1
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de Havenon A, Parasuram NR, Crawford AL, Mazurek MH, Chavva IR, Yadlapalli V, Iglesias JE, Rosen MS, Falcone GJ, Payabvash S, Sze G, Sharma R, Schiff SJ, Safdar B, Wira C, Kimberly WT, Sheth KN. Identification of White Matter Hyperintensities in Routine Emergency Department Visits Using Portable Bedside Magnetic Resonance Imaging. J Am Heart Assoc 2023; 12:e029242. [PMID: 37218590 PMCID: PMC10381997 DOI: 10.1161/jaha.122.029242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/27/2023] [Indexed: 05/24/2023]
Abstract
Background White matter hyperintensity (WMH) on magnetic resonance imaging (MRI) of the brain is associated with vascular cognitive impairment, cardiovascular disease, and stroke. We hypothesized that portable magnetic resonance imaging (pMRI) could successfully identify WMHs and facilitate doing so in an unconventional setting. Methods and Results In a retrospective cohort of patients with both a conventional 1.5 Tesla MRI and pMRI, we report Cohen's kappa (κ) to measure agreement for detection of moderate to severe WMH (Fazekas ≥2). In a subsequent prospective observational study, we enrolled adult patients with a vascular risk factor being evaluated in the emergency department for a nonstroke complaint and measured WMH using pMRI. In the retrospective cohort, we included 33 patients, identifying 16 (49.5%) with WMH on conventional MRI. Between 2 raters evaluating pMRI, the interrater agreement on WMH was strong (κ=0.81), and between 1 rater for conventional MRI and the 2 raters for pMRI, intermodality agreement was moderate (κ=0.66, 0.60). In the prospective cohort we enrolled 91 individuals (mean age, 62.6 years; 53.9% men; 73.6% with hypertension), of which 58.2% had WMHs on pMRI. Among 37 Black and Hispanic individuals, the Area Deprivation Index was higher (versus White, 51.8±12.9 versus 37.9±11.9; P<0.001). Among 81 individuals who did not have a standard-of-care MRI in the preceding year, we identified WMHs in 43 of 81 (53.1%). Conclusions Portable, low-field imaging could be useful for identifying moderate to severe WMHs. These preliminary results introduce a novel role for pMRI outside of acute care and the potential role for pMRI to reduce disparities in neuroimaging.
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Affiliation(s)
- Adam de Havenon
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
| | | | - Anna L. Crawford
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | - Mercy H. Mazurek
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | - Isha R. Chavva
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | | | - Juan E. Iglesias
- Department of Neurology, Division of Neurocritical CareMassachusetts General HospitalBostonMAUSA
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMAUSA
- Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolDepartment of Physics, Harvard UniversityBostonMAUSA
| | - Matthew S. Rosen
- Department of Neurology, Division of Neurocritical CareMassachusetts General HospitalBostonMAUSA
| | - Guido J. Falcone
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
| | - Seyedmehdi Payabvash
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
- Department of RadiologyYale University School of MedicineNew HavenCOUSA
| | - Gordon Sze
- Department of RadiologyYale University School of MedicineNew HavenCOUSA
| | - Richa Sharma
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
| | - Steven J. Schiff
- Department of NeurosurgeryYale University School of MedicineNew HavenCOUSA
| | - Basmah Safdar
- Department of Emergency MedicineYale University School of MedicineNew HavenCOUSA
| | - Charles Wira
- Department of Emergency MedicineYale University School of MedicineNew HavenCOUSA
| | - William T. Kimberly
- Department of Neurology, Division of Neurocritical CareMassachusetts General HospitalBostonMAUSA
| | - Kevin N. Sheth
- Department of NeurologyYale University School of MedicineNew HavenCTUSA
- Center for Brain and Mind HealthYale University School of MedicineNew HavenCTUSA
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2
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Iglesias JE, Billot B, Balbastre Y, Magdamo C, Arnold SE, Das S, Edlow BL, Alexander DC, Golland P, Fischl B. SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry. Sci Adv 2023; 9:eadd3607. [PMID: 36724222 PMCID: PMC9891693 DOI: 10.1126/sciadv.add3607] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 01/04/2023] [Indexed: 05/10/2023]
Abstract
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.
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Affiliation(s)
- Juan E. Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Billot
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Yaël Balbastre
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Colin Magdamo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven E. Arnold
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sudeshna Das
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Brian L. Edlow
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, USA
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3
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Sujijantarat N, Koo A, Jambor I, Malhotra A, Crawford A, Mazurek M, Parasuram N, Yadlapalli V, Chavva IR, Antonios J, Elsamadicy A, Renedo D, Hebert R, Schindler JL, Sansing LH, De Havenon AH, Olexa M, Iglesias JE, Rosen M, Kimberly WTT, Petersen NH, Sheth KN, Matouk C. Abstract WP154: Low-field Portable Mri For Routine Post-thrombectomy Assessment Of Ongoing Brain Injury. Stroke 2023. [DOI: 10.1161/str.54.suppl_1.wp154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Introduction:
Conventional MRI (cMRI) is not routinely available post-mechanical thrombectomy (MT), which can preclude accurate infarction assessment. Our objective was to evaluate the use of low-field portable MRI (pMRI) for bedside evaluation post-MT, including its use as a post-procedural baseline monitor.
