌; 且受设备安全性与便携性等因素制约, 难以满足老年群体长期干预需求。本研究拟构建一套网络连接靶向的近红外神经反馈干预框架: 首先, 采用多变量模式分析识别能显著区分重度衰退与功能保持群体的特异性网络连接模式, 用以网络靶点设定; 其次, 以保留拓扑属性的个体实时网络连接模式与目标模式的空间相似性为反馈指标, 构建闭环神经反馈系统, 引导个体自主调控, 并系统评估其干预效果; 最后, 从多维度全面考察认知迁移效应的表现特征, 并通过中介效应分析验证“网络直接驱动”与“工作记忆中介”的潜在双路径迁移机制。本研究有望为改善工作记忆重度衰退提供新型干预方案, 并为网络靶向调控策略提供理论框架与方法借鉴。
中图分类号:
侯鑫. (2026). 网络连接靶向的近红外神经反馈对工作记忆重度衰退的干预效果与机制. 心理科学进展 , 34(9), 1500-1513.
HOU Xin. (2026). The effects and mechanisms of connectome-targeted fNIRS neurofeedback intervention for severe working memory decline. Advances in Psychological Science, 34(9), 1500-1513.
| [1] Baddeley, A. (2012). Working memory: Theories, models, and controversies.Annual Review of Psychology, 63(1), 1-29. [2] Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles.Trends in Cognitive Sciences, 14(6), 277-290. [3] Brunoni, A. R., & Vanderhasselt, M.-A. (2014). Working memory improvement with non-invasive brain stimulation of the dorsolateral prefrontal cortex: A systematic review and meta-analysis.Brain and Cognition, 86, 1-9. [4] Chai W. J., Abd Hamid A. I., & Abdullah J. M. (2018). Working memory from the psychological and neurosciences perspectives: A review.Frontiers in Psychology, 9, 401. [5] Charroud C., Le Bars E., Deverdun J., Steffener J., Molino F., Abdennour M., … Menjot de Champfleur, N. (2016). Working memory performance is related to intrinsic resting state functional connectivity changes in community- dwelling elderly cohort.Neurobiology of Learning and Memory, 132, 57-66. [6] Chein J. M., Moore A. B., & Conway, A. R. A. (2011). Domain-general mechanisms of complex working memory span.NeuroImage, 54(1), 550-559. [7] Chen, X., & Sui, L. (2023). Alpha band neurofeedback training based on a portable device improves working memory performance of young people.Biomedical Signal Processing and Control, 80, 104308. [8] Christophel T. B., Klink P. C., Spitzer B., Roelfsema P. R., & Haynes J.-D. (2017). The distributed nature of working memory.Trends in Cognitive Sciences, 21(2), 111-124. [9] D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory.Annual Review of Psychology, 66(1), 115-142. [10] Dima D., Jogia J., & Frangou S. (2014). Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network.Human Brain Mapping, 35(7), 3025-3035. [11] Ehlis A.-C., Barth B., Hudak J., Storchak H., Weber L., Kimmig A.-C. S., … Fallgatter A. J. (2018). Near-infrared spectroscopy as a new tool for neurofeedback training: Applications in psychiatry and methodological considerations: NIRS neurofeedback in psychiatry.Japanese Psychological Research, 60(4), 225-241. [12] Emberson L. L., Zinszer B. D., Raizada R. D. S., & Aslin R. N. (2017). Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS.PLOS ONE, 12(4), e0172500. [13] Gazzaley A., Rissman J., & D’esposito M. (2004). Functional connectivity during working memory maintenance.Cognitive, Affective, & Behavioral Neuroscience, 4(4), 580-599. [14] Grady, C. (2012). The cognitive neuroscience of ageing.Nature Reviews Neuroscience, 13(7), 491-505. [15] Grover S., Wen W., Viswanathan V., Gill C. T., & Reinhart, R. M. G. (2022). Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation.Nature Neuroscience, 25(9), 1237-1246. [16] Gutchess, A. (2014). Plasticity of the aging brain: New directions in cognitive neuroscience.Science, 346(6209), 579-582. [17] Hara T., Shanmugalingam A., McIntyre A., & Burhan A. M. (2021). The effect of non-invasive brain stimulation (NIBS) on attention and memory function in stroke rehabilitation patients: A systematic review and meta-analysis.Diagnostics, 11(2), 227. [18] Hou X., Xiao X., Gong Y., Jiang Y., Sun P., Li J., … Zhu C. (2021). Functional near-infrared spectroscopy neurofeedback of cortical target enhances hippocampal activation and memory performance.Neuroscience Bulletin, 37(8), 1251-1255. [19] Hou X., Xiao X., Gong Y., Li Z., Chen A., & Zhu C. (2021). Functional near-infrared spectroscopy neurofeedback enhances human spatial memory.Frontiers in Human Neuroscience, 15, 681193. [20] Hsueh J., Chen T., Chen J., & Shaw F. (2016). Neurofeedback training of EEG alpha rhythm enhances episodic and working memory.Human Brain Mapping, 37(7), 2662-2675. [21] Hudak J., Blume F., Dresler T., Haeussinger F. B., Renner T. J., Fallgatter A. J., … Ehlis A.-C. (2017). Near-infrared spectroscopy-based frontal lobe neurofeedback integrated in virtual reality modulates brain and behavior in highly impulsive adults.Frontiers in Human Neuroscience, 11, 425-437. [22] Jimura K., Chushak M. S., Westbrook A., & Braver T. S. (2018). Intertemporal decision-making involves prefrontal control mechanisms associated with working memory.Cerebral Cortex, 28(4), 1105-1116. [23] Johnson M. D., Lim H. H., Netoff T. I., Connolly A. T., Johnson N., Roy A., … He B. (2013). Neuromodulation for brain disorders: Challenges and opportunities.IEEE Transactions on Biomedical Engineering, 60(3), 610-624. [24] Kim C., Kroger J. K., Calhoun V. D., & Clark V. P. (2015). The role of the frontopolar cortex in manipulation of integrated information in working memory.Neuroscience Letters, 595, 25-29. [25] Kohl S. H., Mehler D. M. A., Lührs M., Thibault R. T., Konrad K., & Sorger B. (2020). The potential of functional near-infrared spectroscopy-based neurofeedback-a systematic review and recommendations for best practice.Frontiers in Neuroscience, 14, 594. [26] Ma L., Steinberg J. L., Hasan K. M., Narayana P. A., Kramer L. A., & Moeller F. G. (2012). Working memory load modulation of parieto-frontal connections: Evidence from dynamic causal modeling.Human Brain Mapping, 33(8), 1850-1867. [27] Misaki M., Tsuchiyagaito A., Guinjoan S. M., Rohan M. L., & Paulus M. P. (2024). Whole-brain mechanism of neurofeedback therapy: Predictive modeling of neurofeedback outcomes on repetitive negative thinking in depression.Translational Psychiatry, 14(1), 354-362. [28] Nissim N. R., O’Shea A., Indahlastari A., Telles R., Richards L., Porges E., … Woods A. J. (2019). Effects of in-Scanner bilateral frontal tDCS on functional connectivity of the working memory network in older adults.Frontiers in Aging Neuroscience, 11, 51. [29] Park, H.-J., & Friston, K. (2013). Structural and functional brain networks: From connections to cognition.Science, 342(6158), 1238411. [30] Preacher K. J., Rucker D. D., MacCallum R. C., & Nicewander W. A. (2005). Use of the extreme groups approach: A critical reexamination and new recommendations.Psychological Methods, 10(2), 178-192. [31] Reuter-Lorenz, P., & Sylvester, C.