1
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Teghipco A, Newman-Norlund R, Fridriksson J, Rorden C, Bonilha L. Distinct brain morphometry patterns revealed by deep learning improve prediction of post-stroke aphasia severity. COMMUNICATIONS MEDICINE 2024; 4:115. [PMID: 38866977 PMCID: PMC11169346 DOI: 10.1038/s43856-024-00541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
BACKGROUND Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, substantial interindividual variability remains unaccounted. One explanatory factor may be the spatial distribution of morphometry beyond the lesion (e.g., atrophy), including not just specific brain areas, but distinct three-dimensional patterns. METHODS Here, we test whether deep learning with Convolutional Neural Networks (CNNs) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy better predicts chronic stroke individuals with severe aphasia (N = 231) than classical machine learning (Support Vector Machines; SVMs), evaluating whether encoding spatial dependencies identifies uniquely predictive patterns. RESULTS CNNs achieve higher balanced accuracy and F1 scores, even when SVMs are nonlinear or integrate linear or nonlinear dimensionality reduction. Parity only occurs when SVMs access features learned by CNNs. Saliency maps demonstrate that CNNs leverage distributed morphometry patterns, whereas SVMs focus on the area around the lesion. Ensemble clustering of CNN saliencies reveals distinct morphometry patterns unrelated to lesion size, consistent across individuals, and which implicate unique networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions depend on both ipsilateral and contralateral features outside the lesion. CONCLUSIONS Three-dimensional network distributions of morphometry are directly associated with aphasia severity, underscoring the potential for CNNs to improve outcome prognostication from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.
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Affiliation(s)
- Alex Teghipco
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
| | - Roger Newman-Norlund
- Department of Psychology, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Christopher Rorden
- Department of Psychology, College of Arts and Sciences, University of South Carolina, Columbia, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, School of Medicine, University of South Carolina, Columbia, SC, USA
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2
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Yang Q, Cai S, Chen G, Yu X, Cattell RF, Raviv TR, Huang C, Zhang N, Gao Y. Fine scale hippocampus morphology variation cross 552 healthy subjects from age 20 to 80. Front Neurosci 2023; 17:1162096. [PMID: 37719158 PMCID: PMC10501455 DOI: 10.3389/fnins.2023.1162096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 07/26/2023] [Indexed: 09/19/2023] Open
Abstract
The cerebral cortex varies over the course of a person's life span: at birth, the surface is smooth, before becoming more bumpy (deeper sulci and thicker gyri) in middle age, and thinner in senior years. In this work, a similar phenomenon was observed on the hippocampus. It was previously believed the fine-scale morphology of the hippocampus could only be extracted only with high field scanners (7T, 9.4T); however, recent studies show that regular 3T MR scanners can be sufficient for this purpose. This finding opens the door for the study of fine hippocampal morphometry for a large amount of clinical data. In particular, a characteristic bumpy and subtle feature on the inferior aspect of the hippocampus, which we refer to as hippocampal dentation, presents a dramatic degree of variability between individuals from very smooth to highly dentated. In this report, we propose a combined method joining deep learning and sub-pixel level set evolution to efficiently obtain fine-scale hippocampal segmentation on 552 healthy subjects. Through non-linear dentation extraction and fitting, we reveal that the bumpiness of the inferior surface of the human hippocampus has a clear temporal trend. It is bumpiest between 40 and 50 years old. This observation should be aligned with neurodevelopmental and aging stages.