Methods:
Low-field pMRI was used to obtain bedside imaging in post-MT patients between December 2021 to August 2022 at Yale-New Haven Hospital. All pMRI exams were conducted in the standard ferromagnetic environment of the IR suite. Volumetric analyses were performed by a neuroradiologist using 3D Slicer software. If cMRI was not available for comparison, a CT was used. Patients’ charts were reviewed for pre-revascularization MAP and occurrences of MAP dropping by 10% and 20% from individual baselines between the time of pMRI and delayed imaging.
Results:
A total of 25 patients (64% females, median age 77 years-old [IQR 69.5-84.5]) underwent bedside pMRIs in the IR suite post-MT. The median time from last known normal to access was 6 hours [IQR 4-17]. The median pMRI examination time was 30 minutes [IQR 17-32]. Of the 24 patients with available delayed imaging, 7 (29.2%) had infarct progression compared to immediate post-MT pMRI, while 15 patients (62.5%) had stable/decreased stroke volume. Two patients (8.3%) had parenchymal hemorrhage type 2 and were excluded from further analysis. There was no statistically significant difference between the proportions of favorable TICI scores (85.7% in the infarct progression group vs. 92.3% in the stable/decreased infarct group, p=1.00). Patients with infarct progression had comparable pre-revascularization MAP compared to those with stable/decreased delayed infarct volume (mean of 100.3±4.6 vs. 101.9±15.9 respectively, p=0.727) but had more occurrences of MAP dropping by 10% and 20% of their baseline between the time of pMRI and delayed imaging (mean of 35.0±23.3 vs. 14.7±11.3 occurrences, p=0.011; and mean of 21.7±16.5 vs. 8.5±9.5 occurrences, p=0.026, respectively).
Conclusions:
The use of low-field MRI in the post-MT setting can facilitate benchmark brain monitoring and serial examinations to evaluate the impact of potential physiological perturbations that may impact ongoing brain injury.
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Affiliation(s)
| | | | - Ivan Jambor
- Neurosurgery, Yale-New Haven Hosp, New Haven, CT
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Bhome R, Zarkali A, Thomas GEC, Iglesias JE, Cole JH, Weil RS. Thalamic white matter macrostructure and subnuclei volumes in Parkinson's disease depression. NPJ Parkinsons Dis 2022; 8:2. [PMID: 35013327 PMCID: PMC8748828 DOI: 10.1038/s41531-021-00270-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 12/20/2021] [Indexed: 12/04/2022] Open
Abstract
Depression is a common non-motor feature of Parkinson's disease (PD) which confers significant morbidity and is challenging to treat. The thalamus is a key component in the basal ganglia-thalamocortical network critical to the pathogenesis of PD and depression but the precise thalamic subnuclei involved in PD depression have not been identified. We performed structural and diffusion-weighted imaging (DWI) on 76 participants with PD to evaluate the relationship between PD depression and grey and white matter thalamic subnuclear changes. We used a thalamic segmentation method to divide the thalamus into its 50 constituent subnuclei (25 each hemisphere). Fixel-based analysis was used to calculate mean fibre cross-section (FC) for white matter tracts connected to each subnucleus. We assessed volume and FC at baseline and 14-20 months follow-up. A generalised linear mixed model was used to evaluate the relationship between depression, subnuclei volume and mean FC for each thalamic subnucleus. We found that depression scores in PD were associated with lower right pulvinar anterior (PuA) subnucleus volume. Antidepressant use was associated with higher right PuA volume suggesting a possible protective effect of treatment. After follow-up, depression scores were associated with reduced white matter tract macrostructure across almost all tracts connected to thalamic subnuclei. In conclusion, our work implicates the right PuA as a relevant neural structure in PD depression and future work should evaluate its potential as a therapeutic target for PD depression.
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Affiliation(s)
- R Bhome
- Dementia Research Centre, University College London, London, UK.
