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DIFFUSION TENSOR IMAGING ABNORMALITIES IN THE UNCINATE FASCICULUS AND INFERIOR LONGITUDINAL FASCICULUS IN PHELAN-MCDERMID SYNDROME
Journal Pre-proof DIFFUSION TENSOR IMAGING ABNORMALITIES IN THE UNCINATE FASCICULUS AND INFERIOR LONGITUDINAL FASCICULUS IN PHELANMCDERMID SYNDROME Julia Bassell, Siddharth Srivastava, Anna K. Prohl, Benoit Scherrer, Kush Kapur, Rajna Filip-Dhima, Elizabeth Berry-Kravis, Latha Soorya, Audrey Thurm, Craig M. Powell, Jonathan A. Bernstein, Joseph Buxbaum, Alexander Kolevzon, Simon K. Warfield, Mustafa Sahin, on behalf of Developmental Synaptopathies Consortium PII:
S0887-8994(20)30032-1
DOI:
https://doi.org/10.1016/j.pediatrneurol.2020.01.006
Reference:
PNU 9713
To appear in:
Pediatric Neurology
Received Date: 19 October 2019 Revised Date:
13 January 2020
Accepted Date: 21 January 2020
Please cite this article as: Bassell J, Srivastava S, Prohl AK, Scherrer B, Kapur K, Filip-Dhima R, Berry-Kravis E, Soorya L, Thurm A, Powell CM, Bernstein JA, Buxbaum J, Kolevzon A, Warfield SK, Sahin M, on behalf of Developmental Synaptopathies Consortium, DIFFUSION TENSOR IMAGING ABNORMALITIES IN THE UNCINATE FASCICULUS AND INFERIOR LONGITUDINAL FASCICULUS IN PHELAN-MCDERMID SYNDROME, Pediatric Neurology (2020), doi: https://doi.org/10.1016/ j.pediatrneurol.2020.01.006. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.
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DIFFUSION TENSOR IMAGING ABNORMALITIES IN THE UNCINATE FASCICULUS AND INFERIOR LONGITUDINAL FASCICULUS IN PHELAN-MCDERMID SYNDROME Short title: Phelan-McDermid Syndrome DTI tractography Julia Bassell16#, Siddharth Srivastava1#, Anna K Prohl3, Benoit Scherrer3, Kush Kapur1, Rajna Filip-Dhima 2, Elizabeth Berry-Kravis8, 9, 10, Latha Soorya11, Audrey Thurm12, Craig M. Powell13, 14
, Jonathan A Bernstein15, Joseph Buxbaum4, 5, 6, 7 , Alexander Kolevzon4, 5, Simon K. Warfield3 and Mustafa Sahin1, 2* on behalf of Developmental Synaptopathies Consortiuma
1
Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston,
Massachusetts, USA 2
F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School,
Boston, MA, USA 3
Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital &
Harvard Medical School Boston, Massachusetts, USA 4
Seaver Autism Center for Research and Treatment, Mount Sinai School of Medicine, New York,
NY, USA
*
5
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
6
Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York,
Correspondence to: Mustafa Sahin, M.D., Department of Neurology, Boston Children’s Hospital. Email:
[email protected]. Phone: 617-355-6258. Address: 300 Longwood Avenue, Boston, Massachusetts 02115, U.S.A.
2
NY, USA 7
Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA
8
Department of Pediatrics, Rush University Medical Center, Chicago, IL, USA
9
Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
10
Department of Biochemistry, Rush University Medical Center, Chicago, IL, USA
11
Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA
12
Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health,
National Institutes of Health, Bethesda, MD, USA 13
Department of Neurobiology, University of Alabama at Birmingham School of Medicine,
Birmingham, AL 14
Civitan International Research Center, University of Alabama at Birmingham School of
Medicine, Birmingham, AL 15
Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
16
Warren Alpert Medical School of Brown University, Providence, RI, USA
a
See Acknowledgments
#
Authors contributed equally
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ABSTRACT Objective: This cohort study utilized diffusion tensor imaging tractography to compare the uncinate fasciculus and inferior longitudinal fasciculus in children with Phelan-McDermid Syndrome to age matched controls and investigated trends between autism spectrum diagnosis symptoms and the integrity of the uncinate fasciculus and inferior longitudinal fasciculus white matter tracts. Methods: This research was conducted under a longitudinal, network study that aims to map the genotype, phenotype, and natural history of Phelan-McDermid Syndrome and identify biomarkers using neuroimaging (ClinicalTrial NCT02461420). Patients were 3-21 years of age and underwent longitudinal neuropsychological assessment over 24 months. MRI processing and analyses were completed using previously validated image analysis software distributed as the Computational Radiology Kit (http://crl.med.harvard.edu/). Whole brain connectivity was generated for each subject using a stochastic streamline tractography algorithm, and automatically defined regions of interest were used to map the uncinate fasciculus and inferior longitudinal fasciculus. Results: There were 10 participants (50% male; mean age 11.17 years) with Phelan-McDermid Syndrome (n=8 with autism). Age-matched controls, enrolled in a separate longitudinal study (NIH R01 NS079788), underwent the same protocol. There was a statistically significant decrease in the uncinate fasciculus fractional anisotropy measure and a statistically significant increase in uncinate fasciculus mean diffusivity measure, in the patient group vs controls in both right and left tracts (p ≤ 0.024).
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Conclusion: Because the uncinate fasciculus plays a critical role in social and emotional interaction, this tract may underlie some deficits seen in the Phelan-McDermid Syndrome population. These findings need to be replicated in a larger cohort.
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KEYWORDS 22q13.3 deletion, SHANK3, DTI, autism
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INTRODUCTION Autism spectrum disorder (ASD) is a common and phenotypically heterogeneous neurodevelopmental disorder (1, 2). There are a growing number of different gene defects implicated in ASD. These different genetic causes may culminate in presentations of ASD through common pathways (1). As a result, it is compelling to study ASD by examining genetic disorders associated with a high prevalence of ASD. Phelan McDermid syndrome (PMS), caused by either a 22q13.3 deletion encompassing the SHANK3 gene or SHANK3 mutation, is one compelling genetic model of ASD. PMS is characterized by intellectual disability (ID), generalized hypotonia, delayed or absent speech, normal to accelerated growth, and mild dysmorphic features (3, 4). PMS is a rare, although underdiagnosed disorder. The true prevalence of PMS is unknown; however according to the PMS Foundation, over 2000 individuals have been diagnosed with PMS throughout the world. Up to 84% of children with PMS have ASD (5). SHANK3 encodes a scaffolding protein located at the postsynaptic density of glutamatergic synapses and has important functions in synaptic scaffolding and regulating dendritic spine morphology (6, 7) (8). SHANK3 mutations have also been identified in approximately 1% of patients with ASD (9), and mouse models lacking Shank3 show autistic features (8, 10-16). Thus, PMS is a valuable genetic model of ASD to study the effects of SHANK3 on ASD clinical features and brain networks (17). Because ASD is characterized by deficits in social behavior, it is important to study relevant brain networks and phenotypic correlates in PMS. Social behavior, learning, and expression are all factors important in the ASD phenotype and are dependent on integrated activity of an extended, overlapping cortical socio-emotional processing network (18). There has been a wealth of foundational data showing support for a long distance cortical processing
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deficit in ASD as a contributing factor to core social deficits in ASD (19, 20). Two major tracts that together expand a circuit running between frontal, temporal, and occipital regions and are also involved in socioemotional functions are the uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF) (21). The UF plays a role in episodic memory (22), emotional recognition and empathy (23-25), and social reward (22), and the ILF plays a role in object and facial recognition (26, 27) and more broadly in semantic learning (28) and reading comprehension (29). Furthermore, the UF and ILF have been shown through diffusion tensor imaging (DTI) to have altered microstructure in children with ASD (21, 30-36). These two tracts were investigated in this study. To examine specificity for our findings, we also examined the corticospinal tract (CST) as a control tract. To date no published studies have examined white matter tracts in PMS using DTI. We utilized DTI tractography to compare the UF and ILF in children with PMS to age matched controls and investigated the relationship between ASD diagnosis and the integrity of the UF and ILF white matter tracts. We hypothesized that when comparing the PMS group vs controls, there would be greater integrity of the UF and ILF white matter tracts in the control group.
