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Chen YS, Kuo CY, Lu CH, Wang YW, Chou KH, Lin WC. Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease. Neurobiol Aging 2024; 140:122-129. [PMID: 38776615 DOI: 10.1016/j.neurobiolaging.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 10/26/2023] [Revised: 04/20/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024]
Abstract
Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.
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Affiliation(s)
- Yueh-Sheng Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Chen-Yuan Kuo
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yuan-Wei Wang
- The Science & Technology Policy Research and Information Center, National Applied Research Laboratories(NARLabs), Taipei, Taiwan
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
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2
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Coupeau P, Fasquel JB, Hertz-Pannier L, Dinomais M. GNN-based structural information to improve DNN-based basal ganglia segmentation in children following early brain lesion. Comput Med Imaging Graph 2024; 115:102396. [PMID: 38744197 DOI: 10.1016/j.compmedimag.2024.102396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 01/04/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024]
Abstract
Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by the presence of a lesion. Although current deep neural networks (DNN) perform well in segmenting subcortical brain structures in healthy brains, they lack robustness when faced with lesion variability, leading to structural inconsistencies. Given the established spatial organization of the basal ganglia, we propose enhancing the DNN-based segmentation through post-processing with a graph neural network (GNN). The GNN conducts node classification on graphs encoding both class probabilities and spatial information regarding the regions segmented by the DNN. In this study, we focus on neonatal arterial ischemic stroke (NAIS) in children. The approach is evaluated on both healthy children and children after NAIS using three DNN backbones: U-Net, UNETr, and MSGSE-Net. The results show an improvement in segmentation performance, with an increase in the median Dice score by up to 4% and a reduction in the median Hausdorff distance (HD) by up to 93% for healthy children (from 36.45 to 2.57) and up to 91% for children suffering from NAIS (from 40.64 to 3.50). The performance of the method is compared with atlas-based methods. Severe cases of neonatal stroke result in a decline in performance in the injured hemisphere, without negatively affecting the segmentation of the contra-injured hemisphere. Furthermore, the approach demonstrates resilience to small training datasets, a widespread challenge in the medical field, particularly in pediatrics and for rare pathologies.
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Affiliation(s)
- Patty Coupeau
- Universite d'Angers, LARIS, SFR MATHSTIC, F-49000 Angers, France.
| | | | - Lucie Hertz-Pannier
- UNIACT/Neurospin/JOLIOT/DRF/CEA-Saclay, and U1141 NeuroDiderot/Inserm, CEA, Paris University, France
| | - Mickaël Dinomais
- Universite d'Angers, LARIS, SFR MATHSTIC, F-49000 Angers, France; Departement de medecine physique et de readaptation, Centre Hospitalier Universitaire d'Angers, France
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Song L, Peng Y, Ouyang M, Peng Q, Feng L, Sotardi S, Yu Q, Kang H, Sindabizera KL, Liu S, Huang H. Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels. Hum Brain Mapp 2024; 45:e26695. [PMID: 38727010 PMCID: PMC11083905 DOI: 10.1002/hbm.26695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/11/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.
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Affiliation(s)
- Limei Song
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- School of Medical ImagingWeifang Medical UniversityWeifangChina
| | - Yun Peng
- Department of Radiology, Beijing Children's HospitalCapital Medical UniversityBeijingChina
| | - Minhui Ouyang
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Qinmu Peng
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Lei Feng
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Susan Sotardi
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Qinlin Yu
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Huiying Kang
- Department of Radiology, Beijing Children's HospitalCapital Medical UniversityBeijingChina
| | - Kay L. Sindabizera
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Shuwei Liu
- Research Center for Sectional and Imaging AnatomyShandong University School of MedicineJinanShandongChina
| | - Hao Huang
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Flusund AMH, Bø LE, Reinertsen I, Solheim O, Skandsen T, Håberg A, Andelic N, Vik A, Moen KG. Lesion Frequency Distribution Maps of Traumatic Axonal Injury on Early Magnetic Resonance Imaging After Moderate and Severe Traumatic Brain Injury and Associations to 12 Months Outcome. J Neurotrauma 2024. [PMID: 38588255 DOI: 10.1089/neu.2023.0534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024] Open
Abstract
Traumatic axonal injury (TAI) is a common finding on magnetic resonance imaging (MRI) in patients with moderate-severe traumatic brain injury (TBI), and the burden of TAI is associated with outcome in this patient group. Lesion mapping offers a way to combine imaging findings from numerous individual patients into common lesion maps where the findings from a whole patient cohort can be assessed. The aim of this study was to evaluate the spatial distribution of TAI lesions on different MRI sequences and its associations to outcome with use of lesion mapping. Included prospectively were 269 patients (8-70 years) with moderate or severe TBI and MRI within six weeks after injury. The TAI lesions were evaluated and manually segmented on fluid-attenuated inversed recovery (FLAIR), diffusion weighted imaging (DWI), and either T2* gradient echo (T2*GRE) or susceptibility weighted imaging (SWI). The segmentations were registered to the Montreal Neurological Institute space and combined to lesion frequency distribution maps. Outcome was assessed with Glasgow Outcome Scale Extended (GOSE) score at 12 months. The frequency and distribution of TAI was assessed qualitatively by visual reading. Univariable associations to outcome were assessed qualitatively by visual reading and also quantitatively with use of voxel-based lesion-symptom mapping (VLSM). The highest frequency of TAI was found in the posterior half of corpus callosum. The frequency of TAI was higher in the frontal and temporal lobes than in the parietal and occipital lobes, and in the upper parts of the brainstem than in the lower. At the group level, all voxels in mesencephalon had TAI on FLAIR. The patients with poorest outcome (GOSE scores ≤4) had higher frequencies of TAI. On VLSM, poor outcome was associated with TAI lesions bilaterally in the splenium, the right side of tectum, tegmental mesencephalon, and pons. In conclusion, we found higher frequency of TAI in posterior corpus callosum, and TAI in splenium, mesencephalon, and pons were associated with poor outcome. If lesion frequency distribution maps containing outcome information based on imaging findings from numerous patients in the future can be compared with the imaging findings from individual patients, it would offer a new tool in the clinical workup and outcome prediction of the patient with TBI.
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Affiliation(s)
- Anne-Mari Holte Flusund
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology, Møre and Romsdal Hospital Trust, Molde Hospital, Molde, Norway
| | - Lars Eirik Bø
- SINTEF Digital, Department of Health Research, Trondheim, Norway
| | - Ingerid Reinertsen
- SINTEF Digital, Department of Health Research, Trondheim, Norway
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ole Solheim
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Toril Skandsen
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Asta Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Nada Andelic
- Institute of Health and Society, Research Centre for Habilitation and Rehabilitation Models and Services (CHARM), Faculty of Medicine, University of Oslo, Oslo, Norway
- Department of Physical Medicine and Rehabilitation, Oslo University Hospital, Ullevål, Norway
| | - Anne Vik
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kent Gøran Moen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Radiology, Vestre Viken Hospital Trust, Drammen Hospital, Drammen, Norway
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5
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Staer K, Iranzo A, Stokholm MG, Hvingelby VS, Danielsen EH, Østergaard K, Serradell M, Otto M, Svendsen KB, Garrido A, Vilas D, Santamaria J, Møller A, Gaig C, Brooks DJ, Borghammer P, Tolosa E, Pavese N. Microglial Activation and Progression of Nigrostriatal Dysfunction in Isolated REM Sleep Behavior Disorder. Mov Disord 2024. [PMID: 38477376 DOI: 10.1002/mds.29767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 02/06/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Using 11 C-(R)-PK11195-PET, we found increased microglia activation in isolated REM sleep behavior disorder (iRBD) patients. Their role remains to be clarified. OBJECTIVES The objective is to assess relationships between activated microglia and progression of nigrostriatal dysfunction in iRBD. METHODS Fifteen iRBD patients previously scanned with 11 C-(R)-PK11195 and 18 F-DOPA-PET underwent repeat 18 F-DOPA-PET after 3 years. 18 F-DOPA Ki changes from baseline were evaluated with volumes-of-interest and voxel-based analyses. RESULTS Significant 18 F-DOPA Ki reductions were found in putamen and caudate. Reductions were larger and more widespread in patients with increased nigral microglia activation at baseline. Left nigral 11 C-(R)-PK11195 binding at baseline was a predictor of 18 F-DOPA Ki reduction in left caudate (coef = -0.0426, P = 0.016). CONCLUSIONS Subjects with increased baseline 11 C-(R)-PK11195 binding have greater changes in nigrostriatal function, suggesting a detrimental rather than protective effect of microglial activation. Alternatively, both phenomena occur in patients with prominent nigrostriatal dysfunction without a causative link. The clinical and therapeutic implications of these findings need further elucidation. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Kristian Staer
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
| | - Alex Iranzo
- Department of Neurology, Hospital Clínic de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Multidisciplinary Sleep Unit, Hospital Clinic, Barcelona, Spain
| | - Morten Gersel Stokholm
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Victor S Hvingelby
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine-Nuclear Medicine and PET, Aarhus University, Aarhus, Denmark
| | | | - Karen Østergaard
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark
| | - Mónica Serradell
- Department of Neurology, Hospital Clínic de Barcelona, Barcelona, Spain
- Multidisciplinary Sleep Unit, Hospital Clinic, Barcelona, Spain
| | - Marit Otto
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Alicia Garrido
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Dolores Vilas
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Joan Santamaria
- Department of Neurology, Hospital Clínic de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Multidisciplinary Sleep Unit, Hospital Clinic, Barcelona, Spain
| | - Arne Møller
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
| | - Carles Gaig
- Department of Neurology, Hospital Clínic de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Multidisciplinary Sleep Unit, Hospital Clinic, Barcelona, Spain
| | - David J Brooks
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
| | - Per Borghammer
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
| | - Eduardo Tolosa
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain
- Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Nicola Pavese
- Department of Nuclear Medicine & PET, Aarhus University Hospital, Aarhus, Denmark
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
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Gandia-Ferrero MT, Adrián-Ventura J, Cháfer-Pericás C, Alvarez-Sanchez L, Ferrer-Cairols I, Martinez-Sanchis B, Torres-Espallardo I, Baquero-Toledo M, Marti-Bonmati L. Relationship between neuroimaging and emotion recognition in mild cognitive impairment patients. Behav Brain Res 2024; 461:114844. [PMID: 38176615 DOI: 10.1016/j.bbr.2023.114844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/27/2023] [Accepted: 12/29/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE Dementia is a major public health problem with high needs for early detection, efficient treatment, and prognosis evaluation. Social cognition impairment could be an early dementia indicator and can be assessed with emotion recognition evaluation tests. The purpose of this study is to investigate the link between different brain imaging modalities and cognitive status in Mild Cognitive Impairment (MCI) patients, with the goal of uncovering potential physiopathological mechanisms based on social cognition performance. METHODS The relationship between the Reading the Mind in the Eyes Test (RMET) and some clinical and biochemical variables ([18 F]FDG PET-CT and anatomical MR parameters, neuropsychological evaluation, and CSF biomarkers) was studied in 166 patients with MCI by using a correlational approach. RESULTS The RMET correlated with neuropsychological variables, as well as with structural and functional brain parameters obtained from the MR and FDG-PET imaging evaluation. However, significant correlations between the RMET and CSF biomarkers were not found. DISCUSSION Different neuroimaging parameters were found to be related to an emotion recognition task in MCI. This analysis identified potential minimally-invasive biomarkers providing some knowledge about the physiopathological mechanisms in MCI.
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Affiliation(s)
- Maria Teresa Gandia-Ferrero
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Jesús Adrián-Ventura
- Department of Psychology and Sociology, University of Zaragoza, Atarazanas 4, 44003 Teruel, Spain
| | - Consuelo Cháfer-Pericás
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain.
| | - Lourdes Alvarez-Sanchez
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Neurology Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Inés Ferrer-Cairols
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Begoña Martinez-Sanchis
- Nuclear Medicine Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Irene Torres-Espallardo
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Nuclear Medicine Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Miquel Baquero-Toledo
- Grupo de investigación en Enfermedad de Alzheimer (GINEA), Instituto de Investigación Sanitaria La Fe (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Neurology Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
| | - Luis Marti-Bonmati
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, 46026 Valencia, Spain; Radiology Service, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, 46026 Valencia, Spain
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Mekki L, Acharya S, Ladra M, Lee J. Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy. J Appl Clin Med Phys 2024; 25:e14310. [PMID: 38373283 DOI: 10.1002/acm2.14310] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/29/2024] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
PURPOSE Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive functions are often not included. This paper introduces a novel automatic segmentation tool specifically designed for the unique challenges posed by pediatric patients undergoing brain RT, as well as its seamless integration into the existing clinical workflow. METHODS AND MATERIALS Images of 47 pediatric brain cancer patients aged 1 to 20 years old and 33 two-year-old healthy infants were used to train a vision transformer, UNesT, for the segmentation of five brain OARs. The trained model was then incorporated to clinical workflow via DICOM connections between a treatment planning system (TPS) and a server hosting the trained model such that scans are sent from TPS to the server, automatically segmented, and sent back to TPS for treatment planning. RESULTS The proposed automatic segmentation framework achieved a median dice similarity coefficient of 0.928 (frontal white matter), 0.908 (corpus callosum), 0.933 (hippocampi), 0.819 (temporal lobes), and 0.960 (brainstem) with a mean ± SD run time of 1.8 ± 0.67 s over 20 test cases. CONCLUSIONS The pediatric brain segmentation tool showed promising performance on five OARs linked to neurocognitive functions and can easily be extended for additional structures. The proposed integration to the clinic enables easy access to the tool from clinical platforms and minimizes disruption to existing workflow while maximizing its benefits.
