BRAIN CONNECTIVITY Volume 7, Number 8, 2017 ª Mary Ann Liebert, Inc. DOI: 10.1089/brain.2017.0539
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On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture Maxime Chamberland,1,2 Gabriel Girard,3,4 Michae¨l Bernier,1 David Fortin,5 Maxime Descoteaux,3 and Kevin Whittingstall1
Abstract
Fingerprint patterns derived from functional connectivity (FC) can be used to identify subjects across groups and sessions, indicating that the topology of the brain substantially differs between individuals. However, the source of FC variability inferred from resting-state functional magnetic resonance imaging remains unclear. One possibility is that these variations are related to individual differences in white matter structural connectivity (SC). However, directly comparing FC with SC is challenging given the many potential biases associated with quantifying their respective strengths. In an attempt to circumvent this, we employed a recently proposed test–retest approach that better quantifies inter-subject variability by first correcting for intra-subject nuisance variability (i.e., head motion, physiological differences in brain state, etc.) that can artificially influence FC and SC measures. Therefore, rather than directly comparing the strength of FC with SC, we asked whether brain regions with, for example, low inter-subject FC variability also exhibited low SC variability. From this, we report two main findings: First, at the whole-brain level, SC variability was significantly lower than FC variability, indicating that an individual’s structural connectome is far more similar to another relative to their functional counterpart even after correcting for noise. Second, although FC and SC variability were mutually low in some brain areas (e.g., primary somatosensory cortex) and high in others (e.g., memory and language areas), the two were not significantly correlated across all cortical and sub-cortical regions. Taken together, these results indicate that even after correcting for factors that may differently affect FC and SC, the two, nonetheless, remain largely independent of one another. Further work is needed to understand the role that direct anatomical pathways play in supporting vascular-based measures of FC and to what extent these measures are dictated by anatomical connectivity. Keywords:
connectivity; diffusion MRI; inter-subject variability; resting-state fMRI; tractography
Introduction
E
ach brain is uniquely organized, both functionally (Wang and Liu, 2014) and structurally (Lange et al., 1997). The cerebral architecture is known to evolve across time due to brain development and plasticity (Poldrack et al., 2015; Wierenga et al., 2016), and the complexity of these changes during development is often reflected by regional differences across subjects. These so-called inter-individual differences in brain function and structure are of great interest as they allow
researchers to directly compare how these cerebral differences correlate with other measures such as—among others—musical (Gaser and Schlaug, 2003) and athletic (Yarrow et al., 2009) ability. Recently, two magnetic resonance imaging (MRI)-based methodologies have emerged as non-invasive measures of the brain’s functional connectivity (FC) and structural connectivity (SC). Functional MRI (fMRI) provides fourdimensional whole-brain images that reflect changes in cortical blood flow, volume, and oxygen as measured by the
1 Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Canada. 2 Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom. 3 Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, Canada. 4 Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. 5 Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Canada.
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blood-oxygenation-level-dependent (BOLD) signal (Bandettini et al., 1993; Kwong et al., 1992; Turner, 1992). At rest, the spontaneous low-frequency fluctuations (2M seeds) also aimed at avoiding potential seed bias and ensured a stabilized edge metric (i.e., weights of M represented by the normalized streamline count) (Colon-Perez et al., 2015; Girard et al., 2014). In a pioneer study by the group of Hagmann and associates (2007), the authors reported that one-third to half of streamlines did not reach the WM/GM interface mask and, thus, were excluded from the SC analysis. The tractography algorithm employed in this study ensured that streamlines did not prematurely terminate in the WM. The inclusion of subscortical regions (e.g., thalamus) also enabled a better representation of important pathways such as the optic radiations in the derived connectivity matrices. Weak link between functional and structural inter-subject variability
As described earlier, certain brain regions showing the SC lowest VFC inter also exhibited low Vinter , as did areas with the FC highest Vinter . However, across all regions, VSC inter was significantly lower than VFC (0.07 – 0.04 vs. 0.25 – 0.04, respecinter tively) and they were poorly correlated (r = 0.15, p = 0.05). It is possible that functional variability is greater on average than structural variability because functional variability can be sensitive to previous life experience and genetic factors (Vaidya and Gordon, 2013) and can vary dynamically (Hutchison et al., 2013). Indeed, FC is known to reflect a combination of dynamic signals that varies across the brain of each individual (Smith et al., 2013). For example, in the motor cortex, both variability measures were relatively low, though VFC inter was almost five times higher than VSC inter , indicating that the SC profile of the motor cortex is far more similar across healthy subjects than that of its FC profile. The exact reasons behind this mismatch are unclear. One possible explanation for this result is that the bulk of the reconstructed streamlines emanating from the motor cortex are made up of either the CC or the CST, with little to no terminations observed in other cortical areas. FC, on the other hand, may be visible in these areas due to indirect anatomical connections, thus explaining its higher inter-individual variability as shown in Supplementary Figure S4. Even though FC of the left central sulcus showed similar patterns between different subjects, individual differences are identifiable in the cerebellum and are supported by direct anatomical pathways. However, SC revealed no direct link between parahippocampal connectivity and the left central sulcus. Indirect structural pathways may, therefore, play a key role in explaining the SC mismatch between VFC inter and Vinter , which remains an active area of research nowadays ( Jbabdi et al., 2015). Another possible explanation is that diffusion- and BOLDbased measures of brain structure and function, respectively,
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are sensitive to different sources of noise. For instance, BOLD signal correlations in areas that are highly vascularized may be artificially inflated compared with other regions (Curtis et al., 2014; Kazan et al., 2016; Vigneau-Roy et al., 2014). On the other hand, blood vessels may interfere with diffusion measures, potentially leading to an incorrect estimation of SC due to intravoxel incoherent motion (Le Bihan, 2008). Taken together, the sum of all those sources of variability effectively contributes toward establishing inter-subject variability. Clinical implications. Fingerprint patterns of FC can be used to identify subjects across sessions and individuals (Finn et al., 2015), indicating that the topology of the brain substantially differs between individuals. Assuming that patterns of spatial heterogeneity are also present in SC, our finding is also of clinical importance with the recent advances in surgical approaches based on connectomics (Griffa et al., 2013; Meola et al., 2016; Yu et al., 2016). For instance, a region of inter-individual variability can have potential influence on preoperative planning as the spatial localization of this area may vary from one subject to another. As of today, rs-fMRI imaging protocols are often used in clinical studies to guide surgical gesture. In addition, tractography has been shown to be valuable for surgical planning, and neurosurgeons and radiologists are mostly convinced of the importance of achieving an accurate delineation of WM pathways using tractography (Duffau, 2005; Leclercq et al., 2010; Nimsky et al., 2016). Defining accurate boundaries is critical in surgery targeting frontal and temporal regions (e.g., spaceoccupying lesion, temporal lobe epilepsy), as these areas exhibit higher variability between subjects. With the advent of personalized medicine (Wang et al., 2015), we believe that such a tailored mapping may improve decision making and is likely to impact pre-operative planning approaches, where capturing the idiosyncrasies of individuals is paramount. Conclusion
