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Altered Structural Brain Networks in Tuberous Sclerosis Complex Downloaded from Kiho Im1,2, Banu Ahtam1,2, Daniel Haehn2,3, Jurriaan M

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Altered Structural Brain Networks in Tuberous Sclerosis Complex Downloaded from Kiho Im1,2, Banu Ahtam1,2, Daniel Haehn2,3, Jurriaan M Cerebral Cortex Advance Access published March 5, 2015 Cerebral Cortex, 2015, 1–13 doi: 10.1093/cercor/bhv026 Original Article ORIGINAL ARTICLE Altered Structural Brain Networks in Tuberous Sclerosis Complex Downloaded from Kiho Im1,2, Banu Ahtam1,2, Daniel Haehn2,3, Jurriaan M. Peters4,5, Simon K. Warfield3,5, Mustafa Sahin4, and P. Ellen Grant1,2,3,6 1Division of Newborn Medicine, 2Fetal Neonatal Neuroimaging and Developmental Science Center, 3Department of Radiology, 4Department of Neurology, 5Computational Radiology Laboratory, Boston Children’s Hospital, http://cercor.oxfordjournals.org/ Harvard Medical School, Boston, MA 02115, USA, and 6Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02119, USA Address correspondence to Kiho Im, Boston Children’s Hospital, 1 Autumn Street, Boston, MA 02115, USA. Email: [email protected] Kiho Im and Banu Ahtam contributed equally to this study. Abstract at University of Victoria on November 14, 2015 Tuberous sclerosis complex (TSC) is characterized by benign hamartomas in multiple organs including the brain and its clinical phenotypes may be associated with abnormal neural connections. We aimed to provide the first detailed findings on disrupted structural brain networks in TSC patients. Structural whole-brain connectivity maps were constructed using structural and diffusion MRI in 20 TSC (age range: 3–24 years) and 20 typically developing (TD; 3–23 years) subjects. We assessed global (short- and long-association and interhemispheric fibers) and regional white matter connectivity, and performed graph theoretical analysis using gyral pattern- and atlas-based node parcellations. Significantly higher mean diffusivity (MD) was shown in TSC patients than in TD controls throughout the whole brain and positively correlated with tuber load severity. A significant increase in MD was mainly influenced by an increase in radial diffusivity. Furthermore, interhemispheric connectivity was particularly reduced in TSC, which leads to increased network segregation within hemispheres. TSC patients with developmental delay (DD) showed significantly higher MD than those without DD primarily in intrahemispheric connections. Our analysis allows non- biased determination of differential white matter involvement, which may provide better measures of “lesion load” and lead to a better understanding of disease mechanisms. Key words: brain networks, developmental delay, diffusion tensor imaging, structural connectivity, tuberous sclerosis complex Introduction neuronal connections. Tsc1-deleted mice are shown to display Tuberous sclerosis complex (TSC) is an autosomal dominant dis- seizures and have delayed myelination (Meikle et al. 2007). Tsc2 order caused primarily by mutations in TSC1 or TSC2 genes, and heterozygous mice show aberrant topographic projections of characterized by benign hamartomatous lesions in multiple or- axon pathways in the reticulogeniculate tract (Nie et al. 2010). gans including the brain (Baskin 2008). The clinical phenotype Thus, there is evidence from mouse models of aberrant connect- is widely variable, but the majority of TSC patients show neuro- ivity in TSC. logical manifestations of the disorder including seizures (90%), Although cortical tubers are one of the hallmarks of TSC (Cur- developmental delay (DD) or intellectual disability (50%), neuro- atolo et al. 2002), there is no consistent correlation between the behavioral abnormalities, and autism spectrum disorders (ASDs, number and location of tubers, and epileptic seizures (Major 50%; Baskin 2008; Curatolo et al. 2008). Mouse model studies have et al. 2009) or autistic features (Bolton et al. 2002; Numis et al. found that mutations in Tsc1 or Tsc2 genes cause abnormal 2011). Therefore, it has been suggested that the broad spectrum © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected] 1 2 | Cerebral Cortex of TSC clinical phenotypes may arise from abnormal neural con- was visually assessed using fluid-attenuated inversion recovery nections that are independent of these benign tumors (Tsai and (FLAIR) images separately by 2 independent readers, a certified Sahin 2011; Peters, Taquet, Vega, et al. 2013). Human diffusion neuroradiologist and a certified neurologist (P.E.G. and J.M.P.). tensor imaging (DTI) studies have reported decreased fractional The reviewers qualitatively placed TSC brains into 1 of 4 different anisotropy (FA) and increased mean diffusivity (MD) values in groups, defined by tuber volume relative to whole-brain volume, certain brain structures such as the