MATERIALS AND METHODS Study Design This research was conducted under an ongoing, longitudinal, network study that aims to map the genotype, phenotype, and natural history of PMS, and to identify biomarkers using neuroimaging (ClinicalTrial NCT02461420). Patients were recruited at one of four network sites: Icahn School of Medicine at Mount Sinai, Boston Children’s Hospital (BCH), Rush University
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Medical Center (RUMC), and the National Institutes of Health (NIH). Inclusion criteria required patients to be English speaking, between 3-21 years of age, and have pathogenic 22q13 deletions including SHANK3 or pathogenic sequence variants of the SHANK3 gene. Of note, for loss of function point mutations in SHANK3 we used 58.571 kb as the deletion size. Participants underwent longitudinal physical, medical, and neuropsychological assessment over a period of 24 months. Brain MRIs were only performed if clinically indicated and were not collected longitudinally. All MRI scans were transmitted to BCH for quality assurance and analysis. Age matched controls enrolled in a separate longitudinal study (through the NIH R01 NS079788) underwent the same standardized clinical imaging protocol and were imaged at Boston Children’s Hospital. Controls were recruited from the Boston area by targeted advertisements and letters in the community and at Children’s Hospital Boston, and through the research volunteer registry at Boston Children’s Hospital. Controls have no history of neurological or psychological disease. Data was collected between 2013-2018.
Sample 18 MRI brain scans from 18 PMS patients were available for analysis. Seven scans did not pass diffusion weighted imaging quality assurance and were excluded. One scan was removed because of prior brain surgery. After excluding these scans, the present study included 10 subjects (50% male; for genotype see Table 1) and 10 age- and sex-matched controls with at least one high quality diffusion-weighted MRI, for a total of 20 scans analyzed.
MRI Acquisition
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MRI brain scans were acquired on 3T GE DiscoveryMR 750, Siemens Skyra, and Siemens TrioTim scanners. Subjects were imaged under a consensus clinical imaging protocol that included a 1mm3 sagittal T1-weighted MPRAGE, 1 mm3 T2-weighted SPACE, 30 high angular resolution diffusion weighted images (DWI) (30 b=1000 s/mm2 images, 5 b=0 s/mm2 images, matrix=128x128, contiguous slice thickness=2mm), and reversed phase-encoding directions for distortion compensation, covering the entire brain. Patients were sedated when clinically indicated. Controls were not sedated.
MRI Processing All MRI processing and analyses were completed using previously published and validated image analysis software distributed as the Computational Radiology Kit (http://crl.med.harvard.edu/). For each scan, the T2w image was aligned to the T1w image, and an intracranial cavity was segmented using a previously validated multispectral method (37). DWI data were screened for intra-volume motion and scanner artifact by expert raters, and artefactual volumes were removed. None of the PMS scans analyzed were affected by artifact, and no volumes were removed. Three b=0 volumes were removed from 1 of 10 PMS scans due to patient motion. All control scans analyzed were not affected by artifact, and no volumes were removed. Magnetic susceptibility distortion was mitigated with FSLtopup (38), and inter-volume motion correction was performed via affine registration of each DWI volume to the mean b=0 s/mm2 image. DWI were up sampled to the T1w image and skull stripped with the intracranial cavity segmentation. A single tensor diffusion model was estimated using robust least squares in each brain voxel from which fractional anisotropy [FA =
diffusivity [MD = (λ1 + λ2 + λ3)/3] were computed.
√
] and mean
10 Tractography
For each subject, a whole brain connectivity was generated using a stochastic streamline tractography algorithm that combines the speed and efficacy of deterministic decision making at each voxel with probabilistic sampling from the space of all streamlines (39). A single streamline was stochastically initialized within each white matter voxel and streamlines were propagated with multiple steps per voxel. Streamlines were terminated if the step angle exceeded 35 degrees, or if the FA fell below 0.20. The left and right UF, ILF, and CST were selected from the whole brain connectivity using automatically defined regions of interest (ROIs), for a total of 6 tracts selected per subject. The CST was used as a control tract because this tract would not be affected in ASD. The mean was measured over all tract voxels. The ROIs were automatically delineated on the subject scans using a fully automatic, multi-template approach. Briefly, a template library was constructed from whole brain DTI of 15 healthy individuals, with each scan in its native space. For each template, scalar FA and color maps of the principal diffusion directions were computed from the DTI, and ROIs were hand drawn by an expert rater on the color map. To delineate the same white matter ROIs in the native space of each subject scan, the following procedure was performed for every template: the template DTI was aligned to the subject DTI using a nonlinear, dense registration, and the dense deformation field was then used to resample the template white matter ROIs to the subject DTI using nearest neighbor interpolation (40). Consensus ROIs were computed from the aligned template ROIs using the STAPLE algorithm (41). ROI Delineation
The UF and ILF tracts were investigated. The ILF was selected from the whole brain connectivity using two selection ROIs located in 1) the anterior temporal lobe and 2) the
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occipital lobe with a ventral limit of the white matter of the lingual and fusiform gyrus and a dorsal limit of the white matter of the joining splenium fibers (42). To avoid nearby aberrant fibers, two regions of exclusion (ROE) were manually placed on 1) a single midsagittal slice encompassing the corpus callosum and 2) a single axial slice at the brain stem. Similarly, the UF was selected from the whole brain connectivity using two selection ROIs located in 1) the anterior temporal lobe and 2) the external capsule (42) (Figure 1). The UF anterior temporal lobe ROI was constrained to the anterior medial aspect of the anterior temporal lobe to minimize selection of non-UF fibers. To further optimize fiber selection, three ROEs were manually placed on 1) a single coronal slice posterior to the anterior temporal lobe spanning the full hemisphere 2) a single whole brain midsagittal slice and 3) sagittal slices on the extreme capsule. We examined the CST as a control tract. The left and right CSTs were selected using three ROIs. A ROI was drawn in the posterior limb of the internal capsule in the axial plane, with its inferior limit defined on the first axial slice superior to the anterior commissure and the superior limit was defined on the axial slice where the lenticular nucleus separates the internal and external capsules (42). A second ROI was drawn in the cerebral peduncle, covering the inferior-superior traversing white matter of the anterior pons (42). A final ROI covered the precentral gyrus. A region of exclusion was placed over the midline corpus callosum and cerebellar white matter.
Clinical Measures Patient phenotype was evaluated with neuropsychological assessment at baseline, 12 months visit, and the 24 months visit. ASD diagnosis was made based on clinical consensus using the Diagnostic and Statistical Manual for Mental Disorders Fifth Edition (DSM-5) (43) and informed by evaluation with the ADOS2 (44) and ADI-R (45). The ADOS2 was given at
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baseline, 12 months into the study, and 24 months and confirmed based on clinical consensus using all three measures. (44). Best estimate IQ was derived from standard scores on IQ tests and ratio IQ estimates in participants who received out-of-range testing with developmental cognitive tests. Specifically, as is standard in such studies (46) a hierarchy of tests was used, including the Mullen Scales of Early Learning (47) in 10 participants, and the Stanford Binet (48).
Statistical Analysis A Wilcoxon sign rank testing was used to compare PMS vs. age matched controls regarding UF FA and MD (left and right) and ILF FA and MD (left and right) DTI metrics. For patients, we used Spearman’s rank correlation coefficients test to see if there was an effect of DTI metric on IQ. Due to small sample size, we used exact logistic regression to understand the relationship between ASD diagnosis and DTI metric while adjusting for age. In post-hoc analysis (given that control data was from different study), we employed Benjamini-Hochberg (BH) false discovery rate procedure, to account for multiple comparisons with q = 0.05(49). Because of small sample, we used less conservation BH correction approach instead of Bonferonni, but we used stricter cutpoint because we did not have numerous hypotheses to test. There were 12 comparisons when comparing DTI metric for patients versus controls: three tracts [ILF, UF, CST] x two sides [left, right] x two metrics [FA, MD].
RESULTS
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Of the initial 18 subjects enrolled in the PMS clinical trial (NCT02461420) with MRI available for analysis, 8 were excluded because of either failure to meet quality assurance or because of previous brain surgery. 10 PMS participant scan data were included in this cohort study. In both the PMS and control group, 50% were male (chi2(1) = 0.000, p = 1.000). The mean age of the PMS group was 11.17 (6.17) years, and the mean age of the control group was 11.24 (5.96) years. The mean IQ of the PMS group was 27.1 (sd = 20.7, range = 3.4-61). 80% had a diagnosis of ASD. The mean deletion size was 2457.8 kb (sd = 2694.1, range = 46-6320). In the sample, 7 (70%) were administered ADOS Module 1, 2 (20%) were administered ADOS Module 2, and 1 (10%) was administered ADOS Module 3. There was a statistically significant decrease in the UF FA measure in the PMS vs age matched controls in both the right and left tracts (adjusted p ≤ 0.024), as well as a statistically significant increase in UF MD measure in the PMS group vs controls in both right and left tracts (adjusted p ≤ 0.024) (Figure 2). There was a decrease in the ILF FA measure in the PMS vs age matched controls in both the right and left tracts, as well as an increase in ILF MD measure in the PMS group vs controls in the right and left tracts, but these changes were not statistically significant with adjusted p-values (Figure 3). Furthermore, an investigation of DTI metrics in the CST was used as a control tract between the PMS and control groups. There was no statistically significant difference in DTI metrics in the CST of PMS vs. controls. There was no significant difference found within the PMS group between any DTI metric and diagnosis of ASD. Of note, only 2 of the 10 PMS participants did not have ASD. There was not a statistically significant difference found within the PMS group between any DTI metric and best estimate IQ. There was not a statistically signification correlation between any DTI metric and deletion size.