In this study, we generated brain functional and structural inter-subject variability maps by taking into account the intra-subject variability using a test–retest approach. With this approach, we showed that structural inter-subject variability was globally lower than functional variability. Our approach also revealed that variability indices were smallest in motor areas for both functional and structural analysis, indicating proper reproducibility within this region. Our results also indicate that functional and structural variability profiles were less consistent near the memory and language areas of the brain. These findings suggest that, at least in these regions, functional inter-subject variability can be explained by the structural organization of the brain. In other words, stable FC does not always imply stable SC for the rest of the brain. Altogether, this study highlights a different aspect of brain connectivity analysis, by not only exploring the link between FC and SC but also investigating the variability between them. Acknowledgments
The authors would like to acknowledge the funding agencies that have supported this research: Natural Sciences and Engineering Research Council of Canada (NSERC)
ON THE ORIGIN OF INDIVIDUAL FUNCTIONAL CONNECTIVITY VARIABILITY
Discovery Grants, QBIN (Quebec Bio-Imaging Network), and the FMSS graduate scholarship program. Main author M.C. was supported by the Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (CGS-D3) from the NSERC at the time. Author Disclosure Statement
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No competing financial interests exist. References
Alvarez I, Schwarzkopf DS, Clark CA. 2015. Extrastriate projections in human optic radiation revealed by fMRI-informed tractography. Brain Struct Funct 220:2519–2532. Bandettini PA, Jesmanowicz A, Wong EC, Hyde JS. 1993. Processing strategies for time-course data sets in functional MRI of the human brain. Magn Reson Med 30:161–173. Bastiani M, Shah NJ, Goebel R, Roebroeck A. 2012. Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. Neuroimage 62: 1732–1749. Bernier M, Chamberland M, Houde JC, Descoteaux M, Whittingstall K. 2014. Using fMRI non local means denoising to uncover activation in sub-cortical structures at 1.5 T for guided HARDI tractography. Front Hum Neurosci 8:715. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541. Bowman FD, Zhang L, Derado G, Chen S. 2012. Determining functional connectivity using fMRI data with diffusionbased anatomical weighting. Neuroimage 62:1769–1779. Buckner RL, Krienen FM, Yeo BT. 2013. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci 16:832–837. Burgel U, Amunts K, Hoemke L, Mohlberg H, Gilsbach JM, Zilles K. 2006. White matter fiber tracts of the human brain: threedimensional mapping at microscopic resolution, topography and inter-subject variability. Neuroimage 29:1092–1105. Cammoun L, Gigandet X, Meskaldji D, Thiran JP, Sporns O, Do KQ, et al. 2012. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J Neurosci Methods 203:386–397. Catani M, Jones DK, Donato R, Ffytche DH. 2003. Occipitotemporal connections in the human brain. Brain 126:2093–2107. Catani M, Mesulam M. 2008. The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 44:953–961. Catani M, Thiebaut De Schotten M. 2012. Atlas of Human Brain Connections. New York, NY: Oxford University Press. Includes bibliographical references and index. Chamberland M, Bernier M, Fortin D, Whittingstall K, Descoteaux M. 2015. 3D interactive tractography-informed restingstate fMRI connectivity. Front Neurosci 9:275. Chamberland M, Scherrer B, Prabhu SP, Madsen J, Fortin D, Whittingstall K, et al. 2017. Active delineation of Meyer’s loop using oriented priors through MAGNEtic tractography (MAGNET). Hum Brain Mapp 38:509–527. Chen B, Xu T, Zhou C, Wang L, Yang N, Wang Z, et al. 2015. Individual variability and test–retest reliability revealed by ten repeated resting-state brain scans over one month. PLoS One 10:e0144963. Cheng H, Wang Y, Sheng J, Kronenberger WG, Mathews VP, Hummer TA, Saykin AJ. 2012. Characteristics and
501
variability of structural networks derived from diffusion tensor imaging. Neuroimage 61:1153–1164. Chi JG, Dooling EC, Gilles FH. 1977. Gyral development of the human brain. Ann Neurol 1:86–93. Colon-Perez LM, Spindler C, Goicochea S, Triplett W, Parekh M, Montie E, et al. 2015. Dimensionless, scale invariant, edgeweight metric for the study of complex structural networks. PLoS One 10:e0131493. Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. 2008. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging 27:425–441. Cox RW. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173. Curtis AT, Hutchison RM, Menon RS. 2014. Phase based venous suppression in resting-state BOLD GE-FMRI. Neuroimage 100:51–59. Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF. 2006. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103:13848–13853. Descoteaux M. 2015. High angular resolution diffusion imaging (HARDI). Wiley Encyclopedia of Electrical and Electronics Engineering. John Wiley & Sons, Inc., p. 1. Destrieux C, Fischl B, Dale A, Halgren E. 2010. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53:1–15. Duffau H. 2005. Lessons from brain mapping in surgery for lowgrade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 4:476–486. Farquharson S, Tournier JD, Calamante F, Fabinyi G, SchneiderKolsky M, Jackson GD, Connelly A. 2013. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg 118:1367–1377. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, et al. 2015. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18:1664–1671. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. 2004. Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22. Fornito A, Zalesky A, Breakspear M. 2015. The connectomics of brain disorders. Nat Rev Neurosci 16:159–172. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I. 2014. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform 8:8. Gaser C, Schlaug G. 2003. Brain structures differ between musicians and non-musicians. J Neurosci 23:9240–9245. Geschwind N, Levitsky W. 1968. Human brain: left-right asymmetries in temporal speech region. Science 161:186–187. Ghaziri J, Tucholka A, Girard G, Houde JC, Boucher O, Gilbert G, et al. 2017. The corticocortical structural connectivity of the human insula. Cereb Cortex 27:1216–1228. Ghumman S, Fortin D, Noel-Lamy M, Cunnane SC, Whittingstall K. 2016. Exploratory study of the effect of brain tumors on the default mode network. J Neurooncol 128:437–444. Girard G, Whittingstall K, Deriche R, Descoteaux M. 2014. Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage 98:266–278. Girard G, Whittingstall K, Deriche R, Descoteaux M. Studying White Matter Tractography Reproducibility Through Connectivity Matrices. In International Symposium on Magnetic Resonance in Medicine, Toronto, Canada, 2015.
