Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Computational Modeling of Neural Networks of the Human Brain

  • Living reference work entry
  • First Online:
Handbook of Computational Neurodegeneration

Abstract

Computational modeling of the neural networks of the human brain has numerous applications including research into neurodegeneration diseases, more specifically Alzheimer ‘s disease and mild cognitive impairment. Changes in functional connectivity as a feature of the neural networks of the human brain might precede alterations in structural connections in Alzheimer’s disease, hence the extensive research in the application of computational modeling to understand the alterations in the functional connectivity. Before functional networks and their alterations can be modeled, optimal data preprocessing steps and computational approaches need to be elected. Approaches to data preprocessing and computational modeling including seed-based, data-driven, and graph theory are discussed. The challenges associated with these approaches and their implications to research and applications are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  • Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3(2):e17

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Agosta F, Pievani M, Geroldi C, Copetti M, Frisoni GB, Filippi M (2012) Resting state fMRI in Alzheimer's disease: beyond the default mode network. Neurobiol Aging 33(8):1564–1578

    Article  PubMed  Google Scholar 

  • Allen G, Barnard H, McColl R, Hester AL, Fields JA, Weiner MF, Ringe WK, Lipton AM, Brooker M, McDonald E (2007) Reduced hippocampal functional connectivity in Alzheimer disease. Arch Neurol 64(10):1482–1487

    Article  PubMed  Google Scholar 

  • Bai F, Zhang Z, Watson DR, Yu H, Shi Y, Yuan Y, Zang Y, Zhu C, Qian Y (2009) Abnormal functional connectivity of hippocampus during episodic memory retrieval processing network in amnestic mild cognitive impairment. Biol Psychiatry 65(11):951–958

    Article  PubMed  Google Scholar 

  • Birn RM, Molloy EK, Patriat R, Parker T, Meier TB, Kirk GR, Nair VA, Meyerand ME, Prabhakaran V (2013) The effect of scan length on the reliability of resting-state fMRI connectivity estimates. NeuroImage 83:550–558

    Article  PubMed  Google Scholar 

  • Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34(4):537–541

    Article  CAS  PubMed  Google Scholar 

  • Brier MR, Thomas JB, Ances BM (2014) Network dysfunction in Alzheimer's disease: refining the disconnection hypothesis. Brain Connect 4(5):299–311

    Article  PubMed  PubMed Central  Google Scholar 

  • Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, Sheline YI, Klunk WE, Mathis CA, Morris JC (2005) Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25(34):7709–7717

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Buzsáki G, Draguhn A (2004) Neuronal oscillations in cortical networks. Science 304(5679):1926–1929

    Article  PubMed  CAS  Google Scholar 

  • Cao J, Worsley K (1999) The geometry of correlation fields with an application to functional connectivity of the brain. Ann Appl Probab 9(4):1021–1057

    Article  Google Scholar 

  • Castellanos FX, Di Martino A, Craddock RC, Mehta AD, Milham MP (2013) Clinical applications of the functional connectome. NeuroImage 80:527–540

    Article  CAS  PubMed  Google Scholar 

  • Chaubey YP (2000) Resampling methods: a practical guide to data analysis. Technometrics 42(3):311–311

    Article  Google Scholar 

  • Daianu M, Jahanshad N, Nir TM, Leonardo CD, Jack CR, Weiner MW, Bernstein MA, Thompson PM (2014) Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. Comput Diffus MRI 39:55–64. Springer

    Article  Google Scholar 

  • de Haan W, Pijnenburg YA, Strijers RL, van der Made Y, van der Flier WM, Scheltens P, Stam CJ (2009) Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory. BMC Neurosci 10(1):101

    Article  PubMed  PubMed Central  Google Scholar 

  • Delbeuck X, Van der Linden M, Collette F (2003) Alzheimer'disease as a disconnection syndrome? Neuropsychol Rev 13(2):79–92

    Article  CAS  PubMed  Google Scholar 

  • Derflinger S, Sorg C, Gaser C, Myers N, Arsic M, Kurz A, Zimmer C, Wohlschläger A, Mühlau M (2011) Grey-matter atrophy in Alzheimer's disease is asymmetric but not lateralized. J Alzheimers Dis 25(2):347–357

