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Yogi A, Hirata Y, Linetsky M, Ellingson BM, Salamon N. Qualitative and quantitative evaluation for the heterogeneity of cortical tubers using structural imaging and diffusion-weighted imaging to predict the epileptogenicity in tuberous sclerosis complex patients. Neuroradiology 2023; 65:845-853. [PMID: 36456893 DOI: 10.1007/s00234-022-03094-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/20/2022] [Indexed: 12/03/2022]
Abstract
PURPOSE We aimed to evaluate whether the heterogeneity of tuber imaging features, evaluated on the structural imaging and apparent diffusion coefficient (ADC) map, can facilitate detecting epileptogenic tubers before surgery in tuberous sclerosis complex (TSC) patients. METHODS Twenty-three consecutive patients, who underwent tuber resection at our institute, were retrospectively selected. A total of 125 tubers (39 epileptogenic, 86 non-epileptogenic) were used for the analysis. Tuber heterogeneity was evaluated, using a 5-point visual scale and standard deviation of ADC values (ADCsd). A 5-point visual scale reflected the degree of T1/T2 prolongation, presence of internal cystic degeneration, and their spatial distribution within the tuber. These results were statistically compared between epileptogenic and non-epileptogenic groups, and their performance in predicting the epileptogenicity was also evaluated by receiver operating characteristic (ROC) analysis. RESULTS A 5-point visual scale demonstrated that more heterogeneous tubers were significantly more epileptogenic (p < 0.001). Multiplicity of internal cystic degeneration moderately correlated with epileptogenicity (p < 0.03) based on the comparison between class 4 and class 5 tubers. ADCsd was significantly higher in epileptogenic tubers (p < 0.001). ROC curves revealed that a 5-point visual scale demonstrated higher area under the curve (AUC) value than ADCsd (0.75 and 0.72, respectively). CONCLUSION Tuber heterogeneity may help identify the epileptogenic tubers in presurgical TSC patients. Visual assessment and standard deviation of ADC value, which are easier to implement in clinical use, may be a useful tool predicting epileptogenic tubers, improving presurgical clinical management for TSC patients with intractable epilepsy.
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Affiliation(s)
- Akira Yogi
- Department of Radiology, University of the Ryukyus Hospital, 207 Uehara, Nishihara-Cho, Nakagami-Gun, Okinawa, 903-0215, Japan.
- Department of Radiological Science, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Los AngelesLos Angeles, CA, 90024, USA.
| | - Yoko Hirata
- Department of Radiological Science, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Los AngelesLos Angeles, CA, 90024, USA
- Department of Neurosurgery, Toho University Ohashi Medical Center, 2-22-36 Ohashi, Meguro-Ku, Tokyo, 153-8515, Japan
| | - Michael Linetsky
- Department of Radiological Science, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Los AngelesLos Angeles, CA, 90024, USA
| | - Benjamin M Ellingson
- Department of Radiological Science, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Los AngelesLos Angeles, CA, 90024, USA
| | - Noriko Salamon
- Department of Radiological Science, David Geffen School of Medicine, University of California, 924 Westwood Blvd, Los AngelesLos Angeles, CA, 90024, USA
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Majithia J, Mahajan A, Vaish R, Prakash G, Patwardhan S, Sarin R. Imaging Recommendations for Diagnosis, Staging, and Management of Hereditary Malignancies. Indian J Med Paediatr Oncol 2023. [DOI: 10.1055/s-0042-1760325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
AbstractHereditary cancer syndromes, characterized by genetically distinct neoplasms developing in specific organs in more than one family members, predispose an individual to early onset of distinct site-specific tumors. Early age of onset, multiorgan involvement, multiple and bilateral tumors, advanced disease at presentation, and aggressive tumor histology are few characteristic features of hereditary cancer syndromes. A multidisciplinary approach to hereditary cancers has led to a paradigm shift in the field of preventive oncology and precision medicine. Imaging plays a pivotal role in the screening, testing, and follow-up of individuals and their first- and second-degree relatives with hereditary cancers. In fact, a radiologist is often the first to apprise the clinician about the possibility of an underlying hereditary cancer syndrome based on pathognomonic imaging findings. This article focuses on the imaging spectrum of few common hereditary cancer syndromes with specific mention of the imaging features of associated common and uncommon tumors in each syndrome. The screening and surveillance recommendations for each condition with specific management approaches, in contrast to sporadic cases, have also been described.
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Affiliation(s)
- Jinita Majithia
- Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Abhishek Mahajan
- Department of Radiology, The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Richa Vaish
- Department of Head and Neck Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Gagan Prakash
- Department of Uro-Oncology, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Saket Patwardhan
- Department of Radiodiagnosis, Tata Memorial Hospital, Mumbai, Maharashtra, India
| | - Rajiv Sarin
- Department of Radiation Oncology and In-Charge Cancer Genetics, Tata Memorial Hospital and Advanced Centre for Treatment Research and Education in Cancer (ACTREC), Mumbai, Maharashtra, India
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Cohen AL, Kroeck MR, Wall J, McManus P, Ovchinnikova A, Sahin M, Krueger DA, Bebin EM, Northrup H, Wu JY, Warfield SK, Peters JM, Fox MD. Tubers Affecting the Fusiform Face Area Are Associated with Autism Diagnosis. Ann Neurol 2023; 93:577-590. [PMID: 36394118 PMCID: PMC9974824 DOI: 10.1002/ana.26551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Tuberous sclerosis complex (TSC) is associated with focal brain "tubers" and a high incidence of autism spectrum disorder (ASD). The location of brain tubers associated with autism may provide insight into the neuroanatomical substrate of ASD symptoms. METHODS We delineated tuber locations for 115 TSC participants with ASD (n = 31) and without ASD (n = 84) from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network. We tested for associations between ASD diagnosis and tuber burden within the whole brain, specific lobes, and at 8 regions of interest derived from the ASD neuroimaging literature, including the anterior cingulate, orbitofrontal and posterior parietal cortices, inferior frontal and fusiform gyri, superior temporal sulcus, amygdala, and supplemental motor area. Next, we performed an unbiased data-driven voxelwise lesion symptom mapping (VLSM) analysis. Finally, we calculated the risk of ASD associated with positive findings from the above analyses. RESULTS There were no significant ASD-related differences in tuber burden across the whole brain, within specific lobes, or within a priori regions derived from the ASD literature. However, using VLSM analysis, we found that tubers involving the right fusiform face area (FFA) were associated with a 3.7-fold increased risk of developing ASD. INTERPRETATION Although TSC is a rare cause of ASD, there is a strong association between tuber involvement of the right FFA and ASD diagnosis. This highlights a potentially causative mechanism for developing autism in TSC that may guide research into ASD symptoms more generally. ANN NEUROL 2023;93:577-590.