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Affiliation(s)
- Qinzhu Yang
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Shuxiu Cai
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Guojing Chen
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaxia Yu
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
| | - Renee F. Cattell
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States
- Department of Radiation Oncology, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, United States
| | - Tammy Riklin Raviv
- The School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Chuan Huang
- Department of Psychiatry, Stony Brook University, Stony Brook, NY, United States
- Department of Radiology, Stony Brook University, Stony Brook, NY, United States
| | - Nu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yi Gao
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China
- Shenzhen Key Laboratory of Precision Medicine for Hematological Malignancies, Shenzhen, China
- Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China
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3
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Schell M, Foltyn-Dumitru M, Bendszus M, Vollmuth P. Automated hippocampal segmentation algorithms evaluated in stroke patients. Sci Rep 2023; 13:11712. [PMID: 37474622 PMCID: PMC10359355 DOI: 10.1038/s41598-023-38833-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 07/16/2023] [Indexed: 07/22/2023] Open
Abstract
Deep learning segmentation algorithms can produce reproducible results in a matter of seconds. However, their application to more complex datasets is uncertain and may fail in the presence of severe structural abnormalities-such as those commonly seen in stroke patients. In this investigation, six recent, deep learning-based hippocampal segmentation algorithms were tested on 641 stroke patients of a multicentric, open-source dataset ATLAS 2.0. The comparisons of the volumes showed that the methods are not interchangeable with concordance correlation coefficients from 0.266 to 0.816. While the segmentation algorithms demonstrated an overall good performance (volumetric similarity [VS] 0.816 to 0.972, DICE score 0.786 to 0.921, and Hausdorff distance [HD] 2.69 to 6.34), no single out-performing algorithm was identified: FastSurfer performed best in VS, QuickNat in DICE and average HD, and Hippodeep in HD. Segmentation performance was significantly lower for ipsilesional segmentation, with a decrease in performance as a function of lesion size due to the pathology-based domain shift. Only QuickNat showed a more robust performance in volumetric similarity. Even though there are many pre-trained segmentation methods, it is important to be aware of the possible decrease in performance for the segmentation results on the lesion side due to the pathology-based domain shift. The segmentation algorithm should be selected based on the research question and the evaluation parameter needed. More research is needed to improve current hippocampal segmentation methods.
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Affiliation(s)
- Marianne Schell
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martha Foltyn-Dumitru
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Philipp Vollmuth
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
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4
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Teghipco A, Newman-Norlund R, Fridriksson J, Rorden C, Bonilha L. Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity. RESEARCH SQUARE 2023:rs.3.rs-3126126. [PMID: 37461696 PMCID: PMC10350198 DOI: 10.21203/rs.3.rs-3126126/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the stroke lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, significant interindividual variability remains unaccounted for. A possible explanatory factor may be the spatial distribution of brain atrophy beyond the lesion. This includes not just the specific brain areas showing atrophy, but also distinct three-dimensional patterns of atrophy. Here, we tested whether deep learning with Convolutional Neural Networks (CNN) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy can better predict which individuals with chronic stroke (N=231) have severe aphasia, and whether encoding spatial dependencies in the data might be capable of improving predictions by identifying unique individualized spatial patterns. We observed that CNN achieves significantly higher accuracy and F1 scores than Support Vector Machine (SVM), even when the SVM is nonlinear or integrates linear and nonlinear dimensionality reduction techniques. Performance parity was only achieved when the SVM was directly trained on the latent features learned by the CNN. Saliency maps demonstrated that the CNN leveraged widely distributed patterns of brain atrophy predictive of aphasia severity, whereas the SVM focused almost exclusively on the area around the lesion. Ensemble clustering of CNN saliency maps revealed distinct morphometry patterns that were unrelated to lesion size, highly consistent across individuals, and implicated unique brain networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions of severity depended on both ipsilateral and contralateral features outside of the location of stroke. Our findings illustrate that three-dimensional network distributions of atrophy in individuals with aphasia are directly associated with aphasia severity, underscoring the potential for deep learning to improve prognostication of behavioral outcomes from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.
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5
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Haddad E, Pizzagalli F, Zhu AH, Bhatt RR, Islam T, Ba Gari I, Dixon D, Thomopoulos SI, Thompson PM, Jahanshad N. Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3. Hum Brain Mapp 2023; 44:1515-1532. [PMID: 36437735 PMCID: PMC9921222 DOI: 10.1002/hbm.26147] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/29/2022] Open
Abstract
Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.