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Affiliation(s)
- Lina Mekki
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sahaja Acharya
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Matthew Ladra
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
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Park JS, Fadnavis S, Garyfallidis E. Multi-scale V-net architecture with deep feature CRF layers for brain extraction. Commun Med (Lond) 2024; 4:29. [PMID: 38396078 PMCID: PMC10891085 DOI: 10.1038/s43856-024-00452-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Brain extraction is a computational necessity for researchers using brain imaging data. However, the complex structure of the interfaces between the brain, meninges and human skull have not allowed a highly robust solution to emerge. While previous methods have used machine learning with structural and geometric priors in mind, with the development of Deep Learning (DL), there has been an increase in Neural Network based methods. Most proposed DL models focus on improving the training data despite the clear gap between groups in the amount and quality of accessible training data between. METHODS We propose an architecture we call Efficient V-net with Additional Conditional Random Field Layers (EVAC+). EVAC+ has 3 major characteristics: (1) a smart augmentation strategy that improves training efficiency, (2) a unique way of using a Conditional Random Fields Recurrent Layer that improves accuracy and (3) an additional loss function that fine-tunes the segmentation output. We compare our model to state-of-the-art non-DL and DL methods. RESULTS Results show that even with limited training resources, EVAC+ outperforms in most cases, achieving a high and stable Dice Coefficient and Jaccard Index along with a desirable lower Surface (Hausdorff) Distance. More importantly, our approach accurately segmented clinical and pediatric data, despite the fact that the training dataset only contains healthy adults. CONCLUSIONS Ultimately, our model provides a reliable way of accurately reducing segmentation errors in complex multi-tissue interfacing areas of the brain. We expect our method, which is publicly available and open-source, to be beneficial to a wide range of researchers.
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Affiliation(s)
- Jong Sung Park
- Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, USA.
| | - Shreyas Fadnavis
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Mantovani DBA, Pitombeira MS, Schuck PN, de Araújo AS, Buchpiguel CA, de Paula Faria D, M da Silva AM. Evaluation of Non-Invasive Methods for (R)-[ 11C]PK11195 PET Image Quantification in Multiple Sclerosis. J Imaging 2024; 10:39. [PMID: 38392087 PMCID: PMC10889702 DOI: 10.3390/jimaging10020039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 11/24/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/24/2024] Open
Abstract
This study aims to evaluate non-invasive PET quantification methods for (R)-[11C]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[11C]PK11195 PET and magnetic resonance images. The total volume of distribution (VT) and distribution volume ratio (DVR) were measured in the gray matter, white matter, caudate nucleus, putamen, pallidum, thalamus, cerebellum, and brainstem using AIF, the image-derived input function (IDIF) from the carotid arteries, and pseudo-reference regions from supervised clustering analysis (SVCA). Uptake differences between MS and HC groups were tested using statistical tests adjusted for age and sex, and correlations between the results from the different quantification methods were also analyzed. Significant DVR differences were observed in the gray matter, white matter, putamen, pallidum, thalamus, and brainstem of MS patients when compared to the HC group. Also, strong correlations were found in DVR values between non-invasive methods and AIF (0.928 for IDIF and 0.975 for SVCA, p < 0.0001). On the other hand, (R)-[11C]PK11195 uptake could not be differentiated between MS patients and HC using VT values, and a weak correlation (0.356, p < 0.0001) was found between VTAIF and VTIDIF. Our study shows that the best alternative for AIF is using SVCA for reference region modeling, in addition to a cautious and appropriate methodology.
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Affiliation(s)
| | - Milena S Pitombeira
- Laboratory of Nuclear Medicine (LIM 43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 05403-911, Brazil
| | | | - Adriel S de Araújo
- Graduate Program in Computer Science, Pontificia Universidade Catolica do Rio Grande do Sul PUCRS, Porto Alegre 90619-900, Brazil
| | - Carlos Alberto Buchpiguel
- Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 05403-911, Brazil
- Laboratory of Nuclear Medicine (LIM 43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 05403-911, Brazil
| | - Daniele de Paula Faria
- Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 05403-911, Brazil
- Laboratory of Nuclear Medicine (LIM 43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 05403-911, Brazil
| | - Ana Maria M da Silva
- Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo 05403-911, Brazil
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van Dinther M, Hooghiemstra AM, Bron EE, Versteeg A, Leeuwis AE, Kalay T, Moonen JE, Kuipers S, Backes WH, Jansen JFA, van Osch MJP, Biessels G, Staals J, van Oostenbrugge RJ. Lower cerebral blood flow predicts cognitive decline in patients with vascular cognitive impairment. Alzheimers Dement 2024; 20:136-144. [PMID: 37491840 PMCID: PMC10917014 DOI: 10.1002/alz.13408] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION Chronic cerebral hypoperfusion is one of the assumed pathophysiological mechanisms underlying vascular cognitive impairment (VCI). We investigated the association between baseline cerebral blood flow (CBF) and cognitive decline after 2 years in patients with VCI and reference participants. METHODS One hundred eighty-one participants (mean age 66.3 ± 7.4 years, 43.6% women) underwent arterial spin labeling (ASL) magnetic resonance imaging (MRI) and neuropsychological assessment at baseline and at 2-year follow-up. We determined the association between baseline global and lobar CBF and cognitive decline with multivariable regression analysis. RESULTS Lower global CBF at baseline was associated with more global cognitive decline in VCI and reference participants. This association was most profound in the domain of attention/psychomotor speed. Lower temporal and frontal CBF at baseline were associated with more cognitive decline in memory. DISCUSSION Our study supports the role of hypoperfusion in the pathophysiological and clinical progression of VCI. HIGHLIGHTS Impaired cerebral blood flow (CBF) at baseline is associated with faster cognitive decline in VCI and normal aging. Our results suggest that low CBF precedes and contributes to the development of vascular cognitive impairment. CBF determined by ASL might be used as a biomarker to monitor disease progression or treatment responses in VCI.
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Affiliation(s)
- Maud van Dinther
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Astrid M. Hooghiemstra
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Esther E. Bron
- Department of Radiology & Nuclear MedicineErasmus MC—University Medical Center RotterdamRotterdamThe Netherlands
| | - Adriaan Versteeg
- Department of Radiology & Nuclear MedicineErasmus MC—University Medical Center RotterdamRotterdamThe Netherlands
| | - Anna E. Leeuwis
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
- Department of Old Age PsychiatryGGZ inGeestAmsterdamThe Netherlands
| | - Tugba Kalay
- Department of NeurologySt. Antonius ZiekenhuisNieuwegeinThe Netherlands
| | - Justine E. Moonen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMCVrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Sanne Kuipers
- Department of NeurologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology and Nuclear MedicineMaastricht University Medical CenterMaastrichtThe Netherlands
| | - Mathias J. P. van Osch
- C.J. Gorter MRI Center, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Geert‐Jan Biessels
- Department of NeurologyUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Julie Staals
- Department of NeurologyMaastricht University Medical CenterMaastrichtThe Netherlands
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11
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Jin SO, Mérida I, Stavropoulos I, Elwes RDC, Lam T, Guedj E, Girard N, Costes N, Hammers A. Characterisation of a novel [ 18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example. EJNMMI Res 2023; 13:98. [PMID: 37964137 PMCID: PMC10645721 DOI: 10.1186/s13550-023-01023-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 07/26/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21-78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23-65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13-48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12). RESULTS Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11). CONCLUSIONS As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation.
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Affiliation(s)
- Sameer Omer Jin
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- King's College London & Guy's and St Thomas' PET Centre, London, UK
| | - Inés Mérida
- Centre d'Etude et de Recherche Multimodale et Pluridisciplinaire en Imagerie du Vivant (CERMEP), Lyon, France
| | - Ioannis Stavropoulos
- Department of Clinical Neurophysiology, King's College Hospital, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Robert D C Elwes
- Department of Clinical Neurophysiology, King's College Hospital, London, UK
| | - Tanya Lam
- Children's Neuroscience Centre, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, UK
| | - Eric Guedj
- Nuclear Medicine Department, APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Aix Marseille University, Marseille, France
| | - Nadine Girard
- Department of Neuroradiology, APHM, CRMBM, UMR CNRS 7339, Timone Hospital, Aix Marseille University, Marseille, France
| | - Nicolas Costes
- Centre d'Etude et de Recherche Multimodale et Pluridisciplinaire en Imagerie du Vivant (CERMEP), Lyon, France
| | - Alexander Hammers
- Faculty of Life Sciences and Medicine, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
- King's College London & Guy's and St Thomas' PET Centre, London, UK.
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12
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Clementi L, Arnone E, Santambrogio MD, Franceschetti S, Panzica F, Sangalli LM. Anatomically compliant modes of variations: New tools for brain connectivity. PLoS One 2023; 18:e0292450. [PMID: 37934760 PMCID: PMC10629624 DOI: 10.1371/journal.pone.0292450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 09/20/2023] [Indexed: 11/09/2023] Open
Abstract
Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.
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Affiliation(s)
- Letizia Clementi
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- CHDS, Center for Health Data Science, Human Technopole, Milan, Italy
| | | | - Marco D. Santambrogio
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | | | | | - Laura M. Sangalli
- MOX - Department of Mathematics, Politecnico di Milano, Milan, Italy
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13
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Kitazaki Y, Ikawa M, Hamano T, Sasaki H, Yamaguchi T, Enomoto S, Shirafuji N, Hayashi K, Yamamura O, Tsujikawa T, Okazawa H, Kimura H, Nakamoto Y. Magnetic resonance imaging arterial spin labeling hypoperfusion with diffusion-weighted image hyperintensity is useful for diagnostic imaging of Creutzfeldt-Jakob disease. Front Neurol 2023; 14:1242615. [PMID: 37885479 PMCID: PMC10598551 DOI: 10.3389/fneur.2023.1242615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/14/2023] [Indexed: 10/28/2023] Open
Abstract
Background and objectives Magnetic resonance imaging with arterial spin labeling (ASL) perfusion imaging is a noninvasive method for quantifying cerebral blood flow (CBF). We aimed to evaluate the clinical utility of ASL perfusion imaging to aid in the diagnosis of Creutzfeldt-Jakob disease (CJD). Methods This retrospective study enrolled 10 clinically diagnosed with probable sporadic CJD (sCJD) based on the National CJD Research & Surveillance Unit and EuroCJD criteria and 18 healthy controls (HCs). Diffusion-weighted images (DWIs), CBF images obtained from ASL, N-isopropyl-(123I)-p-iodoamphetamine (123IMP)-single-photon emission computed tomography (SPECT) images, and 18F-fluorodeoxyglucose (18FDG)-positron emission tomography (PET) images were analyzed. First, the cortical values obtained using volume-of-interest (VOI) analysis were normalized using the global mean in each modality. The cortical regions were classified into DWI-High (≥ +1 SD) and DWI-Normal (< +1 SD) regions according to the DWI-intensity values. The normalized cortical values were compared between the two regions for each modality. Second, each modality value was defined as ASL hypoperfusion (< -1 SD), SPECT hypoperfusion (< -1 SD), and PET low accumulation (< -1 SD). The overall agreement rate of DWIs with ASL-CBF, SPECT, and PET was calculated. Third, regression analyses between the normalized ASL-CBF values and normalized SPECT or PET values derived from the VOIs were performed using a scatter plot. Results The mean values of ASL-CBF (N = 10), 123IMP-SPECT (N = 8), and 18FDG-PET (N = 3) in DWI-High regions were significantly lower than those in the DWI-Normal regions (p < 0.001 for all); however, HCs (N = 18) showed no significant differences in ASL-CBF between the two regions. The overall agreement rate of DWI (high or normal) with ASL-CBF (hypoperfusion or normal) (81.8%) was similar to that of SPECT (85.2%) and PET (78.5%) in CJD. The regression analysis showed that the normalized ASL-CBF values significantly correlated with the normalized SPECT (r = 0.44, p < 0.001) and PET values (r = 0.46, p < 0.001) in CJD. Discussion Patients with CJD showed ASL hypoperfusion in lesions with DWI hyperintensity, suggesting that ASL-CBF could be beneficial for the diagnostic aid of CJD.