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Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mallory R Kroeck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Juliana Wall
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter McManus
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arina Ovchinnikova
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School at University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Joyce Y Wu
- Division of Neurology & Epilepsy, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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The longitudinal evolution of cerebral blood flow in children with tuberous sclerosis assessed by arterial spin labeling magnetic resonance imaging may be related to cognitive performance. Eur Radiol 2022; 33:196-206. [PMID: 36066730 DOI: 10.1007/s00330-022-09036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 07/11/2022] [Accepted: 07/18/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To study longitudinal changes in tuber and whole-brain perfusion in children with tuberous sclerosis complex (TSC) using arterial spin labeling (ASL) perfusion MRI and correlate them with pathological EEG slow wave activity and neurodevelopmental outcomes. METHODS Retrospective longitudinal cohort study of 13 children with TSC, 3 to 6 serial ASL-MRI scans between 2 months and 7 years of age (53 scans in total), and an EEG examination performed within 2 months of the last MRI. Tuber cerebral blood flow (CBF) values were calculated in tuber segmentation masks, and tuber:cortical CBF ratios were used to study tuber perfusion. Logistic regression analysis was performed to identify which initial tuber characteristics (CBF value, volume, location) in the first MRI predicted tubers subsequently associated with EEG slow waves. Whole-brain and lobar CBF values were extracted for all patient scans and age-matched controls. CBF ratios were compared in patients and controls to study longitudinal changes in whole-brain CBF. RESULTS Perfusion was reduced in tubers associated with EEG slow waves compared with other tubers. Low tuber CBF values around 6 months of age and large tuber volumes were predictive of tubers subsequently associated with EEG slow waves. Patients with severe developmental delay had more severe whole-brain hypoperfusion than those with no/mild delay, which became apparent after 2 years of age and were not associated with a higher tuber load. CONCLUSIONS Dynamic changes in tuber and brain perfusion occur over time. Perfusion is significantly reduced in tubers associated with EEG slow waves. Whole-brain perfusion is significantly reduced in patients with severe delay. KEY POINTS • Tubers associated with EEG slow wave activity were significantly more hypoperfused than other tubers, especially after 1 year of age. • Larger and more hypoperfused tubers at 6 months of age were more likely to subsequently be associated with pathological EEG slow wave activity. • Patients with severe developmental delay had more extensive and severe global hypoperfusion than those without developmental delay.
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Nijman M, Yang E, Jaimes C, Prohl AK, Sahin M, Krueger DA, Wu JY, Northrup H, Stone SSD, Madsen JR, Fallah A, Blount JP, Weiner HL, Grayson L, Bebin EM, Porter BE, Warfield SK, Prabhu SP, Peters JM. Limited utility of structural MRI to identify the epileptogenic zone in young children with tuberous sclerosis. J Neuroimaging 2022; 32:991-1000. [PMID: 35729081 DOI: 10.1111/jon.13016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE The success of epilepsy surgery in children with tuberous sclerosis complex (TSC) hinges on identification of the epileptogenic zone (EZ). We studied structural MRI markers of epileptogenic lesions in young children with TSC. METHODS We included 26 children with TSC who underwent epilepsy surgery before the age of 3 years at five sites, with 12 months or more follow-up. Two neuroradiologists, blinded to surgical outcome data, reviewed 10 candidate lesions on preoperative MRI for characteristics of the tuber (large affected area, calcification, cyst-like properties) and of focal cortical dysplasia (FCD) features (cortical malformation, gray-white matter junction blurring, transmantle sign). They selected lesions suspect for the EZ based on structural MRI, and reselected after unblinding to seizure onset location on electroencephalography (EEG). RESULTS None of the tuber characteristics and FCD features were distinctive for the EZ, indicated by resected lesions in seizure-free children. With structural MRI alone, the EZ was identified out of 10 lesions in 31%, and with addition of EEG data, this increased to 48%. However, rates of identification of resected lesions in non-seizure-free children were similar. Across 251 lesions, interrater agreement was moderate for large size (κ = .60), and fair (κ = .24) for all other features. CONCLUSIONS In young children with TSC, the utility of structural MRI features is limited in the identification of the epileptogenic tuber, but improves when combined with EEG data.
