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Use of Nuclear Medicine Molecular Neuroimaging to Model Brain Molecular Connectivity

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PET and SPECT in Neurology

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Abstract

We introduce basic concepts of brain networks and discuss methods to model and analyze brain molecular connectivity using positron emission tomography (PET) and single-photon emission computed tomography (SPECT). Basic elements of network analytic methods, including graph theory, and the connectivity matrix as a basis for network analysis will be discussed in more detail. Statistical methods to compare networks will be reviewed. A specific brain network analysis method called sparse inverse covariance estimation (SICE) is presented as an alternative to Pearson correlation to estimate the brain molecular connectivity matrix. Finally, we will discuss examples from published research to illustrate the practical application of brain molecular connectivity analysis concepts.

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References

  • Anticevic A, Cole MW, Murray JD, Corlett PR, Wang XJ, Krystal JH (2012) The role of default network deactivation in cognition and disease. Trends Cogn Sci 16(12):584–592

    Article  PubMed  PubMed Central  Google Scholar 

  • Bassett DS, Bullmore ET (2017) Small-world brain networks revisited. Neuroscientist 23(5):499–516

    Article  PubMed  Google Scholar 

  • Batalle D, Munoz-Moreno E, Figueras F, Bargallo N, Eixarch E, Gratacos E (2013) Normalization of similarity-based individual brain networks from gray matter MRI and its association with neurodevelopment in infants with intrauterine growth restriction. NeuroImage 83:901–911

    Article  PubMed  Google Scholar 

  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B 1:289–300

    Google Scholar 

  • Biggs N, Lloyd E, Wilson R (1986) Graph theory. Oxford University Press, Oxford, pp 1736–1936

    Google Scholar 

  • Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82(4):239–259

    Article  CAS  PubMed  Google Scholar 

  • Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198

    Article  CAS  PubMed  Google Scholar 

  • Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13(5):336–349

    Article  CAS  PubMed  Google Scholar 

  • Caminiti SP, Tettamanti M, Sala A, Presotto L, Iannaccone S, Cappa SF et al (2017) Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J Cereb Blood Flow Metab 37(4):1311–1325

    Article  CAS  PubMed  Google Scholar 

  • Clark CM, Stoessl AJ (1986) Glucose use correlations: a matter of inference. J Cereb Blood Flow Metab 6(4):511–512

    Article  CAS  PubMed  Google Scholar 

  • David O, Guillemain I, Saillet S, Reyt S, Deransart C, Segebarth C et al (2008) Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol 6(12):2683–2697

    Article  CAS  PubMed  Google Scholar 

  • Eidelberg D, Moeller JR, Dhawan V, Spetsieris P, Takikawa S, Ishikawa T et al (1994) The metabolic topography of parkinsonism. J Cereb Blood Flow Metab 14(5):783–801

    Article  CAS  PubMed  Google Scholar 

  • Fornito A, Bullmore ET (2015) Connectomics: a new paradigm for understanding brain disease. Eur Neuropsychopharmacol 25(5):733–748

    Article  CAS  PubMed  Google Scholar 

  • Fornito A, Zalesky A, Breakspear M (2013) Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 80:426–444

    Article  PubMed  Google Scholar 

  • Friston KJ (2011) Functional and effective connectivity: a review. Brain Connect 1(1):13–36

    Article  PubMed  Google Scholar 

  • Garcia JO, Ashourvan A, Muldoon SF, Vettel JM, Bassett DS (2018) Applications of community detection techniques to brain graphs: algorithmic considerations and implications for neural function. Proc IEEE Inst Electr Electron Eng 106(5):846–867

    Article  PubMed  PubMed Central  Google Scholar 

  • Gu SC, Ye Q, Yuan CX (2019) Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson's disease: a systematic review and meta-analysis. Rev Neurosci

    Google Scholar 

  • Hahn A, Lanzenberger R, Kasper S (2019) Making sense of connectivity. Int J Neuropsychopharmacol 22(3):194–207

    Article  CAS  PubMed  Google Scholar 

  • Honey CJ, Thivierge JP, Sporns O (2010) Can structure predict function in the human brain? NeuroImage 52(3):766–776

    Article  PubMed  Google Scholar 

  • Horwitz B, Duara R, Rapoport SI (1984) Intercorrelations of glucose metabolic rates between brain regions: application to healthy males in a state of reduced sensory input. J Cereb Blood Flow Metab 4(4):484–499