Downloaded by Gothenburg University Library from online.liebertpub.com at 10/17/17. For personal use only.
502
Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, Beaulieu C. 2009. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19:524–536. Goni J, van den Heuvel MP, Avena-Koenigsberger A, Velez de Mendizabal N, Betzel RF, Griffa A, et al. 2014. Resting-brain functional connectivity predicted by analytic measures of network communication. Proc Natl Acad Sci U S A 111: 833–838. Gonzalez-Castillo J, Handwerker DA, Robinson ME, Hoy CW, Buchanan LC, Saad ZS, Bandettini PA. 2014. The spatial structure of resting state connectivity stability on the scale of minutes. Front Neurosci 8:138. Griffa A, Baumann PS, Thiran JP, Hagmann P. 2013. Structural connectomics in brain diseases. Neuroimage 80:515–526. Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran JP. 2007. Mapping human whole-brain structural networks with diffusion MRI. PLoS One 2:e597. Hermundstad AM, Bassett DS, Brown KS, Aminoff EM, Clewett D, Freeman S, et al. 2013. Structural foundations of resting-state and task-based functional connectivity in the human brain. Proc Natl Acad Sci U S A 110:6169–6174. Hill J, Dierker D, Neil J, Inder T, Knutsen A, Harwell J, et al. 2010. A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J Neurosci 30:2268–2276. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P. 2009. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A 106:2035–2040. Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS. 2013. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp 34:2154–2177. Jbabdi S, Sotiropoulos SN, Haber SN, Van Essen DC, Behrens TE. 2015. Measuring macroscopic brain connections in vivo. Nat Neurosci 18:1546–1555. Jeon T, Mishra V, Ouyang M, Chen M, Huang H. 2015. Synchronous changes of cortical thickness and corresponding white matter microstructure during brain development accessed by diffusion MRI tractography from parcellated cortex. Front Neuroanat 9:158. Jones DK, Knosche TR, Turner R. 2013. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254. Kazan SM, Mohammadi S, Callaghan MF, Flandin G, Huber L, Leech R, et al. 2016. Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: a pilot study. Neuroimage 124:794–805. Kinney HC, Brody BA, Kloman AS, Gilles FH. 1988. Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. J Neuropathol Exp Neurol 47:217–234. Kiriyama I, Miki H, Kikuchi K, Ohue S, Matsuda S, Mochizuki T. 2009. Topographic analysis of the inferior parietal lobule in high-resolution 3D MR imaging. AJNR Am J Neuroradiol 30:520–524. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, et al. 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A 89:5675–5679. Lange N, Giedd JN, Castellanos FX, Vaituzis AC, Rapoport JL. 1997. Variability of human brain structure size: ages 4–20 years. Psychiatry Res 74:1–12.
CHAMBERLAND ET AL.
Le Bihan D. 2008. Intravoxel incoherent motion perfusion MR imaging: a wake-up call. Radiology 249:748–752. Leclercq D, Delmaire C, de Champfleur NM, Chiras J, Lehericy S. 2011. Diffusion tractography: methods, validation and applications in patients with neurosurgical lesions. Neurosurg Clin N Am 22:253–268. Leclercq D, Duffau H, Delmaire C, Capelle L, Gatignol P, Ducros M, et al. 2010. Comparison of diffusion tensor imaging tractography of language tracts and intraoperative subcortical stimulations. J Neurosurg 112:503–511. Lurito JT, Dzemidzic M. 2001. Determination of cerebral hemisphere language dominance with functional magnetic resonance imaging. Neuroimage Clin N Am 11:355–363. Maier-Hein K, Neher P, Houde JC, Cote MA, Garyfallidis E, Zhong J, et al. 2016. Tractography-based connectomes are dominated by false-positive connections. bioRxiv [Epub ahead of print]; DOI: 10.1101/084137. Mattay VS, Fera F, Tessitore A, Hariri A, Das S, Callicott J, Weinberger D. 2002. Neurophysiological correlates of age-related changes in human motor function. Neurology 58:630–635. Meola A, Yeh FC, Fellows-Mayle W, Weed J, FernandezMiranda JC. 2016. Human connectome-based tractographic atlas of the brainstem connections and surgical approaches. Neurosurgery 79:437–455. Mueller S, Wang D, Fox MD, Yeo BTT, Sepulcre J, Sabuncu MR, et al. 2013. Individual variability in functional connectivity architecture of the human brain. Neuron 77:586–595. Murphy K, Birn RM, Bandettini PA. 2013. Resting-state fMRI confounds and cleanup. Neuroimage 80:349–359. Neher PF, Descoteaux M, Houde JC, Stieltjes B, Maier-Hein KH. 2015. Strengths and weaknesses of state of the art fiber tractography pipelines—a comprehensive in-vivo and phantom evaluation study using tractometer. Med Image Anal 26:287–305. Nenonen M, Hakulinen U, Brander A, Ohman J, Dastidar P, Luoto TM. 2015. Possible confounding factors on cerebral diffusion tensor imaging measurements. Acta Radiol Open 4:2047981614546795. Nimsky C, Bauer M, Carl B. 2016. Merits and limits of tractography techniques for the uninitiated. Adv Tech Stand Neurosurg 43:37–60. Nimsky C, Ganslandt O, Hastreiter P, Wang R, Benner T, Sorensen AG, Fahlbusch R. 2005. Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 56:130–137. Owen JP, Ziv E, Bukshpun P, Pojman N, Wakahiro M, Berman JI, et al. 2013. Test–retest reliability of computational network measurements derived from the structural connectome of the human brain. Brain Connect 3:160–176. Penfield W, Jasper H. 1954. Epilepsy and the Functional Anatomy of the Human Brain. Boston, MA: Little, Brown. Poldrack RA, Laumann TO, Koyejo O, Gregory B, Hover A, Chen MY, et al. 2015. Long-term neural and physiological phenotyping of a single human. Nat Commun 6:8885. Powell HW, Parker GJ, Alexander DC, Symms MR, Boulby PA, Wheeler-Kingshott CA, et al. 2006. Hemispheric asymmetries in language-related pathways: a combined functional MRI and tractography study. Neuroimage 32:388–399. Renauld E, Descoteaux M, Bernier M, Garyfallidis E, Whittingstall K. 2016. Semi-automatic segmentation of optic radiations and LGN, and their relationship to EEG alphawaves. PLoS One 11:e0156436. Reveley C, Seth AK, Pierpaoli C, Silva AC, Yu D, Saunders RC, et al. 2015. Superficial white matter fiber systems impede
Downloaded by Gothenburg University Library from online.liebertpub.com at 10/17/17. For personal use only.