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Affiliation(s)
- Yuki Kitazaki
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Masamichi Ikawa
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
- Biomedical Imaging Research Center, University of Fukui, Fukui, Japan
- Department of Advanced Medicine for Community Healthcare, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Tadanori Hamano
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
- Department of Aging and Dementia (DAD), University of Fukui, Fukui, Japan
- Life Science Innovation Center, University of Fukui, Fukui, Japan
| | - Hirohito Sasaki
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Tomohisa Yamaguchi
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Soichi Enomoto
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Norimichi Shirafuji
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Kouji Hayashi
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
- Department of Rehabilitation, Faculty of Health Science, Fukui Health Science University, Fukui, Japan
| | - Osamu Yamamura
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Tetsuya Tsujikawa
- Biomedical Imaging Research Center, University of Fukui, Fukui, Japan
| | - Hidehiko Okazawa
- Biomedical Imaging Research Center, University of Fukui, Fukui, Japan
| | - Hirohiko Kimura
- Department of Radiology, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
| | - Yasunari Nakamoto
- Second Department of Internal Medicine, Faculty of Medical Sciences, University of Fukui, Fukui, Japan
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14
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Hwang U, Kim SW, Jung D, Kim S, Lee H, Seo SW, Seong JK, Yoon S. Real-world prediction of preclinical Alzheimer's disease with a deep generative model. Artif Intell Med 2023; 144:102654. [PMID: 37783547 DOI: 10.1016/j.artmed.2023.102654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 10/04/2023]
Abstract
Amyloid positivity is an early indicator of Alzheimer's disease and is necessary to determine the disease. In this study, a deep generative model is utilized to predict the amyloid positivity of cognitively normal individuals using proxy measures, such as structural MRI scans, demographic variables, and cognitive scores, instead of invasive direct measurements. Through its remarkable efficacy in handling imperfect datasets caused by missing data or labels, and imbalanced classes, the model outperforms previous studies and widely used machine learning approaches with an AUROC of 0.8609. Furthermore, this study illuminates the model's adaptability to diverse clinical scenarios, even when feature sets or diagnostic criteria differ from the training data. We identify the brain regions and variables that contribute most to classification, including the lateral occipital lobes, posterior temporal lobe, and APOE ϵ4 allele. Taking advantage of deep generative models, our approach can not only provide inexpensive, non-invasive, and accurate diagnostics for preclinical Alzheimer's disease, but also meet real-world requirements for clinical translation of a deep learning model, including transferability and interpretability.
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Affiliation(s)
- Uiwon Hwang
- Division of Digital Healthcare, Yonsei University, Wonju, 26493, Republic of Korea
| | - Sung-Woo Kim
- Department of Bio-convergence Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Dahuin Jung
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - SeungWook Kim
- Department of Bio-convergence Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Hyejoo Lee
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea; Neuroscience Center, Samsung Medical Center, Seoul, 06351, Republic of Korea
| | - Joon-Kyung Seong
- Department of Artificial Intelligence, Korea University, Seoul, 02841, Republic of Korea; School of Biomedical Engineering, Korea University, Seoul, 02841, Republic of Korea; Interdisciplinary Program in Precision Public Health, College of Health Science, Korea University, Seoul, 02841, Republic of Korea.
| | - Sungroh Yoon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea; Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, 08826, Republic of Korea.
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15
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Gandia-Ferrero MT, Torres-Espallardo I, Martínez-Sanchis B, Morera-Ballester C, Muñoz E, Sopena-Novales P, González-Pavón G, Martí-Bonmatí L. Objective Image Quality Comparison Between Brain-Dedicated PET and PET/CT Scanners. J Med Syst 2023; 47:88. [PMID: 37589893 DOI: 10.1007/s10916-023-01984-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 08/02/2023] [Indexed: 08/18/2023]
Abstract
As part of a clinical validation of a new brain-dedicated PET system (CMB), image quality of this scanner has been compared to that of a whole-body PET/CT scanner. To that goal, Hoffman phantom and patient data were obtined with both devices. Since CMB does not use a CT for attenuation correction (AC) which is crucial for PET images quality, this study includes the evaluation of CMB PET images using emission-based or CT-based attenuation maps. PET images were compared using 34 image quality metrics. Moreover, a neural network was used to evaluate the degree of agreement between both devices on the patients diagnosis prediction. Overall, results showed that CMB images have higher contrast and recovery coefficient but higher noise than PET/CT images. Although SUVr values presented statistically significant differences in many brain regions, relative differences were low. An asymmetry between left and right hemispheres, however, was identified. Even so, the variations between the two devices were minor. Finally, there is a greater similarity between PET/CT and CMB CT-based AC PET images than between PET/CT and the CMB emission-based AC PET images.
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Affiliation(s)
- Maria Teresa Gandia-Ferrero
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain.
| | - Irene Torres-Espallardo
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain
- Nuclear Medicine Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
| | - Begoña Martínez-Sanchis
- Nuclear Medicine Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
| | | | - Enrique Muñoz
- Oncovision, Carrer de Jeroni de Montsoriu, 92, València, 46022, Spain
| | - Pablo Sopena-Novales
- Nuclear Medicine Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
| | | | - Luis Martí-Bonmatí
- Biomedical Imaging Research Group (GIBI230), La Fe Health Research Institute (IIS La Fe), Avenida Fernando Abril Martorell, València, 46026, Spain
- Radiology Department, La Fe University and Polytechnic Hospital, Avenida Fernando Abril Martorell, València, 46026, Spain
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16
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Kojima K, Liu C, Ehrlich S, Kline-Fath BM, Jain S, Parikh NA. Early surgery in very preterm infants is associated with brain abnormalities on term MRI: a propensity score analysis. J Perinatol 2023; 43:877-883. [PMID: 36966211 PMCID: PMC10382249 DOI: 10.1038/s41372-023-01645-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/22/2023] [Accepted: 03/07/2023] [Indexed: 03/27/2023]
Abstract
OBJECTIVE To investigate the association between exposure to surgery under general anesthesia and brain abnormalities and neurodevelopmental outcomes in very preterm infants. STUDY DESIGN This prospective observational study includes 392 infants born at or below 32 weeks' gestational age. Participants completed brain MRI at term-equivalent age and Bayley-III assessment at 2 years corrected age. We evaluated the independent effects of surgery on brain MRI abnormalities and neurodevelopmental outcomes after propensity score matching. RESULTS All infants completed brain MRI, and 341 (87%) completed neurodevelopmental testing. Forty-five received surgery. Surgery was associated with worse MRI abnormalities (p < 0.0001) but with none of the developmental outcomes after propensity score matching. The global brain abnormality score was associated with the Bayley Cognitive (p = 0.005) and Motor (p = 0.028) composite scores. CONCLUSIONS Very preterm infants exposed to surgery under general anesthesia were at higher risk of brain abnormalities on MRI at term.
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Affiliation(s)
- Katsuaki Kojima
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
| | - Chunyan Liu
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA
| | - Shelley Ehrlich
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA
| | - Beth M Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA
| | - Shipra Jain
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229-3039, USA.
- Department of Pediatrics, University of Cincinnati College of Medicine, 3230 Eden Avenue, Cincinnati, OH, 45267, USA.
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17
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Green MA, Crawford JL, Kuhnen CM, Samanez-Larkin GR, Seaman KL. Multivariate associations between dopamine receptor availability and risky investment decision-making across adulthood. Cereb Cortex Commun 2023; 4:tgad008. [PMID: 37255569 PMCID: PMC10225308 DOI: 10.1093/texcom/tgad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/08/2023] [Accepted: 05/10/2023] [Indexed: 06/01/2023] Open
Abstract
Enhancing dopamine increases financial risk taking across adulthood but it is unclear whether baseline individual differences in dopamine function are related to risky financial decisions. Here, thirty-five healthy adults completed an incentive-compatible risky investment decision task and a PET scan at rest using [11C]FLB457 to assess dopamine D2-like receptor availability. Participants made choices between a safe asset (bond) and a risky asset (stock) with either an expected value less than the bond ("bad stock") or expected value greater than the bond ("good stock"). Five measures of behavior (choice inflexibility, risk seeking, suboptimal investment) and beliefs (absolute error, optimism) were computed and D2-like binding potential was extracted from four brain regions of interest (midbrain, amygdala, anterior cingulate, insula). We used canonical correlation analysis to evaluate multivariate associations between decision-making and dopamine function controlling for age. Decomposition of the first dimension (r = 0.76) revealed that the strongest associations were between measures of choice inflexibility, incorrect choice, optimism, amygdala binding potential, and age. Follow-up univariate analyses revealed that amygdala binding potential and age were both independently associated with choice inflexibility. The findings suggest that individual differences in dopamine function may be associated with financial risk taking in healthy adults.
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Affiliation(s)
- Mikella A Green
- Department of Psychology & Neuroscience, 417 Chapel Dr, Durham, NC 27708, Center for Cognitive Neuroscience, Duke University, 308 Research Drive, Durham, NC 27708
| | - Jennifer L Crawford
- Department of Psychology, Brandeis University, 415 South Street, Waltham, MA 02453
| | - Camelia M Kuhnen
- UNC Kenan-Flagler Business School, 300 Kenan Center Drive, Chapel Hill, NC 27599, National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA 02138
| | - Gregory R Samanez-Larkin
- Department of Psychology & Neuroscience, 417 Chapel Dr, Durham, NC 27708, Center for Cognitive Neuroscience, Duke University, 308 Research Drive, Durham, NC 27708
| | - Kendra L Seaman
- Department of Psychology, University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080-3021, Center for Vital Longevity, University of Texas at Dallas, 1600 Viceroy Drive, Suite 800, Dallas, TX 75235
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18
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Tada T, Hara K, Fujita N, Ito Y, Yamaguchi H, Ohdake R, Kawabata K, Ogura A, Kato T, Yokoi T, Masuda M, Abe S, Miyao S, Naganawa S, Katsuno M, Watanabe H, Sobue G, Kato K. Comparative examination of the pons and corpus callosum as reference regions for quantitative evaluation in positron emission tomography imaging for Alzheimer's disease using 11C-Pittsburgh Compound-B. Ann Nucl Med 2023:10.1007/s12149-023-01843-y. [PMID: 37160863 DOI: 10.1007/s12149-023-01843-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 12/18/2022] [Accepted: 04/24/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVES Standardised uptake value ratio (SUVR) is usually obtained by dividing the SUV of the region of interest (ROI) by that of the cerebellar cortex. Cerebellar cortex is not a valid reference in cases where amyloid β deposition or lesions are present. Only few studies have evaluated the use of other regions as references. We compared the validity of the pons and corpus callosum as reference regions for the quantitative evaluation of brain positron emission tomography (PET) using 11C-PiB compared to the cerebellar cortex. METHODS We retrospectively evaluated data from 86 subjects with or without Alzheimer's disease (AD). All subjects underwent magnetic resonance imaging, PET imaging, and cognitive function testing. For the quantitative analysis, three-dimensional ROIs were automatically placed, and SUV and SUVR were obtained. We compared these values between AD and healthy control (HC) groups. RESULTS SUVR data obtained using the pons and corpus callosum as reference regions strongly correlated with that using the cerebellar cortex. The sensitivity and specificity were high when either the pons or corpus callosum was used as the reference region. However, the SUV values of the corpus callosum were different between AD and HC (p < 0.01). CONCLUSIONS Our data suggest that the pons and corpus callosum might be valid reference regions.
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Affiliation(s)
- Tomohiro Tada
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Naotoshi Fujita
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Yoshinori Ito
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan
| | - Hiroshi Yamaguchi
- Nagoya University Radioisotope Research Center Medical Branch, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Reiko Ohdake
- Department of Neurology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
| | - Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Medical University of Innsbruck, Innrain 52, 6020, Innsbruck, Austria
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Anjo Kosei Hospital, 28 Higashihirokute Anjo-Cho, Anjo, 446-8602, Japan
| | - Takamasa Yokoi
- Department of Neurology, Toyohashi Municipal Hospital, 50 Hachikennishi, Aotake-Cho, Toyohashi, 441-8570, Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Neurology, Okazaki City Hospital, 1-3 Gosyoai, Kouryuji-Cho, Okazaki, 444-8553, Japan
| | - Shinji Abe
- Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Shinichi Miyao
- Department of Neurology, Meitetsu Hospital, 2-26-11 Sakou, Nishiku, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8550, Japan
- Department of Clinical Research Education, Nagoya University Graduate School of Medicine, 65 Tsurumai-Cho, Showa-Ku, Nagoya, 466-8560, Japan
| | - Hirohisa Watanabe
- Department of Neurology, Fujita Health University School of Medicine, 1-98 Dengakugakubo, Kutsukake-Cho, Toyoake, Aichi, 470-1192, Japan
| | - Gen Sobue
- Aichi Medical University, 1-1 Yazakokarimata, Nagakute, Japan
| | - Katsuhiko Kato
- Functional Medical Imaging, Biomedical Imaging Sciences, Division of Advanced Information Health Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-Ku, Nagoya, 461-8673, Japan.