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Affiliation(s)
- Maaike Nijman
- Localization Laboratory, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Edward Yang
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mustafa Sahin
- Rosamund Stone Zander Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Darcy A Krueger
- Division of Neurology, Cincinnati Children's Hospital Medical Center, and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Joyce Y Wu
- Division of Neurology, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.,Departments of Pediatrics and Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, at University of Texas Health Science Center at Houston (UTHealth) and Children's Memorial Hermann Hospital, Houston, Texas, USA
| | - Scellig S D Stone
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph R Madsen
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aria Fallah
- Department of Neurosurgery, Division of Pediatric Neurosurgery, University of California Los Angeles Mattel Children's Hospital, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA
| | - Jeffrey P Blount
- Department of Neurosurgery, Division of Pediatric Neurosurgery, Children's of Alabama, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Howard L Weiner
- Department of Surgery, Division of Pediatric Neurosurgery, Texas Children's Hospital, Houston, Texas, USA.,Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Leslie Grayson
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Brenda E Porter
- Department of Neurology, Stanford University Medical Center, Stanford, California, USA
| | - Simon K Warfield
- Rosamund Stone Zander Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sanjay P Prabhu
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jurriaan M Peters
- Localization Laboratory, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Park DK, Kim W, Thornburg O, McBrian D, McKhann G, Feldstein N, Maddocks A, Gonzalez E, Shen MY, Akman C, Provenzano F. Convolutional Neural Network-aided Tuber Segmentation in Tuberous Sclerosis Complex Patients Correlates with EEG. Epilepsia 2022; 63:1530-1541. [PMID: 35301716 DOI: 10.1111/epi.17227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 11/27/2022]
Abstract
OBJECTIVE One of the clinical hallmarks of tuberous sclerosis complex is radiologically-identified cortical tubers present in most patients. Intractable epilepsy may require surgery, often involving invasive diagnostic procedures such as intracranial EEG. Identifying the location of the dominant tuber responsible for generating epileptic activities, is a critical issue. However, the link between cortical tubers and epileptogenesis is poorly understood. Given this, we hypothesized that tuber voxel intensity may be an indicator of the dominant epileptogenic tuber. Also, via tuber segmentation based on deep learning, we explore whether an automatic quantification of the tuber burden is feasible. METHODS We annotated tubers from structural MRIs across 29 TSC subjects, summarized tuber statistics in eight brain lobes, and determined suspected epileptogenic lobes from the same group using EEG monitoring data. Then logistic regression analyses are performed to demonstrate the linkage between the statistics of cortical tuber and the epileptogenic zones. Furthermore, we test the ability of a neural network to identify and quantify tuber burden. RESULTS Logistic regression analyses show that the volume and count of tubers per lobe, not the mean or variance of tuber voxel intensity, are positively correlated with electrophysiological data. In 47.6% of subjects, the lobe with the largest tuber volume concurred with the epileptic brain activity. A neural network model on the test dataset shows a sensitivity of 0.83 for localizing individual tubers. The predicted masks from the model highly correlated with the neurologist labels, thus may be a useful tool for determining tuber burden and searching for epileptogenic zone. SIGNIFICANCE we prove the feasibility of an automatic segmentation of tubers and a derivation of tuber burden across brain lobes. Our method may provide crucial insights in the treatment and outcome of TSC patients.
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Affiliation(s)
- David K Park
- Department of Biomedical Engineering, Columbia University
| | - Woojoong Kim
- Columbia University Irving Medical Center.,Child Neurology, Columbia University Medical Center
| | | | | | - Guy McKhann
- Neurological Surgery, Columbia University Medical Center
| | - Neil Feldstein
- Neurological Surgery, Columbia University Medical Center
| | | | | | - Min Y Shen
- Columbia University Irving Medical Center
| | - Cigdem Akman
- Columbia University Irving Medical Center.,Child Neurology, Columbia University Medical Center
| | - Frank Provenzano
- Columbia University Irving Medical Center.,Department of Neurology, Columbia University
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Sato A, Tominaga K, Iwatani Y, Kato Y, Wataya-Kaneda M, Makita K, Nemoto K, Taniike M, Kagitani-Shimono K. Abnormal White Matter Microstructure in the Limbic System Is Associated With Tuberous Sclerosis Complex-Associated Neuropsychiatric Disorders. Front Neurol 2022; 13:782479. [PMID: 35359647 PMCID: PMC8963953 DOI: 10.3389/fneur.2022.782479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 02/18/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveTuberous sclerosis complex (TSC) is a genetic disease that arises from TSC1 or TSC2 abnormalities and induces the overactivation of the mammalian/mechanistic target of rapamycin pathways. The neurological symptoms of TSC include epilepsy and tuberous sclerosis complex-associated neuropsychiatric disorders (TAND). Although TAND affects TSC patients' quality of life, the specific region in the brain associated with TAND remains unknown. We examined the association between white matter microstructural abnormalities and TAND, using diffusion tensor imaging (DTI).MethodsA total of 19 subjects with TSC and 24 age-matched control subjects were enrolled. Tract-based spatial statistics (TBSS) were performed to assess group differences in fractional anisotropy (FA) between the TSC and control groups. Atlas-based association analysis was performed to reveal TAND-related white matter in subjects with TSC. Multiple linear regression was performed to evaluate the association between TAND and the DTI parameters; FA and mean diffusivity in seven target regions and projection fibers.ResultsThe TBSS showed significantly reduced FA in the right hemisphere and particularly in the inferior frontal occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), and genu of corpus callosum (CC) in the TSC group relative to the control group. In the association analysis, intellectual disability was widely associated with all target regions. In contrast, behavioral problems and autistic features were associated with the limbic system white matter and anterior limb of the internal capsule (ALIC) and CC.ConclusionThe disruption of white matter integrity may induce underconnectivity between cortical and subcortical regions. These findings suggest that TANDs are not the result of an abnormality in a specific brain region, but rather caused by connectivity dysfunction as a network disorder. This study indicates that abnormal white matter connectivity including the limbic system is relevant to TAND. The analysis of brain and behavior relationship is a feasible approach to reveal TAND related white matter and neural networks. TAND should be carefully assessed and treated at an early stage.
