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Li T, Guo Y, Jin X, Liu T, Wu G, Huang W, Chen F. Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging. Brain Res 2024; 1833:148851. [PMID: 38479491 DOI: 10.1016/j.brainres.2024.148851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/24/2024]
Abstract
PURPOSE To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). METHODS We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. RESULTS In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. CONCLUSIONS White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.
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Affiliation(s)
- Tiansheng Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Xin Jin
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Tao Liu
- Department of Geriatric Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China.
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2
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Mendoza C, Román C, Mangin JF, Hernández C, Guevara P. Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter. Front Neurosci 2024; 18:1394681. [PMID: 38737100 PMCID: PMC11088237 DOI: 10.3389/fnins.2024.1394681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/14/2024] Open
Abstract
In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires dedicated methodologies to identify the main fiber bundle shape and deal with spurious fibers. This paper presents an enhanced short fiber bundle segmentation based on a SWM bundle atlas and the filtering of noisy fibers. The method was tuned and evaluated over HCP test-retest probabilistic tractography datasets (44 subjects). We propose four fiber bundle filters to remove spurious fibers. Furthermore, we include the identification of the main fiber fascicle to obtain well-defined fiber bundles. First, we identified four main bundle shapes in the SWM atlas, and performed a filter tuning in a subset of 28 subjects. The filter based on the Convex Hull provided the highest similarity between corresponding test-retest fiber bundles. Subsequently, we applied the best filter in the 16 remaining subjects for all atlas bundles, showing that filtered fiber bundles significantly improve test-retest reproducibility indices when removing between ten and twenty percent of the fibers. Additionally, we applied the bundle segmentation with and without filtering to the ABIDE-II database. The fiber bundle filtering allowed us to obtain a higher number of bundles with significant differences in fractional anisotropy, mean diffusivity, and radial diffusivity of Autism Spectrum Disorder patients relative to controls.
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Affiliation(s)
- Cristóbal Mendoza
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Claudio Román
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Universidad de Valparaíso, Valparaíso, Chile
| | | | - Cecilia Hernández
- Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
- Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Department of Electrical Engineering, Faculty of Engineering, Universidad de Concepción, Concepción, Chile
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3
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Xiao J, Sun C, Chen R, Zhao Z, Wang G, Wu D. Reproducibility of Diffusion MRI-Based Tractography in the Fetal Brain. J Magn Reson Imaging 2024. [PMID: 38284561 DOI: 10.1002/jmri.29253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Tractography based on diffusion MRI (dMRI) is a useful tool to study white matter of the developing brain. However, its application in fetal brains is limited due to motion artifacts and low resolution of in utero dMRI, leading to reduced reliability, which was scarcely investigated in previous studies. PURPOSE To identify reliably traceable fibers in fetal brains and assess whether reproducibility varies with gestational age (GA) and varies between brain regions. STUDY TYPE Prospective cohort study. SUBJECTS A total of 44 healthy fetuses with GAs between 25 and 37 (31 ± 6). FIELD STRENGTH/SEQUENCE 3-T, diffusion-weighted echo-planar imaging sequence (2-5 repeated dMRI scans within the same session per subject). ASSESSMENT We fitted dMRI with constrained spherical deconvolution model and conducted tractography on eight fibers. We extracted volume, fractional anisotropy, and fiber count for each fiber and assessed the reproducibility of these metrics between repeated scans within each subject. Data were divided into two age-based subgroups (≤30 weeks, N = 28, and >30 weeks, N = 16) for further tests. STATISTICAL TESTS The reproducibility were compared between fibers by analysis of variance and two-sample t tests. Multiple comparisons were corrected by the false discovery rate (5% was accepted). RESULTS The reproducibility of the anterior thalamic radiation, inferior longitudinal fasciculus (ILF), genu of the corpus callosum (GCC), and body of the corpus callosum (BCC) significantly decreased with advancing GA (correlation coefficient = 0.525-0.823), as confirmed by group comparisons between fetuses in early GA (≤30 weeks) and late GA (>30 weeks) groups. Corticospinal tract, inferior fronto-occipital fasciculus, and GCC showed high reproducibility for fiber count (weighted dice average = 0.846 vs. 0.814), while BCC and ILF exhibited the lowest reproducibility in both age groups. DATA CONCLUSION The study indicates that the reliability of fetal brain tractography depends on GA and varies among different fibers. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jiaxin Xiao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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4
