《中国康复理论与实践》 ›› 2026, Vol. 32 ›› Issue (5): 522-533.doi: 10.3969/j.issn.1006-9771.2026.05.003
韩天星1, 刘昊天1, 徐子淇1, 彭晓涵1, 王杨2, 曹丹娜2(
)
收稿日期:2025-11-19
出版日期:2026-05-25
发布日期:2026-05-26
通讯作者:
曹丹娜,E-mail:hljanna@126.com作者简介:韩天星(2001-),男,汉族,辽宁辽阳市人,硕士研究生,主要研究方向:中枢神经系统的临床与医学影像学研究。基金资助:
HAN Tianxing1, LIU Haotian1, XU Ziqi1, PENG Xiaohan1, WANG Yang2, CAO Danna2(
)
Received:2025-11-19
Published:2026-05-25
Online:2026-05-26
Contact:
CAO Danna, E-mail: hljanna@126.comSupported by:摘要:
目的 探讨静息态功能磁共振成像(rs-fMRI)对阿尔茨海默病(AD)轻度认知障碍(MCI)期影像学诊断价值,探索在默认网络(DMN)与内侧颞叶区(MTL)脑功能连接变化特征。
方法 系统检索2020年1月至2025年10月中国知网、PubMed、Medline、Embase等多个数据库,基于rs-fMRI比较MCI患者与健康对照(HC) DMN和MTL功能连接的相关研究。由2名研究人员独立采用QUADAS-2筛查并提取文献,将纳入文献进行方法学质量检测。采用Review Manager 5.4对MCI患者和HC受试者神经影像指标进行Meta分析,通过Stata、SDM软件进行诊断效能合并和脑功能连接坐标整合分析,计算合并敏感性、特异性、集成受试者工作特征曲线下面积(SROC-AUC),绘制Deeks漏斗图,分析文献权重并进行亚组分析。
结果 最终纳入10篇文献,涉及MCI患者1 010例,HC 1 714例。MCI患者在DMN存在血氧水平依赖信号下降或异常,以双侧内侧前额皮质(mPFC)和后扣带回为核心,SROC-AUC=0.91。在MTL存在与海马区之间的自发神经功能连接减少,反映MCI患者从情景记忆中提取信息至DMN中枢节点的信息传递过程受阻。DMN和MTL的功能连接异常会引起其他子网络或局部功能连接代偿性增强,以额顶网络和海马体-顶叶网络最为明显,mPFC的异常激活提示了海马区萎缩或脑功能活动异常。DMN和MTL功能连接可以提示MCI从早期发展到记忆障碍、信息加工功能衰退等临床进展。
结论 DMN和MTL间早期功能解耦是AD-MCI期关键的神经机制,为研究AD早期诊断提供重要神经影像学依据。
中图分类号:
韩天星, 刘昊天, 徐子淇, 彭晓涵, 王杨, 曹丹娜. 基于静息态磁共振功能成像对阿尔茨海默病轻度认知障碍期默认网络与内侧颞叶区脑功能连接研究的Meta分析[J]. 《中国康复理论与实践》, 2026, 32(5): 522-533.
HAN Tianxing, LIU Haotian, XU Ziqi, PENG Xiaohan, WANG Yang, CAO Danna. Functional connectivity in default mode network and medial temporal lobe in mild cognitive impairment stage of Alzheimer's disease based on resting-state functional magnetic resonance imaging: a meta-analysis[J]. Chinese Journal of Rehabilitation Theory and Practice, 2026, 32(5): 522-533.
