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Brain overlapping system-level architecture influenced by external magnetic stimulation and internal gene expression in AD-spectrum patients

Abstract

The brain overlapping system-level architecture is associated with functional information integration in the multiple roles of the same region, and it has been developed as an underlying novel biomarker of brain disease and may characterise the indicators for the treatment of Alzheimer’s disease (AD). However, it remains uncertain whether these changes are influenced by external magnetic stimulation and internal gene expression. A total of 73 AD-spectrum patients (52 with true stimulation and 21 with sham stimulation) were underwent four-week neuronavigated transcranial magnetic stimulation (rTMS). Shannon-entropy diversity coefficient analysis was used to explore the brain overlapping system of the neuroimaging data in these pre- and posttreatment patients. Transcription-neuroimaging association analysis was further performed via gene expression data from the Allen Human Brain Atlas. Compared with the rTMS_sham stimulation group, the rTMS_true stimulation group achieved the goal of cognitive improvement through the reconstruction of functional information integration in the multiple roles of 27 regions associated with the brain overlapping system, involving the attentional network, sensorimotor network, default mode network and limbic network. Furthermore, these overlapping regions were closely linked to gene expression on cellular homeostasis and immune inflammation, and support vector regression analysis revealed that the baseline diversity coefficients of the attentional and sensorimotor networks can effectively predict memory improvement after rTMS treatment. These findings highlight the brain overlapping system associated with cognitive improvement, and provide the first evidence that external magnetic stimulation and internal gene expression could influence these overlapping regions in AD-spectrum patients.

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Fig. 1: Study design and analysis pipeline schematic overview.
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Fig. 2: Schematic diagram of the brain networks.
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Fig. 3: Differences in the distribution of the Shannon-entropy diversity coefficient in 27 differential brain overlapping regions after four weeks of neuronavigated rTMS intervention (Bonferroni corrected, P < 0.05).
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Fig. 4
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Fig. 5: Functional enrichment of the differential brain overlapping system-related genes.
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Fig. 6: Specific expression of the differential brain overlapping system-related genes.
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Fig. 7: The differential brain overlapping system-related genes were used to construct the PPI network.
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Data availability

Data supporting the findings of this study are available from the corresponding author upon request. The data are not publicly available because they contain information that could compromise the privacy of the research participants.

References

  1. Ogbodo JO, Agbo CP, Njoku UO, Ogugofor MO, Egba SI, Ihim SA, et al. Alzheimer’s disease: pathogenesis and therapeutic interventions. Curr Aging Sci. 2022;15:2–25.

    Article  PubMed  Google Scholar 

  2. Passeri E, Elkhoury K, Morsink M, Broersen K, Linder M, Tamayol A, et al. Alzheimer’s disease: treatment strategies and their limitations. Int J Mol Sci. 2022;23:13954.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Di Lorenzo F, Motta C, Casula EP, Bonnì S, Assogna M, Caltagirone C, et al. LTP-like cortical plasticity predicts conversion to dementia in patients with memory impairment. Brain Stimul. 2020;13:1175–82.

    Article  PubMed  Google Scholar 

  4. McNerney MW, Kraybill EP, Narayanan S, Mojabi FS, Venkataramanan V, Heath A. Memory-related hippocampal brain-derived neurotrophic factor activation pathways from repetitive transcranial magnetic stimulation in the 3xTg-AD mouse line. Exp Gerontol. 2023;183:112323.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev. 2014;24:49–62.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Nabizadeh F. Disruption in functional networks mediated tau spreading in Alzheimer’s disease. Brain Commun. 2024;6:fcae198.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Guo T, Zhang Y, Xue Y, Qiao L, Shen D. Brain function network: higher order vs. more discrimination. Front Neurosci. 2021;15:696639.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Yuan Y, et al. Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment. Biol Psychiatry. 2009;65:951–8.