ON THE ORIGIN OF INDIVIDUAL FUNCTIONAL CONNECTIVITY VARIABILITY
detection of long-range cortical connections in diffusion MR tractography. Proc Natl Acad Sci U S A 112:E2820–E2828. Schmahmann JD, Pandya DN. 2006. Fiber Pathways of the Brain. Oxford, New York, Auckland: Oxford University Press. www.opac.inria.fr/record=b1121214 Last accessed December 2016. Seunarine K, Alexander D. Multiple fibers: beyond the diffusion tensor. www.eprints.ucl.ac.uk/112334/2009 Last accessed December 2016. Smith RE, Tournier JD, Calamante F, Connelly A. 2012. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62:1924–1938. Smith RE, Tournier JD, Calamante F, Connelly A. 2015. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage 104:253–265. Smith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, et al. 2013. Resting-state fMRI in the Human Connectome Project. Neuroimage 80:144–168. Sporns O. 2013. Structure and function of complex brain networks. Dialogues Clin Neurosci 15:247–262. Steinmetz H. 1996. Structure, functional and cerebral asymmetry: in vivo morphometry of the planum temporale. Neurosci Biobehav Rev 20:587–591. Tax CM, Chamberland M, van Stralen M, Viergever MA,Whittingstall K, Fortin D, et al. 2015. Seeing more by showing less: orientation-dependent transparency rendering for fiber tractography visualization. PLoS One 10:e0139434. Tournier JD, Calamante F, Connelly A. 2007. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35:1459–1472. Tournier JD, Calamante F, Connelly A. 2012. MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66. Tournier JD, Mori S, Leemans A. 2011. Diffusion tensor imaging and beyond. Magn Reson Med 65:1532–1556. Turner R. 1992. Magnetic resonance imaging of brain function. Am J Physiol Imaging 7:136–145. Tyszka JM, Kennedy DP, Adolphs R, Paul LK. 2011. Intact bilateral resting-state networks in the absence of the corpus callosum. J Neurosci 31:15154–15162. Vaidya CJ, Gordon EM. 2013. Phenotypic variability in restingstate functional connectivity: current status. Brain Connect 3:99–120. van den Heuvel MP, Mandl RCW, Kahn RS, Hulshoff Pol HE. 2009. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 30:3127–3141. Varkuti B, Cavusoglu M, Kullik A, Schiffler B, Veit R, Yilmaz O, et al. 2011. Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: an integrative MRI study. PLoS One 6:e14801. Vassal F, Schneider F, Boutet C, Jean B, Sontheimer A, Lemaire JJ. 2016. Combined DTI tractography and functional MRI study of the language connectome in healthy volunteers: extensive mapping of white matter fascicles and cortical activations. PLoS One 11:e0152614.
503
Vernooij MW, Smits M, Wielopolski PA, Houston GC, Krestin GP, van der Lugt A. 2007. Fiber density asymmetry of the arcuate fasciculus in relation to functional hemispheric language lateralization in both right- and left-handed healthy subjects: a combined fMRI and DTI study. Neuroimage 35: 1064–1076. Vigneau-Roy N, Bernier M, Descoteaux M, Whittingstall K. 2014. Regional variations in vascular density correlate with resting-state and task-evoked blood oxygen level-dependent signal amplitude. Hum Brain Mapp 35:1906–1920. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, et al. 2015. Parcellating cortical functional networks in individuals. Nat Neurosci 18:1853–1860. Wang D, Liu H. 2014. Functional connectivity architecture of the human brain: not all the same. Neuroscientist 20:432–438. Ward AM, Schultz AP, Huijbers W, Van Dijk KR, Hedden T, Sperling RA. 2014. The parahippocampal gyrus links the default-mode cortical network with the medial temporal lobe memory system. Hum Brain Mapp 35:1061–1073. Whittingstall K, Bernier M, Houde JC, Fortin D, Descoteaux M. 2014. Structural network underlying visuospatial imagery in humans. Cortex 56:85–98. Wierenga LM, van den Heuvel MP, van Dijk S, Rijks Y, de Reus MA, Durston S. 2016. The development of brain network architecture. Hum Brain Mapp 37:717–729. Yarrow K, Brown P, Krakauer JW. 2009. Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nat Rev Neurosci 10:585–596. Yogarajah M, Powell HW, Parker GJ, Alexander DC, Thompson PJ, Symms MR, et al. 2008. Tractography of the parahippocampal gyrus and material specific memory impairment in unilateral temporal lobe epilepsy. Neuroimage 40:1755–1764. Yu Z, Tao L, Qian Z, Wu J, Liu H, Yu Y, et al. 2016. Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J Comput Assist Radiol Surg 11:2007–2019. Zalesky A, Fornito A, Cocchi L, Gollo LL, van den Heuvel MP, Breakspear M. 2016. Connectome sensitivity or specificity: which is more important? Neuroimage 142:407–420. Zhang Y, Brady M, Smith S. 2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57. Zhu D, Zhang T, Jiang X, Hu X, Chen H, Yang N, et al. 2014. Fusing DTI and fMRI data: a survey of methods and applications. Neuroimage 102P1:184–191.