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DISCUSSION This is the first study to date to use DTI in a PMS population in order to investigate potential microstructural changes in tracts critical for social and emotional interaction. This cohort study utilized DTI tractography to compare the UF and ILF in 10 children with PMS to age matched controls and investigated the integrity of the UF and ILF white matter tracts. In this study, there was a statistically significant decrease in the UF (and increase in the MD) in both the right and left tracts of PMS vs. age-matched controls. The CST was used as a control tract, and no difference was found in DTI metrics in the PMS vs. control groups. This control tract investigation further supports the specificity of the DTI changes in the UF. Based on previous studies in conjunction with our PMS investigation, UF microstructural abnormalities have been highlighted as an important tract underlying aspects of emotional and social behavior. The UF is a medial fronto-temporal long-range white matter tract that links anterior portions of superior, middle, and inferior temporal lobe, including the hippocampus and amygdala, with the insular and orbitofrontal cortex (50-52). The UF has been shown to play a critical role in emotional recognition (23, 24), self-awareness (53), social reward processing (22), and proper name retrieval (54). One study found that participants with UF lesions have deficits in an emotional empathy task, highlighting the role of the UF in social interaction and the ability to relate to others (25). Past ASD DTI studies have found significant, yet conflicting results, when examining the UF in patients with ASD vs controls (21, 30-34, 55-57). In this PMS study, the ILF FA DTI metric values were found to be decreased, and the ILF MD DTI increased, in the PMS group when compared to age matched controls, but these findings were not statistically significant with adjusted p-values. The ILF is the main pathway
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connecting brain structures that play a critical role in not only facial recognition, but also modulating visual stream socially salient information (58). Visual processing and emotional recognition are crucial for effective social communication (59-64). The ILF extends from the occipital cortex into the temporal lobes which mediates connections to the superior temporal sulcus, fusiform face area, and the amygdala (65-67). Previous ASD DTI studies have found changes in the ILF microstructure in ASD when compared to controls, reinforcing that the ILF is a relevant and important white matter tract to study in the context of ASD. This study also investigated a potential relationship between ASD diagnosis and DTI metric within the PMS group. No significant differences were found between any DTI metric and ASD diagnosis or IQ within the PMS group. Both the UF and ILF have temporal and amygdala connectivity which provides further support to study these two relevant tracts (50-52, 65-67). A previous positron emission tomography (PET) study detected localized dysfunction of the left temporal polar lobe and amygdala hypoperfusion in children with PMS (68) suggesting that there may be abnormalities in amygdalo-cortical connectivity that may contribute to the social interaction deficits found in PMS. By studying white matter tracts in the context of a known genetic mutation, we can now also lay the foundation to study the role SHANK3 may play in specific axonal tracts.
LIMITATIONS One main limitation of this study was the small sample size. It is possible the small sample size of this study is one limitation that potentially prevented ILF differences between patients and controls from maintaining statistical significance. In addition, there was a wide age range in this study and age significantly affects the development of both the UF and ILF.
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Of note, 2 of the 10 PMS participants did not have ASD, so the analysis was unbalanced in a setting of a small cohort, which likely limited our ability to make further conclusions about the DTI findings in relation to ASD diagnosis. In other words, the main DTI findings of this investigation may be specific to PMS rather than ASD more broadly, although conclusions are limited with this small sample size. While PMS is rare, through the ongoing longitudinal, network study encompassing multiple institutions, we plan to grow our sample size for further studies (ClinicalTrial NCT02461420). In addition, in future studies with greater samples sizes, ADOS severity score should be evaluated as a continuous measure. Furthermore, in addition to including an estimate of Full Scale IQ, which is important, future directions with a greater sample size include investigating aspects of phenotypic measurements that more specifically relate to the function of the UF or ILF in PMS or ASD populations. For example, the Vineland Adaptive Behavior Scale, specifically the socialization domain, can be used to more effectively evaluate whether changes in UF white matter microstructure correlates with changes in specific adaptive behaviors in areas previously found to be functionally relevant in the UF and ILF. In addition, future work can utilize a low IQ control group in order to specify findings further to PMS related differences, and not merely to gross cognitive differences between PMS and control groups.
CONCLUSIONS There have been no published studies that have examined white matter tracts in PMS using DTI. We utilized DTI tractography to compare the UF and ILF in children with PMS to age matched controls and investigated the relationship between ASD diagnosis and the integrity of the UF and ILF white matter tracts. Overall, this study found significant decrease in the FA (and increase in the MD) of the right and left UF in the PMS group when compared to
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age matched controls. The UF plays a critical role in social and emotional interaction and may underlie some deficits seen in the PMS and therefor ASD population. Further work with greater sample sizes needs to be done to study these two important tracts, ILF and UF, and their relationship to PMS and ASD. In addition, more work needs to be done to investigate on a neurological systems level the role of SHANK3 in different neural networks. Altogether, PMS provides a unique context to study contributions of SHANK3 to the development of ASD.
TRIAL REGISTRATION The study uses data from a clinical trial (ClinicalTrials.gov NCT02461420; https://clinicaltrials.gov/ct2/show/NCT02461420). Date of registration was June 3, 2015.
LIST OF ABBREVIATIONS diffusion tensor imaging (DTI) uncinate fasciculus (UF) inferior longitudinal fasciculus (ILF) autism spectrum diagnosis (ASD) Phelan McDermid syndrome (PMS) autism Diagnostic Observation Schedule 2nd edition (ADOS2) diagnostic and Statistical Manual for Mental Disorders Fifth Edition (DSM-5) diffusion weighted images (DWI) region of interest (ROI) regions of exclusion (ROE) cortical spinal tract (CST) mean diffusivity (MD)
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fractional anisotropy (FA)
DECLARATIONS Ethics approval and consent to participate The study was approved by the Institutional Review Board (IRB) at Boston Children’s Hospital, which serves as the central IRB for all the sites included in this study. Informed consent was obtained from the caregivers and legal guardians of all participants. The study was conducted in accordance with Good Clinical Practice guidelines.
Consent for Publication Not applicable
Availability of data and materials Data was entered in a web-based system created and maintained by the data management and coordinating center at the University of South Florida, which met the Health Insurance Portability and Accountability Act privacy regulations. Additional MRI data was maintained at the Computational Radiology Laboratory at Boston Children’s Hospital. Both clinical and MRI data presented here have been made available to the National Database for Autism Research, which is an NIH-funded data repository which aims to house and share with qualified researchers’ data related to Autism Spectrum Disorder.
Competing interests Siddharth Srivastava has received consulting fees from Guidepoint. Mustafa Sahin reports grant support from Novartis, Roche, Pfizer, Ipsen, LAM Therapeutics and Quadrant Biosciences,
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unrelated to this project. He has served on Scientific Advisory Boards for Sage, Roche, Celgene and Takeda. AK receives research support from AMO Pharma and consults to Ovid Therapeutics, Coronis, 5AM Ventures, sema4, LabCorp, and Takeda. EBK has received funding from Seaside Therapeutics, Novartis, Roche, Alcobra, Neuren, Cydan, Fulcrum, GW, Neurotrope, Marinus, Zynerba, BioMarin, Ovid, Yamo, Acadia, Ionis, Ultragenyx, and Lumos Pharmaceuticals to consult on trial design or development strategies and/or conduct clinical studies in FXS or other NDDs or neurodegenerative disorders; from Vtesse/Sucampo/Mallinckrodt Pharmaceuticals to conduct clinical trials in NP-C; and from Asuragen Inc. to develop testing standards for FMR1 testing. All funding to Dr. Berry-Kravis is directed to Rush University Medical Center (RUMC) in support of rare disease programs. C.M.P. has accepted travel funds and honoraria to speak once at each of the following companies: Psychogenics, Inc.; Astra-Zeneca; Roche; Pfizer; and Dainippon Sumitomo Pharma Co. C.M.P. also has investigator-initiated grant funding for clinical research with Novartis. None of these activities represents a competing interest to the current study.