    Article  CAS  PubMed  Google Scholar 

  • Horwitz B, Grady CL, Schlageter NL, Duara R, Rapoport SI (1987) Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer's disease. Brain Res 407(2):294–306

    Article  CAS  PubMed  Google Scholar 

  • Hosseini SM, Hoeft F, Kesler SR (2012) GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS One 7(7):e40709

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang S, Li J, Sun L, Ye J, Fleisher A, Wu T et al (2010) Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. NeuroImage 50(3):935–949

    Article  PubMed  Google Scholar 

  • Koch MA, Norris DG, Hund-Georgiadis M (2002) An investigation of functional and anatomical connectivity using magnetic resonance imaging. NeuroImage 16(1):241–250

    Article  PubMed  Google Scholar 

  • Lee DS, Kang H, Kim H, Park H, Oh JS, Lee JS et al (2008) Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults. Eur J Nucl Med Mol Imaging 35(9):1681–1691

    Article  PubMed  Google Scholar 

  • Li Q, Wu X, Xie F, Chen K, Yao L, Zhang J et al (2018) Aberrant connectivity in mild cognitive impairment and Alzheimer disease revealed by multimodal neuroimaging data. Neurodegener Dis 18(1):5–18

    Article  PubMed  Google Scholar 

  • Liu H, Roeder K, Wasserman L (2010) Stability approach to regularization selection (StARS) for high dimensional graphical models. Adv Neural Inf Process Syst 24:1432–1440

    PubMed  PubMed Central  Google Scholar 

  • Liu Z, Ke L, Liu H, Huang W, Hu Z (2014) Changes in topological organization of functional PET brain network with normal aging. PLoS One 9(2):e88690

    Article  PubMed  PubMed Central  Google Scholar 

  • Manzanera OM, Meles SK, Leenders KL, Renken RJ, Pagani M, Arnaldi D et al (2019) Scaled subprofile modeling and convolutional neural networks for the identification of Parkinson's disease in 3D nuclear imaging data. Int J Neural Syst 1950010

    Google Scholar 

  • Melie-García L, Sanabria-Diaz G, Iturria-Medina Y, Alemán-Gómez Y (2010) MorphoConnect: toolbox for studying structural brain networks using morphometric descriptors. In: 16th annual meeting of the Organization for Human Brain Mapping. Human Brain Mapping (HBM), Barcelona

    Google Scholar 

  • Melie-Garcia L, Sanabria-Diaz G, Sanchez-Catasus C (2013) Studying the topological organization of the cerebral blood flow fluctuations in resting state. NeuroImage 64:173–184

    Article  PubMed  Google Scholar 

  • Metter EJ, Riege WH, Kuhl DE, Phelps ME (1984) Cerebral metabolic relationships for selected brain regions in healthy adults. J Cereb Blood Flow Metab 4(1):1–7

    Article  CAS  PubMed  Google Scholar 

  • Mijalkov M, Kakaei E, Pereira JB, Westman E, Volpe G (2017) BRAPH: a graph theory software for the analysis of brain connectivity. PLoS One 12(8):e0178798

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Mišić B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR et al (2016) Network-level structure-function relationships in human neocortex. Cereb Cortex 26(7):3285–3296

    Article  PubMed  PubMed Central  Google Scholar 

  • Moeller JR, Strother SC, Sidtis JJ, Rottenberg DA (1987) Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data. J Cereb Blood Flow Metab 7(5):649–658

    Article  CAS  PubMed  Google Scholar 

  • Monakow C (1914) Die Lokalisation im Grosshirn und der Abbau der Funktion durch Kortikale Herde. J. F. Bergman, Wiesbaden, pp 26–34

    Google Scholar 

  • Murphy K, Fox MD (2017) Towards a consensus regarding global signal regression for resting state functional connectivity MRI. NeuroImage 154:169–173

    Article  PubMed  Google Scholar 

  • Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 44(3):893–905

    Article  PubMed  Google Scholar 

  • Passow S, Specht K, Adamsen TC, Biermann M, Brekke N, Craven AR et al (2015) Default-mode network functional connectivity is closely related to metabolic activity. Hum Brain Mapp 36(6):2027–2038

    Article  PubMed  PubMed Central  Google Scholar 

  • Pereira JB, Strandberg TO, Palmqvist S, Volpe G, van Westen D, Westman E et al (2018) Amyloid network topology characterizes the progression of Alzheimer's disease during the Predementia stages. Cereb Cortex 28(1):340–349