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19
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Park S, Hong CH, Lee DG, Park K, Shin H. Prospective classification of Alzheimer's disease conversion from mild cognitive impairment. Neural Netw 2023; 164:335-344. [PMID: 37163849 DOI: 10.1016/j.neunet.2023.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 11/03/2022] [Revised: 02/26/2023] [Accepted: 04/12/2023] [Indexed: 05/12/2023]
Abstract
Alzheimer's disease (AD) is emerging as a serious problem with the rapid aging of the population, but due to the unclear cause of the disease and the absence of therapy, appropriate preventive measures are the next best thing. For this reason, it is important to early detect whether the disease converts from mild cognitive impairment (MCI) which is a prodromal phase of AD. With the advance in brain imaging techniques, various machine learning algorithms have become able to predict the conversion from MCI to AD by learning brain atrophy patterns. However, at the time of diagnosis, it is difficult to distinguish between the conversion group and the non-conversion group of subjects because the difference between groups is small, but the within-group variability is large in brain images. After a certain period of time, the subjects of conversion group show significant brain atrophy, whereas subjects of non-conversion group show only subtle changes due to the normal aging effect. This difference on brain atrophy makes the brain images more discriminative for learning. Motivated by this, we propose a method to perform classification by projecting brain images into the future, namely prospective classification. The experiments on the Alzheimer's Disease Neuroimaging Initiative dataset show that the prospective classification outperforms ordinary classification. Moreover, the features of prospective classification indicate the brain regions that significantly influence the conversion from MCI to AD.
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Affiliation(s)
- Sunghong Park
- Department of Psychiatry, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Chang Hyung Hong
- Department of Psychiatry, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Dong-Gi Lee
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kanghee Park
- Korea Institute of Science and Technology Information, Seoul, 02456, Republic of Korea
| | - Hyunjung Shin
- Department of Artificial Intelligence, Ajou University, Suwon, 16499, Republic of Korea; Department of Industrial Engineering, Ajou University, Suwon, 16499, Republic of Korea.
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20
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Chen Y, Yue H, Kuang H, Wang J. RBS-Net: Hippocampus segmentation using multi-layer feature learning with the region, boundary and structure loss. Comput Biol Med 2023; 160:106953. [PMID: 37120987 DOI: 10.1016/j.compbiomed.2023.106953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 10/19/2022] [Revised: 04/10/2023] [Accepted: 04/15/2023] [Indexed: 05/02/2023]
Abstract
Hippocampus has great influence over the Alzheimer's disease (AD) research because of its essential role as a biomarker in the human brain. Thus the performance of hippocampus segmentation influences the development of clinical research for brain disorders. Deep learning using U-net-like networks becomes prevalent in hippocampus segmentation on Magnetic Resonance Imaging (MRI) due to its efficiency and accuracy. However, current methods lose sufficient detailed information during pooling, which hinders the segmentation results. And weak supervision on the details like edges or positions results in fuzzy and coarse boundary segmentation, causing great differences between the segmentation and ground-truth. In view of these drawbacks, we propose a Region-Boundary and Structure Net (RBS-Net), which consists of a primary net and an auxiliary net. (1) Our primary net focuses on the region distribution of hippocampus and introduces a distance map for boundary supervision. Furthermore the primary net adds a multi-layer feature learning module to compensate the information loss during pooling and strengthen the differences between the foreground and background, improving the region and boundary segmentation. (2) The auxiliary net concentrates on the structure similarity and also utilizes the multi-layer feature learning module, and this parallel task can refine encoders by similarizing the structure of the segmentation and ground-truth. We train and test our network using 5-fold cross-validation on HarP, a public available hippocampus dataset. Experimental results demonstrate that our proposed RBS-Net achieves a Dice of 89.76% in average, outperforming several state-of-the-art hippocampus segmentation methods. Furthermore, in few shot circumstances, our proposed RBS-Net achieves better results in terms of a comprehensive evaluation compared to several state-of-the-art deep learning-based methods. Finally we can observe that visual segmentation results for the boundary and detailed regions are improved by our proposed RBS-Net.
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Affiliation(s)
- Yu Chen
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Hailin Yue
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Hulin Kuang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.
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21
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Steinbart D, Yaakub SN, Steinbrenner M, Guldin LS, Holtkamp M, Keller SS, Weber B, Rüber T, Heckemann RA, Ilyas-Feldmann M, Hammers A. Automatic and manual segmentation of the piriform cortex: Method development and validation in patients with temporal lobe epilepsy and Alzheimer's disease. Hum Brain Mapp 2023; 44:3196-3209. [PMID: 37052063 PMCID: PMC10171523 DOI: 10.1002/hbm.26274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 02/10/2023] [Accepted: 02/24/2023] [Indexed: 04/14/2023] Open
Abstract
The piriform cortex (PC) is located at the junction of the temporal and frontal lobes. It is involved physiologically in olfaction as well as memory and plays an important role in epilepsy. Its study at scale is held back by the absence of automatic segmentation methods on MRI. We devised a manual segmentation protocol for PC volumes, integrated those manually derived images into the Hammers Atlas Database (n = 30) and used an extensively validated method (multi-atlas propagation with enhanced registration, MAPER) for automatic PC segmentation. We applied automated PC volumetry to patients with unilateral temporal lobe epilepsy with hippocampal sclerosis (TLE; n = 174 including n = 58 controls) and to the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 151, of whom with mild cognitive impairment (MCI), n = 71; Alzheimer's disease (AD), n = 33; controls, n = 47). In controls, mean PC volume was 485 mm3 on the right and 461 mm3 on the left. Automatic and manual segmentations overlapped with a Jaccard coefficient (intersection/union) of ~0.5 and a mean absolute volume difference of ~22 mm3 in healthy controls, ~0.40/ ~28 mm3 in patients with TLE, and ~ 0.34/~29 mm3 in patients with AD. In patients with TLE, PC atrophy lateralised to the side of hippocampal sclerosis (p < .001). In patients with MCI and AD, PC volumes were lower than those of controls bilaterally (p < .001). Overall, we have validated automatic PC volumetry in healthy controls and two types of pathology. The novel finding of early atrophy of PC at the stage of MCI possibly adds a novel biomarker. PC volumetry can now be applied at scale.
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Affiliation(s)
- David Steinbart
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
| | - Siti N Yaakub
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
- School of Psychology, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Mirja Steinbrenner
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
| | - Lynn S Guldin
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
| | - Martin Holtkamp
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Department of Neuroradiology, The Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Bernd Weber
- Center for Economics and Neuroscience, University of Bonn, Bonn, Germany
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Theodor Rüber
- Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Bonn, Germany
| | - Rolf A Heckemann
- Department of Medical Radiation Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Maria Ilyas-Feldmann
- Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt-Universität zu Berlin, Department of Neurology, Epilepsy-Center Berlin-Brandenburg, Berlin, Germany
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, St Thomas' Hospital, London, UK
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22
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Perszyk EE, Davis XS, Djordjevic J, Jones-Gotman M, Trinh J, Hutelin Z, Veldhuizen MG, Koban L, Wager TD, Kober H, Small DM. Odor imagery but not perception drives risk for food cue reactivity and increased adiposity. bioRxiv 2023:2023.02.06.527292. [PMID: 36798231 PMCID: PMC9934556 DOI: 10.1101/2023.02.06.527292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Mental imagery has been proposed to play a critical role in the amplification of cravings. Here we tested whether olfactory imagery drives food cue reactivity strength to promote adiposity in 45 healthy individuals. We measured odor perception, odor imagery ability, and food cue reactivity using self-report, perceptual testing, and neuroimaging. Adiposity was assessed at baseline and one year later. Brain responses to real and imagined odors were analyzed with univariate and multivariate decoding methods to identify pattern-based olfactory codes. We found that the accuracy of decoding imagined, but not real, odor quality correlated with a perceptual measure of odor imagery ability and with greater adiposity changes. This latter relationship was mediated by cue-potentiated craving and intake. Collectively, these findings establish odor imagery ability as a risk factor for weight gain and more specifically as a mechanism by which exposure to food cues promotes craving and overeating.
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Affiliation(s)
- Emily E. Perszyk
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Xue S. Davis
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Jelena Djordjevic
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
| | - Marilyn Jones-Gotman
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montréal, QC H3A 2B4, Canada
| | - Jessica Trinh
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Zach Hutelin
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Maria G. Veldhuizen
- Department of Anatomy, Faculty of Medicine, Mersin University, Ciftlikkoy Campus, Mersin 33343, Turkey
| | - Leonie Koban
- Lyon Neuroscience Research Center (CRNL), CNRS, INSERM, University Claude Bernard Lyon 1, France
| | - Tor D. Wager
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - Hedy Kober
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Department of Psychology, Yale University, New Haven, CT 06511, USA
| | - Dana M. Small
- Modern Diet and Physiology Research Center, New Haven, CT 06510, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
- Department of Psychology, Yale University, New Haven, CT 06511, USA
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23
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Janet R, Costes N, Mérida I, Derrington E, Dreher JC. Relationships between serotonin availability and frontolimbic response to fearful and threatening faces. Sci Rep 2023; 13:1558. [PMID: 36707612 PMCID: PMC9883493 DOI: 10.1038/s41598-023-28667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 01/23/2023] [Indexed: 01/29/2023] Open
Abstract
Serotonin is a critical neurotransmitter in the regulation of emotional behavior. Although emotion processing is known to engage a corticolimbic circuit, including the amygdala and prefrontal cortex, exactly how this brain system is modulated by serotonin remains unclear. Here, we hypothesized that serotonin modulates variability in excitability and functional connectivity within this circuit. We tested whether this modulation contributes to inter-individual differences in emotion processing. Using a multimodal neuroimaging approach with a simultaneous PET-3T fMRI scanner, we simultaneously acquired BOLD signal while participants viewed emotional faces depicting fear and anger, while also measuring serotonin transporter (SERT) levels, regulating serotonin functions. Individuals with higher activity of the medial amygdala BOLD in response to fearful or angry facial expressions, who were temperamentally more anxious, also exhibited lower SERT availability in the dorsal raphe nucleus (DRN). Moreover, higher connectivity of the medial amygdala with the left dorsolateral prefrontal and the anterior cingulate cortex was associated with lower levels of SERT availability in the DRN. These results demonstrate the association between the serotonin transporter level and emotion processing through changes in functional interactions between the amygdala and the prefrontal areas in healthy humans.
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Affiliation(s)
- R Janet
- CNRS-Institut de Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, Reward, and Decision Making Laboratory, Lyon, France
| | - N Costes
- CERMEP-Imagerie du vivant, Lyon, France
| | - I Mérida
- CERMEP-Imagerie du vivant, Lyon, France
| | - E Derrington
- CNRS-Institut de Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, Reward, and Decision Making Laboratory, Lyon, France
| | - J C Dreher
- CNRS-Institut de Sciences Cognitives Marc Jeannerod, UMR5229, Neuroeconomics, Reward, and Decision Making Laboratory, Lyon, France.
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24
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Zatcepin A, Kopczak A, Holzgreve A, Hein S, Schindler A, Duering M, Kaiser L, Lindner S, Schidlowski M, Bartenstein P, Albert N, Brendel M, Ziegler SI. Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients. Z Med Phys 2023:S0939-3889(22)00128-3. [PMID: 36682921 DOI: 10.1016/j.zemedi.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 08/03/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 01/21/2023]
Abstract
INTRODUCTION Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, the gold standard TSPO PET quantification method includes a 90 min scan and continuous arterial blood sampling, which is challenging to perform on a routine basis. In this work, we determine what information is required for a simplified quantification approach using a machine learning algorithm. MATERIALS AND METHODS We analyzed data from 18 patients with ischemic stroke who received 0-90 min [18F]GE-180 PET as well as T1-weigted (T1w), FLAIR, and arterial spin labeling (ASL) MRI scans. During PET scans, five manual venous blood samples at 5, 15, 30, 60, and 85 min post injection (p.i.) were drawn, and plasma activity concentration was measured. Total distribution volume (VT) was calculated using Logan plot with the full dynamic PET and an image-derived input function (IDIF) from the carotid arteries. IDIF was scaled by a calibration factor derived from all the measured plasma activity concentrations. The calculated VT values were used for training a random forest regressor. As input features for the model, we used three late PET frames (60-70, 70-80, and 80-90 min p.i.), the ASL image reflecting perfusion, the voxel coordinates, the lesion mask, and the five plasma activity concentrations. The algorithm was validated with the leave-one-out approach. To estimate the impact of the individual features on the algorithm's performance, we used Shapley Additive Explanations (SHAP). Having determined that the three late PET frames and the plasma activity concentrations were the most important features, we tested a simplified quantification approach consisting of dividing a late PET frame by a plasma activity concentration. All the combinations of frames/samples were compared by means of concordance correlation coefficient and Bland-Altman plots. RESULTS When using all the input features, the algorithm predicted VT values with high accuracy (87.8 ± 8.3%) for both lesion and non-lesion voxels. The SHAP values demonstrated high impact of the late PET frames (60-70, 70-80, and 80-90 min p.i.) and plasma activity concentrations on the VT prediction, while the influence of the ASL-derived perfusion, voxel coordinates, and the lesion mask was low. Among all the combinations of the late PET frames and plasma activity concentrations, the 70-80 min p.i. frame divided by the 30 min p.i. plasma sample produced the closest VT estimate in the ischemic lesion. CONCLUSION Reliable TSPO PET quantification is achievable by using a single late PET frame divided by a late blood sample activity concentration.