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Gonzalez Alam TRJ, Cruz Arias J, Jefferies E, Smallwood J, Leemans A, Marino Davolos J. Ventral and dorsal aspects of the inferior frontal-occipital fasciculus support verbal semantic access and visually-guided behavioural control. Brain Struct Funct 2024; 229:207-221. [PMID: 38070006 PMCID: PMC10827863 DOI: 10.1007/s00429-023-02729-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 11/03/2023] [Indexed: 01/31/2024]
Abstract
The Inferior Frontal Occipital Fasciculus (IFOF) is a major anterior-to-posterior white matter pathway in the ventral human brain that connects parietal, temporal and occipital regions to frontal cortex. It has been implicated in a range of functions, including language, semantics, inhibition and the control of action. The recent research shows that the IFOF can be sub-divided into a ventral and dorsal branch, but the functional relevance of this distinction, as well as any potential hemispheric differences, are poorly understood. Using DTI tractography, we investigated the involvement of dorsal and ventral subdivisions of the IFOF in the left and right hemisphere in a response inhibition task (Go/No-Go), where the decision to respond or to withhold a prepotent response was made on the basis of semantic or non-semantic aspects of visual inputs. The task also varied the presentation modality (whether concepts were presented as written words or images). The results showed that the integrity of both dorsal and ventral IFOF in the left hemisphere were associated with participants' inhibition performance when the signal to stop was meaningful and presented in the verbal modality. This effect was absent in the right hemisphere. The integrity of dorsal IFOF was also associated with participants' inhibition efficiency in difficult perceptually guided decisions. This pattern of results indicates that left dorsal IFOF is implicated in the domain-general control of visually-guided behaviour, while the left ventral branch might interface with the semantic system to support the control of action when the inhibitory signal is based on meaning.
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Affiliation(s)
- Tirso R J Gonzalez Alam
- Department of Psychology and York Neuroimaging Centre, University of York, York, UK.
- School of Psychology, Bangor University, Bangor, UK.
| | | | - Elizabeth Jefferies
- Department of Psychology and York Neuroimaging Centre, University of York, York, UK
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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5
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Meesters S, Landers M, Rutten GJ, Florack L. Subject-Specific Automatic Reconstruction of White Matter Tracts. J Digit Imaging 2023; 36:2648-2661. [PMID: 37537513 PMCID: PMC10584769 DOI: 10.1007/s10278-023-00883-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 07/05/2023] [Accepted: 07/05/2023] [Indexed: 08/05/2023] Open
Abstract
MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.
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Affiliation(s)
- Stephan Meesters
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Maud Landers
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Geert-Jan Rutten
- Department of Neurosurgery, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| | - Luc Florack
- Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
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6
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Yen C, Lin CL, Chiang MC. Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life (Basel) 2023; 13:1472. [PMID: 37511847 PMCID: PMC10381462 DOI: 10.3390/life13071472] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/12/2023] [Accepted: 06/27/2023] [Indexed: 07/30/2023] Open
Abstract
Neuroimaging has revolutionized our understanding of brain function and has become an essential tool for researchers studying neurological disorders. Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two widely used neuroimaging techniques to review changes in brain activity. fMRI is a noninvasive technique that uses magnetic fields and radio waves to produce detailed brain images. An EEG is a noninvasive technique that records the brain's electrical activity through electrodes placed on the scalp. This review overviews recent developments in noninvasive functional neuroimaging methods, including fMRI and EEG. Recent advances in fMRI technology, its application to studying brain function, and the impact of neuroimaging techniques on neuroscience research are discussed. Advances in EEG technology and its applications to analyzing brain function and neural oscillations are also highlighted. In addition, advanced courses in neuroimaging, such as diffusion tensor imaging (DTI) and transcranial electrical stimulation (TES), are described, along with their role in studying brain connectivity, white matter tracts, and potential treatments for schizophrenia and chronic pain. Application. The review concludes by examining neuroimaging studies of neurodevelopmental and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease (AD), and Parkinson's disease (PD). We also described the role of transcranial direct current stimulation (tDCS) in ASD, ADHD, AD, and PD. Neuroimaging techniques have significantly advanced our understanding of brain function and provided essential insights into neurological disorders. However, further research into noninvasive treatments such as EEG, MRI, and TES is necessary to continue to develop new diagnostic and therapeutic strategies for neurological disorders.