Address correspondence to: Maxime Chamberland Cardiff University Brain Research Imaging Centre (CUBRIC) Cardiff University Maindy Road Cardiff CF24 4HQ United Kingdom E-mail: [email protected]
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On the Origin of Individual Functional Connectivity Variability: The Role of White Matter Architecture Maxime Chamberland,1,2 Gabriel Girard,3,4 Michae¨l Bernier,1 David Fortin,5 Maxime Descoteaux,3 and Kevin Whittingstall1
Abstract
Fingerprint patterns derived from functional connectivity (FC) can be used to identify subjects across groups and sessions, indicating that the topology of the brain substantially differs between individuals. However, the source of FC variability inferred from resting-state functional magnetic resonance imaging remains unclear. One possibility is that these variations are related to individual differences in white matter structural connectivity (SC). However, directly comparing FC with SC is challenging given the many potential biases associated with quantifying their respective strengths. In an attempt to circumvent this, we employed a recently proposed test–retest approach that better quantifies inter-subject variability by first correcting for intra-subject nuisance variability (i.e., head motion, physiological differences in brain state, etc.) that can artificially influence FC and SC measures. Therefore, rather than directly comparing the strength of FC with SC, we asked whether brain regions with, for example, low inter-subject FC variability also exhibited low SC variability. From this, we report two main findings: First, at the whole-brain level, SC variability was significantly lower than FC variability, indicating that an individual’s structural connectome is far more similar to another relative to their functional counterpart even after correcting for noise. Second, although FC and SC variability were mutually low in some brain areas (e.g., primary somatosensory cortex) and high in others (e.g., memory and language areas), the two were not significantly correlated across all cortical and sub-cortical regions. Taken together, these results indicate that even after correcting for factors that may differently affect FC and SC, the two, nonetheless, remain largely independent of one another. Further work is needed to understand the role that direct anatomical pathways play in supporting vascular-based measures of FC and to what extent these measures are dictated by anatomical connectivity. Keywords:
connectivity; diffusion MRI; inter-subject variability; resting-state fMRI; tractography
Introduction
E
ach brain is uniquely organized, both functionally (Wang and Liu, 2014) and structurally (Lange et al., 1997). The cerebral architecture is known to evolve across time due to brain development and plasticity (Poldrack et al., 2015; Wierenga et al., 2016), and the complexity of these changes during development is often reflected by regional differences across subjects. These so-called inter-individual differences in brain function and structure are of great interest as they allow
researchers to directly compare how these cerebral differences correlate with other measures such as—among others—musical (Gaser and Schlaug, 2003) and athletic (Yarrow et al., 2009) ability. Recently, two magnetic resonance imaging (MRI)-based methodologies have emerged as non-invasive measures of the brain’s functional connectivity (FC) and structural connectivity (SC). Functional MRI (fMRI) provides fourdimensional whole-brain images that reflect changes in cortical blood flow, volume, and oxygen as measured by the
1 Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Canada. 2 Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom. 3 Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, Canada. 4 Signal Processing Lab (LTS5), Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland. 5 Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Canada.
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blood-oxygenation-level-dependent (BOLD) signal (Bandettini et al., 1993; Kwong et al., 1992; Turner, 1992). At rest, the spontaneous low-frequency fluctuations (2M seeds) also aimed at avoiding potential seed bias and ensured a stabilized edge metric (i.e., weights of M represented by the normalized streamline count) (Colon-Perez et al., 2015; Girard et al., 2014). In a pioneer study by the group of Hagmann and associates (2007), the authors reported that one-third to half of streamlines did not reach the WM/GM interface mask and, thus, were excluded from the SC analysis. The tractography algorithm employed in this study ensured that streamlines did not prematurely terminate in the WM. The inclusion of subscortical regions (e.g., thalamus) also enabled a better representation of important pathways such as the optic radiations in the derived connectivity matrices. Weak link between functional and structural inter-subject variability
As described earlier, certain brain regions showing the SC lowest VFC inter also exhibited low Vinter , as did areas with the FC highest Vinter . However, across all regions, VSC inter was significantly lower than VFC (0.07 – 0.04 vs. 0.25 – 0.04, respecinter tively) and they were poorly correlated (r = 0.15, p = 0.05). It is possible that functional variability is greater on average than structural variability because functional variability can be sensitive to previous life experience and genetic factors (Vaidya and Gordon, 2013) and can vary dynamically (Hutchison et al., 2013). Indeed, FC is known to reflect a combination of dynamic signals that varies across the brain of each individual (Smith et al., 2013). For example, in the motor cortex, both variability measures were relatively low, though VFC inter was almost five times higher than VSC inter , indicating that the SC profile of the motor cortex is far more similar across healthy subjects than that of its FC profile. The exact reasons behind this mismatch are unclear. One possible explanation for this result is that the bulk of the reconstructed streamlines emanating from the motor cortex are made up of either the CC or the CST, with little to no terminations observed in other cortical areas. FC, on the other hand, may be visible in these areas due to indirect anatomical connections, thus explaining its higher inter-individual variability as shown in Supplementary Figure S4. Even though FC of the left central sulcus showed similar patterns between different subjects, individual differences are identifiable in the cerebellum and are supported by direct anatomical pathways. However, SC revealed no direct link between parahippocampal connectivity and the left central sulcus. Indirect structural pathways may, therefore, play a key role in explaining the SC mismatch between VFC inter and Vinter , which remains an active area of research nowadays ( Jbabdi et al., 2015). Another possible explanation is that diffusion- and BOLDbased measures of brain structure and function, respectively,
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are sensitive to different sources of noise. For instance, BOLD signal correlations in areas that are highly vascularized may be artificially inflated compared with other regions (Curtis et al., 2014; Kazan et al., 2016; Vigneau-Roy et al., 2014). On the other hand, blood vessels may interfere with diffusion measures, potentially leading to an incorrect estimation of SC due to intravoxel incoherent motion (Le Bihan, 2008). Taken together, the sum of all those sources of variability effectively contributes toward establishing inter-subject variability. Clinical implications. Fingerprint patterns of FC can be used to identify subjects across sessions and individuals (Finn et al., 2015), indicating that the topology of the brain substantially differs between individuals. Assuming that patterns of spatial heterogeneity are also present in SC, our finding is also of clinical importance with the recent advances in surgical approaches based on connectomics (Griffa et al., 2013; Meola et al., 2016; Yu et al., 2016). For instance, a region of inter-individual variability can have potential influence on preoperative planning as the spatial localization of this area may vary from one subject to another. As of today, rs-fMRI imaging protocols are often used in clinical studies to guide surgical gesture. In addition, tractography has been shown to be valuable for surgical planning, and neurosurgeons and radiologists are mostly convinced of the importance of achieving an accurate delineation of WM pathways using tractography (Duffau, 2005; Leclercq et al., 2010; Nimsky et al., 2016). Defining accurate boundaries is critical in surgery targeting frontal and temporal regions (e.g., spaceoccupying lesion, temporal lobe epilepsy), as these areas exhibit higher variability between subjects. With the advent of personalized medicine (Wang et al., 2015), we believe that such a tailored mapping may improve decision making and is likely to impact pre-operative planning approaches, where capturing the idiosyncrasies of individuals is paramount. Conclusion