    Article  PubMed  Google Scholar 

  • Power JD, Schlaggar BL, Lessov-Schlaggar CN, Petersen SE (2013) Evidence for hubs in human functional brain networks. Neuron 79(4):798–813

    Article  CAS  PubMed  Google Scholar 

  • Raj A, Mueller SG, Young K, Laxer KD, Weiner M (2010) Network-level analysis of cortical thickness of the epileptic brain. NeuroImage 52(4):1302–1313

    Article  CAS  PubMed  Google Scholar 

  • Riedl V, Utz L, Castrillón G, Grimmer T, Rauschecker JP, Ploner M et al (2016) Metabolic connectivity mapping reveals effective connectivity in the resting human brain. Proc Natl Acad Sci U S A 113(2):428–433

    Article  CAS  PubMed  Google Scholar 

  • Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52(3):1059–1069

    Article  PubMed  Google Scholar 

  • Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A et al (2012) Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2(1):25–32

    Article  PubMed  PubMed Central  Google Scholar 

  • Saggar M, Hosseini SM, Bruno JL, Quintin EM, Raman MM, Kesler SR et al (2015) Estimating individual contribution from group-based structural correlation networks. NeuroImage 120:274–284

    Article  PubMed  Google Scholar 

  • Sala A, Perani D (2019) Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using positron emission tomography. Front Neurosci 13:617

    Article  PubMed  PubMed Central  Google Scholar 

  • Sala A, Caminiti SP, Presotto L, Premi E, Pilotto A, Turrone R et al (2017) Altered brain metabolic connectivity at multiscale level in early Parkinson's disease. Sci Rep 7(1):4256

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Sanabria-Diaz G, Martinez-Montes E, Melie-Garcia L (2013) Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer's disease and mild cognitive impairment. PLoS One 8(7):e68860

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sanchez-Catasus CA, Sanabria-Diaz G, Willemsen A, Martinez-Montes E, Samper-Noa J, Aguila-Ruiz A et al (2017) Subtle alterations in cerebrovascular reactivity in mild cognitive impairment detected by graph theoretical analysis and not by the standard approach. Neuroimage Clin 15:151–160

    Article  PubMed  PubMed Central  Google Scholar 

  • Sanchez-Catasus CA, Willemsen A, Boellaard R, Juarez-Orozco LE, Samper-Noa J, Aguila-Ruiz A et al (2018) Episodic memory in mild cognitive impairment inversely correlates with the global modularity of the cerebral blood flow network. Psychiatry Res Neuroimaging 282:73–81

    Article  PubMed  Google Scholar 

  • Sepulcre J, Sabuncu MR, Becker A, Sperling R, Johnson KA (2013) In vivo characterization of the early states of the amyloid-beta network. Brain 136(Pt 7):2239–2252

    Article  PubMed  PubMed Central  Google Scholar 

  • Skudlarski P, Jagannathan K, Calhoun VD, Hampson M, Skudlarska BA, Pearlson G (2008) Measuring brain connectivity: diffusion tensor imaging validates resting state temporal correlations. NeuroImage 43(3):554–561

    Article  PubMed  Google Scholar 

  • Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1(4):e42

    Article  PubMed  PubMed Central  Google Scholar 

  • Tijms BM, Series P, Willshaw DJ, Lawrie SM (2012) Similarity-based extraction of individual networks from gray matter MRI scans. Cereb Cortex 22(7):1530–1541

    Article  PubMed  Google Scholar 

  • Titov D, Diehl-Schmid J, Shi K, Perneczky R, Zou N, Grimmer T et al (2017) Metabolic connectivity for differential diagnosis of dementing disorders. J Cereb Blood Flow Metab 37(1):252–262

    Article  CAS  PubMed  Google Scholar 

  • Tomasi DG, Shokri-Kojori E, Wiers CE, Kim SW, Demiral SB, Cabrera EA et al (2017) Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest. J Cereb Blood Flow Metab 37(12):3659–3670

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1):273–289

    Article  CAS  PubMed  Google Scholar 

  • Veronese M, Moro L, Arcolin M, Dipasquale O, Rizzo G, Expert P et al (2019) Covariance statistics and network analysis of brain PET imaging studies. Sci Rep 9(1):2496

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Watts DJ, Strogatz SH (1998) Collective dynamics of 'small-world' networks. Nature 393(6684):440–442