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Affiliation(s)
- Artem Zatcepin
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Adrien Holzgreve
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Sandra Hein
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Andreas Schindler
- Department of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany; Medical Image Analysis Center (MIAC) & Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Lena Kaiser
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Simon Lindner
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Martin Schidlowski
- Department of Epileptology, University Hospital Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Nathalie Albert
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Sibylle I Ziegler
- Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany
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25
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Courault P, Lancelot S, Costes N, Colom M, Le Bars D, Redoute J, Gobert F, Dailler F, Isal S, Iecker T, Newman-Tancredi A, Merida I, Zimmer L. [ 18F]F13640: a selective agonist PET radiopharmaceutical for imaging functional 5-HT 1A receptors in humans. Eur J Nucl Med Mol Imaging 2023; 50:1651-1664. [PMID: 36656363 PMCID: PMC10119077 DOI: 10.1007/s00259-022-06103-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 05/23/2022] [Accepted: 12/27/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE F13640 (a.k.a. befiradol, NLX-112) is a highly selective 5-HT1A receptor ligand that was selected as a PET radiopharmaceutical-candidate based on animal studies. Due to its high efficacy agonist properties, [18F]F13640 binds preferentially to functional 5-HT1A receptors, which are coupled to intracellular G-proteins. Here, we characterize brain labeling of 5-HT1A receptors by [18F]F13640 in humans and describe a simplified model for its quantification. METHODS PET/CT and PET-MRI scans were conducted in a total of 13 healthy male volunteers (29 ± 9 years old), with arterial input functions (AIF) (n = 9) and test-retest protocol (n = 8). Several kinetic models were compared (one tissue compartment model, two-tissue compartment model, and Logan); two models with reference region were also evaluated: simplified reference tissue model (SRTM) and the logan reference model (LREF). RESULTS [18F]F13640 showed high uptake values in raphe nuclei and cortical regions. SRTM and LREF models showed a very high correlation with kinetic models using AIF. As concerns test-retest parameters and the prolonged binding kinetics of [18F]F13640, better reproducibility, and reliability were found with the LREF method. Cerebellum white matter and frontal lobe white matter stand out as suitable reference regions. CONCLUSION The favorable brain labeling and kinetic profile of [18F]F13640, its high receptor specificity and its high efficacy agonist properties open new perspectives for studying functionally active 5-HT1A receptors, unlike previous radiopharmaceuticals that act as antagonists. [18F]F13640's kinetic properties allow injection outside of the PET scanner with delayed acquisitions, facilitating the design of innovative longitudinal protocols in neurology and psychiatry. TRIAL REGISTRATION Trial Registration EudraCT 2017-002,722-21.
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Affiliation(s)
- Pierre Courault
- Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon Neuroscience Research Center, Lyon, France.,Hospices Civils de Lyon (HCL), Lyon, France
| | - Sophie Lancelot
- Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon Neuroscience Research Center, Lyon, France.,Hospices Civils de Lyon (HCL), Lyon, France.,CERMEP, Bron, France
| | - Nicolas Costes
- Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon Neuroscience Research Center, Lyon, France.,CERMEP, Bron, France
| | | | - Didier Le Bars
- Hospices Civils de Lyon (HCL), Lyon, France.,CERMEP, Bron, France
| | | | - Florent Gobert
- Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon Neuroscience Research Center, Lyon, France.,Hospices Civils de Lyon (HCL), Lyon, France
| | | | - Sibel Isal
- Hospices Civils de Lyon (HCL), Lyon, France
| | | | | | | | - Luc Zimmer
- Université Claude Bernard Lyon 1, CNRS, INSERM, Lyon Neuroscience Research Center, Lyon, France. .,Hospices Civils de Lyon (HCL), Lyon, France. .,CERMEP, Bron, France.
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Parekh P, Vivek Bhalerao G, John JP, Venkatasubramanian G. Sample size requirement for achieving multisite harmonization using structural brain MRI features. Neuroimage 2022; 264:119768. [PMID: 36435343 PMCID: PMC7615107 DOI: 10.1016/j.neuroimage.2022.119768] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022] Open
Abstract
When data is pooled across multiple sites, the extracted features are confounded by site effects. Harmonization methods attempt to correct these site effects while preserving the biological variability within the features. However, little is known about the sample size requirement for effectively learning the harmonization parameters and their relationship with the increasing number of sites. In this study, we performed experiments to find the minimum sample size required to achieve multisite harmonization (using neuroHarmonize) using volumetric and surface features by leveraging the concept of learning curves. Our first two experiments show that site-effects are effectively removed in a univariate and multivariate manner; however, it is essential to regress the effect of covariates from the harmonized data additionally. Our following two experiments with actual and simulated data showed that the minimum sample size required for achieving harmonization grows with the increasing average Mahalanobis distances between the sites and their reference distribution. We conclude by positing a general framework to understand the site effects using the Mahalanobis distance. Further, we provide insights on the various factors in a cross-validation design to achieve optimal inter-site harmonization.
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Affiliation(s)
- Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Gaurav Vivek Bhalerao
- Translational Psychiatry Lab, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Department of Psychiatry, University of Oxford, United Kingdom
| | - John P John
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India.
| | - G Venkatasubramanian
- Translational Psychiatry Lab, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; ADBS Neuroimaging Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India; Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India.
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Hinge C, Henriksen OM, Lindberg U, Hasselbalch SG, Højgaard L, Law I, Andersen FL, Ladefoged CN. A zero-dose synthetic baseline for the personalized analysis of 2-Deoxy-2-[18F]fluoroglucose: Application in Alzheimer’s disease. Front Neurosci 2022; 16:1053783. [DOI: 10.3389/fnins.2022.1053783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
PurposeBrain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer’s disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient’s own MRI, and showcase its applicability in detecting AD pathology.MethodsWe included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities.ResultsThe model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects.ConclusionThis work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.
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Jain VG, Kline JE, He L, Kline-Fath BM, Altaye M, Muglia LJ, DeFranco EA, Ambalavanan N, Parikh NA. Acute histologic chorioamnionitis independently and directly increases the risk for brain abnormalities seen on magnetic resonance imaging in very preterm infants. Am J Obstet Gynecol 2022; 227:623.e1-623.e13. [PMID: 35644247 PMCID: PMC10008527 DOI: 10.1016/j.ajog.2022.05.042] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 05/19/2022] [Accepted: 05/19/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND The independent risk for neurodevelopmental impairments attributed to chorioamnionitis in premature infants remains controversial. Delayed brain maturation or injury identified on magnetic resonance imaging at term-equivalent age can be used as a surrogate measure of neurodevelopmental impairments that is less confounded by postdelivery neonatal intensive care unit environmental factors to investigate this relationship more clearly. OBJECTIVE This study aimed to determine whether preterm infants born with moderate to severe acute histologic chorioamnionitis would have a higher magnetic resonance imaging-determined global brain abnormality score, independent of early premature birth, when compared with preterm infants with no or mild chorioamnionitis. STUDY DESIGN This was a prospective, multicenter cohort study involving infants born very prematurely ≤32 weeks' gestational age with acute moderate to severe histologic chorioamnionitis, graded using standard histologic criteria. Brain abnormalities were diagnosed and scored using a well-characterized, standardized scoring system captured using a high-resolution 3 Tesla magnetic resonance imaging research magnet. In secondary analyses, total brain volume and 4 magnetic resonance imaging metrics of cortical maturation (cortical surface area, sulcal depth, gyral index, and inner cortical curvature) were calculated using an automated algorithm and correlated with chorioamnionitis. The association of funisitis (any grade) with brain abnormalities was also explored. We investigated if premature birth mediated the relationship between histologic chorioamnionitis and brain abnormality score using mediation analysis. RESULTS Of 353 very preterm infants, 297 infants had mild or no chorioamnionitis (controls), and 56 were diagnosed with moderate to severe acute histologic chorioamnionitis. The primary outcome brain abnormality score was significantly higher in histologic chorioamnionitis-exposed infants than in the controls (median, 4 vs 7; P<.001). Infants with acute histologic chorioamnionitis had significantly lower brain tissue volume (P=.03) and sulcal depth (P=.04), whereas other morphometric indices did not differ statistically. In the multiple regression analysis, we observed persistent significant relationships between moderate to severe acute histologic chorioamnionitis and brain abnormality scores (β=2.84; 1.51-4.16; P<.001), total brain volume (P=.03), and sulcal depth (P=.02). Funisitis was also significantly associated with brain abnormality score after adjustment for clinical confounders (P=.005). Mediation analyses demonstrated that 50% of brain abnormalities was an indirect consequence of premature birth, and the remaining 50% was a direct effect of moderate to severe acute histologic chorioamnionitis when compared with preterm infants with no or mild chorioamnionitis exposure. Examining gestational age as a mediator, funisitis did not exert a significant direct effect on brain abnormalities after the significant indirect effects of preterm birth were accounted for. CONCLUSION Acute histologic chorioamnionitis increases the risk for brain injury and delayed maturation, both directly and indirectly, by inducing premature birth.
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Affiliation(s)
- Viral G Jain
- Division of Neonatology, Department of Pediatrics, The University of Alabama at Birmingham, Birmingham, AL; Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Julia E Kline
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Lili He
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Beth M Kline-Fath
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Mekibib Altaye
- Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Louis J Muglia
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Burroughs Wellcome Fund, Research Triangle Park, NC; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Emily A DeFranco
- Department of Obstetrics & Gynecology, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Namasivayam Ambalavanan
- Division of Neonatology, Department of Pediatrics, The University of Alabama at Birmingham, Birmingham, AL
| | - Nehal A Parikh
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Center for Prevention of Neurodevelopmental Disorders, Cincinnati Children's Hospital Medical Center, Cincinnati, OH.
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Douros IK, Xie Y, Dourou C, Isaieva K, Vuissoz P, Felblinger J, Laprie Y. 3D Dynamic Spatiotemporal Atlas of the Vocal Tract during Consonant–Vowel Production from 2D Real Time MRI. J Imaging 2022; 8:227. [PMID: 36135393 PMCID: PMC9504642 DOI: 10.3390/jimaging8090227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 11/21/2022] Open
Abstract
In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps of the proposed method are the temporal alignment of the real-time MR images acquired in several sagittal planes and their combination with adaptive kernel regression. As a preprocessing step, a reference space was created to be used in order to remove anatomical information of the speakers and keep only the variability in speech production for the construction of the atlas. The adaptive kernel regression makes the choice of atlas time points independently of the time points of the frames that are used as an input for the construction. The evaluation of this atlas construction method was made by mapping two new speakers to the atlas and by checking how similar the resulting mapped images are. The use of the atlas helps in reducing subject variability. The results show that the use of the proposed atlas can capture the dynamic behavior of the articulators and is able to generalize the speech production process by creating a universal-speaker reference space.
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Huang Y, Wu Z, Wang F, Hu D, Li T, Guo L, Wang L, Lin W, Li G. Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age. Proc Natl Acad Sci U S A 2022; 119:e2121748119. [PMID: 35939665 DOI: 10.1073/pnas.2121748119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.
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Li Y, Qiu Z, Fan X, Liu X, Chang EIC, Xu Y. Integrated 3d flow-based multi-atlas brain structure segmentation. PLoS One 2022; 17:e0270339. [PMID: 35969596 PMCID: PMC9377636 DOI: 10.1371/journal.pone.0270339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 03/09/2021] [Accepted: 06/09/2022] [Indexed: 11/18/2022] Open
Abstract
MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method’s potential for general applicability in various brain structures and settings.