In this study, we generated brain functional and structural inter-subject variability maps by taking into account the intra-subject variability using a test–retest approach. With this approach, we showed that structural inter-subject variability was globally lower than functional variability. Our approach also revealed that variability indices were smallest in motor areas for both functional and structural analysis, indicating proper reproducibility within this region. Our results also indicate that functional and structural variability profiles were less consistent near the memory and language areas of the brain. These findings suggest that, at least in these regions, functional inter-subject variability can be explained by the structural organization of the brain. In other words, stable FC does not always imply stable SC for the rest of the brain. Altogether, this study highlights a different aspect of brain connectivity analysis, by not only exploring the link between FC and SC but also investigating the variability between them. Acknowledgments
The authors would like to acknowledge the funding agencies that have supported this research: Natural Sciences and Engineering Research Council of Canada (NSERC)
ON THE ORIGIN OF INDIVIDUAL FUNCTIONAL CONNECTIVITY VARIABILITY
Discovery Grants, QBIN (Quebec Bio-Imaging Network), and the FMSS graduate scholarship program. Main author M.C. was supported by the Alexander Graham Bell Canada Graduate Scholarships-Doctoral Program (CGS-D3) from the NSERC at the time. Author Disclosure Statement
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No competing financial interests exist. References
Alvarez I, Schwarzkopf DS, Clark CA. 2015. Extrastriate projections in human optic radiation revealed by fMRI-informed tractography. Brain Struct Funct 220:2519–2532. Bandettini PA, Jesmanowicz A, Wong EC, Hyde JS. 1993. Processing strategies for time-course data sets in functional MRI of the human brain. Magn Reson Med 30:161–173. Bastiani M, Shah NJ, Goebel R, Roebroeck A. 2012. Human cortical connectome reconstruction from diffusion weighted MRI: the effect of tractography algorithm. Neuroimage 62: 1732–1749. Bernier M, Chamberland M, Houde JC, Descoteaux M, Whittingstall K. 2014. Using fMRI non local means denoising to uncover activation in sub-cortical structures at 1.5 T for guided HARDI tractography. Front Hum Neurosci 8:715. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541. Bowman FD, Zhang L, Derado G, Chen S. 2012. Determining functional connectivity using fMRI data with diffusionbased anatomical weighting. Neuroimage 62:1769–1779. Buckner RL, Krienen FM, Yeo BT. 2013. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci 16:832–837. Burgel U, Amunts K, Hoemke L, Mohlberg H, Gilsbach JM, Zilles K. 2006. White matter fiber tracts of the human brain: threedimensional mapping at microscopic resolution, topography and inter-subject variability. Neuroimage 29:1092–1105. Cammoun L, Gigandet X, Meskaldji D, Thiran JP, Sporns O, Do KQ, et al. 2012. Mapping the human connectome at multiple scales with diffusion spectrum MRI. J Neurosci Methods 203:386–397. Catani M, Jones DK, Donato R, Ffytche DH. 2003. Occipitotemporal connections in the human brain. Brain 126:2093–2107. Catani M, Mesulam M. 2008. The arcuate fasciculus and the disconnection theme in language and aphasia: history and current state. Cortex 44:953–961. Catani M, Thiebaut De Schotten M. 2012. Atlas of Human Brain Connections. New York, NY: Oxford University Press. Includes bibliographical references and index. Chamberland M, Bernier M, Fortin D, Whittingstall K, Descoteaux M. 2015. 3D interactive tractography-informed restingstate fMRI connectivity. Front Neurosci 9:275. Chamberland M, Scherrer B, Prabhu SP, Madsen J, Fortin D, Whittingstall K, et al. 2017. Active delineation of Meyer’s loop using oriented priors through MAGNEtic tractography (MAGNET). Hum Brain Mapp 38:509–527. Chen B, Xu T, Zhou C, Wang L, Yang N, Wang Z, et al. 2015. Individual variability and test–retest reliability revealed by ten repeated resting-state brain scans over one month. PLoS One 10:e0144963. Cheng H, Wang Y, Sheng J, Kronenberger WG, Mathews VP, Hummer TA, Saykin AJ. 2012. Characteristics and
501
variability of structural networks derived from diffusion tensor imaging. Neuroimage 61:1153–1164. Chi JG, Dooling EC, Gilles FH. 1977. Gyral development of the human brain. Ann Neurol 1:86–93. Colon-Perez LM, Spindler C, Goicochea S, Triplett W, Parekh M, Montie E, et al. 2015. Dimensionless, scale invariant, edgeweight metric for the study of complex structural networks. PLoS One 10:e0131493. Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C. 2008. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging 27:425–441. Cox RW. 1996. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173. Curtis AT, Hutchison RM, Menon RS. 2014. Phase based venous suppression in resting-state BOLD GE-FMRI. Neuroimage 100:51–59. Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF. 2006. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci U S A 103:13848–13853. Descoteaux M. 2015. High angular resolution diffusion imaging (HARDI). Wiley Encyclopedia of Electrical and Electronics Engineering. John Wiley & Sons, Inc., p. 1. Destrieux C, Fischl B, Dale A, Halgren E. 2010. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage 53:1–15. Duffau H. 2005. Lessons from brain mapping in surgery for lowgrade glioma: insights into associations between tumour and brain plasticity. Lancet Neurol 4:476–486. Farquharson S, Tournier JD, Calamante F, Fabinyi G, SchneiderKolsky M, Jackson GD, Connelly A. 2013. White matter fiber tractography: why we need to move beyond DTI. J Neurosurg 118:1367–1377. Finn ES, Shen X, Scheinost D, Rosenberg MD, Huang J, Chun MM, et al. 2015. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18:1664–1671. Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. 2004. Automatically parcellating the human cerebral cortex. Cereb Cortex 14:11–22. Fornito A, Zalesky A, Breakspear M. 2015. The connectomics of brain disorders. Nat Rev Neurosci 16:159–172. Garyfallidis E, Brett M, Amirbekian B, Rokem A, van der Walt S, Descoteaux M, Nimmo-Smith I. 2014. Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform 8:8. Gaser C, Schlaug G. 2003. Brain structures differ between musicians and non-musicians. J Neurosci 23:9240–9245. Geschwind N, Levitsky W. 1968. Human brain: left-right asymmetries in temporal speech region. Science 161:186–187. Ghaziri J, Tucholka A, Girard G, Houde JC, Boucher O, Gilbert G, et al. 2017. The corticocortical structural connectivity of the human insula. Cereb Cortex 27:1216–1228. Ghumman S, Fortin D, Noel-Lamy M, Cunnane SC, Whittingstall K. 2016. Exploratory study of the effect of brain tumors on the default mode network. J Neurooncol 128:437–444. Girard G, Whittingstall K, Deriche R, Descoteaux M. 2014. Towards quantitative connectivity analysis: reducing tractography biases. Neuroimage 98:266–278. Girard G, Whittingstall K, Deriche R, Descoteaux M. Studying White Matter Tractography Reproducibility Through Connectivity Matrices. In International Symposium on Magnetic Resonance in Medicine, Toronto, Canada, 2015.