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Affiliation(s)
- Akemi Sato
- United Graduate School of Child Development, Osaka University, Osaka, Japan
| | - Koji Tominaga
- United Graduate School of Child Development, Osaka University, Osaka, Japan
- Molecular Research Center for Children's Mental Development, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yoshiko Iwatani
- United Graduate School of Child Development, Osaka University, Osaka, Japan
- Molecular Research Center for Children's Mental Development, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Yoko Kato
- Molecular Research Center for Children's Mental Development, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Mari Wataya-Kaneda
- Division of Health Science, Department of Neurocutaneous Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
- Department of Dermatology, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Kai Makita
- Research Center for Child Mental Development, University of Fukui, Fukui, Japan
| | - Kiyotaka Nemoto
- Division of Clinical Medicine, Department of Psychiatry, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan
| | - Masako Taniike
- United Graduate School of Child Development, Osaka University, Osaka, Japan
- Molecular Research Center for Children's Mental Development, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Kuriko Kagitani-Shimono
- United Graduate School of Child Development, Osaka University, Osaka, Japan
- Molecular Research Center for Children's Mental Development, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Osaka, Japan
- *Correspondence: Kuriko Kagitani-Shimono
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Cohen AL. Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments. J Neurodev Disord 2022; 14:19. [PMID: 35279095 PMCID: PMC8918299 DOI: 10.1186/s11689-022-09433-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/03/2022] [Indexed: 11/20/2022] Open
Abstract
A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.
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Affiliation(s)
- Alexander Li Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA. .,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Laboratory for Brain Network Imaging and Modulation, Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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9
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Convolutional neural networks to identify malformations of cortical development: A feasibility study. Seizure 2021; 91:81-90. [PMID: 34130195 DOI: 10.1016/j.seizure.2021.05.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE To develop and test a deep learning model to automatically detect malformations of cortical development (MCD). METHODS We trained a deep learning model to distinguish between diffuse cortical malformation (CM), periventricular nodular heterotopia (PVNH), and normal magnetic resonance imaging (MRI). We trained 4 different convolutional neural network (CNN) architectures. We used batch normalization, global average pooling, dropout layers, transfer learning, and data augmentation to minimize overfitting. RESULTS There were 45 subjects (866 images) with a normal MRI, 52 subjects (790 images) with CM, and 32 subjects (750 images) with PVNH. There was no subject overlap between the training, validation, and test sets. The InceptionResNetV2 architecture performed best in the validation set in all models and was evaluated in the test set with the following results: 1) the model distinguishing between CM and normal MRI yielded an area under the curve (AUC) of 0.89 and accuracy of 0.81; 2) the model distinguishing between PVNH and normal MRI yielded an AUC of 0.90 and accuracy of 0.84; 3) the model distinguishing between the three classes (CM, PVNH, and normal MRI) yielded an AUC of 0.88 and accuracy of 0.74. Visualization with gradient-weighted class activation maps and saliency maps showed that the deep learning models classified images based on relevant areas within each image. SIGNIFICANCE This study showed that CNNs can detect MCD at a clinically useful performance level with a fully automated workflow without image feature selection.
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10
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Cohen AL, Mulder BPF, Prohl AK, Soussand L, Davis P, Kroeck MR, McManus P, Gholipour A, Scherrer B, Bebin EM, Wu JY, Northrup H, Krueger DA, Sahin M, Warfield SK, Fox MD, Peters JM. Tuber Locations Associated with Infantile Spasms Map to a Common Brain Network. Ann Neurol 2021; 89:726-739. [PMID: 33410532 PMCID: PMC7969435 DOI: 10.1002/ana.26015] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Approximately 50% of patients with tuberous sclerosis complex develop infantile spasms, a sudden onset epilepsy syndrome associated with poor neurological outcomes. An increased burden of tubers confers an elevated risk of infantile spasms, but it remains unknown whether some tuber locations confer higher risk than others. Here, we test whether tuber location and connectivity are associated with infantile spasms. METHODS We segmented tubers from 123 children with (n = 74) and without (n = 49) infantile spasms from a prospective observational cohort. We used voxelwise lesion symptom mapping to test for an association between spasms and tuber location. We then used lesion network mapping to test for an association between spasms and connectivity with tuber locations. Finally, we tested the discriminability of identified associations with logistic regression and cross-validation as well as statistical mediation. RESULTS Tuber locations associated with infantile spasms were heterogenous, and no single location was significantly associated with spasms. However, >95% of tuber locations associated with spasms were functionally connected to the globi pallidi and cerebellar vermis. These connections were specific compared to tubers in patients without spasms. Logistic regression found that globus pallidus connectivity was a stronger predictor of spasms (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.10-3.50, p = 0.02) than tuber burden (OR = 1.65, 95% CI = 0.90-3.04, p = 0.11), with a mean receiver operating characteristic area under the curve of 0.73 (±0.1) during repeated cross-validation. INTERPRETATION Connectivity between tuber locations and the bilateral globi pallidi is associated with infantile spasms. Our findings lend insight into spasm pathophysiology and may identify patients at risk. ANN NEUROL 2021;89:726-739.