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Affiliation(s)
- Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.,Department of Neurosciences, University of Turin, Turin, Italy
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Ravi R Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
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6
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Zavaliangos‐Petropulu A, Lo B, Donnelly MR, Schweighofer N, Lohse K, Jahanshad N, Barisano G, Banaj N, Borich MR, Boyd LA, Buetefisch CM, Byblow WD, Cassidy JM, Charalambous CC, Conforto AB, DiCarlo JA, Dula AN, Egorova‐Brumley N, Etherton MR, Feng W, Fercho KA, Geranmayeh F, Hanlon CA, Hayward KS, Hordacre B, Kautz SA, Khlif MS, Kim H, Kuceyeski A, Lin DJ, Liu J, Lotze M, MacIntosh BJ, Margetis JL, Mohamed FB, Piras F, Ramos‐Murguialday A, Revill KP, Roberts PS, Robertson AD, Schambra HM, Seo NJ, Shiroishi MS, Stinear CM, Soekadar SR, Spalletta G, Taga M, Tang WK, Thielman GT, Vecchio D, Ward NS, Westlye LT, Werden E, Winstein C, Wittenberg GF, Wolf SL, Wong KA, Yu C, Brodtmann A, Cramer SC, Thompson PM, Liew S. Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis. J Am Heart Assoc 2022; 11:e025109. [PMID: 35574963 PMCID: PMC9238563 DOI: 10.1161/jaha.121.025109] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/29/2022] [Indexed: 11/22/2022]
Abstract
Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P<0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. In exploratory analyses, we tested for a sensorimotor impairment and sex interaction and relationships between lesion volume, sensorimotor damage, and hippocampal volume. Greater sensorimotor impairment was significantly associated with ipsilesional (P=0.005; β=0.16) but not contralesional (P=0.96; β=0.003) hippocampal volume, independent of lesion volume and other covariates (P=0.001; β=0.26). Women showed progressively worsening sensorimotor impairment with smaller ipsilesional (P=0.008; β=-0.26) and contralesional (P=0.006; β=-0.27) hippocampal volumes compared with men. Hippocampal volume was associated with lesion size (P<0.001; β=-0.21) and extent of sensorimotor damage (P=0.003; β=-0.15). Conclusions The present study identifies novel associations between chronic poststroke sensorimotor impairment and ipsilesional hippocampal volume that are not caused by lesion size and may be stronger in women.
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Affiliation(s)
- Artemis Zavaliangos‐Petropulu
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCA
| | - Bethany Lo
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCA
| | - Miranda R. Donnelly
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCA
| | - Nicolas Schweighofer
- Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCA
| | - Keith Lohse
- Physical Therapy and NeurologyWashington University School of Medicine in Saint LouisMO
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
| | - Giuseppe Barisano
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
- Neuroscience Graduate ProgramUniversity of Southern CaliforniaLos AngelesCA
| | - Nerisa Banaj
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Michael R. Borich
- Division of Physical TherapyDepartment of Rehabilitation MedicineEmory University School of MedicineAtlantaGA
| | - Lara A. Boyd
- Department of Physical TherapyUniversity of British ColumbiaVancouverCanada
| | | | - Winston D. Byblow
- Department of Exercise Sciences, and Centre for Brain ResearchUniversity of AucklandNew Zealand
| | - Jessica M. Cassidy
- Department of Allied Health SciencesUniversity of North Carolina at Chapel HillNC
| | - Charalambos C. Charalambous
- Department of Basic and Clinical SciencesUniversity of Nicosia Medical SchoolNicosiaCyprus
- Center for Neuroscience and Integrative Brain Research (CENIBRE)NicosiaCyprus
| | - Adriana B. Conforto
- Hospital das ClínicasSão Paulo UniversitySão PauloBrazil
- Hospital Israelita Albert EinsteinSão PauloBrazil
| | - Julie A. DiCarlo
- Center for Neurotechnology and Neurorecovery (CNTR)Massachusetts General HospitalBostonMA
| | - Adrienne N. Dula
- Department of NeurologyDell Medical SchoolUniversity of Texas at AustinTX
| | | | - Mark R. Etherton
- Department of NeurologyJ. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMA
| | - Wuwei Feng
- Department of NeurologyDuke University School of MedicineDurhamNC
| | - Kelene A. Fercho
- Basic Biomedical SciencesUniversity of South DakotaVermillionSD
- Federal Aviation AdministrationCivil Aerospace Medical InstituteOklahoma CityOK
| | | | | | - Kathryn S. Hayward
