Frontiers Commentary ARTICLE
Commentary: Microstructure, length, and connection of limbic tracts in normal human brain development
- The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
by Yu, Q., Peng, Y., Mishra, V., Ouyang, A., Li, H., Zhang, H., et al. (2014). Front. Aging Neurosci. 6:228. doi: 10.3389/fnagi.2014.00228
Limbic tracts are affected in various neurological and psychiatric diseases/disorders (Bogerts et al., 1985; Modell et al., 1989; Braak and Braak, 1991; Tamminga et al., 1992; Becker et al., 2001; Amaral et al., 2008; Sheth et al., 2013; Gottlich et al., 2014; Posner et al., 2014). The onset of the abnormality is of particular interest since many psychiatric disorders are known to have genetic backgrounds that might result in a specific phenotype of the brain anatomy (endophenotype) before the onset of the symptoms (Menzies et al., 2008; Hajek et al., 2009; Fornito et al., 2013; Nery et al., 2013; Dixson et al., 2014; Scognamiglio and Houenou, 2014; Zannas et al., 2014; Chakravarty et al., 2015; Ordóñez et al., 2016). There is the potential that such endophenotypes could be detected even in very early developmental stages, therefore, quantification of the developmental status of the limbic fibers might be useful for identifying groups at high-risk for developing psychiatric disorders in the future. However, little is known about the normal developmental trajectories of these fiber tracts from a neonatal age to young adulthood, which is essential for the evaluation of a pathological deviation from normal brain development (Oishi et al., 2013). MRI scans are particularly challenging in subjects less than 4 years of age without sedation (Oishi et al., 2012), and therefore, establishing the normal developmental trajectory of this age-range will be an important asset for research communities(Oishi et al., 2011; Akazawa et al., 2015; Chang et al., 2016).
Yu and his colleagues aimed to identify the normal developmental characteristics of the limbic fibers (Yu et al., 2014). They used diffusion tensor imaging (DTI) to characterize the microscopic, anatomical features of the live human brain from the neonatal period to 25 years of age, based on cross-sectional observation of 65 healthy individuals. Diffusion property, length, and anatomical connections of the three limbic tracts were evaluated: The cingulate gyrus part of the cingulum (cgc); the hippocampal part of the cingulum (cgh); and the fornix. The tracts-of-interest (TOI) approach was applied to accurately identify the white matter tracts and has been used to evaluate the microscopic status of the developing brain. Tractography was also used to measure the length of the limbic tracts, as well as to evaluate the connectivity of the anatomical structures that are related to the default mode network (DMN), which is defined by resting-state fMRI. In adult brains, the limbic fibers are known to connect the DMN-related structures. However, the role of these limbic fibers in connecting DMN structures has been unclear in early development.
There were three major findings. First, the developmental curve of four DTI-derived measures were mostly logarithmic, with rapid changes until 2 years of age, followed by slow changes that went on until 25 years of age. The important contribution to science here is that these results were compared with and without free water elimination (FWE) (Pasternak et al., 2009), which was introduced to eliminate the partial volume artifact that is caused by the inclusion of cerebrospinal fluid (CSF) into each pixel, which results in falsely high diffusivity of such pixels. There was a concern about whether to apply the FEW to brains under development, since it could potentially eliminate the effect of physiological changes of the brain related to development by eliminating the intercellular water content that is prominent in early development. The authors first demonstrated that contamination of the CSF is the major source of free water that is eliminated by the FWE algorithm. Importantly, the application of the FWE did not change the pattern of the developmental curves of the limbic fibers, which means that both values, with and without FWE, are legitimate for the evaluation of brain development, as long as the method for evaluation is consistent. Second, the authors demonstrated that the length of the cgc, normalized by the anterior-to-posterior length of the brain, increased with age, but the normalized length of the cgh and the fornix were unchanged. This means that the development of the cgc is disproportionally rapid during this age-range, but the development of the cgh and the fornix is proportional. Third, the anatomical connections of DMN-related structures were similar in both neonates and young adults, which means that the functional and anatomical connectivity of the DMN is already established in the early postnatal period. This suggested the importance of the DMN in the basic brain functions that are already needed at birth.
The simultaneous evaluation of the DTI-derived measures—length and connectivity of the limbic fibers from early to late development—provide an important foundation with which to assess the types and onset of abnormalities in brain development related to various neurological or psychiatric disorders. Indeed, during the two years since publication, the results of this paper were referenced to interpret DTI findings of traumatic brain injury (TBI) and major depressive disorder (MDD); the left-side dominancy in the effect of TBI was interpreted in relation to the leftward asymmetry observed in normal development (Ewing-Cobbs et al., 2016), and disrupted functional connectivity in MDD was interpreted in the context of chronological patterns in white matter maturation (Sacchet et al., 2016).