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Affiliation(s)
- Yeshu Li
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Ziming Qiu
- Electrical and Computer Engineering, Tandon School of Engineering, New York University, Brooklyn, NY, United States of America
| | - Xingyu Fan
- Bioengineering College, Chongqing University, Chongqing, China
| | - Xianglong Liu
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | | | - Yan Xu
- School of Biological Science and Medical Engineering, State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics, Mechanobiology of Ministry of Education and Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
- Microsoft Research, Beijing, China
- * E-mail:
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Rischka L, Vraka C, Pichler V, Rasul S, Nics L, Gryglewski G, Handschuh P, Murgaš M, Godbersen GM, Silberbauer LR, Unterholzner J, Wotawa C, Haider A, Ahmed H, Schibli R, Mindt T, Mitterhauser M, Wadsak W, Hahn A, Lanzenberger R, Hacker M, Ametamey SM. First-in-Humans Brain PET Imaging of the GluN2B-Containing N-methyl-d-aspartate Receptor with ( R)- 11C-Me-NB1. J Nucl Med 2022; 63:936-941. [PMID: 34620732 PMCID: PMC9157734 DOI: 10.2967/jnumed.121.262427] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/26/2021] [Indexed: 11/16/2022] Open
Abstract
The N-methyl-d-aspartate receptor (NMDAR) plays a crucial role in neurodegenerative diseases such as Alzheimer disease and in the treatment of major depression by fast-acting antidepressants such as ketamine. Given their broad implications, GluN2B-containing NMDARs have been of interest as diagnostic and therapeutic targets. Recently, (R)-11C-Me-NB1 was investigated preclinically and shown to be a promising radioligand for imaging GluN2B subunits. Here, we report on the performance characteristics of this radioligand in a first-in-humans PET study. Methods: Six healthy male subjects were scanned twice on a fully integrated PET/MR scanner with (R)-11C-Me-NB1 for 120 min. Brain uptake and tracer distribution over time were investigated by SUVs. Test-retest reliability was assessed with the absolute percentage difference and the coefficient of variation. Exploratory total volumes of distribution (VT) were computed using an arterial input function and the Logan plot as well as a constrained 2-tissue-compartment model with the ratio of rate constants between plasma and tissue compartments (K1/k2) coupled (2TCM). SUV was correlated with VT to investigate its potential as a surrogate marker of GluN2B expression. Results: High and heterogeneous radioligand uptake was observed across the entire gray matter with reversible kinetics within the scan time. SUV absolute percentage difference ranged from 6.9% to 8.5% and coefficient of variation from 4.9% to 6.0%, indicating a high test-retest reliability. A moderate correlation was found between SUV averaged from 70 to 90 min and VT using Logan plot (Spearman ρ = 0.44). Correlation between VT Logan and 2TCM was r = 0.76. Conclusion: The radioligand (R)-11C-Me-NB1 was highly effective in mapping GluN2B-enriched NMDARs in the human brain. With a heterogeneous uptake and a high test-retest reliability, this radioligand offers promise to deepen our understanding of the GluN2B-containing NMDAR in the pathophysiology and treatment of neuropsychiatric disease such as Alzheimer disease and major depression. Additionally, it could help in the selection of appropriate doses of GluN2B-targeting drugs.
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Affiliation(s)
- Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Chrysoula Vraka
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Verena Pichler
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Pharmaceutical Chemistry, University of Vienna, Vienna, Austria
| | - Sazan Rasul
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Lukas Nics
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Patricia Handschuh
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Godber M Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Leo R Silberbauer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Christoph Wotawa
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Ahmed Haider
- Centre for Radiopharmaceutical Sciences ETH-PSI-USZ, Institute of Pharmaceutical Sciences ETH, Zurich, Switzerland
| | - Hazem Ahmed
- Centre for Radiopharmaceutical Sciences ETH-PSI-USZ, Institute of Pharmaceutical Sciences ETH, Zurich, Switzerland
| | - Roger Schibli
- Centre for Radiopharmaceutical Sciences ETH-PSI-USZ, Institute of Pharmaceutical Sciences ETH, Zurich, Switzerland
| | - Thomas Mindt
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
- Institute of Inorganic Chemistry, University of Vienna, Vienna, Austria; and
| | - Markus Mitterhauser
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| | - Wolfgang Wadsak
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
- Center for Biomarker Research in Medicine (CBmed), Graz, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria;
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria;
| | - Simon M Ametamey
- Centre for Radiopharmaceutical Sciences ETH-PSI-USZ, Institute of Pharmaceutical Sciences ETH, Zurich, Switzerland;
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Yamaguchi T, Ikawa M, Enomoto S, Shirafuji N, Yamamura O, Tsujikawa T, Okazawa H, Kimura H, Nakamoto Y, Hamano T. Arterial spin labeling imaging for the detection of cerebral blood flow asymmetry in patients with corticobasal syndrome. Neuroradiology. [DOI: 10.1007/s00234-022-02942-9] [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] [Received: 02/21/2022] [Accepted: 04/01/2022] [Indexed: 10/18/2022]
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Shiohama T, Tsujimura K. Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome. Front Neurosci 2022; 16:835964. [PMID: 35450016 PMCID: PMC9016334 DOI: 10.3389/fnins.2022.835964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Congenital genetic disorders often present with neurological manifestations such as neurodevelopmental disorders, motor developmental retardation, epilepsy, and involuntary movement. Through qualitative morphometric evaluation of neuroimaging studies, remarkable structural abnormalities, such as lissencephaly, polymicrogyria, white matter lesions, and cortical tubers, have been identified in these disorders, while no structural abnormalities were identified in clinical settings in a large population. Recent advances in data analysis programs have led to significant progress in the quantitative analysis of anatomical structural magnetic resonance imaging (MRI) and diffusion-weighted MRI tractography, and these approaches have been used to investigate psychological and congenital genetic disorders. Evaluation of morphometric brain characteristics may contribute to the identification of neuroimaging biomarkers for early diagnosis and response evaluation in patients with congenital genetic diseases. This mini-review focuses on the methodologies and attempts employed to study Rett syndrome using quantitative structural brain MRI analyses, including voxel- and surface-based morphometry and diffusion-weighted MRI tractography. The mini-review aims to deepen our understanding of how neuroimaging studies are used to examine congenital genetic disorders.
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Affiliation(s)
- Tadashi Shiohama
- Department of Pediatrics, Chiba University Hospital, Chiba, Japan
- *Correspondence: Tadashi Shiohama,
| | - Keita Tsujimura
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Nagoya, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya, Japan
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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Cabeza-Ruiz R, Velázquez-Pérez L, Linares-Barranco A, Pérez-Rodríguez R. Convolutional Neural Networks for Segmenting Cerebellar Fissures from Magnetic Resonance Imaging. Sensors (Basel) 2022; 22:1345. [PMID: 35214268 DOI: 10.3390/s22041345] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/08/2022] [Indexed: 02/06/2023]
Abstract
The human cerebellum plays an important role in coordination tasks. Diseases such as spinocerebellar ataxias tend to cause severe damage to the cerebellum, leading patients to a progressive loss of motor coordination. The detection of such damages can help specialists to approximate the state of the disease, as well as to perform statistical analysis, in order to propose treatment therapies for the patients. Manual segmentation of such patterns from magnetic resonance imaging is a very difficult and time-consuming task, and is not a viable solution if the number of images to process is relatively large. In recent years, deep learning techniques such as convolutional neural networks (CNNs or convnets) have experienced an increased development, and many researchers have used them to automatically segment medical images. In this research, we propose the use of convolutional neural networks for automatically segmenting the cerebellar fissures from brain magnetic resonance imaging. Three models are presented, based on the same CNN architecture, for obtaining three different binary masks: fissures, cerebellum with fissures, and cerebellum without fissures. The models perform well in terms of precision and efficiency. Evaluation results show that convnets can be trained for such purposes, and could be considered as additional tools in the diagnosis and characterization of neurodegenerative diseases.
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Finze A, Wahl H, Janowitz D, Buerger K, Linn J, Rominger A, Stöcklein S, Bartenstein P, Wollenweber FA, Catak C, Brendel M. Regional Associations of Cortical Superficial Siderosis and β-Amyloid-Positron-Emission-Tomography Positivity in Patients With Cerebral Amyloid Angiopathy. Front Aging Neurosci 2022; 13:786143. [PMID: 35185518 PMCID: PMC8851392 DOI: 10.3389/fnagi.2021.786143] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/20/2021] [Indexed: 11/20/2022] Open
Abstract
Objective This is a cross-sectional study to evaluate whether β-amyloid-(Aβ)-PET positivity and cortical superficial siderosis (cSS) in patients with cerebral amyloid angiopathy (CAA) are regionally colocalized. Methods Ten patients with probable or possible CAA (73.3 ± 10.9 years, 40% women) underwent MRI examination with a gradient-echo-T2*-weighted-imaging sequence to detect cSS and 18F-florbetaben PET examination to detect fibrillar Aβ. In all cortical regions of the Hammers Atlas, cSS positivity (MRI: ITK-SNAP segmentation) and Aβ-PET positivity (PET: ≥ mean value + 2 standard deviations of 14 healthy controls) were defined. Regional agreement of cSS- and Aβ-PET positivity was evaluated. Aβ-PET quantification was compared between cSS-positive and corresponding contralateral cSS-negative atlas regions. Furthermore, the Aβ-PET quantification of cSS-positive regions was evaluated in voxels close to cSS and in direct cSS voxels. Results cSS- and Aβ-PET positivity did not indicate similarity of their regional patterns, despite a minor association between the frequency of Aβ-positive patients and the frequency of cSS-positive patients within individual regions (rs = 0.277, p = 0.032). However, this association was driven by temporal regions lacking cSS- and Aβ-PET positivity. When analyzing all composite brain regions, Aβ-PET values in regions close to cSS were significantly higher than in regions directly affected with cSS (p < 0.0001). However, Aβ-PET values in regions close to cSS were not different when compared to corresponding contralateral cSS-negative regions (p = 0.603). Conclusion In this cross-sectional study, cSS and Aβ-PET positivity did not show regional association in patients with CAA and deserve further exploitation in longitudinal designs. In clinical routine, a specific cross-sectional evaluation of Aβ-PET in cSS-positive regions is probably not useful for visual reading of Aβ-PETs in patients with CAA.
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Affiliation(s)
- Anika Finze
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Hannes Wahl
- Department of Neuroradiology, University Hospital of Dresden, Carl Gustav Carus University Dresden, Dresden, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Katharina Buerger
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Jennifer Linn
- Department of Neuroradiology, University Hospital of Dresden, Carl Gustav Carus University Dresden, Dresden, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sophia Stöcklein
- Department of Radiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Peter Bartenstein
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Frank Arne Wollenweber
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Cihan Catak
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- *Correspondence: Matthias Brendel,
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Myoraku A, Klein G, Landau S, Tosun D. Regional uptakes from early-frame amyloid PET and 18F-FDG PET scans are comparable independent of disease state. Eur J Hybrid Imaging 2022; 6:2. [PMID: 35039928 PMCID: PMC8763988 DOI: 10.1186/s41824-021-00123-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 09/13/2021] [Accepted: 12/10/2021] [Indexed: 12/04/2022] Open
Abstract
Purpose Positron emission tomography (PET) imaging with amyloid-beta (Aβ) tracers and 2-[18F] fluoro-2-Deoxy-d-glucose (18F-FDG) is extensively employed in Alzheimer’s disease (AD) studies as biomarkers of AD pathology and neurodegeneration. To reduce cost and additional burdens to the patient, early-frame uptake during Aβ PET scanning has been proposed as a surrogate measure of regional glucose metabolism. Considering the disease state specific impact of AD on neurovascular coupling, we investigated to what extent the information captured in the early frames of an Aβ-PET (18F-florbetapir or 18F-florbetaben) scan is comparable to that of a 18F-FDG PET scan, independent of disease state. Method A partial correlation was performed on early-frame 18F-florbetapir and 18F-FDG regional data from 100 participants. In a secondary analysis, we compared 92 18F-florbetapir and 21 18F-florbetaben early-frame Aβ scans from cognitively unimpaired and mild cognitive impairment participants to ascertain if regional early-frame information was similar across different Aβ-PET radioligands. Results The partial correlation of early-frame 18F-florbetapir with 18F-FDG was significant in all 84 brain ROIs, with correlation values ranging from 0.61 to 0.94. There were no significant differences between early-frame 18F-florbetapir and 18F-florbetaben images. Conclusion Overall, we find that the regional uptake measurements from early-frame 18F-florbetapir are strongly correlated with regional glucose metabolism as measured in ground-truth 18F-FDG PET scans, regardless of disease state. Future studies should focus on longitudinal early-frame amyloid PET imaging studies to further assess the value of early-frame imaging as a marker of brain metabolic decline.
Supplementary Information The online version contains supplementary material available at 10.1186/s41824-021-00123-0.
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Affiliation(s)
- Alison Myoraku
- Northern California Institute for Research and Education, VA Medical Center, 4150 Clement Street, 114M, San Francisco, CA, 94121, USA. .,Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, USA.
| | - Gregory Klein
- Roche Pharma Research and Early Development, Basel, Switzerland
| | - Susan Landau
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720-3190, USA
| | - Duygu Tosun
- Northern California Institute for Research and Education, VA Medical Center, 4150 Clement Street, 114M, San Francisco, CA, 94121, USA.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, 94143, USA
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Livingston NR, Calsolaro V, Hinz R, Nowell J, Raza S, Gentleman S, Tyacke RJ, Myers J, Venkataraman AV, Perneczky R, Gunn RN, Rabiner EA, Parker CA, Murphy PS, Wren PB, Nutt DJ, Matthews PM, Edison P. Relationship between astrocyte reactivity, using novel (11)C-BU99008 PET, and glucose metabolism, grey matter volume and amyloid load in cognitively impaired individuals. Mol Psychiatry 2022; 27:2019-29. [PMID: 35125495 DOI: 10.1038/s41380-021-01429-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 12/10/2021] [Accepted: 12/23/2021] [Indexed: 12/01/2022]
Abstract
Post mortem neuropathology suggests that astrocyte reactivity may play a significant role in neurodegeneration in Alzheimer's disease. We explored this in vivo using multimodal PET and MRI imaging. Twenty subjects (11 older, cognitively impaired patients and 9 age-matched healthy controls) underwent brain scanning using the novel reactive astrocyte PET tracer 11C-BU99008, 18F-FDG and 18F-florbetaben PET, and T1-weighted MRI. Differences between cognitively impaired patients and healthy controls in regional and voxel-wise levels of astrocyte reactivity, glucose metabolism, grey matter volume and amyloid load were explored, and their relationship to each other was assessed using Biological Parametric Mapping (BPM). Amyloid beta (Aβ)-positive patients showed greater 11C-BU99008 uptake compared to controls, except in the temporal lobe, whilst further increased 11C-BU99008 uptake was observed in Mild Cognitive Impairment subjects compared to those with Alzheimer's disease in the frontal, temporal and cingulate cortices. BPM correlations revealed that regions which showed reduced 11C-BU99008 uptake in Aβ-positive patients compared to controls, such as the temporal lobe, also showed reduced 18F-FDG uptake and grey matter volume, although the correlations with 18F-FDG uptake were not replicated in the ROI analysis. BPM analysis also revealed a regionally-dynamic relationship between astrocyte reactivity and amyloid uptake: increased amyloid load in cortical association areas of the temporal lobe and cingulate cortices was associated with reduced 11C-BU99008 uptake, whilst increased amyloid uptake in primary motor and sensory areas (in which amyloid deposition occurs later) was associated with increased 11C-BU99008 uptake. These novel observations add to the hypothesis that while astrocyte reactivity may be triggered by early Aβ-deposition, sustained pro-inflammatory astrocyte reactivity with greater amyloid deposition may lead to astrocyte dystrophy and amyloid-associated neuropathology such as grey matter atrophy and glucose hypometabolism, although the evidence for glucose hypometabolism here is less strong.