    Article  CAS  PubMed  Google Scholar 

  • Evans AC, Collins DL, Mills S, Brown E, Kelly R, Peters TM (1993) 3D statistical neuroanatomical models from 305 MRI volumes. In: 1993 IEEE conference record nuclear science symposium and medical imaging conference. IEEE, p 1813–1817

    Google Scholar 

  • Filippi M, Agosta F (2011) Structural and functional network connectivity breakdown in Alzheimer's disease studied with magnetic resonance imaging techniques. J Alzheimers Dis 24(3):455–474

    Article  PubMed  Google Scholar 

  • Filippini N, MacIntosh BJ, Hough MG, Goodwin GM, Frisoni GB, Smith SM, Matthews PM, Beckmann CF, Mackay CE (2009) Distinct patterns of brain activity in young carriers of the APOE-ε4 allele. Proceedings of the National Academy of Sciences 106(17):7209–14

    Google Scholar 

  • Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8(9):700–711

    Article  CAS  PubMed  Google Scholar 

  • Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci 102(27):9673–9678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Friston KJ, Frith CD, Liddle PF, Frackowiak RS (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13(1):5–14

    Article  CAS  PubMed  Google Scholar 

  • Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RS (1994) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2(4):189–210

    Article  Google Scholar 

  • Gould R, Arroyo B, Brown R, Owen A, Bullmore E, Howard R (2006) Brain mechanisms of successful compensation during learning in Alzheimer disease. Neurology 67(6):1011–1017

    Article  CAS  PubMed  Google Scholar 

  • Greicius MD, Krasnow B, Reiss AL, Menon V (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci 100(1):253–258

    Article  CAS  PubMed  Google Scholar 

  • Hlinka J, Paluš M, Vejmelka M, Mantini D, Corbetta M (2011) Functional connectivity in resting-state fMRI: is linear correlation sufficient? NeuroImage 54(3):2218–2225

    Article  PubMed  Google Scholar 

  • Jack CR, Petersen RC, Xu Y, O’brien P, Smith GE, Ivnik RJ, Boeve BF, Tangalos EG, Kokmen E (2000) Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55(4):484–490

    Article  PubMed  Google Scholar 

  • Janke AL, Gd Z, Rose SE, Griffin M, Chalk JB, Galloway GJ (2001) 4D deformation modeling of cortical disease progression in Alzheimer's dementia. Magn Reson Med 46(4):661–666

    Article  CAS  PubMed  Google Scholar 

  • Jie B, Zhang D, Suk H-I, Wee C-Y, Shen D (2013) Integrating multiple network properties for MCI identification. In: International workshop on machine learning in medical imaging. Springer, pp 9–16

    Google Scholar 

  • Koch K, Myers NE, Göttler J, Pasquini L, Grimmer T, Förster S, Manoliu A, Neitzel J, Kurz A, Förstl H (2015) Disrupted intrinsic networks link amyloid-β pathology and impaired cognition in prodromal Alzheimer's disease. Cereb Cortex 25(12):4678–4688

    Article  PubMed  Google Scholar 

  • Kruschwitz JD, List D, Waller L, Rubinov M, Walter H (2015) GraphVar: a user-friendly toolbox for comprehensive graph analyses of functional brain connectivity. J Neurosci Methods 245:107–115

    Article  CAS  PubMed  Google Scholar 

  • Laird AR, Robinson JL, McMillan KM, Tordesillas-Gutiérrez D, Moran ST, Gonzales SM, Ray KL, Franklin C, Glahn DC, Fox PT (2010) Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform. NeuroImage 51(2):677–683

    Article  PubMed  Google Scholar 

  • Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Physical review letters 87(19):198701

    Google Scholar 

  • Li K, Guo L, Nie J, Li G, Liu T (2009) Review of methods for functional brain connectivity detection using fMRI. Comput Med Imaging Graph 33(2):131–139