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Affiliation(s)
- Chiahui Yen
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Chia-Li Lin
- Department of International Business, Ming Chuan University, Taipei 111, Taiwan
| | - Ming-Chang Chiang
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan
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7
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Craig BT, Geeraert B, Kinney-Lang E, Hilderley AJ, Yeates KO, Kirton A, Noel M, MacMaster FP, Bray S, Barlow KM, Brooks BL, Lebel C, Carlson HL. Structural brain network lateralization across childhood and adolescence. Hum Brain Mapp 2023; 44:1711-1724. [PMID: 36478489 PMCID: PMC9921220 DOI: 10.1002/hbm.26169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Developmental lateralization of brain function is imperative for behavioral specialization, yet few studies have investigated differences between hemispheres in structural connectivity patterns, especially over the course of development. The present study compares the lateralization of structural connectivity patterns, or topology, across children, adolescents, and young adults. We applied a graph theory approach to quantify key topological metrics in each hemisphere including efficiency of information transfer between regions (global efficiency), clustering of connections between regions (clustering coefficient [CC]), presence of hub-nodes (betweenness centrality [BC]), and connectivity between nodes of high and low complexity (hierarchical complexity [HC]) and investigated changes in these metrics during development. Further, we investigated BC and CC in seven functionally defined networks. Our cross-sectional study consisted of 211 participants between the ages of 6 and 21 years with 93% being right-handed and 51% female. Global efficiency, HC, and CC demonstrated a leftward lateralization, compared to a rightward lateralization of BC. The sensorimotor, default mode, salience, and language networks showed a leftward asymmetry of CC. BC was only lateralized in the salience (right lateralized) and dorsal attention (left lateralized) networks. Only a small number of metrics were associated with age, suggesting that topological organization may stay relatively constant throughout school-age development, despite known underlying changes in white matter properties. Unlike many other imaging biomarkers of brain development, our study suggests topological lateralization is consistent across age, highlighting potential nonlinear mechanisms underlying developmental specialization.
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Affiliation(s)
- Brandon T Craig
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
| | - Bryce Geeraert
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Eli Kinney-Lang
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Alicia J Hilderley
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Keith O Yeates
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Adam Kirton
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| | - Melanie Noel
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Frank P MacMaster
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada.,Child and Adolescent Imaging Research (CAIR) Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Strategic Clinical Network for Addictions and Mental Health, Alberta Health Services, Calgary, Alberta, Canada
| | - Signe Bray
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Child and Adolescent Imaging Research (CAIR) Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Karen M Barlow
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Child and Adolescent Imaging Research (CAIR) Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Brian L Brooks
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada.,Department of Psychology, University of Calgary, Calgary, Alberta, Canada.,Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada
| | - Catherine Lebel
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Child and Adolescent Imaging Research (CAIR) Program, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Helen L Carlson
- University of Calgary, Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
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8
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Kai J, Khan AR, Haast RA, Lau JC. Mapping the subcortical connectome using in vivo diffusion MRI: Feasibility and reliability. Neuroimage 2022; 262:119553. [PMID: 35961469 DOI: 10.1016/j.neuroimage.2022.119553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 07/15/2022] [Accepted: 08/08/2022] [Indexed: 10/31/2022] Open
Abstract
Tractography combined with regions of interest (ROIs) has been used to non-invasively study the structural connectivity of the cortex as well as to assess the reliability of these connections. However, the subcortical connectome (subcortex to subcortex) has not been comprehensively examined, in part due to the difficulty of performing tractography in this complex and compact region. In this study, we performed an in vivo investigation using tractography to assess the feasibility and reliability of mapping known connections between structures of the subcortex using the test-retest dataset from the Human Connectome Project (HCP). We further validated our observations using a separate unrelated subjects dataset from the HCP. Quantitative assessment was performed by computing tract densities and spatial overlap of identified connections between subcortical ROIs. Further, known connections between structures of the basal ganglia and thalamus were identified and visually inspected, comparing tractography reconstructed trajectories with descriptions from tract-tracing studies. Our observations demonstrate both the feasibility and reliability of using a data-driven tractography-based approach to map the subcortical connectome in vivo.
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Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada; Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada
| | - Roy Am Haast
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Aix-Marseille University, CNRS, CRMBM, UMR 7339, Marseille, France
| | - Jonathan C Lau
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada; Department of Clinical Neurological Sciences, Division of Neurosurgery, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada.