表1
rs-fMRI对AD患者MCI期检查的PICO框架"
| 人群(Population) | 干预(Intervention) | 比较(Comparison) | 结局(Outcome) |
|---|---|---|---|
| 临床诊断为AD源性MCI患者 | 基于种子点rs-fMRI分析所获得的DMN区域影像指标:BOLD、FC、fALFF、ReHo 基于种子点rs-fMRI分析所获得的MTL区域影像指标:BOLD、FC、fALFF、ReHo | HC同期同设备完成的rs-fMRI检测结果 rs-fMRI对DMN和MTL的形态学检测结果 | 诊断效能指标:敏感性、特异性、LR+、LR-、DOR、SROC-AUC 神经影像特征指标:DMN与MTL核心脑区的BOLD 信号变化趋势、rsFC强度值、ReHo/fALFF量化值,异常脑区的MNI坐标、团块大小及t值 |
表3
纳入文献基本特征"
| 纳入文献 | 种子点 | 样本量(男/女)/n | 平均年龄(C/T)/岁 | 诊断标准 | |
|---|---|---|---|---|---|
| C | T | ||||
| Cao等[ | PCC、HIP、MTL | 40/29 | 25/13 | (69.16±4.96)/(68.46±4.97) | AD临床评估、ADAS-Cog |
| Liao等[ | PCC、右HIP、mPFC、PFcm | 37/36 | 76/64 | (68.28±9.64)/(66.30±9.52) | MMSE、MoCA |
| Xiong等[ | PCC、PF | 6/10 | 17/15 | (68.28±9.64)/(66.30±9.52) | ADNI、MMSE、GDS、CDR、FAQ、NPI-Q |
| Sendi等[ | PCC | 498/530 | 166/191 | (75.1±7.85)/(69.83±8.64) | 影像数据分析、CDR |
| Soman等[ | PCC、MTL | 12/19 | 26/34 | (65.7±6.0)/(65.1±6.7) | ACE-M |
| Ghahremani等[ (2023) | PFcm、Pfop、PFt、PFm、 Pga、PGp | 35/28 | 18/14 | (73.9±8.1)/(70.6±6.7) | MBIC、MoCA |
| Hojjati等[ | PCC、HIT、MTL | 176/185 | 88/80 | (68.41±6.15)/(64.96±3.17) | MMSE、MoCA |
| Takao等[ | mPFC、PCC、HIP | 18/13 | 38/42 | (71.9±7.0)/(76.0±7.2) | MMSE |
| Saberian等[ | PCC、MTL | 13/15 | 23/51 | (71.74±6.51)/(72.26±5.95) | CDR、MMSE |
| Mohammadian等[ | PCC、HIT、MTL | 4/10 | 12/17 | (72.87±4.34)/(71.17±2.59) | MMSE |
表4
纳入文献的影像特征和文献质量评价"
| 纳入文献 | 敏感性/% | 特异性/% | 影像特征 | 异常脑区MNI坐标与团块大小 | NOS评分 | |||
|---|---|---|---|---|---|---|---|---|
| x | y | z | 体积/mm3 | |||||
| Cao等[ | 95.30 | 74.49 | DMN中双侧内侧前额皮质、后扣带楔前叶、腹侧前扣带回BOLD活动降低;mPFC与PCC之间FC下降;楔前叶与PCC fALFF下降;前额叶皮质ReHo降低 | 3 | -47 | 22 | 442 | 8 |
| Liao等[ | 88.43 | 78.21 | 右中额回、右扣带后回BOLD活动降低;PCC和MTL FC下降;内侧前颞叶fALFF升高不显著,楔前叶与PCC fALFF下降;后部DMN、海马区、前额叶皮质ReHo下降 | 6 | -55 | 30 | 376 | 8 |
| Xiong等[ | 96.90 | 81.20 | 尾状核与小脑亚区、尾状核与额下回、尾状核、内侧前额叶皮质BOLD活动降低;DMN内部FC广泛降低;双侧内侧前额皮质和PCC之间fALFF下降,后扣带皮质ReHo降低 | -4 | -47 | 24 | 209 | 9 |
| Sendi等[ | 95.00 | 72.00 | 视交叉上核、运动神经元存活基因、自动决策支持系统、细胞间基质蛋白族BOLD活动降低;后扣带楔前页和腹侧前扣带回存在FC下降情况,内侧前颞叶ReHo变化不一致,楔前叶ReHo下降 | ±47 | -57 | 33 | 588 | 9 |
| Soman等[ | 89.30 | 73.50 | DMN、运动神经元存活基因、顶网络、背侧注意网络BOLD活动降低;PCC与海马体之间FC下降;尾状和前额叶皮质之间fALFF下降,DMN内ReHo普遍性降低 | ±38 | -66 | 28 | 381 | 7 |
| Ghahremani等[ | 95.35 | 80.15 | DMN、脑皮质区域BOLD活动降低;MTL内FC下降;右扣带后回fALFF下降;海马体边缘与楔前叶ReHo下降 | 2 | -46 | 24 | 761 | 8 |
| Hojjati等[ | 91.40 | 77.30 | 视觉网络、运动网络、背侧注意网络、凸显网络、控制网络、边缘网络和DMN中tau蛋白和Aβ蛋白沉积,BOLD活动降低;楔前叶ReHo下降 | -5 | 46 | -13 | 447 | 8 |