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Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Brechtje P F Mulder
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- VUmc School of Medical Sciences, VU University Medical Center Amsterdam, Amsterdam, the Netherlands
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Louis Soussand
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Peter Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Mallory R Kroeck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Peter McManus
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL
| | - Joyce Y Wu
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Hope Northrup
- Division of Medical Genetics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Michael D Fox
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
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11
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Steriade C, French J, Devinsky O. Epilepsy: key experimental therapeutics in early clinical development. Expert Opin Investig Drugs 2020; 29:373-383. [DOI: 10.1080/13543784.2020.1743678] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Claude Steriade
- Division of Epilepsy, Department of Neurology, NYU Comprehensive Epilepsy Center, New York, NY, USA
| | - Jacqueline French
- Division of Epilepsy, Department of Neurology, NYU Comprehensive Epilepsy Center, New York, NY, USA
| | - Orrin Devinsky
- Division of Epilepsy, Department of Neurology, NYU Comprehensive Epilepsy Center, New York, NY, USA
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12
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Lesion-Constrained Electrical Source Imaging: A Novel Approach in Epilepsy Surgery for Tuberous Sclerosis Complex. J Clin Neurophysiol 2020; 37:79-86. [PMID: 31261349 DOI: 10.1097/wnp.0000000000000615] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Electrical source imaging may yield ambiguous results in multilesional epilepsy. The aim of this study was to test the clinical utility of lesion-constrained electrical source imaging in epilepsy surgery in children with tuberous sclerosis complex. METHODS Lesion-constrained electrical source imaging is a novel method based on a proposed head model in which the source solution is constrained to lesions. Using a goodness of fit analysis, we rank-ordered individual tubers by their ability to approximate interictal and ictal EEG data. The overlap with the surgical resection cavity was determined qualitatively, and placed findings in the context of epilepsy surgical outcome, and compared with the low-resolution brain electromagnetic tomography solution. RESULTS Low-resolution brain electromagnetic tomography predicted the surgical cavity in only one patient with good outcome (true positive) and localized to outside of the cavity in two patients with a good outcome (false negative). In one patient with a poor outcome, the interictal low-resolution brain electromagnetic tomography solution overlapped with the cavity (false positive). Lesion-constrained electrical source imaging of ictal EEG data identified tubers concordant with the resection zone in three patients with a good surgical outcome (true positive) and appropriately discordant in three other patients with a poor outcome (true negative). CONCLUSIONS Lesion-constrained electrical source imaging on low-resolution EEG data provides complementary information in the presurgical workup for patients with tuberous sclerosis complex, although further validation is required. In the appropriate clinical context, the yield of source localization on low-resolution EEG data may be increased by reduction of the solution space.
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13
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Prohl AK, Scherrer B, Tomas-Fernandez X, Davis PE, Filip-Dhima R, Prabhu SP, Peters JM, Bebin EM, Krueger DA, Northrup H, Wu JY, Sahin M, Warfield SK. Early white matter development is abnormal in tuberous sclerosis complex patients who develop autism spectrum disorder. J Neurodev Disord 2019; 11:36. [PMID: 31838998 PMCID: PMC6912944 DOI: 10.1186/s11689-019-9293-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 11/11/2019] [Indexed: 11/23/2022] Open
Abstract
Background Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. Methods TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. Results Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. Conclusions Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.
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Affiliation(s)
- Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Peter E Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Rajna Filip-Dhima
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Sanjay P Prabhu
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Jurriaan M Peters
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Darcy A Krueger
- Department of Neurology and Rehabilitation Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Joyce Y Wu
- Division of Pediatric Neurology, University of California at Los Angeles Mattel Children's Hospital, David Geffen School of Medicine, University of California, California, Los Angeles, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA.,F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts, USA.
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14
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Baumer FM, Peters JM, Clancy S, Prohl AK, Prabhu SP, Scherrer B, Jansen FE, Braun KPJ, Sahin M, Stamm A, Warfield SK. Corpus Callosum White Matter Diffusivity Reflects Cumulative Neurological Comorbidity in Tuberous Sclerosis Complex. Cereb Cortex 2019; 28:3665-3672. [PMID: 29939236 DOI: 10.1093/cercor/bhx247] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION Neurological manifestations in Tuberous Sclerosis Complex (TSC) are highly variable. Diffusion tensor imaging (DTI) may reflect the neurological disease burden. We analyzed the association of autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy with callosal DTI metrics in subjects with and without TSC. METHODS 186 children underwent 3T MRI DTI: 51 with TSC (19 with concurrent ASD), 46 with non-syndromic ASD and 89 healthy controls (HC). Subgroups were based on presence of TSC, ASD, ID, and epilepsy. Density-weighted DTI metrics obtained from tractography of the corpus callosum were fitted using a 2-parameter growth model. We estimated distributions using bootstrapping and calculated half-life and asymptote of the fitted curves. RESULTS TSC was associated with a lower callosal fractional anisotropy (FA) than ASD, and ASD with a lower FA than HC. ID, epilepsy and ASD diagnosis were each associated with lower FA values, demonstrating additive effects. In TSC, the largest change in FA was related to a comorbid diagnosis of ASD. Mean diffusivity (MD) showed an inverse relationship to FA. Some subgroups were too small for reliable data fitting. CONCLUSIONS Using a cross-disorder approach, this study demonstrates cumulative abnormality of callosal white matter diffusion with increasing neurological comorbidity.