corpus callosum, internal such as 1. minimal: <1/8 of the brain volume, 2. mild: >1/8 but capsule, and external capsule in patients with TSC compared <1/4 of the brain volume, 3. moderate: >1/4 but <1/2 of the brain with typically developing (TD) individuals (Makki et al. 2007; volume, and 4. severe: >1/2 of the brain volume. After the initial in- Krishnan et al. 2010; Peters et al. 2012). However, although select- dependent review, a consensus was reached between the 2 physi- ive tracts have been analyzed, unbiased whole-brain connectiv- cians on the 4 cases who differed by only one grade. Demographic ity measures have not been performed. and clinical data for all TSC patients are presented in Table 1.The To assess whole-brain white matter connectivity in an un- 20 TSC patients are a subgroup of the TSC patients whose data biased manner, a human brain connectome approach can be were analyzed by Peters et al. (2012) where only corpus callosum used. This approach models the complex network of brain con- connectivity was assessed. The TD control participants were nectivity with a graph using a set of nodes and interconnecting chosen from individuals who were clinically normal apart from edges to provide measures of whole-brain structural connectivity symptoms of headache who underwent MRI examinations as a (Sporns et al. 2005). To define the nodes, most studies parcellated screening precaution; control datasets were chosen from those Downloaded from cortical regions using volume- or surface-based registration to an individuals who had normal brain MRI examinations as deter- atlas (Tzourio-Mazoyer et al. 2002; Desikan et al. 2006). However, mined by 2 pediatric neuroradiologists and no clinical diagnosis the use of atlas-based parcellation techniques causes many short associated with cerebral disorders including migraine. This intergyral connections to be ignored and heterogeneously con- study was approved by the institutional review board of Boston nected brain regions to be lumped into single nodes. To overcome Children’sHospital. http://cercor.oxfordjournals.org/ these limitations, we used a gyral topology-based parcellation fi scheme, which we believe is a more appropriate node de nition MRI Acquisition method for the description of the whole-brain white matter net- work (Im et al. 2014). The imaging protocol consisted of a structural and diffusion- T The aim of this study was to perform a non-biased whole- weighted sequence. The structural sequence was a 1-weighted brain analysis of global white matter connectivity (short- and high-resolution magnetization-prepared rapid-acquisition gradi- – long-association and interhemispheric fiber connections) using ent-echo (MPRAGE) acquisition (voxel size [mm] = 0.5 × 0.5 × 1 1 fi – – gyral pattern-based cortical node parcellations and regional con- ×1×1; eld of view [FOV] = 19.2 25.6 cm; echo time [TE] = 1.66 – fl – nectivity using atlas-based parcellations. We also performed 3.39 ms; repetition time [TR] = 1130 2530 ms; ip angle = 7 9°). graph theoretical global network analysis using both gyral and The diffusion sequence was an axial echo-planar sequence at University of Victoria on November 14, 2015 atlas-based approaches, and compared their results. In TSC (voxelsize[mm]=1.7×1.7×2.2;FOV=22cm;TE=88ms;TR= fl patients, all connectivity and network measurements were 1000 ms; ip angle = 90°; in-plane GRAPPA acceleration factor = 2, compared between subgroups defined by the presence of an number of averages acquired = 1; 30 gradient diffusion directions b 2 b 2 ASD, epilepsy, and DD, as well as tuber load. at = 1000 s/mm ; 5 acquisitions with =0s/mm ). Sedation, typically dexmedetomidine, was used only when necessary (e.g., very young children, intellectual impairment, and behavior- Materials and Methods al difficulties), to prevent excessive motion. Dexmedetomidine has been proved to be a safe and effective sedation technique, Participants producing a condition similar to natural sleep (Mason 2010)for Twenty patients (age range, 3–24 years; 11 males and 9 females) MRI (Siddappa et al. 2011; Ambi et al. 2012), and is the suggested with a diagnosis of TSC and 20 age- and gender-matched TD par- sedation technique that is commonly used for children with ticipants (age range, 2–23 years; 7 males and 13 females) were im- neurobehavioral disorders such as autism (Lubisch et al. 2009). aged using a 3-T MRI. All TSC patients had a neurological As there is only minor perturbation of neuronal activity with examination, and their clinical data were obtained through dexmedetomidine, diffusion metrics such as FA and MD, which their office visits and medical records. All patients fulfilled the are modulated by water compartmentalization, are unlikely to fi clinical criteria for a definite TSC diagnosis, as defined by the Tu- show any signi cant change. Currently, there are no
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