- Departments of Physiotherapy and Medicine, University of MelbourneHeidelbergVictoriaAustralia
- The Florey Institute of Neuroscience and Mental HealthHeidelbergVictoriaAustralia
| | - Brenton Hordacre
- Innovation, Implementation and Clinical Translation (IIMPACT) in HealthAllied Health and Human PerformanceUniversity of South AustraliaAdelaideSouth AustraliaAustralia
| | - Steven A. Kautz
- Ralph H Johnson Veterans Affairs Medical CenterCharlestonSC
- Department of Health Sciences & ResearchMedical University of South CarolinaCharlestonSC
| | - Mohamed Salah Khlif
- The Florey Institute of Neuroscience and Mental HealthHeidelbergVictoriaAustralia
| | - Hosung Kim
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
| | - Amy Kuceyeski
- Department of RadiologyWeill Cornell MedicineNew YorkNY
| | - David J. Lin
- Center for Neurotechnology and Neurorecovery (CNTR)Massachusetts General HospitalBostonMA
| | - Jingchun Liu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Martin Lotze
- Functional ImagingInstitute for Diagnostic Radiology and NeuroradiologyUniversity Medicine GreifswaldGermany
| | - Bradley J. MacIntosh
- Hurvitz Brain Sciences ProgramSunnybrook Research InstituteTorontoCanada
- Department of Medical BiophysicsUniversity of TorontoOntarioCanada
| | - John L. Margetis
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCA
| | - Feroze B. Mohamed
- Department of RadiologyJefferson Integrated MR CenterThomas Jefferson UniversityPhiladelphiaPA
| | - Fabrizio Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Ander Ramos‐Murguialday
- Institute of Medical Psychology and Behavioral NeurobiologyUniversity of TübingenGermany
- Health DivisionTECNALIASan SebastianSpain
| | | | - Pamela S. Roberts
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCA
- Department of Physical Medicine and RehabilitationCedars‐SinaiLos AngelesCA
| | - Andrew D. Robertson
- Department of Kinesiology and Health SciencesUniversity of WaterlooOntarioCanada
| | - Heidi M. Schambra
- Departments of Neurology & Rehabilitation MedicineNYU LangoneNew YorkNY
| | - Na Jin Seo
- Ralph H Johnson Veterans Affairs Medical CenterCharlestonSC
- Department of Rehabilitation SciencesDepartment of Health Science and ResearchMedical University of South CarolinaCharlestonSC
| | - Mark S. Shiroishi
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
- Department of RadiologyKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCA
| | | | - Surjo R. Soekadar
- Clinical Neurotechnology LaboratoryDepartment of Psychiatry and Neurosciences (CCM)Charité ‐ Universitätsmedizin BerlinBerlinGermany
| | | | - Myriam Taga
- NYU Langone Department of NeurologyNew YorkNY
| | - Wai Kwong Tang
- Department of PsychiatryThe Chinese University of Hong KongChina
| | - Gregory T. Thielman
- Department of Physical Therapy and NeuroscienceUniversity of the SciencesPhiladelphiaPA
| | - Daniela Vecchio
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Nick S. Ward
- University College London Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloNorway
- Department of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Emilio Werden
- The Florey Institute of Neuroscience and Mental HealthHeidelbergVictoriaAustralia
- Melbourne Dementia Research CenterUniversity of MelbourneVictoriaAustralia
| | - Carolee Winstein
- Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCA
| | - George F. Wittenberg
- Department of NeurologyUniversity of PittsburghPA
- Department of Veterans AffairsGeriatrics Research Educational & Clinical CenterVeterans Affairs Pittsburgh Healthcare System (VAPHS)PittsburghPA
| | - Steven L. Wolf
- Division of Physical TherapyDepartment of Rehabilitation MedicineEmory University School of MedicineAtlantaGA
- Department of MedicineEmory University School of MedicineAtlantaGA
| | - Kristin A. Wong
- Department of Physical Medicine & RehabilitationDell Medical SchoolUniversity of Texas at AustinTX
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina
| | - Amy Brodtmann
- The Florey Institute of Neuroscience and Mental HealthHeidelbergVictoriaAustralia
| | - Steven C. Cramer
- Department of NeurologyUniversity of California Los AngelesDavid Geffen School of MedicineLos AngelesCA
- California Rehabilitation HospitalLos AngelesCA
| | - Paul M. Thompson
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
| | - Sook‐Lei Liew
- Mark and Mary Stevens Neuroimaging and Informatics InstituteKeck School of Medicine, University of Southern CaliforniaLos AngelesCA
- Chan Division of Occupational Science and Occupational TherapyUniversity of Southern CaliforniaLos AngelesCA