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Affiliation(s)
- Fiona M Baumer
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Brain Center Rudolf Magnus, Department of Pediatric Neurology, University Medical Center Utrecht, The Netherlands
| | - Sean Clancy
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Anna K Prohl
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sanjay P Prabhu
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Floor E Jansen
- Brain Center Rudolf Magnus, Department of Pediatric Neurology, University Medical Center Utrecht, The Netherlands
| | - Kees P J Braun
- Brain Center Rudolf Magnus, Department of Pediatric Neurology, University Medical Center Utrecht, The Netherlands
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Aymeric Stamm
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Laboratory for Modeling and Scientific Computing (MOX), Dipartimento di Matematica, Politecnico di Milano, Italy
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
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15
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Ahtam B, Dehaes M, Sliva DD, Peters JM, Krueger DA, Bebin EM, Northrup H, Wu JY, Warfield SK, Sahin M, Grant PE. Resting-State fMRI Networks in Children with Tuberous Sclerosis Complex. J Neuroimaging 2019; 29:750-759. [PMID: 31304656 DOI: 10.1111/jon.12653] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 06/16/2019] [Accepted: 06/20/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND PURPOSE There are no published studies examining resting state networks (RSNs) and their relationship with neurodevelopmental metrics in tuberous sclerosis complex (TSC). We aimed to identify major resting-state functional magnetic resonance imaging (rs-fMRI) networks in infants with TSC and correlate network analyses with neurodevelopmental assessments, autism diagnosis, and seizure history. METHODS Rs-fMRI data from 34 infants with TSC, sedated with propofol during the scan, were analyzed to identify auditory, motor, and visual RSNs. We examined the correlations between auditory, motor, and visual RSNs at approximately 11.5 months, neurodevelopmental outcome at approximately 18.5 months, and diagnosis of autism spectrum disorders at approximately 36 months of age. RESULTS RSNs were obtained in 76.5% (26/34) of infants. We observed significant negative correlations between auditory RSN and auditory comprehension test scores (p = .038; r = -.435), as well as significant positive correlations between motor RSN and gross motor skills test scores (p = .023; r = .564). Significant positive correlations between motor RSNs and gross motor skills (p = .012; r = .754) were observed in TSC infants without autism, but not in TSC infants with autism, which could suggest altered motor processing. There were no significant differences in RSNs according to seizure history. CONCLUSIONS Negative correlation between auditory RSN, as well as positive correlation between motor RSN and developmental outcome measures might reflect different brain mechanisms and, when identified, may be helpful in predicting later function. A larger study of TSC patients with a healthy control group is needed before auditory and motor RSNs could be considered as neurodevelopmental outcome biomarkers.
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Affiliation(s)
- Banu Ahtam
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Mathieu Dehaes
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal and CHU Sainte-Justine, Montreal, QC, Canada
| | - Danielle D Sliva
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Department of Neuroscience, Brown University, Providence, RI
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | | | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX
| | - Joyce Y Wu
- Division of Pediatric Neurology, University of California at Los Angeles Mattel Children's Hospital, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Mustafa Sahin
- Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA.,F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA
| | - Patricia Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA.,Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
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- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA
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16
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Peters JM, Struyven RR, Prohl AK, Vasung L, Stajduhar A, Taquet M, Bushman JJ, Lidov H, Singh JM, Scherrer B, Madsen JR, Prabhu SP, Sahin M, Afacan O, Warfield SK. White matter mean diffusivity correlates with myelination in tuberous sclerosis complex. Ann Clin Transl Neurol 2019; 6:1178-1190. [PMID: 31353853 PMCID: PMC6649396 DOI: 10.1002/acn3.793] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 04/18/2019] [Accepted: 04/22/2019] [Indexed: 11/26/2022] Open
Abstract
Objective Diffusion tensor imaging (DTI) of the white matter is a biomarker for neurological disease burden in tuberous sclerosis complex (TSC). To clarify the basis of abnormal diffusion in TSC, we correlated ex vivo high‐resolution diffusion imaging with histopathology in four tissue types: cortex, tuber, perituber, and white matter. Methods Surgical specimens of three children with TSC were scanned in a 3T or 7T MRI with a structural image isotropic resolution of 137–300 micron, and diffusion image isotropic resolution of 270‐1,000 micron. We stained for myelin (luxol fast blue, LFB), gliosis (glial fibrillary acidic protein, GFAP), and neurons (NeuN) and registered the digitized histopathology slides (0.686 micron resolution) to MRI for visual comparison. We then performed colocalization analysis in four tissue types in each specimen. Finally, we applied a linear mixed model (LMM) for pooled analysis across the three specimens. Results In white matter and perituber regions, LFB optical density measures correlated with fractional anisotropy (FA) and inversely with mean diffusivity (MD). In white matter only, GFAP correlated with MD, and inversely with FA. In tubers and in the cortex, there was little variation in mean LFB and GFAP signal intensity, and no correlation with MRI metrics. Neuronal density correlated with MD. In the analysis of the combined specimens, the most robust correlation was between white matter MD and LFB metrics. Interpretation In TSC, diffusion imaging abnormalities in microscopic tissue types correspond to specific histopathological markers. Across all specimens, white matter diffusivity correlates with myelination.