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Schöne M, Seidenbecher S, Kaufmann J, Antonucci LA, Frodl T, Koutsouleris N, Schiltz K, Bogerts B. Appetitive aggression is associated with lateralized activation in nucleus accumbens. Psychiatry Res Neuroimaging 2022; 319:111425. [PMID: 34891023 DOI: 10.1016/j.pscychresns.2021.111425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/14/2021] [Accepted: 12/02/2021] [Indexed: 12/01/2022]
Abstract
Aggression can have a hedonistic aspect in predisposed individuals labeled as appetitive aggression. The present study investigates the neurobiological correlates of this appetitive type of aggression in non-clinical samples from community. Applying functional magnet resonance imaging (fMRI), we tested whether 20 martial artists compared to 26 controls had a higher activation in the nucleus accumbens (NAcc), a central part of the dopaminergic, mesolimbic reward system. Subjects had to watch violent vs. neutral pictures representing appetitive aggression. The affinity towards hedonistic violence was assessed by the Appetitive and Facilitative Aggression Scale (AFAS). Furthermore, the subjects rated all the pictures with regard to how pleasant and violent they were. The martial artists reported a higher AFAS-score and a more positive perception of violent pictures. On the neural level, across all subjects, there was a significant positive correlation between the AFAS-score and the activation in the left NAcc and an inverse association with the activation of the right NAcc when watching violent compared to neutral pictures. This lateralization effect indicates a different processing of hedonistic aspects of aggression in the two hemispheres.
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Affiliation(s)
- Maria Schöne
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Salus Institute, Salus gGmbH, Magdeburg, Germany.
| | - Stephanie Seidenbecher
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Salus Institute, Salus gGmbH, Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto-von-Guericke University, Magdeburg, Germany
| | - Linda Antonella Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Bari, Italy
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry and Institute of Neuroscience, Dublin, Ireland; Center for Behavioral Brain Sciences, Otto-von-Guericke University, Magdeburg, Germany; German Center for Neurodegenerative Diseases, Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatic, RWTH-University, Aachen, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilians-University, Munich, Germany
| | - Kolja Schiltz
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Department of Forensic Psychiatry, Mental Hospital of the Ludwig-Maximilians-University, Munich, Germany
| | - Bernhard Bogerts
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany; Salus Institute, Salus gGmbH, Magdeburg, Germany
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Szpak M, Collins SC, Li Y, Liu X, Ayub Q, Fischer MC, Vancollie VE, Lelliott CJ, Xue Y, Yalcin B, Yang H, Tyler-Smith C. A Positively Selected MAGEE2 LoF Allele Is Associated with Sexual Dimorphism in Human Brain Size and Shows Similar Phenotypes in Magee2 Null Mice. Mol Biol Evol 2021; 38:5655-5663. [PMID: 34464968 PMCID: PMC8662591 DOI: 10.1093/molbev/msab243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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] [Indexed: 12/14/2022] Open
Abstract
A nonsense allele at rs1343879 in human MAGEE2 on chromosome X has previously been reported as a strong candidate for positive selection in East Asia. This premature stop codon causing ∼80% protein truncation is characterized by a striking geographical pattern of high population differentiation: common in Asia and the Americas (up to 84% in the 1000 Genomes Project East Asians) but rare elsewhere. Here, we generated a Magee2 mouse knockout mimicking the human loss-of-function mutation to study its functional consequences. The Magee2 null mice did not exhibit gross abnormalities apart from enlarged brain structures (13% increased total brain area, P = 0.0022) in hemizygous males. The area of the granular retrosplenial cortex responsible for memory, navigation, and spatial information processing was the most severely affected, exhibiting an enlargement of 34% (P = 3.4×10-6). The brain size in homozygous females showed the opposite trend of reduced brain size, although this did not reach statistical significance. With these insights, we performed human association analyses between brain size measurements and rs1343879 genotypes in 141 Chinese volunteers with brain MRI scans, replicating the sexual dimorphism seen in the knockout mouse model. The derived stop gain allele was significantly associated with a larger volume of gray matter in males (P = 0.00094), and smaller volumes of gray (P = 0.00021) and white (P = 0.0015) matter in females. It is unclear whether or not the observed neuroanatomical phenotypes affect behavior or cognition, but it might have been the driving force underlying the positive selection in humans.
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Affiliation(s)
- Michał Szpak
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Stephan C Collins
- Inserm UMR1231, Genetics of Developmental Disorders Laboratory, University of Bourgogne Franche-Comté, Dijon, France.,IGBMC, UMR7104, Illkirch, Inserm, France
| | - Yan Li
- BGI-Shenzhen, Shenzhen, China
| | - Xiao Liu
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Qasim Ayub
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom.,Monash University Malaysia Genomics Facility, School of Science, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | | | | | | | - Yali Xue
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Binnaz Yalcin
- Inserm UMR1231, Genetics of Developmental Disorders Laboratory, University of Bourgogne Franche-Comté, Dijon, France.,IGBMC, UMR7104, Illkirch, Inserm, France
| | | | - Chris Tyler-Smith
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
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Ferro DA, Kuijf HJ, Hilal S, van Veluw SJ, van Veldhuizen D, Venketasubramanian N, Tan BY, Biessels GJ, Chen C. Association Between Cerebral Cortical Microinfarcts and Perilesional Cortical Atrophy on 3T MRI. Neurology 2021; 98:e612-e622. [PMID: 34862322 DOI: 10.1212/wnl.0000000000013140] [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] [Received: 01/10/2021] [Accepted: 11/16/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Cerebral cortical microinfarcts (CMIs) are a novel MRI-marker of cerebrovascular disease (CeVD) that predicts accelerated cognitive decline. Presence of CMIs is known to be associated with global cortical atrophy, although the mechanism linking the two is unclear. Our primary objective was to examine the relation between CMIs and cortical atrophy and establish possible perilesional atrophy surrounding CMIs. Our secondary objective was to examine the role of cortical atrophy in CMI-associated cognitive impairment. METHODS Patients were recruited from two Singapore memory clinics between December 2010 and September 2013 and included if they received the diagnosis no objective cognitive impairment, cognitive impairment (with or without a history of stroke) or Alzheimer's or vascular dementia. Cortical thickness, chronic cortical microinfarcts and MRI-markers of CeVD were assessed on 3T MRI. Patients underwent cognitive testing. Cortical thickness was compared globally between patients with and without CMIs, regionally within individual patients with CMIs comparing brain regions with CMIs to the corresponding contralateral region without CMIs and locally within individuals patients in a 50 mm radius of CMIs. Global cortical thickness was analyzed as mediator in the relation between CMI and cognitive performance. RESULTS Of the 238 patients (mean age 72.5 SD 9.1 years) enrolled, 75 had ≥1 CMIs. Patient with CMIs had a 2.1% lower global cortical thickness (B=-.049 mm, 95% CI [.091; -.007] p=.022) compared to patients without CMIs, after correction for age, sex, education and intracranial volume. In patients with CMIs, cortical thickness in brain regions with CMIs was 2.2 % lower than in contralateral regions without CMIs (B=-.048 mm [-.071; -.026] p<.001). In a 20 mm radius area surrounding the CMI-core, cortical thickness was lower than in the area 20-50 mm from the CMI-core (Mean difference -.06 mm 95% CI [-.10; -.02] p=.002). Global cortical thickness was a significant mediator in the relationship between CMI presence and cognitive performance as measure with the Mini-Mental State Examination (B=-.12 [-.22; -.01] p=.025). DISCUSSION We found cortical atrophy surrounding CMIs, suggesting a perilesional effect in a cortical area many times larger than the CMI-core. Our findings support the notion that CMIs affect brain structure beyond the actual lesion site.
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Affiliation(s)
- Doeschka A Ferro
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Hugo J Kuijf
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Saima Hilal
- Memory Aging and Cognition Centre, Department of Pharmacology, National University of Singapore, Singapore
| | - Susanne J van Veluw
- Department of Neurology, J.P.K. Stroke Research Center, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Christopher Chen
- Memory Aging and Cognition Centre, Department of Pharmacology, National University of Singapore, Singapore
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Zhu J, Zhang H, Chong YS, Shek LP, Gluckman PD, Meaney MJ, Fortier MV, Qiu A. Integrated structural and functional atlases of Asian children from infancy to childhood. Neuroimage 2021; 245:118716. [PMID: 34767941 DOI: 10.1016/j.neuroimage.2021.118716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 05/28/2021] [Revised: 11/06/2021] [Accepted: 11/08/2021] [Indexed: 12/21/2022] Open
Abstract
The developing brain grows exponentially in the first few years of life. There is a need to have age-appropriate brain atlases that coherently characterize the geometry of the cerebral cortex, white matter tracts, and functional organization. This study employed multi-modal brain images of an Asian cohort and constructed brain structural and functional atlases for 6-month-old infants, 4.5-, 6-, and 7.5-year-old children. We exploited large deformation diffeomorphic metric mapping and probabilistic atlas generation approaches to integrate structural MRI and diffusion weighted images (DWIs) and to create the atlas where white matter tracts well fit into the cortical folding pattern. Based on this structural atlas, we then employed spectral clustering to parcellate the brain into functional networks from resting-state fMRI (rs-fMRI). Our results provided the atlas that characterizes the cortical folding geometry, subcortical regions, deep white matter tracts, as well as functional networks in a stereotaxic coordinate space for the four different age groups. The functional networks consisting of the primary cortex were well established in infancy and remained stable to childhood, while specific higher-order functional networks showed specific patterns of hemispherical, subcortical-cerebellar, and cortical-cortical integration and segregation from infancy to childhood. Our multi-modal fusion analysis demonstrated the use of the integrated structural and functional atlas for understanding coherent patterns of brain anatomical and functional development during childhood. Hence, our atlases can be potentially used to study coherent patterns of brain anatomical and functional development.
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Affiliation(s)
- Jingwen Zhu
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Block E4 #04-08, 11758, Singapore
| | - Han Zhang
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Block E4 #04-08, 11758, Singapore
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences, Singapore; Department of Obstetrics & Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Lynette P Shek
- Department of Pediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Michael J Meaney
- Singapore Institute for Clinical Sciences, Singapore; Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Block E4 #04-08, 11758, Singapore; The N.1 Institute for Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore; NUS (Suzhou) Research Institute, National University of Singapore, China; Department of Biomedical Engineering, The Johns Hopkins University, USA.
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43
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Huizinga W, Poot DHJ, Vinke EJ, Wenzel F, Bron EE, Toussaint N, Ledig C, Vrooman H, Ikram MA, Niessen WJ, Vernooij MW, Klein S. Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis. Front Big Data 2021; 4:577164. [PMID: 34723175 PMCID: PMC8552517 DOI: 10.3389/fdata.2021.577164] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 06/28/2020] [Accepted: 05/21/2021] [Indexed: 12/03/2022] Open
Abstract
For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good (>0.75), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients’ z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.