| | - A Zarkali
- Dementia Research Centre, University College London, London, UK
| | - G E C Thomas
- Dementia Research Centre, University College London, London, UK
| | - J E Iglesias
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Cambridge, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA
| | - J H Cole
- Dementia Research Centre, University College London, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - R S Weil
- Dementia Research Centre, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
- Movement Disorders Consortium, National Hospital for Neurology and Neurosurgery, London, UK
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5
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Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to magnetic resonance imaging (MRI) contrast. While classical methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning methods are fast at test time but limited to images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency using a generative strategy that exposes networks to a wide range of images synthesized from segmentations during training, forcing them to generalize across contrasts. We show that networks trained within this framework generalize to a broad array of unseen MRI contrasts and surpass classical state-of-the-art brain registration accuracy by up to 12.4 Dice points for a variety of tested contrast combinations. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images during training.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Billot
- Centre for Medical Image Computing, University College London, WC1E 6BT, UK
| | - Juan E Iglesias
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Centre for Medical Image Computing, University College London, WC1E 6BT, UK
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
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6
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Etherton MR, Fotiadis P, Giese AK, Iglesias JE, Wu O, Rost NS. White Matter Hyperintensity Burden Is Associated With Hippocampal Subfield Volume in Stroke. Front Neurol 2020; 11:588883. [PMID: 33193055 PMCID: PMC7649326 DOI: 10.3389/fneur.2020.588883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 10/05/2020] [Indexed: 12/05/2022] Open
Abstract
White matter hyperintensities of presumed vascular origin (WMH) are a prevalent form of cerebral small-vessel disease and an important risk factor for post-stroke cognitive dysfunction. Despite this prevalence, it is not well understood how WMH contributes to post-stroke cognitive dysfunction. Preliminary findings suggest that increasing WMH volume is associated with total hippocampal volume in chronic stroke patients. The hippocampus, however, is a complex structure with distinct subfields that have varying roles in the function of the hippocampal circuitry and unique anatomical projections to different brain regions. For these reasons, an investigation into the relationship between WMH and hippocampal subfield volume may further delineate how WMH predispose to post-stroke cognitive dysfunction. In a prospective study of acute ischemic stroke patients with moderate/severe WMH burden, we assessed the relationship between quantitative WMH burden and hippocampal subfield volumes. Patients underwent a 3T MRI brain within 2–5 days of stroke onset. Total WMH volume was calculated in a semi-automated manner. Mean cortical thickness and hippocampal volumes were measured in the contralesional hemisphere. Total and subfield hippocampal volumes were measured using an automated, high-resolution, ex vivo computational atlas. Linear regression analyses were performed for predictors of total and subfield hippocampal volumes. Forty patients with acute ischemic stroke and moderate/severe white matter hyperintensity burden were included in this analysis. Median WMH volume was 9.0 cm3. Adjusting for intracranial volume and stroke laterality, age (β = −3.7, P < 0.001), hypertension (β = −44.7, P = 0.04), WMH volume (β = −0.89, P = 0.049), and mean cortical thickness (β = 286.2, P = 0.006) were associated with total hippocampal volume. In multivariable analysis, age (β = −3.3, P < 0.001) and cortical thickness (β = 205.2, P = 0.028) remained independently associated with total hippocampal volume. In linear regression for predictors of hippocampal subfield volume, increasing WMH volume was associated with decreased hippocampal-amygdala transition area volume (β = −0.04, P = 0.001). These finding suggest that in ischemic stroke patients, increased WMH burden is associated with selective hippocampal subfield degeneration in the hippocampal-amygdala transition area.
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Affiliation(s)
- Mark R Etherton
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Panagiotis Fotiadis
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Juan E Iglesias
- Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Ona Wu
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Department of Radiology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Natalia S Rost
- Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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7
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Aganj I, Frau-Pascual A, Iglesias JE, Yendiki A, Augustinack JC, Salat DH, Fischl B. COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging 2020; 2020:283-287. [PMID: 32587665 PMCID: PMC7316404 DOI: 10.1109/isbi45749.2020.9098440] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Identification of the specific brain networks that are vulnerable or resilient in neurodegenerative diseases can help to better understand the disease effects and derive new connectomic imaging biomarkers. In this work, we use brain connectivity to find pairs of structural connections that are negatively correlated with each other across Alzheimer's disease (AD) and healthy populations. Such anti-correlated brain connections can be informative for identification of compensatory neuronal pathways and the mechanism of brain networks' resilience to AD. We find significantly anti-correlated connections in a public diffusion-MRI database, and then validate the results on other databases.