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Affiliation(s)
- Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts.,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Robbert R Struyven
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Lana Vasung
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Andrija Stajduhar
- Croatian Institute for Brain Research and Center of Research Excellence for Basic, Clinical and Translational Neuroscience, University of Zagreb, Zagreb, Croatia
| | - Maxime Taquet
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - John J Bushman
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Hart Lidov
- Division of Neuropathology, Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Jolene M Singh
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Joseph R Madsen
- Department of Neurosurgery, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sanjay P Prabhu
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mustafa Sahin
- Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts
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17
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Despouy E, Curot J, Denuelle M, Deudon M, Sol JC, Lotterie JA, Reddy L, Nowak LG, Pariente J, Thorpe SJ, Valton L, Barbeau EJ. Neuronal spiking activity highlights a gradient of epileptogenicity in human tuberous sclerosis lesions. Clin Neurophysiol 2019; 130:537-547. [DOI: 10.1016/j.clinph.2018.12.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 12/12/2018] [Accepted: 12/25/2018] [Indexed: 11/26/2022]
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18
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Longitudinal Effects of Everolimus on White Matter Diffusion in Tuberous Sclerosis Complex. Pediatr Neurol 2019; 90:24-30. [PMID: 30424962 PMCID: PMC6314307 DOI: 10.1016/j.pediatrneurol.2018.10.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 10/10/2018] [Accepted: 10/14/2018] [Indexed: 11/21/2022]
Abstract
OBJECTIVE We studied the longitudinal effects of everolimus, an inhibitor of the mammalian target of rapamycin (mTOR), on callosal white matter diffusion tensor imaging (DTI) in patients with tuberous sclerosis complex (TSC). METHODS Serial imaging data spanning nine years were used from the open label, Phase I/II trial (NCT00411619) and open-ended extension phase of everolimus for the treatment of subependymal giant cell astrocytoma associated with TSC. From 28 patients treated with everolimus and 25 untreated control patients, 481 MRI scans were available. Rigorous quality control resulted in omission of all scans with diffusion weighted imaging data in less than 15 directions or more than eight artifacted volumes, and all postsurgical scans. We applied a linear mixed-effects model to the remaining 125 scans (17 treated, 24 controls) for longitudinal analysis of each DTI metric of manually drawn callosal regions of interest. RESULTS On a population level, mTOR inhibition was associated with a decrease in mean diffusivity. In addition, in treated patients only, a decrease of radial diffusivity was observed; in untreated patients only, an increase of axial diffusivity was seen. In patients below age 10, effect-sizes were consistently greater, and longer treatment was associated with greater rate of diffusion change. There was no correlation between DTI metrics and reduction of subependymal giant cell astrocytoma volume, or everolimus serum levels. CONCLUSIONS Effects from mTOR overactivity on white matter microstructural integrity in TSC were modified through pharmacologic inhibition of mTOR. These changes sustained over time, were greater with longer treatment and in younger patients during a time of rapid white matter maturation.
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19
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Liu W, Yan B, An D, Niu R, Tang Y, Tong X, Gong Q, Zhou D. Perilesional and contralateral white matter evolution and integrity in patients with periventricular nodular heterotopia and epilepsy: a longitudinal diffusion tensor imaging study. Eur J Neurol 2017; 24:1471-1478. [PMID: 28872216 DOI: 10.1111/ene.13441] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 08/31/2017] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND PURPOSE This study aimed to assess the evolution of perinodular and contralateral white matter abnormalities in patients with periventricular nodular heterotopia (PNH) and epilepsy. METHODS Diffusion tensor imaging (DTI) (64 directions) and 3 T structural magnetic resonance imaging were performed in 29 PNH patients (mean age 27.3 years), and 16 patients underwent a second scan (average time between the two scans 1.1 years). Fractional anisotropy and mean diffusivity were measured within the perilesional and contralateral white matter. RESULTS Longitudinal analysis showed that white matter located 10 mm from the focal nodule displayed characteristics intermediate to tissue 5 mm away, and normal-appearing white matter (NAWM) also established evolution profiles of perinodular white matter in different cortical lobes. Compared to 29 age- and sex-matched healthy controls, significant decreased fractional anisotropy and elevated mean diffusivity values were observed in regions 5 and 10 mm from nodules (P < 0.01), whilst DTI metrics of the remaining NAWM did not differ significantly from controls. Additionally, normal DTI metrics were shown in the contralateral region in patients with unilateral PNH. CONCLUSIONS Periventricular nodular heterotopia is associated with microstructural abnormalities within the perilesional white matter and the extent decreases with increasing distance from the nodule. In the homologous contralateral region, white matter diffusion metrics were unchanged in unilateral PNH. These findings have clinical implications with respect to the medical and surgical interventions of PNH-related epilepsy.
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Affiliation(s)
- W Liu
- Departments of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - B Yan
- Departments of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - D An
- Departments of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - R Niu
- Departments of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Y Tang
- Departments of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - X Tong
- Departments of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Q Gong
- Departments of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - D Zhou
- Departments of Neurology, West China Hospital, Sichuan University, Chengdu, China
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Boom M, Raskin JS, Curry DJ, Weiner HL, Peters JM. Technological advances in pediatric epilepsy surgery: implications for tuberous sclerosis complex. FUTURE NEUROLOGY 2017. [DOI: 10.2217/fnl-2017-0005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In selected children with tuberous sclerosis complex, epilepsy surgery leads to seizure freedom or seizure reduction. The current standard involves a multimodal pre-surgical workup followed by invasive electrocorticographic monitoring and resective surgery. Recent insights in the disorder and novel technologies are changing the approach to pediatric epilepsy surgery in tuberous sclerosis complex. New evidence suggests tubers are poorly delineated, and epileptogenic activity may originate in the perituber tissue. Novel imaging modalities relevant to surgical planning include high-resolution MRI, α-methyl-l-tryptophan or fluorodeoxyglucose PET with diffusion tensor imaging. Advanced neurophysiological techniques have improved identification of the surgical target, including magnetoencephalography, electrical source imaging of high-density electroencephalograph data, and high-frequency oscillations in electrocorticography data. Simultaneously, novel surgical tools including stereo-electroencephalography and laser-induced thermal therapy have become available for children. This article reviews the literature in the light of these rapidly changing technologies.