| Takao等[ | 91.90 | 74.30 | 感觉运动网络、执行控制网络、视觉网络、DMN、背侧注意网络、凸显网络、双侧内侧前额皮质BOLD活动降低;MTL及海马区fALFF下降 | 4 | 47 | -9 | 736 | 8 |
| Saberian等[ | 92.40 | 80.70 | 神经丝轻链、DMN BOLD活动降低,右扣带后回fALFF下降,PCC与海马体之间FC下降,多基因风险评估显示AD风险增加 | ±66 | -60 | 28 | 337 | 9 |
| Mohammadian等[ | 87.80 | 83.60 | DMN、凸显网络、背侧注意网络、视觉网络、前额顶叶、语言中枢和小脑皮质神经元BOLD活动降低;PCC、内侧前额叶皮质和颞顶联合区存在不同程度FC下降;MTL特别是海马旁回和海马体之间ReHo降低、fALFF下降 | 3 | 55 | -11 | 509 | 7 |
表6
亚组分析"
| 亚组变量 | 纳入分析数量 | 合并敏感性(95%CI) | 合并特异性(95%CI) | Meta回归联合P值 |
|---|---|---|---|---|
| 病例来源 ADNI数据库 非ADNI数据库 | 566 789 | 0.88 (0.82, 0.89) 0.84 (0.81, 0.92) | 0.94 (0.92, 0.96) 0.93 (0.87, 0.95) | 0.67 |
| 场强 1.5 T 3.0 T | 842 204 | 0.85 (0.82, 0.88) 0.90 (0.88, 0.91) | 0.91 (0.88, 0.95) 0.95 (0.91, 0.99) | 0.03 |
| 线圈类型 PA ER | 649 703 | 0.86 (0.82, 0.88) 0.89 (0.86, 0.90) | 0.90 (0.88, 0.93) 0.90 (0.86, 0.92) | 0.86 |
| [1] | 戴西件, 骆嵩. 阿尔茨海默病源性认知障碍早期诊疗专家共识(2025)[J]. 中国医学影像学杂志, 2025, 33(4): 337-346. |
| DAI X J, LUO S. Expert Consensus on Early Diagnosis and Treatment of Alzheimer's Disease-Associated Cognitive Impairment(2025)[J]. Chin J Med Imag, 2025, 33(4): 337-346. | |
| [2] |
JACOBS T, BRIEN C O, FIGUEREDO L, et al. Body mass index and blood volume influence plasma biomarkers and positron emission tomography classification in preclinical Alzheimer's disease[J]. Alzheimers Dement, 2025, 21(10): e70765.
doi: 10.1002/alz.v21.10 |
| [3] |
YUAN S, MA W, YANG R, et al. Sleep duration, genetic susceptibility, and Alzheimer's disease: a longitudinal UK Biobank-based study[J]. BMC Geriatr, 2022, 22(1): 638.
doi: 10.1186/s12877-022-03298-8 pmid: 35918656 |
| [4] |
蒋嘉蕊, 牛振东. 深度学习在阿尔茨海默病诊断中应用近5年文献的可视化分析[J]. 中国康复理论与实践, 2022, 28(11): 1318-1324.
doi: 10.3969/j.issn.1006-9771.2022.11.011 |
| JIANG J R, NIU Z D. Deep learning for diagnosis of Alzheimer's disease in the past five years: a visuallized analysis[J]. Chin J Rehabil Theory Pract, 2022, 28(11): 1318-1324. | |
| [5] | 胡晓飞, 桂莉, 陈晓, 等. 2024版NIA-AA阿尔茨海默病诊断与分期标准更新解读:多模态影像的诊疗意义[J]. 中国医学影像学杂志, 2025, 33(5): 454-460. |
| HU X F, GUI L, CHEN X, et al. Interpretation of updates for diagnosis and staging criterion of NIA-AA Alzheimer's Disease (2024 Edition): diagnostic and therapeutic significance of multimodal imaging[J]. Chin J Med Imag, 2025, 33(5): 454-460. | |
| [6] |
GREICIUS M D, SRIVASTAVA G, REISS A L, et al. Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI[J]. Proc Natl Acad Sci U S A, 2004, 101(13): 4637-4642.