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Affiliation(s)
- Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
| | - Aina Frau-Pascual
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
| | - Juan E Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
- Center for Medical Image Computing (CMIC), University College London, London, UK
| | - Anastasia Yendiki
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
| | - Jean C Augustinack
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
| | - David H Salat
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
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8
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Brown EM, Pierce ME, Clark DC, Fischl BR, Iglesias JE, Milberg WP, McGlinchey RE, Salat DH. Test-retest reliability of FreeSurfer automated hippocampal subfield segmentation within and across scanners. Neuroimage 2020; 210:116563. [DOI: 10.1016/j.neuroimage.2020.116563] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/13/2020] [Accepted: 01/15/2020] [Indexed: 11/26/2022] Open
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9
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Bocchetta M, Iglesias JE, Neason M, Cash DM, Warren JD, Rohrer JD. Thalamic nuclei in frontotemporal dementia: Mediodorsal nucleus involvement is universal but pulvinar atrophy is unique to C9orf72. Hum Brain Mapp 2019; 41:1006-1016. [PMID: 31696638 PMCID: PMC7267940 DOI: 10.1002/hbm.24856] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 10/09/2019] [Accepted: 10/18/2019] [Indexed: 12/12/2022] Open
Abstract
Thalamic atrophy is a common feature across all forms of FTD but little is known about specific nuclei involvement. We aimed to investigate in vivo atrophy of the thalamic nuclei across the FTD spectrum. A cohort of 402 FTD patients (age: mean(SD) 64.3(8.2) years; disease duration: 4.8(2.8) years) was compared with 104 age-matched controls (age: 62.5(10.4) years), using an automated segmentation of T1-weighted MRIs to extract volumes of 14 thalamic nuclei. Stratification was performed by clinical diagnosis (180 behavioural variant FTD (bvFTD), 85 semantic variant primary progressive aphasia (svPPA), 114 nonfluent variant PPA (nfvPPA), 15 PPA not otherwise specified (PPA-NOS), and 8 with associated motor neurone disease (FTD-MND), genetic diagnosis (27 MAPT, 28 C9orf72, 18 GRN), and pathological confirmation (37 tauopathy, 38 TDP-43opathy, 4 FUSopathy). The mediodorsal nucleus (MD) was the only nucleus affected in all FTD subgroups (16-33% smaller than controls). The laterodorsal nucleus was also particularly affected in genetic cases (28-38%), TDP-43 type A (47%), tau-CBD (44%), and FTD-MND (53%). The pulvinar was affected only in the C9orf72 group (16%). Both the lateral and medial geniculate nuclei were also affected in the genetic cases (10-20%), particularly the LGN in C9orf72 expansion carriers. Use of individual thalamic nuclei volumes provided higher accuracy in discriminating between FTD groups than the whole thalamic volume. The MD is the only structure affected across all FTD groups. Differential involvement of the thalamic nuclei among FTD forms is seen, with a unique pattern of atrophy in the pulvinar in C9orf72 expansion carriers.
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Affiliation(s)
- Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Juan E Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.,Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Boston, Massachusetts, USA
| | - Mollie Neason
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK.,Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Jason D Warren
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
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10
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Stewart W, Allinson K, Al-Sarraj S, Bachmeier C, Barlow K, Belli A, Burns MP, Carson A, Crawford F, Dams-O'Connor K, Diaz-Arrastia R, Dixon CE, Edlow BL, Ferguson S, Fischl B, Folkerth RD, Gentleman S, Giza CC, Grady MS, Helmy A, Herceg M, Holton JL, Howell D, Hutchinson PJ, Iacono D, Iglesias JE, Ikonomovic MD, Johnson VE, Keene CD, Kofler JK, Koliatsos VE, Lee EB, Levin H, Lifshitz J, Ling H, Loane DJ, Love S, Maas AI, Marklund N, Master CL, McElvenny DM, Meaney DF, Menon DK, Montine TJ, Mouzon B, Mufson EJ, Ojo JO, Prins M, Revesz T, Ritchie CW, Smith C, Sylvester R, Tang CY, Trojanowski JQ, Urankar K, Vink R, Wellington C, Wilde EA, Wilson L, Yeates K, Smith DH. Primum non nocere: a call for balance when reporting on CTE. Lancet Neurol 2019; 18:231-233. [PMID: 30784550 PMCID: PMC6594011 DOI: 10.1016/s1474-4422(19)30020-1] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 01/21/2019] [Indexed: 12/15/2022]
Affiliation(s)
- William Stewart
- Department of Neuropathology, Queen Elizabeth University Hospital, Glasgow G51 4TF, UK; Department of Neuropathology, Queen Elizabeth University Hospital, Glasgow, UK; Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK.
| | - Kieren Allinson
- Department of Pathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Safa Al-Sarraj
- The Institute of Psychiatry Psychology and Neurosciences, King's College London, London, UK
| | - Corbin Bachmeier
- Roskamp Institute, Sarasota, Florida, USA; The Open University, Milton Keynes, UK; Bay Pines VA Healthcare System, Bay Pines, Florida, USA
| | - Karen Barlow
- Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Antonio Belli
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Mark P Burns
- Georgetown University Medical Center, Washington DC, DC, USA
| | - Alan Carson
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Fiona Crawford
- Roskamp Institute, Sarasota, Florida, USA; The Open University, Milton Keynes, UK; James A Haley Veterans' Hospital, Tampa, FL, USA
| | - Kristen Dams-O'Connor
- Department of Rehabilitation Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Brain Injury and Repair, University of Pennsylvania, Philadelphia, PA, USA
| | - C Edward Dixon
- Department of Neurological Surgery, Brain Trauma Research Center, University of Pittsburgh, Pittsburgh, PA, USA; Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Scott Ferguson
- Roskamp Institute, Sarasota, Florida, USA; Roskamp Institute, Sarasota, Florida, USA; James A Haley Veterans' Hospital, Tampa, FL, USA
| | - Bruce Fischl