    Article  PubMed  Google Scholar 

  • Loewenstein DA, Barker WW, Chang J-Y, Apicella A, Yoshii F, Kothari P, Levin B, Duara R (1989) Predominant left hemisphere metabolic dysfunction in dementia. Arch Neurol 46(2):146–152

    Article  CAS  PubMed  Google Scholar 

  • Minoshima S, Giordani B, Berent S, Frey KA, Foster NL, Kuhl DE (1997) Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease. Ann Neurol 42(1):85–94

    Article  CAS  PubMed  Google Scholar 

  • Pellegrini E, Ballerini L, Hernandez MCV, Chappell FM, González-Castro V, Anblagan D, Danso S, Muñoz-Maniega S, Job D, Pernet C (2018) Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimer's Dementia (Amst) 10:519–535

    Article  Google Scholar 

  • Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE (2011) Statistical parametric mapping: the analysis of functional brain images. Elsevier

    Google Scholar 

  • Rachakonda S, Egolf E, Correa N, Calhoun V (2007) Group ICA of fMRI toolbox (GIFT) manual

    Google Scholar 

  • Raichle ME, Mintun MA (2006) Brain work and brain imaging. Annu Rev Neurosci 29:449–476

    Article  CAS  PubMed  Google Scholar 

  • Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci 98(2):676–682

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reddan MC, Lindquist MA, Wager TD (2017) Effect size estimation in neuroimaging. JAMA Psychiat 74(3):207–208

    Article  Google Scholar 

  • Rombouts S, Stam C, Kuijer J, Scheltens P, Barkhof F (2003) Identifying confounds to increase specificity during a “no task condition”: evidence for hippocampal connectivity using fMRI. NeuroImage 20(2):1236–1245

    Article  CAS  PubMed  Google Scholar 

  • Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SA, Maris E, Barkhof F, Scheltens P, Stam CJ (2010) Loss of ‘small-world’networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 5(11)

    Google Scholar 

  • Sexton CE, Kalu UG, Filippini N, Mackay CE, Ebmeier KP (2011) A meta-analysis of diffusion tensor imaging in mild cognitive impairment and Alzheimer's disease. Neurobiology of aging 32(12):2322. e2325–2322. e2318

    Article  Google Scholar 

  • Shirer WR, Jiang H, Price CM, Ng B, Greicius MD (2015) Optimization of rs-fMRI pre-processing for enhanced signal-noise separation, test-retest reliability, and group discrimination. NeuroImage 117:67–79

    Article  PubMed  Google Scholar 

  • Shulman GL, Fiez JA, Corbetta M, Buckner RL, Miezin FM, Raichle ME, Petersen SE (1997) Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. J Cogn Neurosci 9(5):648–663

    Article  CAS  PubMed  Google Scholar 

  • Smitha K, Akhil Raja K, Arun K, Rajesh P, Thomas B, Kapilamoorthy T, Kesavadas C (2017) Resting state fMRI: a review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J 30(4):305–317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, He Y, Yan CG, Zang YF (2011) REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS One 6:e25031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sorg C, Riedl V, Mühlau M, Calhoun VD, Eichele T, Läer L, Drzezga A, Förstl H, Kurz A, Zimmer C (2007) Selective changes of resting-state networks in individuals at risk for Alzheimer's disease. Proc Natl Acad Sci 104(47):18760–18765

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Stam CJ, Jones B, Nolte G, Breakspear M, Scheltens P (2007) Small-world networks and functional connectivity in Alzheimer's disease. Cereb Cortex 17(1):92–99

    Article  CAS  PubMed  Google Scholar 

  • Stern Y (2006) Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord 20(2):112–117

    Article  PubMed  Google Scholar 

  • Sun FT, Miller LM, D'Esposito M (2004) Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage 21(2):647–658

    Article  PubMed  Google Scholar 

  • Supekar K, Menon V, Rubin D, Musen M, Greicius MD (2008) Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput Biol 4(6)

    Google Scholar 

  • Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain-3-dimensional proportional system. An approach to cerebral imaging. Stuttgart: Thieme