doi: 10.1073/pnas.0308627101 |
| [7] |
SOHN W S, YOO K, NA D L, et al. Progressive changes in hippocampal resting-state connectivity across cognitive impairment: a cross-sectional study from normal to Alzheimer disease[J]. Alzheimer Dis Assoc Disord, 2014, 28(3): 239-246.
doi: 10.1097/WAD.0000000000000027 |
| [8] |
SULLIVAN M D, ANDERSON J A, TURNER G R, et al. Alzheimer's Disease Neuroimaging Initiative. Intrinsic neurocognitive network connectivity differences between normal aging and mild cognitive impairment are associated with cognitive status and age[J]. Neurobiol Aging, 2019, 73: 219-228.
doi: S0197-4580(18)30357-9 pmid: 30391818 |
| [9] |
THERRIAULT J, WANG S, MATHOTAARACHCHI S, et al. Rostral-caudal hippocampal functional convergence is reduced across the Alzheimer's disease spectrum[J]. Mol Neurobiol, 2019, 56(12): 8336-8344.
doi: 10.1007/s12035-019-01671-0 pmid: 31230260 |
| [10] |
WANG S M, KANG D W, UM Y H, et al. Cognitive normal older adults with APOE-2 allele show a distinctive functional connectivity pattern in response to cerebral Aβ deposition[J]. Int J Mol Sci, 2023, 24(14): 11250.
doi: 10.3390/ijms241411250 |
| [11] |
IBRAHIM K M, AGCAOGLU O, DUDA M, et al. Parallel multilink joint ICA for multimodal fusion of gray matter and multiple resting fMRI networks[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2024, 2024: 1-4.
doi: 10.1109/EMBC53108.2024.10781919 pmid: 40039205 |
| [12] |
JALILIANHASANPOUR R, BEHESHTIAN E, SHERBAF G, et al. Functional connectivity in neurodegenerative disorders: Alzheimer's disease and frontotemporal dementia[J]. Top Magn Reson Imaging, 2019, 28(6): 317-324.
doi: 10.1097/RMR.0000000000000223 pmid: 31794504 |
| [13] |
RAO Y L, GANARAJA B, MURLIMANJU B V, et al. Hippocampus and its involvement in Alzheimer's disease: a review[J]. 3 Biotech, 2022, 12(2): 55.
doi: 10.1007/s13205-022-03123-4 pmid: 35116217 |
| [14] |
BECKER M, REPANTIS D, DRESLER M, et al. Cognitive enhancement: Effects of methylphenidate, modafinil, and caffeine on latent memory and resting state functional connectivity in healthy adults[J]. Hum Brain Mapp, 2022, 43(14): 4225-4238.
doi: 10.1002/hbm.25949 pmid: 35670369 |
| [15] |
YANG A, ZHUANG H, DU L, et al. Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD[J]. Neuroimage, 2023, 282: 120381.
doi: 10.1016/j.neuroimage.2023.120381 |
| [16] |
ALMEIDA V N, RADANOVIC M. Semantic processing and neurobiology in Alzheimer's disease and mild cognitive impairment[J]. Neuropsychologia, 2022, 174: 108337.
doi: 10.1016/j.neuropsychologia.2022.108337 |
| [17] | ABDELAZIZ M, WANG T, ANWAAR W, et al. Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis[J]. Comput BiolMed, 2025, 184: 109438. |
| [18] |
LENZI D, SERRA L, PERRI R, et al. Single domain amnestic MCI: a multiple cognitive domains fMRI investigation[J]. Neurobiol Aging, 2011, 32(9): 1542-1557.