Funding This study is funded by supported by the Developmental Synaptopathies Consortium (U54NS092090), which is a part of the National Center for Advancing Translational Sciences Rare Diseases Clinical Research Network. The Rare Diseases Clinical Research Network is an initiative of the Office of Rare Diseases Research of the National Center for Advancing Translational Sciences, and Developmental Synaptopathies Consortium is funded through collaboration between the National Center for Advancing Translational Sciences, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.
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Members of the Developmental Synaptopathies Consortium (DSC) – Phelan-McDermid Syndrome Group include: Mustafa Sahin, MD, PhD a, b Alexander Kolevzon, MD c, d Joseph Buxbaum, PhD c, d, e, f Elizabeth Berry Kravis, MD, PhD g, h, i Latha Soorya, PhD j Audrey Thurm, PhD k Craig Powell, MD, PhD l, m Jonathan A Bernstein, MD, PhD n
Simon Warfield, PhD o Benoit Scherrer, PhD o Rajna Filip-Dhima, MS b Kira Dies, ScM, CGC b Paige Siper, PhD c Ellen Hanson, PhD p Jennifer M. Phillips, PhD q Stormi P. White, Psy D r
Affiliations for above:
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a Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA b F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA c Seaver Autism Center for Research and Treatment, Mount Sinai School of Medicine, New York, NY d Department of Psychiatry, Mount Sinai School of Medicine, New York, NY e Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY f Department of Neuroscience, Mount Sinai School of Medicine, New York, NY g Department of Pediatrics, Rush University Medical Center, Chicago, IL h Department of Neurological Sciences, Rush University Medical Center, Chicago, IL i Department of Biochemistry, Rush University Medical Center, Chicago, IL j Department of Psychiatry, Rush University Medical Center, Chicago, IL k Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD l Department of Neurobiology, University of Alabama at Birmingham School of Medicine, Birmingham, AL m Civitan International Research Center, University of Alabama at Birmingham School of Medicine, Birmingham, AL n Department of Pediatrics, Stanford University School of Medicine, Stanford, CA o Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital & Harvard Medical School, Boston, MA
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p Department of Developmental Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA q Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA r Center for Autism and Developmental Disabilities, University of Texas Southwestern Medical Center, Dallas, TX
Authors' contributions JB drafted the manuscript; analyzed the data. SS analyzed the data; performed critical revision of the manuscript for important intellectual content. AP and BS collected and analyzed the data; performed critical revision of the manuscript for important intellectual content. KK provided statistical analysis; performed critical revision of the manuscript for important intellectual content. RFD performed critical revision of the manuscript for important intellectual content. EBK performed critical revision of the manuscript for important intellectual content. LS performed critical revision of the manuscript for important intellectual content. AT performed critical revision of the manuscript for important intellectual content. CP performed critical revision of the manuscript for important intellectual content. JB performed critical revision of the manuscript for important intellectual content. AK performed critical revision of the manuscript for important intellectual content. SW and MS oversaw study concept and design; performed critical revision of the manuscript for important intellectual content.
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Acknowledgements We are sincerely indebted to the generosity of the families and patients in PMS clinics across the United States who contributed their time and effort to this study. We would also like to thank the Phelan-McDermid Syndrome Foundation for their continued support in PMS research. We acknowledge all the study coordinators who are part of DSC.
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Figure 1. Example images of the ILF (A-B) and UF (C-E) overlaid on a fractional anisotropy map in two different patients with PMS.
A) left midsagittal slice showing a representative ILF in a patient with PMS and the two ROIs (Catani et al 2008) used to capture the ILF: 1) dark pink anterior temporal lobe label 2) purple posterior occipital lobe label. B) A left midsagittal slice showing the sagittal ROE. Note: The axial brain stem ROE slice was applied to generate the ILF indicated, but is not shown. C) A left side midsagittal slice showing a representative UF in a patient with PMS. D) Left side cutaway view showing all three planes (axial, sagittal, and coronal) with orange labels showing the whole brain sagittal ROE slice and the hemisphere coronal ROE slice. E) Left side cutaway view showing the two ROI (Catani et al 2008) used to capture the UF: 1) dark pink anterior temporal lobe label 2) light pink external capsule label.
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Note: The sagittal extreme capsule ROE slice was applied to generate the UF indicated, but is not shown. ILF: inferior longitudinal fasciculus PMS: Phelan McDermid Syndrome ROE: region of exclusion ROI: region of inclusion UF: uncinate fasciculus
26
Figure 2. Mean DTI metric versus age of patients and controls
Plot of mean DTI metric versus age of patients and controls for (A) FA UF left (B) FA UF right (C) MD UF left (CD) MD UF right.
27
Figure 3. Mean DTI metric versus age of patients and controls
Plot of mean DTI metric versus age of patients and controls for (A) FA ILF left (B) FA ILF right (C) MD ILF left (CD) MD ILF right.
28
Table 1. Genotype of participants. Participant
Genotype
1
arr[hg19] 22q13.31q13.33(46967883-51211392)x1
2
arr[hg19] 22q13.33(51116107-51193680)x1
3
arr[hg19] 22q13.33(51127905-51234443)x1
4
arr[hg19] 22q13.33(51130264-51176099)x1
5
22q13 deletion (22q13.3 - q terminus)
6
SHANK3, NM_033517.1:c.5008A>T, p.K1670*, SHANK3, NM_033517.1: c.3872C>T, p.S1291L
7
ish 22q13.3(TelVysion22q-)
8
arr[hg19] 22q13.31q13.33(44831210-51178257)x1; der(22)t(2;22)
9
arr[hg19] 22q13.31-q13.33(44921022-51224252)x1
10
arr[hg19] 22q13.31-q13.33(46350579-51224252)x1
29
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31 40. Suarez RO, Commowick O, Prabhu SP, Warfield SK. Automated delineation of white matter fiber tracts with a multiple region-of-interest approach. Neuroimage. 2012;59(4):3690-700. 41. Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004;23(7):903-21. 42. Catani M, Thiebaut de Schotten M. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 2008;44(8):1105-32. 43. American Psychiatric Association APA, DSM-5 Task Force. . Diagnostic and statistical manual of mental disorders: DSM-5. Arlington, Va: American Psychiatric Association . 2013. 44. Lord C, Luyster RJ, Gotham K, Guthrie W. Adapted ADOS, Modules 1 and 2, Prepublication Research Manual. Western Psychological Corporation. 2012. 45. Rutter M LA, Lord C. . Autism Diagnostic Interview-Revised (ADI-R). Los Angeles, CA: Western Psychological Corporation. . 2003. 46. Soorya LV LJ, Trelles P, & Thurm A. Framework for assessing individuals with profound intellectual and multiple disabilities (PIMD): Case study in Phelan McDermid Syndrome. Clinical Neuropsychologist. 2018;21:1-30. 47. Mullen EM. Mullen Scales of Early Learning. Circle Pines, Minnesota: American Guidance Service Inc.; 1995. 48. Roid GH, Pomplun M. The Stanford-Binet Intelligence Scales: The Guilford Press; 2012. 49. Storey J. The positive false discovery rate: a Bayesian interpretation and the q-value
. The Annals of Statistics. 2003;31:2013-35. 50. Kier EL, Staib LH, Davis LM, Bronen RA. MR imaging of the temporal stem: anatomic dissection tractography of the uncinate fasciculus, inferior occipitofrontal fasciculus, and Meyer's loop of the optic radiation. AJNR Am J Neuroradiol. 2004;25(5):677-91. 51. Ghashghaei HT, Hilgetag CC, Barbas H. Sequence of information processing for emotions based on the anatomic dialogue between prefrontal cortex and amygdala. Neuroimage. 2007;34(3):905-23. 52. Saleem KS, Kondo H, Price JL. Complementary circuits connecting the orbital and medial prefrontal networks with the temporal, insular, and opercular cortex in the macaque monkey. J Comp Neurol. 2008;506(4):659-93. 53. Levine B, Black SE, Cabeza R, Sinden M, McIntosh AR, Toth JP, et al. Episodic memory and the self in a case of isolated retrograde amnesia. Brain. 1998;121 ( Pt 10):1951-73. 54. Papagno C, Miracapillo C, Casarotti A, Romero Lauro LJ, Castellano A, Falini A, et al. What is the role of the uncinate fasciculus? Surgical removal and proper name retrieval. Brain. 2011;134(Pt 2):405-14. 55. Travers BG, Adluru N, Ennis C, Tromp do PM, Destiche D, Doran S, et al. Diffusion tensor imaging in autism spectrum disorder: a review. Autism Res. 2012;5(5):289-313. 56. Sahyoun CP, Belliveau JW, Soulieres I, Schwartz S, Mody M. Neuroimaging of the functional and structural networks underlying visuospatial vs. linguistic reasoning in high-functioning autism. Neuropsychologia. 2010;48(1):86-95. 57. Elison JT, Wolff JJ, Heimer DC, Paterson SJ, Gu H, Hazlett HC, et al. Frontolimbic neural circuitry at 6 months predicts individual differences in joint attention at 9 months. Dev Sci. 2013;16(2):186-97. 58. Rudrauf D, David O, Lachaux JP, Kovach CK, Martinerie J, Renault B, et al. Rapid interactions between the ventral visual stream and emotion-related structures rely on a two-pathway architecture. J Neurosci. 2008;28(11):2793-803.