-Y. (2005). The cognitive neuroscience of working memory and aging. In R. Cabeza, L. Nyberg, & D. Park (Eds.), Cognitive neuroscience of aging: Linking cognitive and cerebral aging (pp. 186-218). Oxford University Press. [32] Rieck J. R., Rodrigue K. M., Boylan M. A., & Kennedy K. M. (2017). Age-related reduction of BOLD modulation to cognitive difficulty predicts poorer task accuracy and poorer fluid reasoning ability.NeuroImage, 147, 262-271. [33] Scheinost D., Hsu T. W., Avery E. W., Hampson M., Constable R. T., Chun M. M., & Rosenberg M. D. (2020). Connectome-based neurofeedback: A pilot study to improve sustained attention.NeuroImage, 212, 116684. [34] Shen X., Finn E. S., Scheinost D., Rosenberg M. D., Chun M. M., Papademetris X., & Constable R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity.Nature Protocols, 12(3), 506-518. [35] Sherwood M. S., Kane J. H., Weisend M. P., & Parker J. G. (2016). Enhanced control of dorsolateral prefrontal cortex neurophysiology with real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training and working memory practice.NeuroImage, 124, 214-223. [36] Sitaram R., Ros T., Stoeckel L., Haller S., Scharnowski F., Lewis-Peacock J., … Sulzer J. (2017). Closed-loop brain training: The science of neurofeedback.Nature Reviews Neuroscience, 18(2), 86-100. [37] Smith, G. E. (2016). Healthy cognitive aging and dementia prevention.American Psychologist, 71(4), 268-275. [38] Son C., Hong J., & Park J.-H. (2022). Effects of functional near-infrared spectroscopy-based neuro-feedback training on cognitive function: Systematic review and meta-analysis.International Journal of Gerontology, 16(4), 316-321. [39] Soreq E., Leech R., & Hampshire A. (2019). Dynamic network coding of working-memory domains and working- memory processes.Nature Communications, 10(1), 936. [40] Taylor J. E., Yamada T., Kawashima T., Kobayashi Y., Yoshihara Y., Miyata J., … Motegi T. (2022). Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback.Scientific Reports, 12(1), 2581. [41] Thibault R. T., MacPherson A., Lifshitz M., Roth R. R., & Raz A. (2018). Neurofeedback with fMRI: A critical systematic review.NeuroImage, 172, 786-807. [42] Westwood S. J., Aggensteiner P.-M., Kaiser A., Nagy P., Donno F., Merkl D., … Sonuga-Barke, E. J. S. (2025). Neurofeedback for attention-deficit/hyperactivity disorder: A systematic review and meta-analysis.JAMA Psychiatry, 82(2), 118-129. [43] Xia M., Xu P., Yang Y., Jiang W., Wang Z., Gu X., … Wang D. (2021). Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants.IEEE Access, 9, 62316-62331. [44] Yang Y., Chen Y., Sang F., Zhao S., Wang J., Li X., … Zhang Z. (2022). Successful or pathological cognitive aging? Converging into a “frontal preservation, temporal impairment (FPTI)” hypothesis.Science Bulletin, 67(22), 2285-2290. [45] Yarkoni T., Poldrack R. A., Nichols T. E., Van Essen D. C., & Wager T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data.Nature Methods, 8(8), 665-670. [46] Yeh W.-H., Ju Y.-J., Liu Y.-T., & Wang T.-Y. (2022). Systematic review and meta-analysis on the effects of neurofeedback training of theta activity on working memory and episodic memory in healthy population.International Journal of Environmental Research and Public Health, 19(17), 11037. [47] Zając-Lamparska L., Zabielska-Mendyk E., Zapała D., & Augustynowicz P. (2024). Compensatory brain activity pattern is not present in older adults during the n-back task performance—findings based on EEG frequency analysis.Frontiers in Psychology, 15, 1371035. [48] Zhao Z., Yao S., Li K., Sindermann C., Zhou F., Zhao W., … Becker B. (2019). Real-time functional connectivity- informed neurofeedback of amygdala-frontal pathways reduces anxiety.Psychotherapy and Psychosomatics, 88(1), 5-15. |
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