- Athinoula A Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Rebecca D Folkerth
- City of New York Office of the Chief Medical Examiner, and New York University School of Medicine, New York NY, USA
| | - Steve Gentleman
- Division of Brain Sciences, Department of Medicine, Imperial College London, London, UK
| | - Christopher C Giza
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA; Departments of Pediatrics and Neurosurgery, David Geffen School of Medicine and UCLA Mattel Children's Hospital, University of California, Los Angeles, CA, USA
| | - M Sean Grady
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Adel Helmy
- Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Mark Herceg
- Department of Physical Medicine and Rehabilitation, Phelps Hospital Northwell Health, New York, NY, USA; School of Public Health, New York Medical College, New York, NY, USA
| | - Janice L Holton
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - David Howell
- Sports Medicine Center, Children's Hospital Colorado, Aurora, CO, USA; Department of Orthopedics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter J Hutchinson
- Division of Neurosurgery, Department of Clinical Neurosciences, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | - Diego Iacono
- Neuropathology Research, Biomedical Research Institute of New Jersey, Cedar Knolls, NJ, USA; Atlantic Neuroscience Institute, Atlantic Health System, Morristown, NJ, USA
| | - Juan E Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Milos D Ikonomovic
- Departments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Victoria E Johnson
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Brain Injury and Repair, University of Pennsylvania, Philadelphia, PA, USA
| | - C Dirk Keene
- Department of Pathology, University of Washington, Seattle, WA, USA
| | - Julia K Kofler
- Department of Pathology, Division of Neuropathology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vassilis E Koliatsos
- Departments of Pathology, Neurology, and Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Neuropsychiatry Program, Sheppard and Enoch Pratt Hospital, Baltimore, MD, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, University of Pennsylvania, Philadelphia, PA, USA
| | - Harvey Levin
- Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
| | - Jonathan Lifshitz
- Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, USA; University of Arizona College of Medicine Phoenix, Child Health, Phoenix, AZ, USA; Phoenix Veteran Affairs Healthcare System, Phoenix, AZ, USA
| | - Helen Ling
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - David J Loane
- Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, MD, USA; Shock Trauma and Anesthesiology Research (STAR) Center, University of Maryland School of Medicine, Baltimore, MD, USA; School of Biochemistry and Immunology and Trinity Biomedical Sciences Institute, Trinity College Dublin, Ireland
| | - Seth Love
- Dementia Research Group, Institute of Clinical Neurosciences, Medical School, University of Bristol, Bristol, UK
| | - Andrew Ir Maas
- Department of Neurosurgery, Antwerp University Hospital and University of Antwerp, Edegem, Belgium
| | - Niklas Marklund
- Skane University Hospital, Department of Clinical Sciences Lund, Neurosurgery, Lund University, Lund, Sweden
| | - Christina L Master
- Center for Injury Research and Prevention and Division of Orthopedic Surgery, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - David F Meaney
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Brain Injury and Repair, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - David K Menon
- NIHR Global Health Research Group on Neurotrauma, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK; Division of Anaesthesia, Department of Medicine, Cambridge Biomedical Campus, University of Cambridge, Cambridge, UK
| | | | - Benoit Mouzon
- Roskamp Institute, Sarasota, Florida, USA; The Open University, Milton Keynes, UK; James A Haley Veterans' Hospital, Tampa, FL, USA
| | - Elliott J Mufson
- Barrow Neurological Institute, Departments of Neurobiology and Neurology, Phoenix, AZ, USA
| | - Joseph O Ojo
- Roskamp Institute, Sarasota, Florida, USA; The Open University, Milton Keynes, UK; James A Haley Veterans' Hospital, Tampa, FL, USA
| | - Mayumi Prins
- UCLA Steve Tisch BrainSPORT Program, Los Angeles, CA, USA
| | - Tamas Revesz
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Craig W Ritchie
- Centre for Dementia Prevention, University of Edinburgh, Edinburgh, UK
| | - Colin Smith
- Academic Neuropathology, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Richard Sylvester
- Homerton University Hospital NHS Trust, National Hospital of Neurology and Neurosurgery, University College London, London, UK
| | - Cheuk Y Tang
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, University of Pennsylvania, Philadelphia, PA, USA; Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn Urankar
- Dementia Research Group, Institute of Clinical Neurosciences, Medical School, University of Bristol, Bristol, UK
| | - Robert Vink
- Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Cheryl Wellington
- Department of Pathology and Laboratory Medicine, Djavad Mowafaghian Centre for Brain Health, International Collaboration on Repair Discoveries, School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah, Salt Lake City, UT, USA; Michael DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX, USA
| | - Lindsay Wilson
- Division of Psychology, University of Stirling, Stirling, UK
| | - Keith Yeates
- Department of Psychology, Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, AB, Canada
| | - Douglas H Smith
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA; Penn Center for Brain Injury and Repair, University of Pennsylvania, Philadelphia, PA, USA
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11
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Arganda-Carreras I, Manoliu T, Mazuras N, Schulze F, Iglesias JE, Bühler K, Jenett A, Rouyer F, Andrey P. A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain. Front Neuroinform 2018; 12:13. [PMID: 29628885 PMCID: PMC5876320 DOI: 10.3389/fninf.2018.00013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 03/01/2018] [Indexed: 11/13/2022] Open
Abstract
Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila, one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.