doi: 10.1016/j.neurobiolaging.2009.09.006 pmid: 19880216 |
| [19] | 厍映霞, 王帅文, 马文玲, 等. 阿尔茨海默病患者静息态局部一致性与体素镜像同伦连接研究[J]. 中国医学影像学杂志, 2018, 26(10): 721-725. |
| SHE Y X, WANG S W, MA W L, et al. Regional homogeneity of Alzheimer's disease at resting-state and voxel-mirrored homotopic connectivity[J]. Chin J Med Imag, 2018, 26(10): 721-725. | |
| [20] |
DING S, LI X, HUANG Z, et al. Alterations of brain structural and functional connectivity networks topology and decoupling in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease[J]. Mult Scler Relat Disord, 2024, 87: 105699.
doi: 10.1016/j.msard.2024.105699 |
| [21] |
IBRAHIM B, SUPPIAH S, IBRAHIM N, et al. Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: a systematic review[J]. Hum Brain Mapp, 2021, 42(9): 2941-2968.
doi: 10.1002/hbm.25369 pmid: 33942449 |
| [22] |
JACKSON D, TURNER R. Power analysis for random-effects meta-analysis[J]. Res Synth Methods, 2017, 8(3): 290-302.
doi: 10.1002/jrsm.1240 pmid: 28378395 |
| [23] | SALAMEH J P, BOSSUYT P M, MCGRATH T A, et al. Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA): explanation, elaboration, and checklist[J]. BMJ, 2020, 370: m2632. |
| [24] |
DE J F, SCHAEFFERS A, KOOREMAN Z E, et al. Botulinum toxin A treatment in facial palsy synkinesis: a systematic review and meta-analysis[J]. Eur Arch Otorhinolaryngol, 2023, 280(4): 1581-1592.
doi: 10.1007/s00405-022-07796-8 |
| [25] |
ISO-MARKKU P, KUJALA U M, KNITTLE K, et al. Physical activity as a protective factor for dementia and Alzheimer's disease: systematic review, meta-analysis and quality assessment of cohort and case-control studies[J]. Br J Sports Med, 2022, 56(12): 701-709.
doi: 10.1136/bjsports-2021-104981 |
| [26] |
LIU S, ZHAO M, YANG X, et al. Exosomal microRNA biomarkers in Alzheimer's disease: systematic review and meta-analysis[J]. Clin Chim Acta, 2026, 578: 120542.
doi: 10.1016/j.cca.2025.120542 |
| [27] |
SEITIDIS G, NIKOLAKOPOULOS S, HENNESSY E A, et al. Network meta-analysis techniques for synthesizing prevention science evidence[J]. Prev Sci, 2022, 23(3): 415-424.
doi: 10.1007/s11121-021-01289-6 |
| [28] |
PULJAK L, BALA M M, MATHES T, et al. AMSTAR 2 is only partially applicable to systematic reviews of non-intervention studies: a meta-research study[J]. J Clin Epidemiol, 2023, 163: 11-20.
doi: 10.1016/j.jclinepi.2023.08.021 pmid: 37659582 |
| [29] |
CAO Y, YANG H, ZHOU Z, et al. Abnormal default-mode network homogeneity in patients with mild cognitive impairment in Chinese communities[J]. Front Neurol, 2021, 11: 569806.
doi: 10.3389/fneur.2020.569806 |
| [30] |
LIAO Z, SUN W, LIU X, et al. Altered dynamic intrinsic brain activity of the default mode network in Alzheimer's disease: a resting-state fMRI study[J]. Front Hum Neurosci, 2022, 16: 951114.
doi: 10.3389/fnhum.2022.951114 |
| [31] |
XIONG Y, YE C, CHEN Y, et al. Altered functional connectivity of basal ganglia in mild cognitive impairment and Alzheimer's disease[J]. Brain Sci, 2022, 12(11): 1555.
doi: 10.3390/brainsci12111555 |
| [32] |
SENDI M S, ZENDEHROUH E, MILLER R L, et al. Alzheimer's disease projection from normal to mild dementia reflected in functional network connectivity: a longitudinal study[J]. Front Neural Circuits, 2021, 14: 593263.
doi: 10.3389/fncir.2020.593263 |
| [33] |
SOMAN S M, RAGHAVAN S, RAJESH P G, et al. Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia?[J]. J Neurol Sci, 2020, 418: 117093.