32 59. Dawson G, Webb SJ, McPartland J. Understanding the nature of face processing impairment in autism: insights from behavioral and electrophysiological studies. Dev Neuropsychol. 2005;27(3):403-24. 60. Kleinhans NM, Richards T, Sterling L, Stegbauer KC, Mahurin R, Johnson LC, et al. Abnormal functional connectivity in autism spectrum disorders during face processing. Brain. 2008;131(Pt 4):100012. 61. Pelphrey KA, Sasson NJ, Reznick JS, Paul G, Goldman BD, Piven J. Visual scanning of faces in autism. J Autism Dev Disord. 2002;32(4):249-61. 62. Lee PS, Foss-Feig J, Henderson JG, Kenworthy LE, Gilotty L, Gaillard WD, et al. Atypical neural substrates of Embedded Figures Task performance in children with Autism Spectrum Disorder. Neuroimage. 2007;38(1):184-93. 63. Bertone A, Mottron L, Jelenic P, Faubert J. Enhanced and diminished visuo-spatial information processing in autism depends on stimulus complexity. Brain. 2005;128(Pt 10):2430-41. 64. Weigelt S, Koldewyn K, Kanwisher N. Face identity recognition in autism spectrum disorders: a review of behavioral studies. Neurosci Biobehav Rev. 2012;36(3):1060-84. 65. Adolphs R, Tranel D. Intact recognition of emotional prosody following amygdala damage. Neuropsychologia. 1999;37(11):1285-92. 66. Pelphrey K, Adolphs R, Morris JP. Neuroanatomical substrates of social cognition dysfunction in autism. Ment Retard Dev Disabil Res Rev. 2004;10(4):259-71. 67. Catani M, Jones DK, Donato R, Ffytche DH. Occipito-temporal connections in the human brain. Brain. 2003;126(Pt 9):2093-107. 68. Philippe A, Boddaert N, Vaivre-Douret L, Robel L, Danon-Boileau L, Malan V, et al. Neurobehavioral profile and brain imaging study of the 22q13.3 deletion syndrome in childhood. Pediatrics. 2008;122(2):e376-82.
S0887-8994(20)30032-1
DOI:
https://doi.org/10.1016/j.pediatrneurol.2020.01.006
Reference:
PNU 9713
To appear in:
Pediatric Neurology
Received Date: 19 October 2019 Revised Date:
13 January 2020
Accepted Date: 21 January 2020
Please cite this article as: Bassell J, Srivastava S, Prohl AK, Scherrer B, Kapur K, Filip-Dhima R, Berry-Kravis E, Soorya L, Thurm A, Powell CM, Bernstein JA, Buxbaum J, Kolevzon A, Warfield SK, Sahin M, on behalf of Developmental Synaptopathies Consortium, DIFFUSION TENSOR IMAGING ABNORMALITIES IN THE UNCINATE FASCICULUS AND INFERIOR LONGITUDINAL FASCICULUS IN PHELAN-MCDERMID SYNDROME, Pediatric Neurology (2020), doi: https://doi.org/10.1016/ j.pediatrneurol.2020.01.006. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.
1
DIFFUSION TENSOR IMAGING ABNORMALITIES IN THE UNCINATE FASCICULUS AND INFERIOR LONGITUDINAL FASCICULUS IN PHELAN-MCDERMID SYNDROME Short title: Phelan-McDermid Syndrome DTI tractography Julia Bassell16#, Siddharth Srivastava1#, Anna K Prohl3, Benoit Scherrer3, Kush Kapur1, Rajna Filip-Dhima 2, Elizabeth Berry-Kravis8, 9, 10, Latha Soorya11, Audrey Thurm12, Craig M. Powell13, 14
, Jonathan A Bernstein15, Joseph Buxbaum4, 5, 6, 7 , Alexander Kolevzon4, 5, Simon K. Warfield3 and Mustafa Sahin1, 2* on behalf of Developmental Synaptopathies Consortiuma
1
Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston,
Massachusetts, USA 2
F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School,
Boston, MA, USA 3
Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital &
Harvard Medical School Boston, Massachusetts, USA 4
Seaver Autism Center for Research and Treatment, Mount Sinai School of Medicine, New York,
NY, USA
*
5
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
6
Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York,
Correspondence to: Mustafa Sahin, M.D., Department of Neurology, Boston Children’s Hospital. Email:
[email protected]. Phone: 617-355-6258. Address: 300 Longwood Avenue, Boston, Massachusetts 02115, U.S.A.
2
NY, USA 7
Department of Neuroscience, Mount Sinai School of Medicine, New York, NY, USA
8
Department of Pediatrics, Rush University Medical Center, Chicago, IL, USA
9
Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
10
Department of Biochemistry, Rush University Medical Center, Chicago, IL, USA
11
Department of Psychiatry, Rush University Medical Center, Chicago, IL, USA
12
Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health,
National Institutes of Health, Bethesda, MD, USA 13
Department of Neurobiology, University of Alabama at Birmingham School of Medicine,
Birmingham, AL 14
Civitan International Research Center, University of Alabama at Birmingham School of
Medicine, Birmingham, AL 15
Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
16
Warren Alpert Medical School of Brown University, Providence, RI, USA
a
See Acknowledgments
#
Authors contributed equally
3
ABSTRACT Objective: This cohort study utilized diffusion tensor imaging tractography to compare the uncinate fasciculus and inferior longitudinal fasciculus in children with Phelan-McDermid Syndrome to age matched controls and investigated trends between autism spectrum diagnosis symptoms and the integrity of the uncinate fasciculus and inferior longitudinal fasciculus white matter tracts. Methods: This research was conducted under a longitudinal, network study that aims to map the genotype, phenotype, and natural history of Phelan-McDermid Syndrome and identify biomarkers using neuroimaging (ClinicalTrial NCT02461420). Patients were 3-21 years of age and underwent longitudinal neuropsychological assessment over 24 months. MRI processing and analyses were completed using previously validated image analysis software distributed as the Computational Radiology Kit (http://crl.med.harvard.edu/). Whole brain connectivity was generated for each subject using a stochastic streamline tractography algorithm, and automatically defined regions of interest were used to map the uncinate fasciculus and inferior longitudinal fasciculus. Results: There were 10 participants (50% male; mean age 11.17 years) with Phelan-McDermid Syndrome (n=8 with autism). Age-matched controls, enrolled in a separate longitudinal study (NIH R01 NS079788), underwent the same protocol. There was a statistically significant decrease in the uncinate fasciculus fractional anisotropy measure and a statistically significant increase in uncinate fasciculus mean diffusivity measure, in the patient group vs controls in both right and left tracts (p ≤ 0.024).
4
Conclusion: Because the uncinate fasciculus plays a critical role in social and emotional interaction, this tract may underlie some deficits seen in the Phelan-McDermid Syndrome population. These findings need to be replicated in a larger cohort.