    Article  PubMed  Google Scholar 

  9. Chen X, Zhang H, Gao Y, Wee CY, Li G, Shen D. High-order resting-state functional connectivity network for MCI classification. Hum Brain Mapp. 2016;37:3282–96.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Schuurman T, Bruner E. Modularity and community detection in human brain morphology. Anat Rec (Hoboken). 2024;307:345–55.

    Article  PubMed  Google Scholar 

  11. Asghari K, Niknam Z, Mohammadpour-Asl S, Chodari L. Cellular junction dynamics and Alzheimer’s disease: a comprehensive review. Mol Biol Rep. 2024;51:273.

    Article  CAS  PubMed  Google Scholar 

  12. Dai Z, Yan C, Li K, Wang Z, Wang J, Cao M, et al. Identifying and mapping connectivity patterns of brain network hubs in Alzheimer’s disease. Cereb Cortex. 2015;25:3723–42.

    Article  PubMed  Google Scholar 

  13. Lee WJ, Cho H, Baek MS, Kim HK, Lee JH, Ryu YH, et al. Dynamic network model reveals distinct tau spreading patterns in early- and late-onset Alzheimer disease. Alzheimers Res Ther. 2022;14:121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Lei T, Liao X, Liang X, Sun L, Xia M, Xia Y, et al. Functional network modules overlap and are linked to interindividual connectome differences during human brain development. PLoS Biol. 2024;22:e3002653.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Faskowitz J, Esfahlani FZ, Jo Y, Sporns O, Betzel RF. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nat Neurosci. 2020;23:1644–54.

    Article  CAS  PubMed  Google Scholar 

  16. Jo Y, Zamani Esfahlani F, Faskowitz J, Chumin EJ, Sporns O, Betzel RF. The diversity and multiplexity of edge communities within and between brain systems. Cell Rep. 2021;37:110032.

    Article  CAS  PubMed  Google Scholar 

  17. Gu Y, Li L, Zhang Y, Ma J, Yang C, Xiao Y, et al. The overlapping modular organization of human brain functional networks across the adult lifespan. Neuroimage. 2022;253:119125.

    Article  PubMed  Google Scholar 

  18. Rubinov M, Sporns O. Weight-conserving characterization of complex functional brain networks. Neuroimage. 2011;56:2068–79.

    Article  PubMed  Google Scholar 

  19. Martínez García SJ, Padilla Longoria P. Analysis of Shannon’s entropy to contrast between the Embodied and Neurocentrist hypothesis of conscious experience. Biosystems. 2024;246:105323.

    Article  PubMed  Google Scholar 

  20. Sirkis DW, Bonham LW, Johnson TP, La Joie R, Yokoyama JS. Dissecting the clinical heterogeneity of early-onset Alzheimer’s disease. Mol Psychiatry. 2022;27:2674–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chhetri A, Goel K, Ludhiadch A, Singh P, Munshi A. Role of imaging genetics in Alzheimer’s disease: a systematic review and current update. CNS Neurol Disord Drug Targets. 2024;23:1143–56.

    Article  PubMed  Google Scholar 

  22. Estevez-Fraga C, Altmann A, Parker CS, Scahill RI, Costa B, Chen Z, et al. Genetic topography and cortical cell loss in Huntington’s disease link development and neurodegeneration. Brain. 2023;146:4532–46.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Baik JY, Kim M, Bao J, Long Q, Shen L. Identifying Alzheimer’s genes via brain transcriptome mapping. BMC Med Genomics. 2022;15:116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Anand C, Torok J, Abdelnour F, Maia PD, Raj A. Selective vulnerability and resilience to Alzheimer’s disease tauopathy as a function of genes and the connectome. bioRxiv. 2024.