- Biokinesiology and Physical TherapyUniversity of Southern CaliforniaLos AngelesCA
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7
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Balboni E, Nocetti L, Carbone C, Dinsdale N, Genovese M, Guidi G, Malagoli M, Chiari A, Namburete AIL, Jenkinson M, Zamboni G. The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects. Hum Brain Mapp 2022; 43:3427-3438. [PMID: 35373881 PMCID: PMC9248306 DOI: 10.1002/hbm.25858] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/04/2022] [Accepted: 03/21/2022] [Indexed: 11/23/2022] Open
Abstract
Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold‐out test set. In addition, 10% of the overall training dataset was used as a hold‐out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (ps < .001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis.
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Affiliation(s)
- Erica Balboni
- Health Physics Unit, Azienda Ospedaliera di Modena, Modena, Italy.,Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Luca Nocetti
- Health Physics Unit, Azienda Ospedaliera di Modena, Modena, Italy
| | - Chiara Carbone
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.,Center for Neurosciences and Neurotechnology, Università di Modena e Reggio Emilia, Modena, Italy
| | - Nicola Dinsdale
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Oxford Machine Learning in NeuroImaging Lab, Department of Computer Science, Oxford, UK
| | | | - Gabriele Guidi
- Health Physics Unit, Azienda Ospedaliera di Modena, Modena, Italy
| | | | - Annalisa Chiari
- Neuroradiology Unit, Azienda Ospedaliera di Modena, Modena, Italy
| | - Ana I L Namburete
- Oxford Machine Learning in NeuroImaging Lab, Department of Computer Science, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Australian Institute for Machine Learning, School of Computer Science, University of Adelaide, Adelaide, South Australia, Australia.,South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Giovanna Zamboni
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy.,Center for Neurosciences and Neurotechnology, Università di Modena e Reggio Emilia, Modena, Italy.,Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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8
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Thompson PM, Jahanshad N, Schmaal L, Turner JA, Winkler AM, Thomopoulos SI, Egan GF, Kochunov P. The Enhancing NeuroImaging Genetics through Meta-Analysis Consortium: 10 Years of Global Collaborations in Human Brain Mapping. Hum Brain Mapp 2021; 43:15-22. [PMID: 34612558 PMCID: PMC8675422 DOI: 10.1002/hbm.25672] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 09/09/2021] [Accepted: 09/16/2021] [Indexed: 12/23/2022] Open
Abstract
This Special Issue of Human Brain Mapping is dedicated to a 10-year anniversary of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium. It reports updates from a broad range of international neuroimaging projects that pool data from around the world to answer fundamental questions in neuroscience. Since ENIGMA was formed in December 2009, the initiative grew into a worldwide effort with over 2,000 participating scientists from 45 countries, and over 50 working groups leading large-scale studies of human brain disorders. Over the last decade, many lessons were learned on how best to pool brain data from diverse sources. Working groups were created to develop methods to analyze worldwide data from anatomical and diffusion magnetic resonance imaging (MRI), resting state and task-based functional MRI, electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance spectroscopy (MRS). The quest to understand genetic effects on human brain development and disease also led to analyses of brain scans on an unprecedented scale. Genetic roadmaps of the human cortex were created by researchers worldwide who collaborated to perform statistically well-powered analyses of common and rare genetic variants on brain measures and rates of brain development and aging. Here, we summarize the 31 papers in this Special Issue, covering: (a) technical approaches to harmonize analysis of different types of brain imaging data, (b) reviews of the last decade of work by several of ENIGMA's clinical and technical working groups, and (c) new empirical papers reporting large-scale international brain mapping analyses in patients with substance use disorders, schizophrenia, bipolar disorders, major depression, posttraumatic stress disorder, obsessive compulsive disorder, epilepsy, and stroke.