5
KEYWORDS 22q13.3 deletion, SHANK3, DTI, autism
6
INTRODUCTION Autism spectrum disorder (ASD) is a common and phenotypically heterogeneous neurodevelopmental disorder (1, 2). There are a growing number of different gene defects implicated in ASD. These different genetic causes may culminate in presentations of ASD through common pathways (1). As a result, it is compelling to study ASD by examining genetic disorders associated with a high prevalence of ASD. Phelan McDermid syndrome (PMS), caused by either a 22q13.3 deletion encompassing the SHANK3 gene or SHANK3 mutation, is one compelling genetic model of ASD. PMS is characterized by intellectual disability (ID), generalized hypotonia, delayed or absent speech, normal to accelerated growth, and mild dysmorphic features (3, 4). PMS is a rare, although underdiagnosed disorder. The true prevalence of PMS is unknown; however according to the PMS Foundation, over 2000 individuals have been diagnosed with PMS throughout the world. Up to 84% of children with PMS have ASD (5). SHANK3 encodes a scaffolding protein located at the postsynaptic density of glutamatergic synapses and has important functions in synaptic scaffolding and regulating dendritic spine morphology (6, 7) (8). SHANK3 mutations have also been identified in approximately 1% of patients with ASD (9), and mouse models lacking Shank3 show autistic features (8, 10-16). Thus, PMS is a valuable genetic model of ASD to study the effects of SHANK3 on ASD clinical features and brain networks (17). Because ASD is characterized by deficits in social behavior, it is important to study relevant brain networks and phenotypic correlates in PMS. Social behavior, learning, and expression are all factors important in the ASD phenotype and are dependent on integrated activity of an extended, overlapping cortical socio-emotional processing network (18). There has been a wealth of foundational data showing support for a long distance cortical processing
7
deficit in ASD as a contributing factor to core social deficits in ASD (19, 20). Two major tracts that together expand a circuit running between frontal, temporal, and occipital regions and are also involved in socioemotional functions are the uncinate fasciculus (UF) and inferior longitudinal fasciculus (ILF) (21). The UF plays a role in episodic memory (22), emotional recognition and empathy (23-25), and social reward (22), and the ILF plays a role in object and facial recognition (26, 27) and more broadly in semantic learning (28) and reading comprehension (29). Furthermore, the UF and ILF have been shown through diffusion tensor imaging (DTI) to have altered microstructure in children with ASD (21, 30-36). These two tracts were investigated in this study. To examine specificity for our findings, we also examined the corticospinal tract (CST) as a control tract. To date no published studies have examined white matter tracts in PMS using DTI. We utilized DTI tractography to compare the UF and ILF in children with PMS to age matched controls and investigated the relationship between ASD diagnosis and the integrity of the UF and ILF white matter tracts. We hypothesized that when comparing the PMS group vs controls, there would be greater integrity of the UF and ILF white matter tracts in the control group.
MATERIALS AND METHODS Study Design This research was conducted under an ongoing, longitudinal, network study that aims to map the genotype, phenotype, and natural history of PMS, and to identify biomarkers using neuroimaging (ClinicalTrial NCT02461420). Patients were recruited at one of four network sites: Icahn School of Medicine at Mount Sinai, Boston Children’s Hospital (BCH), Rush University
8
Medical Center (RUMC), and the National Institutes of Health (NIH). Inclusion criteria required patients to be English speaking, between 3-21 years of age, and have pathogenic 22q13 deletions including SHANK3 or pathogenic sequence variants of the SHANK3 gene. Of note, for loss of function point mutations in SHANK3 we used 58.571 kb as the deletion size. Participants underwent longitudinal physical, medical, and neuropsychological assessment over a period of 24 months. Brain MRIs were only performed if clinically indicated and were not collected longitudinally. All MRI scans were transmitted to BCH for quality assurance and analysis. Age matched controls enrolled in a separate longitudinal study (through the NIH R01 NS079788) underwent the same standardized clinical imaging protocol and were imaged at Boston Children’s Hospital. Controls were recruited from the Boston area by targeted advertisements and letters in the community and at Children’s Hospital Boston, and through the research volunteer registry at Boston Children’s Hospital. Controls have no history of neurological or psychological disease. Data was collected between 2013-2018.
Sample 18 MRI brain scans from 18 PMS patients were available for analysis. Seven scans did not pass diffusion weighted imaging quality assurance and were excluded. One scan was removed because of prior brain surgery. After excluding these scans, the present study included 10 subjects (50% male; for genotype see Table 1) and 10 age- and sex-matched controls with at least one high quality diffusion-weighted MRI, for a total of 20 scans analyzed.
MRI Acquisition
9
MRI brain scans were acquired on 3T GE DiscoveryMR 750, Siemens Skyra, and Siemens TrioTim scanners. Subjects were imaged under a consensus clinical imaging protocol that included a 1mm3 sagittal T1-weighted MPRAGE, 1 mm3 T2-weighted SPACE, 30 high angular resolution diffusion weighted images (DWI) (30 b=1000 s/mm2 images, 5 b=0 s/mm2 images, matrix=128x128, contiguous slice thickness=2mm), and reversed phase-encoding directions for distortion compensation, covering the entire brain. Patients were sedated when clinically indicated. Controls were not sedated.
MRI Processing All MRI processing and analyses were completed using previously published and validated image analysis software distributed as the Computational Radiology Kit (http://crl.med.harvard.edu/). For each scan, the T2w image was aligned to the T1w image, and an intracranial cavity was segmented using a previously validated multispectral method (37). DWI data were screened for intra-volume motion and scanner artifact by expert raters, and artefactual volumes were removed. None of the PMS scans analyzed were affected by artifact, and no volumes were removed. Three b=0 volumes were removed from 1 of 10 PMS scans due to patient motion. All control scans analyzed were not affected by artifact, and no volumes were removed. Magnetic susceptibility distortion was mitigated with FSLtopup (38), and inter-volume motion correction was performed via affine registration of each DWI volume to the mean b=0 s/mm2 image. DWI were up sampled to the T1w image and skull stripped with the intracranial cavity segmentation. A single tensor diffusion model was estimated using robust least squares in each brain voxel from which fractional anisotropy [FA =
diffusivity [MD = (λ1 + λ2 + λ3)/3] were computed.
√
] and mean
10 Tractography
For each subject, a whole brain connectivity was generated using a stochastic streamline tractography algorithm that combines the speed and efficacy of deterministic decision making at each voxel with probabilistic sampling from the space of all streamlines (39). A single streamline was stochastically initialized within each white matter voxel and streamlines were propagated with multiple steps per voxel. Streamlines were terminated if the step angle exceeded 35 degrees, or if the FA fell below 0.20. The left and right UF, ILF, and CST were selected from the whole brain connectivity using automatically defined regions of interest (ROIs), for a total of 6 tracts selected per subject. The CST was used as a control tract because this tract would not be affected in ASD. The mean was measured over all tract voxels. The ROIs were automatically delineated on the subject scans using a fully automatic, multi-template approach. Briefly, a template library was constructed from whole brain DTI of 15 healthy individuals, with each scan in its native space. For each template, scalar FA and color maps of the principal diffusion directions were computed from the DTI, and ROIs were hand drawn by an expert rater on the color map. To delineate the same white matter ROIs in the native space of each subject scan, the following procedure was performed for every template: the template DTI was aligned to the subject DTI using a nonlinear, dense registration, and the dense deformation field was then used to resample the template white matter ROIs to the subject DTI using nearest neighbor interpolation (40). Consensus ROIs were computed from the aligned template ROIs using the STAPLE algorithm (41). ROI Delineation
The UF and ILF tracts were investigated. The ILF was selected from the whole brain connectivity using two selection ROIs located in 1) the anterior temporal lobe and 2) the
11
occipital lobe with a ventral limit of the white matter of the lingual and fusiform gyrus and a dorsal limit of the white matter of the joining splenium fibers (42). To avoid nearby aberrant fibers, two regions of exclusion (ROE) were manually placed on 1) a single midsagittal slice encompassing the corpus callosum and 2) a single axial slice at the brain stem. Similarly, the UF was selected from the whole brain connectivity using two selection ROIs located in 1) the anterior temporal lobe and 2) the external capsule (42) (Figure 1). The UF anterior temporal lobe ROI was constrained to the anterior medial aspect of the anterior temporal lobe to minimize selection of non-UF fibers. To further optimize fiber selection, three ROEs were manually placed on 1) a single coronal slice posterior to the anterior temporal lobe spanning the full hemisphere 2) a single whole brain midsagittal slice and 3) sagittal slices on the extreme capsule. We examined the CST as a control tract. The left and right CSTs were selected using three ROIs. A ROI was drawn in the posterior limb of the internal capsule in the axial plane, with its inferior limit defined on the first axial slice superior to the anterior commissure and the superior limit was defined on the axial slice where the lenticular nucleus separates the internal and external capsules (42). A second ROI was drawn in the cerebral peduncle, covering the inferior-superior traversing white matter of the anterior pons (42). A final ROI covered the precentral gyrus. A region of exclusion was placed over the midline corpus callosum and cerebellar white matter.