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Affiliation(s)
- Ignacio Arganda-Carreras
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, Versailles, France.,Ikerbasque, Basque Foundation for Science, Bilbao, Spain.,Donostia International Physics Center, Donostia-San Sebastian, Spain
| | - Tudor Manoliu
- Institut des Neurosciences Paris-Saclay, Université Paris Sud, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Nicolas Mazuras
- Institut des Neurosciences Paris-Saclay, Université Paris Sud, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Florian Schulze
- VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria
| | - Juan E Iglesias
- Basque Center on Cognition, Brain and Language, Donostia-San Sebastian, Spain
| | - Katja Bühler
- VRVis Zentrum für Virtual Reality und Visualisierung Forschungs-GmbH, Vienna, Austria
| | - Arnim Jenett
- Tefor Core Facility, Institut des Neurosciences Paris-Saclay, Université Paris Sud, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
| | - François Rouyer
- Institut des Neurosciences Paris-Saclay, Université Paris Sud, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Philippe Andrey
- Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay, Versailles, France
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12
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Saygin ZM, Kliemann D, Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M, Stevens A, Van Leemput K, McKee A, Frosch MP, Fischl B, Augustinack JC. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage 2017; 155:370-382. [PMID: 28479476 DOI: 10.1016/j.neuroimage.2017.04.046] [Citation(s) in RCA: 248] [Impact Index Per Article: 35.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Revised: 04/06/2017] [Accepted: 04/19/2017] [Indexed: 10/19/2022] Open
Abstract
The amygdala is composed of multiple nuclei with unique functions and connections in the limbic system and to the rest of the brain. However, standard in vivo neuroimaging tools to automatically delineate the amygdala into its multiple nuclei are still rare. By scanning postmortem specimens at high resolution (100-150µm) at 7T field strength (n = 10), we were able to visualize and label nine amygdala nuclei (anterior amygdaloid, cortico-amygdaloid transition area; basal, lateral, accessory basal, central, cortical medial, paralaminar nuclei). We created an atlas from these labels using a recently developed atlas building algorithm based on Bayesian inference. This atlas, which will be released as part of FreeSurfer, can be used to automatically segment nine amygdala nuclei from a standard resolution structural MR image. We applied this atlas to two publicly available datasets (ADNI and ABIDE) with standard resolution T1 data, used individual volumetric data of the amygdala nuclei as the measure and found that our atlas i) discriminates between Alzheimer's disease participants and age-matched control participants with 84% accuracy (AUC=0.915), and ii) discriminates between individuals with autism and age-, sex- and IQ-matched neurotypically developed control participants with 59.5% accuracy (AUC=0.59). For both datasets, the new ex vivo atlas significantly outperformed (all p < .05) estimations of the whole amygdala derived from the segmentation in FreeSurfer 5.1 (ADNI: 75%, ABIDE: 54% accuracy), as well as classification based on whole amygdala volume (using the sum of all amygdala nuclei volumes; ADNI: 81%, ABIDE: 55% accuracy). This new atlas and the segmentation tools that utilize it will provide neuroimaging researchers with the ability to explore the function and connectivity of the human amygdala nuclei with unprecedented detail in healthy adults as well as those with neurodevelopmental and neurodegenerative disorders.
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Affiliation(s)
- Z M Saygin
- Massachusetts Institute of Technology/ McGovern Institute, 43 Vassar St., Cambridge, MA 02139, USA; Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA.
| | - D Kliemann
- Massachusetts Institute of Technology/ McGovern Institute, 43 Vassar St., Cambridge, MA 02139, USA; Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - J E Iglesias
- University College London, Dept. Medical Physics and Biomedical Engineering Translational Imaging Group, Malet Place Engineering Building, Gower Street, London WC1E 6BT, UK; Basque Center on Cognition, Brain and Language, Paseo Mikeletegi 69, 20009 Donostia - San Sebastian, Spain
| | - A J W van der Kouwe
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - E Boyd
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - M Reuter
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - A Stevens
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
| | - K Van Leemput
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - A McKee
- Department of Neurology and Pathology, Boston University School of Medicine, Boston University Alzheimer's Disease Center, Boston, MA 02118, USA; VA Boston Healthcare System, MA 02130, USA
| | - M P Frosch
- C.S. Kubik Laboratory for Neuropathology, Pathology Service, MGH, 55 Fruit St., Boston, MA 02115, USA
| | - B Fischl
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA; MIT Computer Science and AI Lab, Cambridge, MA 02139, USA
| | - J C Augustinack
- Athinoula A Martinos Center, Dept. of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA
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13
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Ho NF, Iglesias JE, Sum MY, Kuswanto CN, Sitoh YY, De Souza J, Hong Z, Fischl B, Roffman JL, Zhou J, Sim K, Holt DJ. Progression from selective to general involvement of hippocampal subfields in schizophrenia. Mol Psychiatry 2017; 22:142-152. [PMID: 26903271 PMCID: PMC4995163 DOI: 10.1038/mp.2016.4] [Citation(s) in RCA: 114] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2015] [Revised: 10/19/2015] [Accepted: 12/07/2015] [Indexed: 11/08/2022]
Abstract
Volume deficits of the hippocampus in schizophrenia have been consistently reported. However, the hippocampus is anatomically heterogeneous; it remains unclear whether certain portions of the hippocampus are affected more than others in schizophrenia. In this study, we aimed to determine whether volume deficits in schizophrenia are confined to specific subfields of the hippocampus and to measure the subfield volume trajectories over the course of the illness. Magnetic resonance imaging scans were obtained from Data set 1: 155 patients with schizophrenia (mean duration of illness of 7 years) and 79 healthy controls, and Data set 2: an independent cohort of 46 schizophrenia patients (mean duration of illness of 18 years) and 46 healthy controls. In addition, follow-up scans were collected for a subset of Data set 1. A novel, automated method based on an atlas constructed from ultra-high resolution, post-mortem hippocampal tissue was used to label seven hippocampal subfields. Significant cross-sectional volume deficits in the CA1, but not of the other subfields, were found in the schizophrenia patients of Data set 1. However, diffuse cross-sectional volume deficits across all subfields were found in the more chronic and ill schizophrenia patients of Data set 2. Consistent with this pattern, the longitudinal analysis of Data set 1 revealed progressive illness-related volume loss (~2-6% per year) that extended beyond CA1 to all of the other subfields. This decline in volume correlated with symptomatic worsening. Overall, these findings provide converging evidence for early atrophy of CA1 in schizophrenia, with extension to other hippocampal subfields and accompanying clinical sequelae over time.