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9
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García AO, Brambati SM, Desautels A, Marcotte K. Timing stroke: A review on stroke pathophysiology and its influence over time on diffusion measures. J Neurol Sci 2022; 441:120377. [DOI: 10.1016/j.jns.2022.120377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 06/30/2022] [Accepted: 07/31/2022] [Indexed: 11/26/2022]
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10
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Medaglia JD, Erickson BA, Pustina D, Kelkar AS, DeMarco AT, Dickens JV, Turkeltaub PE. Simulated Attack Reveals How Lesions Affect Network Properties in Poststroke Aphasia. J Neurosci 2022; 42:4913-4926. [PMID: 35545436 PMCID: PMC9188386 DOI: 10.1523/jneurosci.1163-21.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/21/2022] Open
Abstract
Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.
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Affiliation(s)
- John D Medaglia
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104
- Department of Neurology, Drexel University, Philadelphia, Pennsylvania 19104
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Brian A Erickson
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104
| | - Dorian Pustina
- Cure Huntingdon's Disease Initiative (CHDI) Foundation, Princeton, New Jersey 08540
| | - Apoorva S Kelkar
- Department of Psychology, Drexel University, Philadelphia, Pennsylvania 19104
| | - Andrew T DeMarco
- Department of Neurology, Georgetown University, Washington, DC 20007
| | - J Vivian Dickens
- Department of Neurology, Georgetown University, Washington, DC 20007
| | - Peter E Turkeltaub
- Department of Neurology, Georgetown University, Washington, DC 20007
- MedStar National Rehabilitation Hospital, Washington, DC 20007
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11
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Rheault F, Schilling KG, Valcourt‐Caron A, Théberge A, Poirier C, Grenier G, Guberman GI, Begnoche J, Legarreta JH, Y. Cai L, Roy M, Edde M, Caceres MP, Ocampo‐Pineda M, Al‐Sharif N, Karan P, Bontempi P, Obaid S, Bosticardo S, Schiavi S, Sairanen V, Daducci A, Cutting LE, Petit L, Descoteaux M, Landman BA. Tractostorm 2: Optimizing tractography dissection reproducibility with segmentation protocol dissemination. Hum Brain Mapp 2022; 43:2134-2147. [PMID: 35141980 PMCID: PMC8996349 DOI: 10.1002/hbm.25777] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/19/2021] [Accepted: 12/31/2021] [Indexed: 11/29/2022] Open
Abstract
The segmentation of brain structures is a key component of many neuroimaging studies. Consistent anatomical definitions are crucial to ensure consensus on the position and shape of brain structures, but segmentations are prone to variation in their interpretation and execution. White-matter (WM) pathways are global structures of the brain defined by local landmarks, which leads to anatomical definitions being difficult to convey, learn, or teach. Moreover, the complex shape of WM pathways and their representation using tractography (streamlines) make the design and evaluation of dissection protocols difficult and time-consuming. The first iteration of Tractostorm quantified the variability of a pyramidal tract dissection protocol and compared results between experts in neuroanatomy and nonexperts. Despite virtual dissection being used for decades, in-depth investigations of how learning or practicing such protocols impact dissection results are nonexistent. To begin to fill the gap, we evaluate an online educational tractography course and investigate the impact learning and practicing a dissection protocol has on interrater (groupwise) reproducibility. To generate the required data to quantify reproducibility across raters and time, 20 independent raters performed dissections of three bundles of interest on five Human Connectome Project subjects, each with four timepoints. Our investigation shows that the dissection protocol in conjunction with an online course achieves a high level of reproducibility (between 0.85 and 0.90 for the voxel-based Dice score) for the three bundles of interest and remains stable over time (repetition of the protocol). Suggesting that once raters are familiar with the software and tasks at hand, their interpretation and execution at the group level do not drastically vary. When compared to previous work that used a different method of communication for the protocol, our results show that incorporating a virtual educational session increased reproducibility. Insights from this work may be used to improve the future design of WM pathway dissection protocols and to further inform neuroanatomical definitions.