Clinical Measures Patient phenotype was evaluated with neuropsychological assessment at baseline, 12 months visit, and the 24 months visit. ASD diagnosis was made based on clinical consensus using the Diagnostic and Statistical Manual for Mental Disorders Fifth Edition (DSM-5) (43) and informed by evaluation with the ADOS2 (44) and ADI-R (45). The ADOS2 was given at
12
baseline, 12 months into the study, and 24 months and confirmed based on clinical consensus using all three measures. (44). Best estimate IQ was derived from standard scores on IQ tests and ratio IQ estimates in participants who received out-of-range testing with developmental cognitive tests. Specifically, as is standard in such studies (46) a hierarchy of tests was used, including the Mullen Scales of Early Learning (47) in 10 participants, and the Stanford Binet (48).
Statistical Analysis A Wilcoxon sign rank testing was used to compare PMS vs. age matched controls regarding UF FA and MD (left and right) and ILF FA and MD (left and right) DTI metrics. For patients, we used Spearman’s rank correlation coefficients test to see if there was an effect of DTI metric on IQ. Due to small sample size, we used exact logistic regression to understand the relationship between ASD diagnosis and DTI metric while adjusting for age. In post-hoc analysis (given that control data was from different study), we employed Benjamini-Hochberg (BH) false discovery rate procedure, to account for multiple comparisons with q = 0.05(49). Because of small sample, we used less conservation BH correction approach instead of Bonferonni, but we used stricter cutpoint because we did not have numerous hypotheses to test. There were 12 comparisons when comparing DTI metric for patients versus controls: three tracts [ILF, UF, CST] x two sides [left, right] x two metrics [FA, MD].
RESULTS
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Of the initial 18 subjects enrolled in the PMS clinical trial (NCT02461420) with MRI available for analysis, 8 were excluded because of either failure to meet quality assurance or because of previous brain surgery. 10 PMS participant scan data were included in this cohort study. In both the PMS and control group, 50% were male (chi2(1) = 0.000, p = 1.000). The mean age of the PMS group was 11.17 (6.17) years, and the mean age of the control group was 11.24 (5.96) years. The mean IQ of the PMS group was 27.1 (sd = 20.7, range = 3.4-61). 80% had a diagnosis of ASD. The mean deletion size was 2457.8 kb (sd = 2694.1, range = 46-6320). In the sample, 7 (70%) were administered ADOS Module 1, 2 (20%) were administered ADOS Module 2, and 1 (10%) was administered ADOS Module 3. There was a statistically significant decrease in the UF FA measure in the PMS vs age matched controls in both the right and left tracts (adjusted p ≤ 0.024), as well as a statistically significant increase in UF MD measure in the PMS group vs controls in both right and left tracts (adjusted p ≤ 0.024) (Figure 2). There was a decrease in the ILF FA measure in the PMS vs age matched controls in both the right and left tracts, as well as an increase in ILF MD measure in the PMS group vs controls in the right and left tracts, but these changes were not statistically significant with adjusted p-values (Figure 3). Furthermore, an investigation of DTI metrics in the CST was used as a control tract between the PMS and control groups. There was no statistically significant difference in DTI metrics in the CST of PMS vs. controls. There was no significant difference found within the PMS group between any DTI metric and diagnosis of ASD. Of note, only 2 of the 10 PMS participants did not have ASD. There was not a statistically significant difference found within the PMS group between any DTI metric and best estimate IQ. There was not a statistically signification correlation between any DTI metric and deletion size.
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DISCUSSION This is the first study to date to use DTI in a PMS population in order to investigate potential microstructural changes in tracts critical for social and emotional interaction. This cohort study utilized DTI tractography to compare the UF and ILF in 10 children with PMS to age matched controls and investigated the integrity of the UF and ILF white matter tracts. In this study, there was a statistically significant decrease in the UF (and increase in the MD) in both the right and left tracts of PMS vs. age-matched controls. The CST was used as a control tract, and no difference was found in DTI metrics in the PMS vs. control groups. This control tract investigation further supports the specificity of the DTI changes in the UF. Based on previous studies in conjunction with our PMS investigation, UF microstructural abnormalities have been highlighted as an important tract underlying aspects of emotional and social behavior. The UF is a medial fronto-temporal long-range white matter tract that links anterior portions of superior, middle, and inferior temporal lobe, including the hippocampus and amygdala, with the insular and orbitofrontal cortex (50-52). The UF has been shown to play a critical role in emotional recognition (23, 24), self-awareness (53), social reward processing (22), and proper name retrieval (54). One study found that participants with UF lesions have deficits in an emotional empathy task, highlighting the role of the UF in social interaction and the ability to relate to others (25). Past ASD DTI studies have found significant, yet conflicting results, when examining the UF in patients with ASD vs controls (21, 30-34, 55-57). In this PMS study, the ILF FA DTI metric values were found to be decreased, and the ILF MD DTI increased, in the PMS group when compared to age matched controls, but these findings were not statistically significant with adjusted p-values. The ILF is the main pathway
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connecting brain structures that play a critical role in not only facial recognition, but also modulating visual stream socially salient information (58). Visual processing and emotional recognition are crucial for effective social communication (59-64). The ILF extends from the occipital cortex into the temporal lobes which mediates connections to the superior temporal sulcus, fusiform face area, and the amygdala (65-67). Previous ASD DTI studies have found changes in the ILF microstructure in ASD when compared to controls, reinforcing that the ILF is a relevant and important white matter tract to study in the context of ASD. This study also investigated a potential relationship between ASD diagnosis and DTI metric within the PMS group. No significant differences were found between any DTI metric and ASD diagnosis or IQ within the PMS group. Both the UF and ILF have temporal and amygdala connectivity which provides further support to study these two relevant tracts (50-52, 65-67). A previous positron emission tomography (PET) study detected localized dysfunction of the left temporal polar lobe and amygdala hypoperfusion in children with PMS (68) suggesting that there may be abnormalities in amygdalo-cortical connectivity that may contribute to the social interaction deficits found in PMS. By studying white matter tracts in the context of a known genetic mutation, we can now also lay the foundation to study the role SHANK3 may play in specific axonal tracts.
LIMITATIONS One main limitation of this study was the small sample size. It is possible the small sample size of this study is one limitation that potentially prevented ILF differences between patients and controls from maintaining statistical significance. In addition, there was a wide age range in this study and age significantly affects the development of both the UF and ILF.
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Of note, 2 of the 10 PMS participants did not have ASD, so the analysis was unbalanced in a setting of a small cohort, which likely limited our ability to make further conclusions about the DTI findings in relation to ASD diagnosis. In other words, the main DTI findings of this investigation may be specific to PMS rather than ASD more broadly, although conclusions are limited with this small sample size. While PMS is rare, through the ongoing longitudinal, network study encompassing multiple institutions, we plan to grow our sample size for further studies (ClinicalTrial NCT02461420). In addition, in future studies with greater samples sizes, ADOS severity score should be evaluated as a continuous measure. Furthermore, in addition to including an estimate of Full Scale IQ, which is important, future directions with a greater sample size include investigating aspects of phenotypic measurements that more specifically relate to the function of the UF or ILF in PMS or ASD populations. For example, the Vineland Adaptive Behavior Scale, specifically the socialization domain, can be used to more effectively evaluate whether changes in UF white matter microstructure correlates with changes in specific adaptive behaviors in areas previously found to be functionally relevant in the UF and ILF. In addition, future work can utilize a low IQ control group in order to specify findings further to PMS related differences, and not merely to gross cognitive differences between PMS and control groups.
CONCLUSIONS There have been no published studies that have examined white matter tracts in PMS using DTI. We utilized DTI tractography to compare the UF and ILF in children with PMS to age matched controls and investigated the relationship between ASD diagnosis and the integrity of the UF and ILF white matter tracts. Overall, this study found significant decrease in the FA (and increase in the MD) of the right and left UF in the PMS group when compared to
17
age matched controls. The UF plays a critical role in social and emotional interaction and may underlie some deficits seen in the PMS and therefor ASD population. Further work with greater sample sizes needs to be done to study these two important tracts, ILF and UF, and their relationship to PMS and ASD. In addition, more work needs to be done to investigate on a neurological systems level the role of SHANK3 in different neural networks. Altogether, PMS provides a unique context to study contributions of SHANK3 to the development of ASD.
TRIAL REGISTRATION The study uses data from a clinical trial (ClinicalTrials.gov NCT02461420; https://clinicaltrials.gov/ct2/show/NCT02461420). Date of registration was June 3, 2015.