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Affiliation(s)
- N F Ho
- Research Division, Institute of Mental Health, Singapore
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - J E Iglesias
- Basque Center on Cognition, Brain and Language, Spain
- AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - M Y Sum
- Research Division, Institute of Mental Health, Singapore
| | - C N Kuswanto
- Research Division, Institute of Mental Health, Singapore
| | - Y Y Sitoh
- Department of Neuroradiology, National Neuroscience Institute, Singapore
| | - J De Souza
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - Z Hong
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - B Fischl
- AA Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - J L Roffman
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - J Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore
| | - K Sim
- Research Division, Institute of Mental Health, Singapore
- Department of General Psychiatry, General Psychiatry, Institute of Mental Health, Singapore
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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14
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Cantero JL, Iglesias JE, Van Leemput K, Atienza M. Regional Hippocampal Atrophy and Higher Levels of Plasma Amyloid-Beta Are Associated With Subjective Memory Complaints in Nondemented Elderly Subjects. J Gerontol A Biol Sci Med Sci 2016; 71:1210-5. [PMID: 26946100 DOI: 10.1093/gerona/glw022] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2015] [Accepted: 01/29/2016] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Evidence suggests a link between the presence of subjective memory complaints (SMC) and lower volume of the hippocampus, one of the first regions to show neuropathological lesions in Alzheimer's disease. However, it remains unknown whether this pattern of hippocampal atrophy is regionally specific and whether SMC are also paralleled by changes in peripheral levels of amyloid-beta (Aβ). METHODS The volume of hippocampal subregions and plasma Aβ levels were cross-sectionally compared between elderly individuals with (SMC(+); N = 47) and without SMC (SMC(-); N = 48). Significant volume differences in hippocampal subregions were further correlated with plasma Aβ levels and with objective memory performance. RESULTS Individuals with SMC exhibited significantly higher Aβ1-42 concentrations and lower volumes of CA1, CA4, dentate gyrus, and molecular layer compared with SMC(-) participants. Regression analyses further showed significant associations between lower volume of the dentate gyrus and both poorer memory performance and higher plasma Aβ1-42 levels in SMC(+) participants. CONCLUSIONS The presence of SMC, lower volumes of specific hippocampal regions, and higher plasma Aβ1-42 levels could be conditions associated with aging vulnerability. If such associations are confirmed in longitudinal studies, the combination may be markers recommending clinical follow-up in nondemented older adults.
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Affiliation(s)
- Jose L Cantero
- Laboratory of Functional Neuroscience, CIBERNED (Network Center for Biomedical Research in Neurodegenerative Diseases), Pablo de Olavide University, Seville, Spain.
| | - Juan E Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston. Basque Center on Cognition, Brain and Language, San Sebastian, Spain
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Mercedes Atienza
- Laboratory of Functional Neuroscience, CIBERNED (Network Center for Biomedical Research in Neurodegenerative Diseases), Pablo de Olavide University, Seville, Spain
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15
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Whelan CD, Hibar DP, van Velzen LS, Zannas AS, Carrillo-Roa T, McMahon K, Prasad G, Kelly S, Faskowitz J, deZubiracay G, Iglesias JE, van Erp TGM, Frodl T, Martin NG, Wright MJ, Jahanshad N, Schmaal L, Sämann PG, Thompson PM. Heritability and reliability of automatically segmented human hippocampal formation subregions. Neuroimage 2016; 128:125-137. [PMID: 26747746 PMCID: PMC4883013 DOI: 10.1016/j.neuroimage.2015.12.039] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 11/28/2015] [Accepted: 12/23/2015] [Indexed: 12/01/2022] Open
Abstract
The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.