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Affiliation(s)
- Francois Rheault
- Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Kurt G. Schilling
- Vanderbilt University Institute of ImagingNashvilleTennesseeUSA
- Department of Radiology and Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Alex Valcourt‐Caron
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Antoine Théberge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
- Videos and Images Theory and Analytics Laboratory (VITAL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Charles Poirier
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Gabrielle Grenier
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Guido I. Guberman
- Department of Neurology and Neurosurgery, Faculty of MedicineMcGill UniversityMontrealQuébecCanada
| | - John Begnoche
- The Center for Cognitive Medicine, Department of PsychiatryVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Jon Haitz Legarreta
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
- Videos and Images Theory and Analytics Laboratory (VITAL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Leon Y. Cai
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Maggie Roy
- Research Center on AgingUniversité de SherbrookeSherbrookeQuébecCanada
| | - Manon Edde
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Marco Perez Caceres
- Département de Radiologie DiagnostiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Mario Ocampo‐Pineda
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Noor Al‐Sharif
- McGill Centre for Integrative Neuroscience (MCIN)McGill UniversityMontrealQuébecCanada
| | - Philippe Karan
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Pietro Bontempi
- Department of Neurosciences, Biomedicine and Movement SciencesUniversity of VeronaVeronaItaly
| | - Sami Obaid
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
- University of Montreal, Health Center Research CenterMontrealCanada
| | - Sara Bosticardo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Simona Schiavi
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Viljami Sairanen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Alessandro Daducci
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Laurie E. Cutting
- Vanderbilt Kennedy CenterVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Laurent Petit
- Groupe d'imagerie neurofonctionnelleCNRS, CEA, IMN, University of BordeauxBordeauxFrance
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d'InformatiqueUniversité de SherbrookeSherbrookeQuébecCanada
| | - Bennett A. Landman
- Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Vanderbilt University Institute of ImagingNashvilleTennesseeUSA
- Department of Radiology and Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
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12
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Maillard P, Lu H, Arfanakis K, Gold BT, Bauer CE, Zachariou V, Stables L, Wang DJ, Jann K, Seshadri S, Duering M, Hillmer LJ, Rosenberg GA, Snoussi H, Sepehrband F, Habes M, Singh B, Kramer JH, Corriveau RA, Singh H, Schwab K, Helmer KG, Greenberg SM, Caprihan A, DeCarli C, Satizabal CL. Instrumental validation of free water, peak-width of skeletonized mean diffusivity, and white matter hyperintensities: MarkVCID neuroimaging kits. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12261. [PMID: 35382232 PMCID: PMC8959640 DOI: 10.1002/dad2.12261] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Indexed: 11/11/2022]
Abstract
Introduction To describe the protocol and findings of the instrumental validation of three imaging-based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1-weighted imaging. Methods The instrumental validation of imaging-based biomarker kits included inter-rater reliability among participating sites, test-retest repeatability, and inter-scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra-class correlation coefficients (ICC). Results The three biomarkers demonstrated excellent inter-rater reliability (ICC >0.94, P-values < .001), very high agreement between test and retest sessions (ICC >0.98, P-values < .001), and were extremely consistent across the three scanners (ICC >0.98, P-values < .001). Discussion The three biomarker kits demonstrated very high inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility, offering robust biomarkers suitable for future multi-site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).
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Affiliation(s)
- Pauline Maillard
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Hanzhang Lu
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Konstantinos Arfanakis
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Department of Diagnostic Radiology and Nuclear Medicine, Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Brian T. Gold
- Department of NeuroscienceUniversity of KentuckyLexingtonKentuckyUSA
| | | | | | - Lara Stables
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Danny J.J. Wang
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Kay Jann
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Marco Duering
- Department of Biomedical EngineeringMedical Image Analysis Center (MIAC AG)University of BaselBaselSwitzerland
| | - Laura J. Hillmer
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Gary A. Rosenberg
- Department of NeurologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Haykel Snoussi
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Farshid Sepehrband
- Laboratory of FMRI Technology (LOFT)Stevens Neuroimaging and Informatics InstituteKeck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
| | - Baljeet Singh
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Joel H. Kramer
- Department of NeurologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | | | - Herpreet Singh
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Kristin Schwab
- Department of NeurologyMassachusetts General HospitalBostonMassachusettsUSA
| | - Karl G. Helmer
- Department of RadiologyMassachusetts General HospitalBostonMassachusettsUSA
- Department of RadiologyHarvard Medical SchoolBostonMassachusettsUSA
| | | | | | - Charles DeCarli
- Department of NeurologyUniversity of California, DavisDavisCaliforniaUSA
| | - Claudia L. Satizabal