LIST OF ABBREVIATIONS diffusion tensor imaging (DTI) uncinate fasciculus (UF) inferior longitudinal fasciculus (ILF) autism spectrum diagnosis (ASD) Phelan McDermid syndrome (PMS) autism Diagnostic Observation Schedule 2nd edition (ADOS2) diagnostic and Statistical Manual for Mental Disorders Fifth Edition (DSM-5) diffusion weighted images (DWI) region of interest (ROI) regions of exclusion (ROE) cortical spinal tract (CST) mean diffusivity (MD)
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fractional anisotropy (FA)
DECLARATIONS Ethics approval and consent to participate The study was approved by the Institutional Review Board (IRB) at Boston Children’s Hospital, which serves as the central IRB for all the sites included in this study. Informed consent was obtained from the caregivers and legal guardians of all participants. The study was conducted in accordance with Good Clinical Practice guidelines.
Consent for Publication Not applicable
Availability of data and materials Data was entered in a web-based system created and maintained by the data management and coordinating center at the University of South Florida, which met the Health Insurance Portability and Accountability Act privacy regulations. Additional MRI data was maintained at the Computational Radiology Laboratory at Boston Children’s Hospital. Both clinical and MRI data presented here have been made available to the National Database for Autism Research, which is an NIH-funded data repository which aims to house and share with qualified researchers’ data related to Autism Spectrum Disorder.
Competing interests Siddharth Srivastava has received consulting fees from Guidepoint. Mustafa Sahin reports grant support from Novartis, Roche, Pfizer, Ipsen, LAM Therapeutics and Quadrant Biosciences,
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unrelated to this project. He has served on Scientific Advisory Boards for Sage, Roche, Celgene and Takeda. AK receives research support from AMO Pharma and consults to Ovid Therapeutics, Coronis, 5AM Ventures, sema4, LabCorp, and Takeda. EBK has received funding from Seaside Therapeutics, Novartis, Roche, Alcobra, Neuren, Cydan, Fulcrum, GW, Neurotrope, Marinus, Zynerba, BioMarin, Ovid, Yamo, Acadia, Ionis, Ultragenyx, and Lumos Pharmaceuticals to consult on trial design or development strategies and/or conduct clinical studies in FXS or other NDDs or neurodegenerative disorders; from Vtesse/Sucampo/Mallinckrodt Pharmaceuticals to conduct clinical trials in NP-C; and from Asuragen Inc. to develop testing standards for FMR1 testing. All funding to Dr. Berry-Kravis is directed to Rush University Medical Center (RUMC) in support of rare disease programs. C.M.P. has accepted travel funds and honoraria to speak once at each of the following companies: Psychogenics, Inc.; Astra-Zeneca; Roche; Pfizer; and Dainippon Sumitomo Pharma Co. C.M.P. also has investigator-initiated grant funding for clinical research with Novartis. None of these activities represents a competing interest to the current study.
Funding This study is funded by supported by the Developmental Synaptopathies Consortium (U54NS092090), which is a part of the National Center for Advancing Translational Sciences Rare Diseases Clinical Research Network. The Rare Diseases Clinical Research Network is an initiative of the Office of Rare Diseases Research of the National Center for Advancing Translational Sciences, and Developmental Synaptopathies Consortium is funded through collaboration between the National Center for Advancing Translational Sciences, the National Institute of Mental Health, the National Institute of Neurological Disorders and Stroke, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.
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Members of the Developmental Synaptopathies Consortium (DSC) – Phelan-McDermid Syndrome Group include: Mustafa Sahin, MD, PhD a, b Alexander Kolevzon, MD c, d Joseph Buxbaum, PhD c, d, e, f Elizabeth Berry Kravis, MD, PhD g, h, i Latha Soorya, PhD j Audrey Thurm, PhD k Craig Powell, MD, PhD l, m Jonathan A Bernstein, MD, PhD n
Simon Warfield, PhD o Benoit Scherrer, PhD o Rajna Filip-Dhima, MS b Kira Dies, ScM, CGC b Paige Siper, PhD c Ellen Hanson, PhD p Jennifer M. Phillips, PhD q Stormi P. White, Psy D r
Affiliations for above:
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a Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA b F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA c Seaver Autism Center for Research and Treatment, Mount Sinai School of Medicine, New York, NY d Department of Psychiatry, Mount Sinai School of Medicine, New York, NY e Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, NY f Department of Neuroscience, Mount Sinai School of Medicine, New York, NY g Department of Pediatrics, Rush University Medical Center, Chicago, IL h Department of Neurological Sciences, Rush University Medical Center, Chicago, IL i Department of Biochemistry, Rush University Medical Center, Chicago, IL j Department of Psychiatry, Rush University Medical Center, Chicago, IL k Pediatrics and Developmental Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD l Department of Neurobiology, University of Alabama at Birmingham School of Medicine, Birmingham, AL m Civitan International Research Center, University of Alabama at Birmingham School of Medicine, Birmingham, AL n Department of Pediatrics, Stanford University School of Medicine, Stanford, CA o Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital & Harvard Medical School, Boston, MA
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p Department of Developmental Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA q Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA r Center for Autism and Developmental Disabilities, University of Texas Southwestern Medical Center, Dallas, TX
Authors' contributions JB drafted the manuscript; analyzed the data. SS analyzed the data; performed critical revision of the manuscript for important intellectual content. AP and BS collected and analyzed the data; performed critical revision of the manuscript for important intellectual content. KK provided statistical analysis; performed critical revision of the manuscript for important intellectual content. RFD performed critical revision of the manuscript for important intellectual content. EBK performed critical revision of the manuscript for important intellectual content. LS performed critical revision of the manuscript for important intellectual content. AT performed critical revision of the manuscript for important intellectual content. CP performed critical revision of the manuscript for important intellectual content. JB performed critical revision of the manuscript for important intellectual content. AK performed critical revision of the manuscript for important intellectual content. SW and MS oversaw study concept and design; performed critical revision of the manuscript for important intellectual content.
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Acknowledgements We are sincerely indebted to the generosity of the families and patients in PMS clinics across the United States who contributed their time and effort to this study. We would also like to thank the Phelan-McDermid Syndrome Foundation for their continued support in PMS research. We acknowledge all the study coordinators who are part of DSC.
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Figure 1. Example images of the ILF (A-B) and UF (C-E) overlaid on a fractional anisotropy map in two different patients with PMS.
A) left midsagittal slice showing a representative ILF in a patient with PMS and the two ROIs (Catani et al 2008) used to capture the ILF: 1) dark pink anterior temporal lobe label 2) purple posterior occipital lobe label. B) A left midsagittal slice showing the sagittal ROE. Note: The axial brain stem ROE slice was applied to generate the ILF indicated, but is not shown. C) A left side midsagittal slice showing a representative UF in a patient with PMS. D) Left side cutaway view showing all three planes (axial, sagittal, and coronal) with orange labels showing the whole brain sagittal ROE slice and the hemisphere coronal ROE slice. E) Left side cutaway view showing the two ROI (Catani et al 2008) used to capture the UF: 1) dark pink anterior temporal lobe label 2) light pink external capsule label.
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Note: The sagittal extreme capsule ROE slice was applied to generate the UF indicated, but is not shown. ILF: inferior longitudinal fasciculus PMS: Phelan McDermid Syndrome ROE: region of exclusion ROI: region of inclusion UF: uncinate fasciculus
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Figure 2. Mean DTI metric versus age of patients and controls
Plot of mean DTI metric versus age of patients and controls for (A) FA UF left (B) FA UF right (C) MD UF left (CD) MD UF right.
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Figure 3. Mean DTI metric versus age of patients and controls
Plot of mean DTI metric versus age of patients and controls for (A) FA ILF left (B) FA ILF right (C) MD ILF left (CD) MD ILF right.
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Table 1. Genotype of participants. Participant
Genotype
1
arr[hg19] 22q13.31q13.33(46967883-51211392)x1
2
arr[hg19] 22q13.33(51116107-51193680)x1
3
arr[hg19] 22q13.33(51127905-51234443)x1
4
arr[hg19] 22q13.33(51130264-51176099)x1
5
22q13 deletion (22q13.3 - q terminus)
6
SHANK3, NM_033517.1:c.5008A>T, p.K1670*, SHANK3, NM_033517.1: c.3872C>T, p.S1291L
7
ish 22q13.3(TelVysion22q-)
8
arr[hg19] 22q13.31q13.33(44831210-51178257)x1; der(22)t(2;22)
9
arr[hg19] 22q13.31-q13.33(44921022-51224252)x1
10
arr[hg19] 22q13.31-q13.33(46350579-51224252)x1
29
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