doi: 10.1016/j.jns.2020.117093 |
| [34] | GHAHREMANI M, NATHAN S, SMITH E E, et al. Functional connectivity and mild behavioral impairment in dementia-free elderly[J]. Alzheimers Dement (N Y), 2023, 9(1): e12371. |
| [35] |
HOJJATI S H, BUTLER T A, DE LEON M, et al. Inter-network functional connectivity increases by beta-amyloid and may facilitate the early stage of tau accumulation[J]. Neurobiol Aging, 2025, 148: 16-26.
doi: 10.1016/j.neurobiolaging.2025.01.005 pmid: 39879839 |
| [36] |
TAKAO H, AMEMIYA S, ABE O. Longitudinal stability of resting-state networks in normal aging, mild cognitive impairment, and Alzheimer's disease[J]. Magn Reson Imaging, 2021, 82: 55-73.
doi: 10.1016/j.mri.2021.06.020 pmid: 34153437 |
| [37] |
SABERIAN P, NASIRI H, KAFFASHIAN F, et al. Potential mediating role of polygenic hazard score in the association between neurofilament light chain and default mode network connectivity across the Alzheimer's disease continuum[J]. IBRO Neurosci Rep, 2025, 18: 732-738.
doi: 10.1016/j.ibneur.2025.04.018 pmid: 40476223 |
| [38] |
MOHAMMADIAN F, ZARE SADEGHI A, NOROOZIAN M, et al. Quantitative assessment of resting-state functional connectivity MRI to differentiate amnestic mild cognitive impairment, late-onset Alzheimer's disease from normal subjects[J]. J Magn Reson Imaging, 2023, 57(6): 1702-1712.
doi: 10.1002/jmri.v57.6 |
| [39] |
WU J, HE Y, LIANG S, et al. Effects of computerized cognitive training on structure-function coupling and topology of multiple brain networks in people with mild cognitive impairment: a randomized controlled trial[J]. Alzheimers Res Ther, 2023, 15(1): 158.
doi: 10.1186/s13195-023-01292-9 |
| [40] |
PAN T, QU H, PAN Y, et al. Effective connectivity of default mode network subsystems in Parkinson's disease with mild cognitive impairment based on spectral dynamic causal modeling[J]. J Integr Neurosci, 2024, 23(6): 110.
doi: 10.31083/j.jin2306110 pmid: 38940086 |
| [41] |
WANG C, JI D, SU X, et al. Cerebral perfusion correlates with amyloid deposition in patients with mild cognitive impairment due to Alzheimer's disease[J]. J Prev Alzheimers Dis, 2025, 12(2): 100031.
doi: 10.1016/j.tjpad.2024.100031 |
| [42] |
COHEN J S, PHILLIPS J, DAS S R, et al. Posterior hippocampal sparing in Lewy body disorders with Alzheimer's copathology: an in vivo MRI study[J]. Neuroimage Clin, 2025, 45: 103714.
doi: 10.1016/j.nicl.2024.103714 |
| [43] |
CHAO L L, LEE J A, MARTINEZ S, et al. Preventing loss of independence through exercise (PLIÉ): a pilot trial in older adults with subjective memory decline and mild cognitive impairment[J]. J Alzheimers Dis, 2021, 82(4): 1543-1557.
doi: 10.3233/JAD-210159 |
| [44] |
MCGETTIGAN C, WARREN J E, EISNER F, et al. Neural correlates of sublexical processing in phonological working memory[J]. J Cogn Neurosci, 2011, 23(4): 961-977.
doi: 10.1162/jocn.2010.21491 |
| [45] |
KIM M, SEKIYA H, YAO G, et al. Diagnosis of Alzheimer disease and tauopathies on whole-slide histopathology images using a weakly supervised deep learning algorithm[J]. Lab Invest, 2023, 103(6): 100127.
doi: 10.1016/j.labinv.2023.100127 |
| [46] |
RABINOVICI G D, KNOPMAN D S, ARBIZU J, et al. Updated appropriate use criteria for amyloid and tau PET: a report from the Alzheimer's Association and Society for Nuclear Medicine and Molecular Imaging Workgroup[J]. Alzheimers Dement. 2025, 21(1): e14338.