    Google Scholar 

  • Thirion B, Pinel P, Mériaux S, Roche A, Dehaene S, Poline JB (2007) Analysis of a large fMRI cohort: statistical and methodological issues for group analyses. NeuroImage 35(1):105–120

    Article  PubMed  Google Scholar 

  • Uddin LQ (2017) Mixed signals: on separating brain signal from noise. Trends Cogn Sci 21(6):405–406

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang L, Zang Y, He Y, Liang M, Zhang X, Tian L, Wu T, Jiang T, Li K (2006) Changes in hippocampal connectivity in the early stages of Alzheimer's disease: evidence from resting state fMRI. NeuroImage 31(2):496–504

    Article  PubMed  Google Scholar 

  • Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, Jiang T (2007) Altered functional connectivity in early Alzheimer's disease: a resting-state fMRI study. Hum Brain Mapp 28(10):967–978

    Article  PubMed  Google Scholar 

  • Wang J, Wang X, Xia M, Liao X, Evans A, He Y (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:386

    CAS  PubMed  PubMed Central  Google Scholar 

  • Whitfield-Gabrieli S, Nieto-Castanon A (2012) Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2(3):125–141

    Article  PubMed  Google Scholar 

  • Whitwell JL, Przybelski SA, Weigand SD, Knopman DS, Boeve BF, Petersen RC, Jack Jr CR (2007) 3D maps from multiple MRI illustrate changing atrophy patterns as subjects progress from mild cognitive impairment to Alzheimer's disease. Brain 130(7):1777–1786

    Article  PubMed  Google Scholar 

  • Woo CW, Chang LJ, Lindquist MA, Wager TD (2017) Building better biomarkers: brain models in translational neuroimaging. Nat Neurosci 20(3):365–377

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wu CW, Chen C-L, Liu P-Y, Chao Y-P, Biswal BB, Lin C-P (2011) Empirical evaluations of slice-timing, smoothing, and normalization effects in seed-based, resting-state functional magnetic resonance imaging analyses. Brain Connect 1(5):401–410

    Article  PubMed  Google Scholar 

  • Yan C, Zang Y (2010) DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13

    Google Scholar 

  • Yildirim E, Soncu Buyukiscan E (2019) Default mode network connectivity in Alzheimer’s disease. Available via ResearchGate https://bit.ly/3azAElu. Accessed 20 March 2020

  • Zhang H-Y, Wang S-J, Xing J, Liu B, Ma Z-L, Yang M, Zhang Z-J, Teng G-J (2009) Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease. Behav Brain Res 197(1):103–108

    Article  PubMed  Google Scholar 

  • Zhou Y, Dougherty Jr JH, Hubner KF, Bai B, Cannon RL, Hutson RK (2008) Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment. Alzheimers Dement 4(4):265–270

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biomedical Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, UK

    Ludmila Kucikova

  2. Edinburgh Dementia Prevention and Centre Clinical Brain Sciences, University of Edinburgh Medical School University of Edinburgh, Edinburgh, UK

    Ludmila Kucikova, Samuel O. Danso, Graciela Muniz-Terrera & Craig W. Ritchie

Authors
  1. Ludmila Kucikova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel O. Danso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Graciela Muniz-Terrera
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Craig W. Ritchie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ludmila Kucikova .

Editor information

Editors and Affiliations

  1. Department of Informatics, Ionian University Department of Informatics, Corfu, Greece

    Panayiotis Vlamos

  2. Wilfrid Laurier University, Brantford, Canada

    Ilias S. Kotsireas

  3. ResearchGate, ETH Zurich ResearchGate, Zurich, Zürich, Switzerland

    Ioannis Tarnanas

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Kucikova, L., Danso, S.O., Muniz-Terrera, G., Ritchie, C.W. (2021). Computational Modeling of Neural Networks of the Human Brain. In: Vlamos, P., Kotsireas, I.S., Tarnanas, I. (eds) Handbook of Computational Neurodegeneration. Springer, Cham. https://doi.org/10.1007/978-3-319-75479-6_63-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-75479-6_63-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-75479-6

  • Online ISBN: 978-3-319-75479-6

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics

Search

Navigation