Downloaded by Gothenburg University Library from online.liebertpub.com at 10/17/17. For personal use only.
502
Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, Beaulieu C. 2009. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19:524–536. Goni J, van den Heuvel MP, Avena-Koenigsberger A, Velez de Mendizabal N, Betzel RF, Griffa A, et al. 2014. Resting-brain functional connectivity predicted by analytic measures of network communication. Proc Natl Acad Sci U S A 111: 833–838. Gonzalez-Castillo J, Handwerker DA, Robinson ME, Hoy CW, Buchanan LC, Saad ZS, Bandettini PA. 2014. The spatial structure of resting state connectivity stability on the scale of minutes. Front Neurosci 8:138. Griffa A, Baumann PS, Thiran JP, Hagmann P. 2013. Structural connectomics in brain diseases. Neuroimage 80:515–526. Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran JP. 2007. Mapping human whole-brain structural networks with diffusion MRI. PLoS One 2:e597. Hermundstad AM, Bassett DS, Brown KS, Aminoff EM, Clewett D, Freeman S, et al. 2013. Structural foundations of resting-state and task-based functional connectivity in the human brain. Proc Natl Acad Sci U S A 110:6169–6174. Hill J, Dierker D, Neil J, Inder T, Knutsen A, Harwell J, et al. 2010. A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. J Neurosci 30:2268–2276. Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P. 2009. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A 106:2035–2040. Hutchison RM, Womelsdorf T, Gati JS, Everling S, Menon RS. 2013. Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp 34:2154–2177. Jbabdi S, Sotiropoulos SN, Haber SN, Van Essen DC, Behrens TE. 2015. Measuring macroscopic brain connections in vivo. Nat Neurosci 18:1546–1555. Jeon T, Mishra V, Ouyang M, Chen M, Huang H. 2015. Synchronous changes of cortical thickness and corresponding white matter microstructure during brain development accessed by diffusion MRI tractography from parcellated cortex. Front Neuroanat 9:158. Jones DK, Knosche TR, Turner R. 2013. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254. Kazan SM, Mohammadi S, Callaghan MF, Flandin G, Huber L, Leech R, et al. 2016. Vascular autorescaling of fMRI (VasA fMRI) improves sensitivity of population studies: a pilot study. Neuroimage 124:794–805. Kinney HC, Brody BA, Kloman AS, Gilles FH. 1988. Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants. J Neuropathol Exp Neurol 47:217–234. Kiriyama I, Miki H, Kikuchi K, Ohue S, Matsuda S, Mochizuki T. 2009. Topographic analysis of the inferior parietal lobule in high-resolution 3D MR imaging. AJNR Am J Neuroradiol 30:520–524. Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, et al. 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A 89:5675–5679. Lange N, Giedd JN, Castellanos FX, Vaituzis AC, Rapoport JL. 1997. Variability of human brain structure size: ages 4–20 years. Psychiatry Res 74:1–12.
CHAMBERLAND ET AL.
Le Bihan D. 2008. Intravoxel incoherent motion perfusion MR imaging: a wake-up call. Radiology 249:748–752. Leclercq D, Delmaire C, de Champfleur NM, Chiras J, Lehericy S. 2011. Diffusion tractography: methods, validation and applications in patients with neurosurgical lesions. Neurosurg Clin N Am 22:253–268. Leclercq D, Duffau H, Delmaire C, Capelle L, Gatignol P, Ducros M, et al. 2010. Comparison of diffusion tensor imaging tractography of language tracts and intraoperative subcortical stimulations. J Neurosurg 112:503–511. Lurito JT, Dzemidzic M. 2001. Determination of cerebral hemisphere language dominance with functional magnetic resonance imaging. Neuroimage Clin N Am 11:355–363. Maier-Hein K, Neher P, Houde JC, Cote MA, Garyfallidis E, Zhong J, et al. 2016. Tractography-based connectomes are dominated by false-positive connections. bioRxiv [Epub ahead of print]; DOI: 10.1101/084137. Mattay VS, Fera F, Tessitore A, Hariri A, Das S, Callicott J, Weinberger D. 2002. Neurophysiological correlates of age-related changes in human motor function. Neurology 58:630–635. Meola A, Yeh FC, Fellows-Mayle W, Weed J, FernandezMiranda JC. 2016. Human connectome-based tractographic atlas of the brainstem connections and surgical approaches. Neurosurgery 79:437–455. Mueller S, Wang D, Fox MD, Yeo BTT, Sepulcre J, Sabuncu MR, et al. 2013. Individual variability in functional connectivity architecture of the human brain. Neuron 77:586–595. Murphy K, Birn RM, Bandettini PA. 2013. Resting-state fMRI confounds and cleanup. Neuroimage 80:349–359. Neher PF, Descoteaux M, Houde JC, Stieltjes B, Maier-Hein KH. 2015. Strengths and weaknesses of state of the art fiber tractography pipelines—a comprehensive in-vivo and phantom evaluation study using tractometer. Med Image Anal 26:287–305. Nenonen M, Hakulinen U, Brander A, Ohman J, Dastidar P, Luoto TM. 2015. Possible confounding factors on cerebral diffusion tensor imaging measurements. Acta Radiol Open 4:2047981614546795. Nimsky C, Bauer M, Carl B. 2016. Merits and limits of tractography techniques for the uninitiated. Adv Tech Stand Neurosurg 43:37–60. Nimsky C, Ganslandt O, Hastreiter P, Wang R, Benner T, Sorensen AG, Fahlbusch R. 2005. Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 56:130–137. Owen JP, Ziv E, Bukshpun P, Pojman N, Wakahiro M, Berman JI, et al. 2013. Test–retest reliability of computational network measurements derived from the structural connectome of the human brain. Brain Connect 3:160–176. Penfield W, Jasper H. 1954. Epilepsy and the Functional Anatomy of the Human Brain. Boston, MA: Little, Brown. Poldrack RA, Laumann TO, Koyejo O, Gregory B, Hover A, Chen MY, et al. 2015. Long-term neural and physiological phenotyping of a single human. Nat Commun 6:8885. Powell HW, Parker GJ, Alexander DC, Symms MR, Boulby PA, Wheeler-Kingshott CA, et al. 2006. Hemispheric asymmetries in language-related pathways: a combined functional MRI and tractography study. Neuroimage 32:388–399. Renauld E, Descoteaux M, Bernier M, Garyfallidis E, Whittingstall K. 2016. Semi-automatic segmentation of optic radiations and LGN, and their relationship to EEG alphawaves. PLoS One 11:e0156436. Reveley C, Seth AK, Pierpaoli C, Silva AC, Yu D, Saunders RC, et al. 2015. Superficial white matter fiber systems impede
Downloaded by Gothenburg University Library from online.liebertpub.com at 10/17/17. For personal use only.