doi: 10.1002/alz.v21.1 |
| [47] |
AHMED N, NGADI A B, SHARIF J M, et al. Network threat detection using machine/deep learning in SDN-Based platforms: a comprehensive analysis of state-of-the-art solutions, discussion, challenges, and future research direction[J]. Sensors (Basel), 2022, 22(20): 7896.
doi: 10.3390/s22207896 |
| [48] |
STEINBERG S N, KING T Z. Within-individual BOLD signal variability and its implications for task-based cognition: a systematic review[J]. Neuropsychol Rev, 2024, 34(4): 1115-1164.
doi: 10.1007/s11065-023-09619-x |
| [1] | 车蕊芯, 唐永甜, 潘世琴, 童雅倩. 经皮穴位电刺激对慢性阻塞性肺疾病效果的Meta分析[J]. 《中国康复理论与实践》, 2026, 32(4): 426-436. |
| [2] | 杜雯倩, 张旭, 陈继伟, 王兴, 朱昆. 运动干预对帕金森病冻结步态效果的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(7): 781-789. |
| [3] | 张勇, 蔡增, 徐凤萍, 刘丹, 常红娟. 音乐治疗对孤独症谱系障碍儿童干预效果的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(4): 423-430. |
| [4] | 杨益成, 王丹丹, 沈群策, 张磊, 吴雪萍. 癌症患者化疗期间运动干预焦虑和抑郁状态效果的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(2): 184-193. |
| [5] | 张琦, 孙文玉, 高振梅, 刘睿, 张天奥. 非侵入性脑刺激在阿尔茨海默病中应用的文献计量分析[J]. 《中国康复理论与实践》, 2025, 31(2): 194-208. |
| [6] | 朱艾荣, 刘培豪, 郭晶, 管婷玉, 陈玺竹, 蔡诗凝, 胡雁, 张玉侠. 运动-认知双任务训练对帕金森病患者认知功能的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(12): 1376-1384. |
| [7] | 刘东斐, 颜会义, 杨帆, 张帅. 运动干预对唐氏综合征儿童青少年多维健康结局影响的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(12): 1398-1413. |
| [8] | 张浩, 许传蕾, 魏振兴, 马丽虹. 不同虚拟现实技术对帕金森病患者运动功能疗效的网状Meta分析[J]. 《中国康复理论与实践》, 2025, 31(11): 1290-1302. |
| [9] | 毛涛, 游茂林. 运动干预对帕金森病患者呼吸功能效果的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(11): 1303-1313. |
| [10] | 陈宇航, 杨瑜爱, 卢轩禹, 王昱航, 张蒙园, 张梓寒, 王荟荧, 常静玲. 卒中后失语和卒中后抑郁自发脑活动改变的静息态功能磁共振成像Meta分析[J]. 《中国康复理论与实践》, 2025, 31(10): 1143-1155. |
| [11] | 杨三峡, 刘清栾, 万嘉敏. 音乐干预对阿尔茨海默病患者激活模式区域差异的功能磁共振成像Meta分析[J]. 《中国康复理论与实践》, 2025, 31(10): 1156-1163. |
| [12] | 赵英楠, 郑鑫彤, 刘俊玲, 王紫丹, 吴泓悦, 李冰, 李燕. 阈值吸气肌训练对肺癌术后患者肺康复影响的Meta分析[J]. 《中国康复理论与实践》, 2025, 31(10): 1164-1171. |
| [13] | 温艳飞, 杨露, 班玥, Ykabaru Daniela BERBESI NORIEGA, 张郝琪, 王丽, 刘华. ICF框架下运动疗法对慢性非特异性颈痛效果的Meta分析[J]. 《中国康复理论与实践》, 2024, 30(7): 778-788. |
| [14] | 吕美玲, 王洁, 曾维斯, 温晓婷, 楚鑫. 虚拟现实技术对帕金森病患者认知功能和生活质量影响的Meta分析[J]. 《中国康复理论与实践》, 2024, 30(6): 648-656. |
| [15] | 崔甜甜, 杨钰琳, 崔腾腾, 马丽虹. 不同强化训练对脑性瘫痪儿童上肢运动功能效果的网状Meta分析[J]. 《中国康复理论与实践》, 2024, 30(4): 437-448. |
| 阅读次数 | ||||||
|
全文 |
|
|||||
|
摘要 |
|
|||||
|
||