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesUniversity of Texas Health San AntonioSan AntonioTexasUSA
- Department of Population Health SciencesUniversity of Texas Health San AntonioSan AntonioTexasUSA
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13
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Cai LY, Yang Q, Kanakaraj P, Nath V, Newton AT, Edmonson HA, Luci J, Conrad BN, Price GR, Hansen CB, Kerley CI, Ramadass K, Yeh FC, Kang H, Garyfallidis E, Descoteaux M, Rheault F, Schilling KG, Landman BA. MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI. Magn Reson Med 2021; 86:3304-3320. [PMID: 34270123 PMCID: PMC9087815 DOI: 10.1002/mrm.28926] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Praitayini Kanakaraj
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Allen T. Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jeffrey Luci
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Cailey I. Kerley
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Maxime Descoteaux
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
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14
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Kruper J, Yeatman JD, Richie-Halford A, Bloom D, Grotheer M, Caffarra S, Kiar G, Karipidis II, Roy E, Chandio BQ, Garyfallidis E, Rokem A. Evaluating the Reliability of Human Brain White Matter Tractometry. APERTURE NEURO 2021; 1:10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669. [PMID: 35079748 PMCID: PMC8785971 DOI: 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Mareike Grotheer
- Center for Mind, Brain and Behavior - CMBB, Hans-Meerwein-Straße 6, Marburg 35032, Germany
- Department of Psychology, University of Marburg, Marburg 35039, Germany
| | - Sendy Caffarra
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, BCBL, 20009, Spain
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, H3A 0E9, Canada
| | - Iliana I Karipidis
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine,Stanford, CA, 94305, USA
| | - Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
| | - Bramsh Q Chandio
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
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15
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Osa García A, Brambati SM, Brisebois A, Désilets-Barnabé M, Houzé B, Bedetti C, Rochon E, Leonard C, Desautels A, Marcotte K. Predicting Early Post-stroke Aphasia Outcome From Initial Aphasia Severity. Front Neurol 2020; 11:120. [PMID: 32153496 PMCID: PMC7047164 DOI: 10.3389/fneur.2020.00120] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 02/03/2020] [Indexed: 12/11/2022] Open
Abstract
Background: The greatest degree of language recovery in post-stroke aphasia takes place within the first weeks. Aphasia severity and lesion measures have been shown to be good predictors of long-term outcomes. However, little is known about their implications in early spontaneous recovery. The present study sought to determine which factors better predict early language outcomes in individuals with post-stroke aphasia. Methods: Twenty individuals with post-stroke aphasia were assessed <72 h (acute) and 10-14 days (subacute) after stroke onset. We developed a composite score (CS) consisting of several linguistic sub-tests: repetition, oral comprehension and naming. Lesion volume, lesion load and diffusion measures [fractional anisotropy (FA) and axial diffusivity (AD)] from both arcuate fasciculi (AF) were also extracted using MRI scans performed at the same time points. A series of regression analyses were performed to predict the CS at the second assessment. Results: Among the diffusion measures, only FA from right AF was found to be a significant predictor of early subacute aphasia outcome. However, when combined in two hierarchical models with FA, age and either lesion load or lesion size, the initial aphasia severity was found to account for most of the variance (R 2 = 0.678), similarly to the complete models (R 2 = 0.703 and R 2 = 0.73, respectively). Conclusions: Initial aphasia severity was the best predictor of early post-stroke aphasia outcome, whereas lesion measures, though highly correlated, show less influence on the prediction model. We suggest that factors predicting early recovery may differ from those involved in long-term recovery.
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Affiliation(s)
- Alberto Osa García
- Centre de Recherche du Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada
- École d'Orthophonie et d'Audiologie, Université de Montréal, Montreal, QC, Canada
| | - Simona Maria Brambati
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
- Département de Psychologie, Université de Montréal, Montreal, QC, Canada
| | - Amélie Brisebois
- Centre de Recherche du Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada
- École d'Orthophonie et d'Audiologie, Université de Montréal, Montreal, QC, Canada
| | - Marianne Désilets-Barnabé
- Centre de Recherche du Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada
- École d'Orthophonie et d'Audiologie, Université de Montréal, Montreal, QC, Canada
| | - Bérengère Houzé
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Christophe Bedetti
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, QC, Canada
| | - Elizabeth Rochon
- Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
- Toronto Rehabilitation Institute, Toronto, ON, Canada
- Heart and Stroke Foundation, Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Carol Leonard
- Department of Speech-Language Pathology, University of Toronto, Toronto, ON, Canada
- Heart and Stroke Foundation, Canadian Partnership for Stroke Recovery, Ottawa, ON, Canada
- School of Rehabilitation Sciences, University of Ottawa, Ottawa, ON, Canada
| | - Alex Desautels
- Centre de Recherche du Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada
- Département de Neurosciences, Université de Montréal, Montreal, QC, Canada
- Centre d'Études Avancées en Médecine du Sommeil, Hôpital du Sacré-Cœur de Montréal, Montreal, QC, Canada
| | - Karine Marcotte
- Centre de Recherche du Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal, Montreal, QC, Canada
- École d'Orthophonie et d'Audiologie, Université de Montréal, Montreal, QC, Canada
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