Several limitations should be noted. The limited number of participants might introduce a selection bias. The limbic pathways investigated were limited; the uncinate fasciculus, stria terminalis, and the mammillothalamic fasciculus were not included. The parameters investigated from diffusion MRI were limited to those derived from the tensor model; parameters acquired through advanced models, such as diffusion spectrum imaging or high-angular resolution diffusion imaging, remain to be investigated. For the evaluation of intra-individual variation, a longitudinal design would be needed (Baltes, 1968). To overcome these general and well-recognized limitations, several longitudinal studies have been launched since the publication of this paper, including the Developing Human Connectome Project, Baby Connectome Project, Lifespan Human Connectome Project, and the Adolescent Brain Cognitive Development (ABCD) study. These projects adopted state-of-the-art scanners and scan protocols. Among them, the ABCD study is the largest study, and involves approximately 10,000 children, who will be followed for 9–10 years. These studies are expected to overcome the limitations of previous cross-sectional studies.
KO contributed to all aspects of the work, including conception and writing, and is accountable for all aspects of this commentary.
The author is supported by NIH grant R01HD065955 and an inHealth Pilot Project grant from the Johns Hopkins Individualized Health Initiative. The content of this commentary is solely the responsibility of the author and does not necessarily represent the official view of NIH/NICHD or the Johns Hopkins Individualized Health Initiative.
Conflict of Interest Statement
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Akazawa, K., Chang, L., Yamakawa, R., Hayama, S., Buchthal, S., Alicata, D., et al. (2015). Probabilistic maps of the white matter tracts with known associated functions on the neonatal brain atlas: application to evaluate longitudinal developmental trajectories in term-born and preterm-born infants. Neuroimage 128, 167–179. doi: 10.1016/j.neuroimage.2015.12.026
Becker, G., Berg, D., Lesch, K. P., and Becker, T. (2001). Basal limbic system alteration in major depression: a hypothesis supported by transcranial sonography and MRI findings. Int. J. Neuropsychopharmacol. 4, 21–31. doi: 10.1017/S1461145701002164
Bogerts, B., Meertz, E., and Schönfeldt-Bausch, R. (1985). Basal ganglia and limbic system pathology in schizophrenia. A morphometric study of brain volume and shrinkage. Arch. Gen. Psychiatry 42, 784–791. doi: 10.1001/archpsyc.1985.01790310046006
Chakravarty, M. M., Rapoport, J. L., Giedd, J. N., Raznahan, A., Shaw, P., Collins, D. L., et al. (2015). Striatal shape abnormalities as novel neurodevelopmental endophenotypes in schizophrenia: a longitudinal study. Hum. Brain Mapp. 36, 1458–1469. doi: 10.1002/hbm.22715
Chang, L., Akazawa, K., Yamakawa, R., Hayama, S., Buchthal, S., Alicata, D., et al. (2016). Delayed early developmental trajectories of white matter tracts of functional pathways in preterm-born infants: longitudinal diffusion tensor imaging data. Data Brief 6, 1007–1015. doi: 10.1016/j.dib.2016.01.064
Dixson, L., Walter, H., Schneider, M., Erk, S., Schafer, A., Haddad, L., et al. (2014). Identification of gene ontologies linked to prefrontal-hippocampal functional coupling in the human brain. Proc. Natl. Acad. Sci. U.S.A. 111, 9657–9662. doi: 10.1073/pnas.1404082111
Ewing-Cobbs, L., Johnson, C. P., Juranek, J., Demaster, D., Prasad, M., Duque, G., et al. (2016). Longitudinal diffusion tensor imaging after pediatric traumatic brain injury: impact of age at injury and time since injury on pathway integrity. Hum. Brain Mapp. 37, 3929–3945. doi: 10.1002/hbm.23286
Fornito, A., Harrison, B. J., Goodby, E., Dean, A., Ooi, C., Nathan, P. J., et al. (2013). Functional dysconnectivity of corticostriatal circuitry as a risk phenotype for psychosis. JAMA Psychiatry 70, 1143–1151. doi: 10.1001/jamapsychiatry.2013.1976
Gottlich, M., Kramer, U. M., Kordon, A., Hohagen, F., and Zurowski, B. (2014). Decreased limbic and increased fronto-parietal connectivity in unmedicated patients with obsessive-compulsive disorder. Hum. Brain Mapp. 35, 5617–5632. doi: 10.1002/hbm.22574