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Affiliation(s)
- Christopher D Whelan
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Derrek P Hibar
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Laura S van Velzen
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Anthony S Zannas
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Tania Carrillo-Roa
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Katie McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Gautam Prasad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Sinéad Kelly
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Greig deZubiracay
- Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Juan E Iglesias
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Thomas Frodl
- Department of Psychiatry, Otto-von Guericke-University of Magdeburg, Germany
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Lianne Schmaal
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Philipp G Sämann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA.
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16
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Iglesias JE. On Patterson′s cyclotomic sets and how to count them. Z KRIST-CRYST MATER 2015. [DOI: 10.1524/zkri.1981.156.14.187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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17
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Abstract
Abstract
The problem of enumerating the possible closest-packings of equal spheres having a period P for each of the possible space groups is solved by systematic exploitation of the properties of the two-color symmetry group of the cyclotomic representation of the Zhdanov symbol. No sophisticated combinatorial or group-theoretical techniques are required, and for some space groups, non-recursive, simple analytical expressions are obtained through the use of Möbius Inversion Formula. A corollary to Möbius theorem, used in the derivation, has been proved.
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18
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Vila E, Landa-Canovas ÁR, Galy J, Iglesias JE, Castro A. Bi2n+4MonO6(n+1) with n=3, 4, 5, 6: A new series of low-temperature stable phases in the mBi2O3 – MoO3 system (1.0 J SOLID STATE CHEM 2007. [DOI: 10.1016/j.jssc.2006.10.036] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Abstract
The relationship between the space-group symmetry of a close packing of equal balls of repeat period P and the symmetry properties of its representing Zhdanov symbol is analyzed. Proofs are straightforward when some symmetry is assumed for the stacking, and it is investigated how this symmetry is reflected in the structure of the Zhdanov symbol. Most of these proofs are documented in the literature, with variable degrees of rigor. However, the proof is somewhat more involved when working backwards, i.e. when some symmetry properties for the Zhdanov symbol are assumed and the corresponding effect on the symmetry of the polytype structure it represents is investigated, which may explain why these proofs are avoided or shrugged off as ;easily seen', 'obvious' and the like.
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Affiliation(s)
- Juan E Iglesias
- Instituto de Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas, Cantoblanco, 28049 Madrid, Spain.
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Iglesias JE. Enumeration of polytypesMXandMX2through the use of the symmetry of the Zhdanov symbol. Acta Crystallogr A 2006; 62:178-94. [PMID: 16614490 DOI: 10.1107/s0108767306003126] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2005] [Accepted: 03/01/2006] [Indexed: 11/10/2022] Open
Abstract
The different close-packed polytypes MX and MX2 have been enumerated for each of the possible space groups by counting the corresponding Zhdanov symbols for each space group and period of stacking, P, by the use of elementary combinatorial techniques. In special cases, simple closed formulae are obtained for these numbers as functions of P. The symmetry properties of the Zhdanov symbol have been investigated with the help of its cyclotomic representation and the two-color symmetry point group thereof. Zhdanov-like rules have been developed for MX2 polytypes. The SiC cases have been generated to P = 18 under the ;1-exclusion' rule and the possible diamond polytypes have been examined.
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Affiliation(s)
- Juan E Iglesias
- Instituto de Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas, Cantoblanco, 28049 Madrid, Spain.
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Losilla ER, Cabeza A, Bruque S, Aranda MA, Sanz J, Iglesias JE, Alonso JA. Syntheses, Structures, and Thermal Expansion of Germanium Pyrophosphates. J SOLID STATE CHEM 2001. [DOI: 10.1006/jssc.2000.8984] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Martínez-Juárez A, Pecharromán C, Iglesias JE, Rojo JM. Relationship between Activation Energy and Bottleneck Size for Li+ Ion Conduction in NASICON Materials of Composition LiMM‘(PO4)3; M, M‘ = Ge, Ti, Sn, Hf. J Phys Chem B 1998. [DOI: 10.1021/jp973296c] [Citation(s) in RCA: 133] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ana Martínez-Juárez
- Instituto Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas (CSIC), Cantoblanco, 28049 Madrid, Spain
| | - Carlos Pecharromán
- Instituto Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas (CSIC), Cantoblanco, 28049 Madrid, Spain
| | - Juan E. Iglesias
- Instituto Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas (CSIC), Cantoblanco, 28049 Madrid, Spain
| | - José M. Rojo
- Instituto Ciencia de Materiales de Madrid, Consejo Superior de Investigaciones Científicas (CSIC), Cantoblanco, 28049 Madrid, Spain
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Pecharromán C, Iglesias JE. Effective dielectric properties of packed mixtures of insulator particles. Phys Rev B Condens Matter 1994; 49:7137-7147. [PMID: 10009450 DOI: 10.1103/physrevb.49.7137] [Citation(s) in RCA: 25] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
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Iglesias JE. A formula for the number of closest packings of equal spheres having a given repeat period. Z KRIST-CRYST MATER 1981. [DOI: 10.1524/zkri.1981.155.14.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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