    Article  CAS  PubMed  Google Scholar 

  • Yakushev I, Drzezga A, Habeck C (2017) Metabolic connectivity: methods and applications. Curr Opin Neurol 30(6):677–685

    Article  PubMed  Google Scholar 

  • Zhou L, Wang Y, Li Y, Yap PT, Shen D (2011) Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures. PLoS One 6(7):e21935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI, USA

    Carlos A. Sanchez-Catasus, Martijn L. T. M. Müller & Nicolaas I. Bohnen

  2. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Carlos A. Sanchez-Catasus & Rudi A. J. O. Dierckx

  3. Morris K. Udall Center of Excellence for Parkinson’s Disease Research, University of Michigan, Ann Arbor, MI, USA

    Martijn L. T. M. Müller & Nicolaas I. Bohnen

  4. Department of Neurology and Alzheimer Center, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Peter Paul De Deyn

  5. Department of Neurology, University of Michigan, Ann Arbor, MI, USA

    Nicolaas I. Bohnen

  6. Neurology Service and GRECC, Veterans Administration Ann Arbor Healthcare System, Ann Arbor, MI, USA

    Nicolaas I. Bohnen

  7. Laboratoire de Recherche en Neuroimagerie (LREN), Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland

    Lester Melie-Garcia

  8. Applied Signal Processing Group, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Lester Melie-Garcia

Authors
  1. Carlos A. Sanchez-Catasus
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  2. Martijn L. T. M. Müller
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  3. Peter Paul De Deyn
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  4. Rudi A. J. O. Dierckx
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  5. Nicolaas I. Bohnen
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  6. Lester Melie-Garcia
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Corresponding author

Correspondence to Carlos A. Sanchez-Catasus .

Editor information

Editors and Affiliations

  1. Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands

    Rudi A. J. O. Dierckx

  2. Faculty of Electrical Engineering, Medical Engineering and Computer Science, Offenburg University, Offenburg, Germany

    Andreas Otte

  3. Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands

    Erik F. J. de Vries

  4. Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands

    Aren van Waarde

  5. Neurology, University Medical Center Groningen, Groningen, The Netherlands

    Klaus L. Leenders

A Lexicon of the Most Commonly Used Network Metrics

A Lexicon of the Most Commonly Used Network Metrics

  • Shortest path: Topological distance between two nodes, also called Length, as the minimum number of edges between two nodes.

  • Path length: Shortest path of a given node to each of the other nodes.

  • Characteristic path length: Average path length node across all nodes.

  • Global efficiency: Average of the inverse shortest path from a given node to all other nodes. At the network level, it is the average of the global efficiency of all nodes.

  • Triad: Formed when a node is connected to any two connected neighbors.

  • Clustering coefficient: The ratio between the number of triads present around a node and the maximum number of triads that could be formed around that node. At the network level, it is the average of the clustering coefficient of all nodes.

  • Local efficiency (nodal level): The global efficiency calculated on the subgraph created by the node’s neighbors. At the network level, it is the average of the local efficiency of all nodes.

  • Modularity: The extent to which a network can be subdivided into modules (communities of nodes) with a maximal within-module and minimal between-module connectivity.

  • The small-worldness (σ): The extent to which a network shows an optimal balance between characteristic path length (integration) and average clustering coefficient (segregation).

  • Degree: The number of direct connections that a node has with other nodes. At the network level, it is the average of the degrees of all nodes.

  • Strength: The sum of the weights of all edges connected to a node. At the network level, it is the average of the strength of all nodes.

  • Closeness centrality: The same as the global efficiency at the nodal level.

  • Betweenness centrality: The ratio between all shortest paths that pass through the node and all shortest paths in the graph.

  • Participation coefficient: The ratio between the number of connections that the node has outside its module (intermodular) and the total number of connections in the whole network.

  • Within-module degree z-score: The nodal degree but restricted to only connections inside the module to which that node belongs.

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Sanchez-Catasus, C.A., Müller, M.L.T.M., De Deyn, P.P., Dierckx, R.A.J.O., Bohnen, N.I., Melie-Garcia, L. (2021). Use of Nuclear Medicine Molecular Neuroimaging to Model Brain Molecular Connectivity. In: Dierckx, R.A.J.O., Otte, A., de Vries, E.F.J., van Waarde, A., Leenders, K.L. (eds) PET and SPECT in Neurology. Springer, Cham. https://doi.org/10.1007/978-3-030-53168-3_8

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  • DOI: https://doi.org/10.1007/978-3-030-53168-3_8

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