  25. Ye F, Funk Q, Rockers E, Shulman JM, Masdeu JC, Pascual B. In Alzheimer-prone brain regions, metabolism and risk-gene expression are strongly correlated. Brain Commun. 2022;4:fcac216.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chen L, Zhao S, Wang Y, Niu X, Zhang B, Li X, et al. Genetic insights into obesity and brain: combine mendelian randomization study and gene expression analysis. Brain Sci. 2023;13:892.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Yao W, Hou X, Zhou H, You S, Lv T, Chen H, et al. Associations between the multitrajectory neuroplasticity of neuronavigated rTMS-mediated angular gyrus networks and brain gene expression in AD spectrum patients with sleep disorders. Alzheimers Dement. 2024;20:7885–901.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Chen HF, Sheng XN, Yang ZY, Shao PF, Xu HH, Qin RM, et al. Multi-networks connectivity at baseline predicts the clinical efficacy of left angular gyrus-navigated rTMS in the spectrum of Alzheimer’s disease: a sham-controlled study. CNS Neurosci Ther. 2023;29:2267–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr., Kawas CH, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7:263–9.

    Article  PubMed  Google Scholar 

  30. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256:183–94.

    Article  CAS  PubMed  Google Scholar 

  31. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37:W305–311.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dougherty JD, Schmidt EF, Nakajima M, Heintz N. Analytical approaches to RNA profiling data for the identification of genes enriched in specific cells. Nucleic Acids Res. 2010;38:4218–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Xu X, Wells AB, O’Brien DR, Nehorai A, Dougherty JD. Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders. J Neurosci. 2014;34:1420–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Peng B, Zhao Y, Li X, Dong K, Li T, Liu D, et al. Understanding the effects of different transcranial magnetic stimulation control protocols: a behavioral and neural perspective. J Neurophysiol. 2024;132:1977–85.

    Article  PubMed  Google Scholar 

  35. Betzel RF, Bassett DS. Multi-scale brain networks. Neuroimage. 2017;160:73–83.

    Article  PubMed  Google Scholar 

  36. Dai Z, Lin Q, Li T, Wang X, Yuan H, Yu X, et al. Disrupted structural and functional brain networks in Alzheimer’s disease. Neurobiol Aging. 2019;75:71–82.

    Article  CAS  PubMed  Google Scholar 

  37. Jin S, Wang J, He Y. The brain network hub degeneration in Alzheimer’s disease. Biophys Rep. 2024;10:213–29.

    PubMed  PubMed Central  Google Scholar 

  38. Rodriguez RX, Noble S, Tejavibulya L, Scheinost D. Leveraging edge-centric networks complements existing network-level inference for functional connectomes. Neuroimage. 2022;264:119742.

    Article  PubMed  Google Scholar 

  39. Chumin EJ, Faskowitz J, Esfahlani FZ, Jo Y, Merritt H, Tanner J, et al. Cortico-subcortical interactions in overlapping communities of edge functional connectivity. Neuroimage. 2022;250:118971.

    Article  PubMed  Google Scholar 

  40. Kabbara A, Paban V, Hassan M. The dynamic modular fingerprints of the human brain at rest. Neuroimage. 2021;227:117674.

    Article  CAS  PubMed  Google Scholar 

  41. Lazarov O, Gupta M, Kumar P, Morrissey Z, Phan T. Memory circuits in dementia: The engram, hippocampal neurogenesis and Alzheimer’s disease. Prog Neurobiol. 2024;236:102601.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bråthen ACS, de Lange AG, Rohani DA, Sneve MH, Fjell AM, Walhovd KB. Multimodal cortical and hippocampal prediction of episodic-memory plasticity in young and older adults. Hum Brain Mapp. 2018;39:4480–92.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Arulchelvan E, Vanneste S. Promising neurostimulation routes for targeting the hippocampus to improve episodic memory: a review. Brain Res. 2023;1815:148457.

    Article  CAS  PubMed  Google Scholar 

  44. Wang JX, Rogers LM, Gross EZ, Ryals AJ, Dokucu ME, Brandstatt KL, et al. Targeted enhancement of cortical-hippocampal brain networks and associative memory. Science. 2014;345:1054–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Lawn T, Howard MA, Turkheimer F, Misic B, Deco G, Martins D, et al. From neurotransmitters to networks: transcending organisational hierarchies with molecular-informed functional imaging. Neurosci Biobehav Rev. 2023;150:105193.