Hajek, T., Gunde, E., Slaney, C., Propper, L., Macqueen, G., Duffy, A., et al. (2009). Amygdala and hippocampal volumes in relatives of patients with bipolar disorder: a high-risk study. Can. J. Psychiatry 54, 726–733. doi: 10.1177/070674370905401102
Menzies, L., Williams, G. B., Chamberlain, S. R., Ooi, C., Fineberg, N., Suckling, J., et al. (2008). White matter abnormalities in patients with obsessive-compulsive disorder and their first-degree relatives. Am. J. Psychiatry 165, 1308–1315. doi: 10.1176/appi.ajp.2008.07101677
Modell, J. G., Mountz, J. M., Curtis, G. C., and Greden, J. F. (1989). Neurophysiologic dysfunction in basal ganglia/limbic striatal and thalamocortical circuits as a pathogenetic mechanism of obsessive-compulsive disorder. J. Neuropsychiatry Clin. Neurosci. 1, 27–36. doi: 10.1176/jnp.1.1.27
Nery, F. G., Monkul, E. S., and Lafer, B. (2013). Gray matter abnormalities as brain structural vulnerability factors for bipolar disorder: a review of neuroimaging studies of individuals at high genetic risk for bipolar disorder. Aust. N. Z. J. Psychiatry 47, 1124–1135. doi: 10.1177/0004867413496482
Oishi, K., Faria, A. V., Yoshida, S., Chang, L., and Mori, S. (2013). Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. Int. J. Dev. Neurosci. 31, 512–524. doi: 10.1016/j.ijdevneu.2013.06.004
Oishi, K., Mori, S., Donohue, P. K., Ernst, T., Anderson, L., Buchthal, S., et al. (2011). Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. Neuroimage 56, 8–20. doi: 10.1016/j.neuroimage.2011.01.051
Ordóñez, A. E., Luscher, Z. I., and Gogtay, N. (2016). Neuroimaging findings from childhood onset schizophrenia patients and their non-psychotic siblings. Schizophr. Res. 173, 124–131. doi: 10.1016/j.schres.2015.03.003
Posner, J., Marsh, R., Maia, T. V., Peterson, B. S., Gruber, A., and Simpson, H. B. (2014). Reduced functional connectivity within the limbic cortico-striato-thalamo-cortical loop in unmedicated adults with obsessive-compulsive disorder. Hum. Brain Mapp. 35, 2852–2860. doi: 10.1002/hbm.22371
Sacchet, M. D., Ho, T. C., Connolly, C. G., Tymofiyeva, O., Lewinn, K. Z., Han, L. K., et al. (2016). Large-scale hypoconnectivity between resting-state functional networks in unmedicated adolescent major depressive disorder. Neuropsychopharmacology 41, 2951–2960. doi: 10.1038/npp.2016.76
Sheth, S. A., Neal, J., Tangherlini, F., Mian, M. K., Gentil, A., Cosgrove, G. R., et al. (2013). Limbic system surgery for treatment-refractory obsessive-compulsive disorder: a prospective long-term follow-up of 64 patients. J. Neurosurg. 118, 491–497. doi: 10.3171/2012.11.JNS12389
Tamminga, C. A., Thaker, G. K., Buchanan, R., Kirkpatrick, B., Alphs, L. D., Chase, T. N., et al. (1992). Limbic system abnormalities identified in schizophrenia using positron emission tomography with fluorodeoxyglucose and neocortical alterations with deficit syndrome. Arch. Gen. Psychiatry 49, 522–530. doi: 10.1001/archpsyc.1992.01820070016003
Yu, Q., Peng, Y., Mishra, V., Ouyang, A., Li, H., Zhang, H., et al. (2014). Microstructure, length, and connection of limbic tracts in normal human brain development. Front. Aging Neurosci. 6:228. doi: 10.3389/fnagi.2014.00228
Zannas, A. S., Mcquoid, D. R., Payne, M. E., Macfall, J. R., Ashley-Koch, A., Steffens, D. C., et al. (2014). Association of gene variants of the renin-angiotensin system with accelerated hippocampal volume loss and cognitive decline in old age. Am. J. Psychiatry 171, 1214–1221. doi: 10.1176/appi.ajp.2014.13111543
Keywords: default mode network, development, diffusion tensor imaging, limbic tracts, free water elimination, neonate, resting-state fMRI, tractography
Citation: Oishi K (2017) Commentary: Microstructure, length, and connection of limbic tracts in normal human brain development. Front. Neurosci. 11:117. doi: 10.3389/fnins.2017.00117
Received: 15 February 2016; Accepted: 24 February 2017;
Published: 13 March 2017.
Edited by:Rodrigo Orlando Kuljiš, University of Miami School of Medicine, USA
Reviewed by:Albert Gjedde, University of Copenhagen, Denmark
Copyright © 2017 Oishi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Kenichi Oishi, email@example.com