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Affiliation(s)
- Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Lianne Schmaal
- Orygen, Parkville, Australia.,Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Jessica A Turner
- Psychology Department, Georgia State University, Atlanta, Georgia, USA
| | - Anderson M Winkler
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia.,Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Peter Kochunov
- Department of Psychiatry, Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland, USA
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9
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Pappas I, Hector H, Haws K, Curran B, Kayser AS, D'Esposito M. Improved normalization of lesioned brains via cohort-specific templates. Hum Brain Mapp 2021; 42:4187-4204. [PMID: 34143540 PMCID: PMC8356997 DOI: 10.1002/hbm.25474] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 04/24/2021] [Accepted: 04/26/2021] [Indexed: 12/16/2022] Open
Abstract
In MRI studies, spatial normalization is required to infer results at the group level. In the presence of a brain lesion, such as in stroke patients, the normalization process can be affected by tissue loss, spatial deformations, signal intensity changes, and other stroke sequelae that introduce confounds into the group analysis results. Previously, most neuroimaging studies with lesioned brains have used normalization methods optimized for intact brains, raising potential concerns about the accuracy of the resulting transformations and, in turn, their reported group level results. In this study, we demonstrate the benefits of creating an intermediate, cohort‐specific template in conjunction with diffeomorphism‐based methods to normalize structural MRI images in stroke patients. We show that including this cohort‐specific template improves accuracy compared to standard methods for normalizing lesioned brains. Critically, this method reduces overall differences in normalization accuracy between stroke patients and healthy controls, and may improve the localization and connectivity of BOLD signal in functional neuroimaging data.
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Affiliation(s)
- Ioannis Pappas
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Henrik Hector
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Kari Haws
- Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Brian Curran
- Department of Neurology, VA Northern California Health Care System, Martinez, California, USA
| | - Andrew S Kayser
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA.,Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Mark D'Esposito
- Department of Psychology, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, USA.,Department of Neurology, VA Northern California Health Care System, Martinez, California, USA.,Department of Psychology, University of California, Berkeley, Berkeley, California, USA
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10
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Neuroimaging Advances in Diagnosis and Differentiation of HIV, Comorbidities, and Aging in the cART Era. Curr Top Behav Neurosci 2021; 50:105-143. [PMID: 33782916 DOI: 10.1007/7854_2021_221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In the "cART era" of more widely available and accessible treatment, aging and HIV-related comorbidities, including symptoms of brain dysfunction, remain common among HIV-infected individuals on suppressive treatment. A better understanding of the neurobiological consequences of HIV infection is essential for developing thorough treatment guidelines and for optimizing long-term neuropsychological outcomes and overall brain health. In this chapter, we first summarize magnetic resonance imaging (MRI) methods used in over two decades of neuroHIV research. These methods evaluate brain volumetric differences and circuitry disruptions in adults living with HIV, and help map clinical correlations with brain function and tissue microstructure. We then introduce and discuss aging and associated neurological complications in people living with HIV, and processes by which infection may contribute to the risk for late-onset dementias. We describe how new technologies and large-scale international collaborations are helping to disentangle the effect of genetic and environmental risk factors on brain aging and neurodegenerative diseases. We provide insights into how these advances, which are now at the forefront of Alzheimer's disease research, may advance the field of neuroHIV. We conclude with a summary of how we see the field of neuroHIV research advancing in the decades to come and highlight potential clinical implications.