    Article  CAS  PubMed  Google Scholar 

  46. Su J, Song Y, Zhu Z, Huang X, Fan J, Qiao J, et al. Cell-cell communication: new insights and clinical implications. Signal Transduct Target Ther. 2024;9:196.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lee CY, Riffle D, Xiong Y, Momtaz N, Lei Y, Pariser JM, et al. Characterizing dysregulations via cell-cell communications in Alzheimer’s brains using single-cell transcriptomes. BMC Neurosci. 2024;25:24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ising C, Venegas C, Zhang S, Scheiblich H, Schmidt SV, Vieira-Saecker A, et al. NLRP3 inflammasome activation drives tau pathology. Nature. 2019;575:669–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Leng F, Edison P. Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol. 2021;17:157–72.

    Article  PubMed  Google Scholar 

  50. Bouakaz L, Bouakaz E, Murgola EJ, Ehrenberg M, Sanyal S. The role of ribosomal protein L11 in class I release factor-mediated translation termination and translational accuracy. J Biol Chem. 2006;281:4548–56.

    Article  CAS  PubMed  Google Scholar 

  51. Slomnicki LP, Hallgren J, Vashishta A, Smith SC, Ellis SR, Hetman M. Proapoptotic requirement of ribosomal protein L11 in ribosomal stress-challenged cortical neurons. Mol Neurobiol. 2018;55:538–53.

    Article  CAS  PubMed  Google Scholar 

  52. Matsutake T, Srivastava PK. The immunoprotective MHC II epitope of a chemically induced tumor harbors a unique mutation in a ribosomal protein. Proc Natl Acad Sci USA. 2001;98:3992–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Qin H, Hu C, Zhao X, Tian M, Zhu B. Usefulness of candidate mRNAs and miRNAs as biomarkers for mild cognitive impairment and Alzheimer’s disease. Int J Neurosci. 2023;133:89–102.

    Article  CAS  PubMed  Google Scholar 

  54. Qiu C, Xu H. A six-gene signature related to liquid-liquid phase separation for diagnosis of Alzheimer’s disease. Actas Esp Psiquiatr. 2024;52:759–68.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Pérez-Oliveira S, Castilla-Silgado J, Painous C, Aldecoa I, Menéndez-González M, Blázquez-Estrada M, et al. Huntingtin CAG repeats in neuropathologically confirmed tauopathies: Novel insights. Brain Pathol. 2024;34:e13250.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Axenhus M, Winblad B, Tjernberg LO, Schedin-Weiss S. Huntingtin levels are elevated in hippocampal post-mortem samples of Alzheimer’s disease brain. Curr Alzheimer Res. 2020;17:858–67.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported partly by grants from the National Natural Science Foundation of China (No. 82371437), the Key Research and Development Program of Jiangsu Province (No. BE2023674), and Clinical Trials from the Affiliated Drum Tower Hospital, Medical School of Nanjing University (No. 2022-LCYG-MS-05).

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WY and FB contributed to the conception and design of the study. WY, XH and WZ performed the analyses, obtained the findings and provided guidance on the interpretation of the results. WY, XH, WZ and XS were involved in the collection of the data. WY, FB and JZ wrote the manuscript and supervised the study.

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Correspondence to JunJian Zhang or Feng Bai.

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The study protocol was approved by the Ethics Committee of Drum Tower Hospital of Nanjing University. All participants submitted their written informed consent to participate before the recruitment.

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Yao, W., Hou, X., Zheng, W. et al. Brain overlapping system-level architecture influenced by external magnetic stimulation and internal gene expression in AD-spectrum patients. Mol Psychiatry 30, 4110–4121 (2025). https://doi.org/10.1038/s41380-025-02991-5

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