ON THE ORIGIN OF INDIVIDUAL FUNCTIONAL CONNECTIVITY VARIABILITY
detection of long-range cortical connections in diffusion MR tractography. Proc Natl Acad Sci U S A 112:E2820–E2828. Schmahmann JD, Pandya DN. 2006. Fiber Pathways of the Brain. Oxford, New York, Auckland: Oxford University Press. www.opac.inria.fr/record=b1121214 Last accessed December 2016. Seunarine K, Alexander D. Multiple fibers: beyond the diffusion tensor. www.eprints.ucl.ac.uk/112334/2009 Last accessed December 2016. Smith RE, Tournier JD, Calamante F, Connelly A. 2012. Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage 62:1924–1938. Smith RE, Tournier JD, Calamante F, Connelly A. 2015. The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage 104:253–265. Smith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, et al. 2013. Resting-state fMRI in the Human Connectome Project. Neuroimage 80:144–168. Sporns O. 2013. Structure and function of complex brain networks. Dialogues Clin Neurosci 15:247–262. Steinmetz H. 1996. Structure, functional and cerebral asymmetry: in vivo morphometry of the planum temporale. Neurosci Biobehav Rev 20:587–591. Tax CM, Chamberland M, van Stralen M, Viergever MA,Whittingstall K, Fortin D, et al. 2015. Seeing more by showing less: orientation-dependent transparency rendering for fiber tractography visualization. PLoS One 10:e0139434. Tournier JD, Calamante F, Connelly A. 2007. Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35:1459–1472. Tournier JD, Calamante F, Connelly A. 2012. MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66. Tournier JD, Mori S, Leemans A. 2011. Diffusion tensor imaging and beyond. Magn Reson Med 65:1532–1556. Turner R. 1992. Magnetic resonance imaging of brain function. Am J Physiol Imaging 7:136–145. Tyszka JM, Kennedy DP, Adolphs R, Paul LK. 2011. Intact bilateral resting-state networks in the absence of the corpus callosum. J Neurosci 31:15154–15162. Vaidya CJ, Gordon EM. 2013. Phenotypic variability in restingstate functional connectivity: current status. Brain Connect 3:99–120. van den Heuvel MP, Mandl RCW, Kahn RS, Hulshoff Pol HE. 2009. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 30:3127–3141. Varkuti B, Cavusoglu M, Kullik A, Schiffler B, Veit R, Yilmaz O, et al. 2011. Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: an integrative MRI study. PLoS One 6:e14801. Vassal F, Schneider F, Boutet C, Jean B, Sontheimer A, Lemaire JJ. 2016. Combined DTI tractography and functional MRI study of the language connectome in healthy volunteers: extensive mapping of white matter fascicles and cortical activations. PLoS One 11:e0152614.
503
Vernooij MW, Smits M, Wielopolski PA, Houston GC, Krestin GP, van der Lugt A. 2007. Fiber density asymmetry of the arcuate fasciculus in relation to functional hemispheric language lateralization in both right- and left-handed healthy subjects: a combined fMRI and DTI study. Neuroimage 35: 1064–1076. Vigneau-Roy N, Bernier M, Descoteaux M, Whittingstall K. 2014. Regional variations in vascular density correlate with resting-state and task-evoked blood oxygen level-dependent signal amplitude. Hum Brain Mapp 35:1906–1920. Wang D, Buckner RL, Fox MD, Holt DJ, Holmes AJ, Stoecklein S, et al. 2015. Parcellating cortical functional networks in individuals. Nat Neurosci 18:1853–1860. Wang D, Liu H. 2014. Functional connectivity architecture of the human brain: not all the same. Neuroscientist 20:432–438. Ward AM, Schultz AP, Huijbers W, Van Dijk KR, Hedden T, Sperling RA. 2014. The parahippocampal gyrus links the default-mode cortical network with the medial temporal lobe memory system. Hum Brain Mapp 35:1061–1073. Whittingstall K, Bernier M, Houde JC, Fortin D, Descoteaux M. 2014. Structural network underlying visuospatial imagery in humans. Cortex 56:85–98. Wierenga LM, van den Heuvel MP, van Dijk S, Rijks Y, de Reus MA, Durston S. 2016. The development of brain network architecture. Hum Brain Mapp 37:717–729. Yarrow K, Brown P, Krakauer JW. 2009. Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nat Rev Neurosci 10:585–596. Yogarajah M, Powell HW, Parker GJ, Alexander DC, Thompson PJ, Symms MR, et al. 2008. Tractography of the parahippocampal gyrus and material specific memory impairment in unilateral temporal lobe epilepsy. Neuroimage 40:1755–1764. Yu Z, Tao L, Qian Z, Wu J, Liu H, Yu Y, et al. 2016. Altered brain anatomical networks and disturbed connection density in brain tumor patients revealed by diffusion tensor tractography. Int J Comput Assist Radiol Surg 11:2007–2019. Zalesky A, Fornito A, Cocchi L, Gollo LL, van den Heuvel MP, Breakspear M. 2016. Connectome sensitivity or specificity: which is more important? Neuroimage 142:407–420. Zhang Y, Brady M, Smith S. 2001. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 20:45–57. Zhu D, Zhang T, Jiang X, Hu X, Chen H, Yang N, et al. 2014. Fusing DTI and fMRI data: a survey of methods and applications. Neuroimage 102P1:184–191.
Address correspondence to: Maxime Chamberland Cardiff University Brain Research Imaging Centre (CUBRIC) Cardiff University Maindy Road Cardiff CF24 4HQ United Kingdom E-mail: [email protected]