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Affiliation(s)
- W Huizinga
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - D H J Poot
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - E J Vinke
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - F Wenzel
- Philips Research Hamburg, Hamburg, Germany
| | - E E Bron
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - N Toussaint
- School of Biomedical Engineering, King's College London, London, United Kingdom
| | - C Ledig
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - H Vrooman
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
| | - M A Ikram
- Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - W J Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands.,Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, Netherlands
| | - M W Vernooij
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, Netherlands
| | - S Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC, Rotterdam, Netherlands
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Kebets V, Favre P, Houenou J, Polosan M, Perroud N, Aubry JM, Van De Ville D, Piguet C. Fronto-limbic neural variability as a transdiagnostic correlate of emotion dysregulation. Transl Psychiatry 2021; 11:545. [PMID: 34675186 DOI: 10.1038/s41398-021-01666-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 09/08/2021] [Accepted: 10/01/2021] [Indexed: 12/15/2022] Open
Abstract
Emotion dysregulation is central to the development and maintenance of psychopathology, and is common across many psychiatric disorders. Neurobiological models of emotion dysregulation involve the fronto-limbic brain network, including in particular the amygdala and prefrontal cortex (PFC). Neural variability has recently been suggested as an index of cognitive flexibility. We hypothesized that within-subject neural variability in the fronto-limbic network would be related to inter-individual variation in emotion dysregulation in the context of low affective control. In a multi-site cohort (N = 166, 93 females) of healthy individuals and individuals with emotional dysregulation (attention deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), and borderline personality disorder (BPD)), we applied partial least squares (PLS), a multivariate data-driven technique, to derive latent components yielding maximal covariance between blood-oxygen level-dependent (BOLD) signal variability at rest and emotion dysregulation, as expressed by affective lability, depression and mania scores. PLS revealed one significant latent component (r = 0.62, p = 0.044), whereby greater emotion dysregulation was associated with increased neural variability in the amygdala, hippocampus, ventromedial, dorsomedial and dorsolateral PFC, insula and motor cortex, and decreased neural variability in occipital regions. This spatial pattern bears a striking resemblance to the fronto-limbic network, which is thought to subserve emotion regulation, and is impaired in individuals with ADHD, BD, and BPD. Our work supports emotion dysregulation as a transdiagnostic dimension with neurobiological underpinnings that transcend diagnostic boundaries, and adds evidence to neural variability being a relevant proxy of neural efficiency.
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Bussas M, Grahl S, Pongratz V, Berthele A, Gasperi C, Andlauer T, Gaser C, Kirschke JS, Wiestler B, Zimmer C, Hemmer B, Mühlau M. Gray matter atrophy in relapsing-remitting multiple sclerosis is associated with white matter lesions in connecting fibers. Mult Scler 2021; 28:900-909. [PMID: 34591698 PMCID: PMC9024016 DOI: 10.1177/13524585211044957] [Citation(s) in RCA: 0] [Impact Index Per Article: 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] [Indexed: 11/16/2022]
Abstract
Background: Lesions of brain white matter (WM) and atrophy of brain gray matter (GM) are well-established surrogate parameters in multiple sclerosis (MS), but it is unclear how closely these parameters relate to each other. Objective: To assess across the whole cerebrum whether GM atrophy can be explained by lesions in connecting WM tracts. Methods: GM images of 600 patients with relapsing-remitting MS (women = 68%; median age = 33.0 years, median expanded disability status scale score = 1.5) were converted to atrophy maps by data from a healthy control cohort. An atlas of WM tracts from the Human Connectome Project and individual lesion maps were merged to identify potentially disconnected GM regions, leading to individual disconnectome maps. Across the whole cerebrum, GM atrophy and potentially disconnected GM were tested for association both cross-sectionally and longitudinally. Results: We found highly significant correlations between disconnection and atrophy across most of the cerebrum. Longitudinal analysis demonstrated a close temporal relation of WM lesion formation and GM atrophy in connecting fibers. Conclusion: GM atrophy is associated with WM lesions in connecting fibers. Caution is warranted when interpreting group differences in GM atrophy exclusively as differences in early neurodegeneration independent of WM lesion formation.
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Affiliation(s)
- Matthias Bussas
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany/TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Sophia Grahl
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany/TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Viola Pongratz
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany/TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Achim Berthele
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christiane Gasperi
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Till Andlauer
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christian Gaser
- Department of Psychiatry and Department of Neurology, Jena University Hospital, Jena, Germany
| | - Jan S Kirschke
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Benedikt Wiestler
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Bernhard Hemmer
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany/Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Mark Mühlau
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany/TUM-Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
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Mérida I, Jung J, Bouvard S, Le Bars D, Lancelot S, Lavenne F, Bouillot C, Redouté J, Hammers A, Costes N. CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [ 18F]FDG PET, T1 and FLAIR MRI, and CT images available for research. EJNMMI Res 2021; 11:91. [PMID: 34529159 PMCID: PMC8446124 DOI: 10.1186/s13550-021-00830-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 06/02/2021] [Accepted: 08/15/2021] [Indexed: 01/05/2023] Open
Abstract
We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants' individual demographics (mean age 38 ± 11.5 years, range 23-65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.
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Affiliation(s)
- Inés Mérida
- CERMEP-Imagerie du Vivant, Lyon, France.
- CHU de Lyon HCL - GH Est, 59 Boulevard Pinel., 69677, Bron Cedex, France.
| | - Julien Jung
- INSERM U1028/CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
- Hospices Civils de Lyon, University Hospitals, Lyon, France
| | - Sandrine Bouvard
- Université Claude Bernard Lyon 1, Lyon Neuroscience Research Center, INSERM, CNRS, Lyon, France
| | - Didier Le Bars
- CERMEP-Imagerie du Vivant, Lyon, France
- Hospices Civils de Lyon, University Hospitals, Lyon, France
| | - Sophie Lancelot
- CERMEP-Imagerie du Vivant, Lyon, France
- INSERM U1028/CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
- Hospices Civils de Lyon, University Hospitals, Lyon, France
| | | | | | | | - Alexander Hammers
- School of Biomedical Engineering and Imaging Sciences, Kings' College London, King's College London and Guy's and St Thomas' PET Centre, London, UK
- Neurodis Foundation, Lyon, France
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Parekh P, Bhalerao GV, Rao R, Sreeraj VS, Holla B, Saini J, Venkatasubramanian G, John JP, Jain S. Protocol for magnetic resonance imaging acquisition, quality assurance, and quality check for the Accelerator program for Discovery in Brain disorders using Stem cells. Int J Methods Psychiatr Res 2021; 30:e1871. [PMID: 33960571 PMCID: PMC8412227 DOI: 10.1002/mpr.1871] [Citation(s) in RCA: 3] [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] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 01/08/2021] [Accepted: 03/10/2021] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVE The Accelerator program for Discovery in Brain disorders using Stem cells (ADBS) is a longitudinal study on five cohorts of patients with major psychiatric disorders from genetically high-risk families, their unaffected first-degree relatives, and healthy subjects. We describe the ADBS protocols for acquisition, quality assurance (QA), and quality check (QC) for multimodal magnetic resonance brain imaging studies. METHODS We describe the acquisition and QC protocols for structural, functional, and diffusion images. For QA, we acquire proton density and functional images on phantoms, along with repeated scans on human volunteer. We describe the analysis of phantom data and test-retest reliability of volumetric and diffusion measures. RESULTS Analysis of acquired phantom data shows linearity of proton density signal with increasing proton fraction, and an overall stability of various spatial and temporal QA measures. Examination of dice coefficient and statistical analyses of coefficient of variation in test-retest data on the human volunteer showed consistency of volumetric and diffusivity measures at whole-brain, regional, and voxel-level. CONCLUSION The described acquisition and QA-QC procedures can yield consistent and reliable quantitative measures. It is expected that this longitudinal neuroimaging dataset will, upon its release, serve the scientific community well and pave the way for interesting discoveries.
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Affiliation(s)
- Pravesh Parekh
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Multimodal Brain Image Analysis LaboratoryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Gaurav V. Bhalerao
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Translational Psychiatry LabNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Rashmi Rao
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Vanteemar S. Sreeraj
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Translational Psychiatry LabNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Bharath Holla
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Jitender Saini
- Department of Neuroimaging and Interventional RadiologyNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Ganesan Venkatasubramanian
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Translational Psychiatry LabNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - John P. John
- ADBS Neuroimaging CentreNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Multimodal Brain Image Analysis LaboratoryNational Institute of Mental Health and NeurosciencesBangaloreIndia
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
| | - Sanjeev Jain
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBangaloreIndia
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Bortolini T, Melo B, Basilio R, Fischer R, Zahn R, de Oliveira-Souza R, Knutson B, Moll J. Striatal and septo-hypothalamic responses to anticipation and outcome of affiliative rewards. Neuroimage 2021; 243:118474. [PMID: 34407439 DOI: 10.1016/j.neuroimage.2021.118474] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 02/15/2021] [Revised: 08/06/2021] [Accepted: 08/12/2021] [Indexed: 11/16/2022] Open
Abstract
Humans are intrinsically motivated to bond with others. The ability to experience affiliative emotions (such as affection/tenderness, sexual attraction, and admiration/awe) may incentivize and promote these affiliative bonds. Here, we interrogate the role of the critical reward circuitry, especially the Nucleus Accumbens (NAcc) and the septo-hypothalamic region, in the anticipation of and response to affiliative rewards using a novel incentive delay task. During Functional Magnetic Resonance Imaging (FMRI), participants (n = 23 healthy humans; 14 female) anticipated and watched videos involving affiliative (tenderness, erotic desire, and awe) and nonaffiliative (i.e., food) rewards, as well as neutral scenes. On the one hand, anticipation of both affiliative and nonaffiliative rewards increased activity in the NAcc, anterior insula, and supplementary motor cortex, but activity in the amygdala and the ventromedial prefrontal cortex (vmPFC) increased in response to reward outcomes. On the other hand, affiliative rewards more specifically increased activity in the septo-hypothalamic area. Moreover, NAcc activity during anticipation correlated with positive arousal for all rewards, whereas septo-hypothalamic activity during the outcome correlated with positive arousal and motivation for subsequent re-exposure only for affiliative rewards. Together, these findings implicate a general appetitive response in the NAcc to different types of rewards but suggests a more specific response in the septo-hypothalamic region in response to affiliative rewards outcomes. This work also presents a new task for distinguishing between neural responses to affiliative and non-affiliative rewards.
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Affiliation(s)
- Tiago Bortolini
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil.
| | - Bruno Melo
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil
| | - Rodrigo Basilio
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil
| | - Ronald Fischer
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil; School of Psychology, PO Box 600, Victoria University of Wellington, Wellington 6021, New Zealand
| | - Roland Zahn
- Centre for Affective Disorders, King's College London, SE5 8AF, United Kingdom
| | - Ricardo de Oliveira-Souza
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil; The Federal University of the State of Rio de Janeiro, Rio de Janeiro 22270-000, Brazil
| | - Brian Knutson
- Department of Psychology, Stanford University, Stanford, CA 94305, USA
| | - Jorge Moll
- Cognitive Neuroscience and Neuroinformatics Unit, D'Or Institute for Research and Education (IDOR), Rio de Janeiro 22281-100, Brazil; Department of Psychology, Stanford University, Stanford, CA 94305, USA; Scients Institute, Palo Alto, CA 94306, USA
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Routier A, Burgos N, Díaz M, Bacci M, Bottani S, El-Rifai O, Fontanella S, Gori P, Guillon J, Guyot A, Hassanaly R, Jacquemont T, Lu P, Marcoux A, Moreau T, Samper-González J, Teichmann M, Thibeau-Sutre E, Vaillant G, Wen J, Wild A, Habert MO, Durrleman S, Colliot O. Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies. Front Neuroinform 2021; 15:689675. [PMID: 34483871 PMCID: PMC8415107 DOI: 10.3389/fninf.2021.689675] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [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: 04/01/2021] [Accepted: 07/19/2021] [Indexed: 12/03/2022] Open
Abstract
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
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Affiliation(s)
- Alexandre Routier
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Ninon Burgos
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Mauricio Díaz
- Inria, Service d'Expérimentation et de Développement, Paris, France
| | - Michael Bacci
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Simona Bottani
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Omar El-Rifai
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Sabrina Fontanella
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Pietro Gori
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Jérémy Guillon
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Alexis Guyot
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Ravi Hassanaly
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Thomas Jacquemont
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Pascal Lu
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Arnaud Marcoux
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Tristan Moreau
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Jorge Samper-González
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Marc Teichmann
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
- Department of Neurology, Institute for Memory and Alzheimer's Disease, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
| | - Elina Thibeau-Sutre
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Ghislain Vaillant
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Junhao Wen
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Adam Wild
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Marie-Odile Habert
- Sorbonne Université, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France
- AP-HP, Hôpital Pitié-Salpêtrière, Médecine Nucléaire, Paris, France
- Centre d'Acquisition et Traitement des Images, Paris, France
| | - Stanley Durrleman
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Olivier Colliot
- Inria, Aramis Project-Team, Paris, France
- Sorbonne Université, Paris, France
- Institut du Cerveau – Paris Brain Institute – ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France
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Fu X, Richards JE. Investigating developmental changes in scalp-to-cortex correspondence using diffuse optical tomography sensitivity in infancy. Neurophotonics 2021; 8:035003. [PMID: 34322572 PMCID: PMC8305752 DOI: 10.1117/1.nph.8.3.035003] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 07/09/2021] [Indexed: 05/25/2023]
Abstract
Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data require accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development. Aim: We aim to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years), and adults (20 to 24 years). Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs. Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs. Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate, realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.
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Affiliation(s)
- Xiaoxue Fu
- University of South Carolina, Department of Psychology, Columbia, South Carolina, United States
| | - John E. Richards
- University of South Carolina, Department of Psychology, Columbia, South Carolina, United States
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