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Affiliation(s)
- Merel Boom
- Division of Epilepsy & Clinical Neurophysiology, Boston Children’s Hospital & Harvard Medical School, 300 Longwood Avenue, BCH 3063, Boston, MA 02115, USA
| | - Jeffrey S Raskin
- Division of Pediatric Neurosurgery, Department of Surgery, Texas Children’s Hospital & Department of Neurosurgery, Baylor College of Medicine, 6701 Fannin St. Suite 1230.01, Houston, TX 77030, USA
| | - Daniel J Curry
- Division of Pediatric Neurosurgery, Department of Surgery, Texas Children’s Hospital & Department of Neurosurgery, Baylor College of Medicine, 6701 Fannin St. Suite 1230.01, Houston, TX 77030, USA
| | - Howard L Weiner
- Division of Pediatric Neurosurgery, Department of Surgery, Texas Children’s Hospital & Department of Neurosurgery, Baylor College of Medicine, 6701 Fannin St. Suite 1230.01, Houston, TX 77030, USA
| | - Jurriaan M Peters
- Division of Epilepsy & Clinical Neurophysiology, Boston Children’s Hospital & Harvard Medical School, 300 Longwood Avenue, BCH 3063, Boston, MA 02115, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School,300 Longwood Avenue, BCH 3429, Boston, MA 02115, SA
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Kaye HL, Peters JM, Gersner R, Chamberland M, Sansevere A, Rotenberg A. Neurophysiological evidence of preserved connectivity in tuber tissue. EPILEPSY & BEHAVIOR CASE REPORTS 2016; 7:64-68. [PMID: 28616385 PMCID: PMC5459951 DOI: 10.1016/j.ebcr.2016.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 09/29/2016] [Accepted: 10/05/2016] [Indexed: 06/07/2023]
Abstract
We present a case of preserved corticospinal connectivity in a cortical tuber, in a 10 year-old boy with intractable epilepsy and tuberous sclerosis complex (TSC). The patient had multiple subcortical tubers, one of which was located in the right central sulcus. In preparation for epilepsy surgery, motor mapping, by neuronavigated transcranial magnetic stimulation (nTMS) coupled with surface electromyography (EMG) was performed to locate the primary motor cortical areas. The resulting functional motor map revealed expected corticospinal connectivity in the left precentral gyrus. Surprisingly, robust contralateral deltoid and tibialis anterior motor evoked potentials (MEPs) were also elicited with direct stimulation of the cortical tuber in the right central sulcus. MRI with diffusion tensor imaging (DTI) tractography confirmed corticospinal fibers originating in the tuber. As there are no current reports of preserved connectivity between a cortical tuber and the corticospinal tract, this case serves to highlight the functional interdigitation of tuber and eloquent cortex. Our case also illustrates the widening spectrum of neuropathological abnormality in TSC that is becoming apparent with modern MRI methodology. Finally, our finding underscores the need for further study of preserved function in tuber tissue during presurgical workup in patients with TSC.
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Affiliation(s)
- HL Kaye
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Neuromodulation Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- The F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - JM Peters
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- The F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - R Gersner
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Neuromodulation Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - M Chamberland
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - A Sansevere
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
| | - A Rotenberg
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Neuromodulation Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- The F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
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Hsieh DT, Whiteway SL, Rohena LO, Thiele EA. Tuberous sclerosis complex: Five new things. Neurol Clin Pract 2016; 6:339-347. [PMID: 29443126 DOI: 10.1212/cpj.0000000000000260] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Purpose of review Tuberous sclerosis complex (TSC) is a variably expressed neurocutaneous genetic disorder characterized by hamartomatous growths in multiple organ systems. Neurologic involvement often confers the most severe symptoms, and can include epilepsy, increased intracranial pressure from hydrocephalus, intellectual deficits, and autism. The purpose of this review is to provide a neurologically focused update in the diagnosis and treatment of these complications in patients with TSC. Recent findings We highlight 5 new areas of understanding in TSC: the neurobiology of TSC and its translation into clinical practice, vigabatrin in the treatment of infantile spasms, the role of tubers and epilepsy surgery, the treatment of subependymal giant cell astrocytomas, and TSC-related neuropsychiatric disorders. Summary These recent advances in diagnosis and treatment give our patients with TSC and their families hope for the future for improved care and possible preventive cures, to the end goal of improving quality of life.
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Affiliation(s)
- David T Hsieh
- Divisions of Child Neurology (DTH), Hematology/Oncology (SLW), and Medical Genetics (LOR), Department of Pediatrics, San Antonio Military Medical Center, JBSA - Ft. Sam Houston, TX; and Pediatric Epilepsy Program (EAT), Department of Neurology, Massachusetts General Hospital, Boston
| | - Susan L Whiteway
- Divisions of Child Neurology (DTH), Hematology/Oncology (SLW), and Medical Genetics (LOR), Department of Pediatrics, San Antonio Military Medical Center, JBSA - Ft. Sam Houston, TX; and Pediatric Epilepsy Program (EAT), Department of Neurology, Massachusetts General Hospital, Boston
| | - Luis O Rohena
- Divisions of Child Neurology (DTH), Hematology/Oncology (SLW), and Medical Genetics (LOR), Department of Pediatrics, San Antonio Military Medical Center, JBSA - Ft. Sam Houston, TX; and Pediatric Epilepsy Program (EAT), Department of Neurology, Massachusetts General Hospital, Boston
| | - Elizabeth A Thiele
- Divisions of Child Neurology (DTH), Hematology/Oncology (SLW), and Medical Genetics (LOR), Department of Pediatrics, San Antonio Military Medical Center, JBSA - Ft. Sam Houston, TX; and Pediatric Epilepsy Program (EAT), Department of Neurology, Massachusetts General Hospital, Boston
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Ess KC, Chugani HT. Dynamic tubers in tuberous sclerosis complex: A window for intervention? Neurology 2015; 85:1530-1. [PMID: 26432847 DOI: 10.1212/wnl.0000000000002056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Kevin C Ess
- From the Division of Pediatric Neurology (K.C.E.), Vanderbilt University Medical Center, Nashville, TN; and Division of Pediatric Neurology (H.T.C.), PET Center Children's Hospital of Michigan, Detroit.
| | - Harry T Chugani
- From the Division of Pediatric Neurology (K.C.E.), Vanderbilt University Medical Center, Nashville, TN; and Division of Pediatric Neurology (H.T.C.), PET Center Children's Hospital of Michigan, Detroit
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