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11
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Ozzoude M, Ramirez J, Raamana PR, Holmes MF, Walker K, Scott CJM, Gao F, Goubran M, Kwan D, Tartaglia MC, Beaton D, Saposnik G, Hassan A, Lawrence-Dewar J, Dowlatshahi D, Strother SC, Symons S, Bartha R, Swartz RH, Black SE. Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions. Front Neurosci 2020; 14:598868. [PMID: 33381009 PMCID: PMC7768006 DOI: 10.3389/fnins.2020.598868] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 11/24/2020] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Regional changes to cortical thickness in individuals with neurodegenerative and cerebrovascular diseases (CVD) can be estimated using specialized neuroimaging software. However, the presence of cerebral small vessel disease, focal atrophy, and cortico-subcortical stroke lesions, pose significant challenges that increase the likelihood of misclassification errors and segmentation failures. PURPOSE The main goal of this study was to examine a correction procedure developed for enhancing FreeSurfer's (FS's) cortical thickness estimation tool, particularly when applied to the most challenging MRI obtained from participants with chronic stroke and CVD, with varying degrees of neurovascular lesions and brain atrophy. METHODS In 155 CVD participants enrolled in the Ontario Neurodegenerative Disease Research Initiative (ONDRI), FS outputs were compared between a fully automated, unmodified procedure and a corrected procedure that accounted for potential sources of error due to atrophy and neurovascular lesions. Quality control (QC) measures were obtained from both procedures. Association between cortical thickness and global cognitive status as assessed by the Montreal Cognitive Assessment (MoCA) score was also investigated from both procedures. RESULTS Corrected procedures increased "Acceptable" QC ratings from 18 to 76% for the cortical ribbon and from 38 to 92% for tissue segmentation. Corrected procedures reduced "Fail" ratings from 11 to 0% for the cortical ribbon and 62 to 8% for tissue segmentation. FS-based segmentation of T1-weighted white matter hypointensities were significantly greater in the corrected procedure (5.8 mL vs. 15.9 mL, p < 0.001). The unmodified procedure yielded no significant associations with global cognitive status, whereas the corrected procedure yielded positive associations between MoCA total score and clusters of cortical thickness in the left superior parietal (p = 0.018) and left insula (p = 0.04) regions. Further analyses with the corrected cortical thickness results and MoCA subscores showed a positive association between left superior parietal cortical thickness and Attention (p < 0.001). CONCLUSION These findings suggest that correction procedures which account for brain atrophy and neurovascular lesions can significantly improve FS's segmentation results and reduce failure rates, thus maximizing power by preventing the loss of our important study participants. Future work will examine relationships between cortical thickness, cerebral small vessel disease, and cognitive dysfunction due to neurodegenerative disease in the ONDRI study.
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Affiliation(s)
- Miracle Ozzoude
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Melissa F. Holmes
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Kirstin Walker
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher J. M. Scott
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Fuqiang Gao
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Donna Kwan
- Centre for Neuroscience Studies, Queens University, Kingston, ON, Canada
| | - Maria C. Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
- Division of Neurology, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
| | - Gustavo Saposnik
- Stroke Outcomes and Decision Neuroscience Research Unit, Division of Neurology, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada
| | - Ayman Hassan
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON, Canada
| | | | - Dariush Dowlatshahi
- Department of Medicine (Neurology), Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Department of Medical Biophysics, Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Richard H. Swartz
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Sandra E. Black
- LC Campbell Cognitive Neurology Research, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
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