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Maximov II, Westlye LT. Comparison of different neurite density metrics with brain asymmetry evaluation. Z Med Phys 2023:S0939-3889(23)00085-5. [PMID: 37562999 DOI: 10.1016/j.zemedi.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.
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Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jensen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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2
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Meisler SL, Gabrieli JDE. A large-scale investigation of white matter microstructural associations with reading ability. Neuroimage 2022; 249:118909. [PMID: 35033675 PMCID: PMC8919267 DOI: 10.1016/j.neuroimage.2022.118909] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 01/18/2023] Open
Abstract
Reading involves the functioning of a widely distributed brain network, and white matter tracts are responsible for transmitting information between constituent network nodes. Several studies have analyzed fiber bundle microstructural properties to shed insights into the neural basis of reading abilities and disabilities. Findings have been inconsistent, potentially due to small sample sizes and varying methodology. To address this, we analyzed a large data set of 686 children ages 5-18 using state-of-the-art neuroimaging acquisitions and processing techniques. We searched for associations between fractional anisotropy (FA) and single-word and single-nonword reading skills in children with diverse reading abilities across multiple tracts previously thought to contribute to reading. We also looked for group differences in tract FA between typically reading children and children with reading disabilities. FA of the white matter increased with age across all participants. There were no significant correlations between overall reading abilities and tract FAs across all children, and no significant group differences in tract FA between children with and without reading disabilities. There were associations between FA and nonword reading ability in older children (ages 9 and above). Higher FA in the right superior longitudinal fasciculus (SLF) and left inferior cerebellar peduncle (ICP) correlated with better nonword reading skills. These results suggest that letter-sound correspondence skills, as measured by nonword reading, are associated with greater white matter coherence among older children in these two tracts, as indexed by higher FA.
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Affiliation(s)
- Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, 43 Vassar Street, Bldg. 46, Room 4033 Cambridge, MA, 02139, USA.
| | - John D E Gabrieli
- Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Bldg. 46, Room 4033 Cambridge, MA, 02139, USA.
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3
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Maximov II, van der Meer D, de Lange AMG, Kaufmann T, Shadrin A, Frei O, Wolfers T, Westlye LT. Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example. Hum Brain Mapp 2021; 42:3141-3155. [PMID: 33788350 PMCID: PMC8193531 DOI: 10.1002/hbm.25424] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 03/10/2021] [Accepted: 03/13/2021] [Indexed: 12/12/2022] Open
Abstract
Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population‐based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large‐scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract‐based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.
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Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,LREN, Centre for Research in Neurosciences - Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland.,Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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4
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Bergamino M, Keeling EG, Walsh RR, Stokes AM. Systematic Assessment of the Impact of DTI Methodology on Fractional Anisotropy Measures in Alzheimer's Disease. Tomography 2021; 7:20-38. [PMID: 33681461 PMCID: PMC7934686 DOI: 10.3390/tomography7010003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/17/2020] [Indexed: 12/16/2022] Open
Abstract
White matter microstructural changes in Alzheimer's disease (AD) are often assessed using fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI). FA depends on the acquisition and analysis methods, including the fitting algorithm. In this study, we compared FA maps from different acquisitions and fitting algorithms in AD, mild cognitive impairment (MCI), and healthy controls (HCs) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Three acquisitions from two vendors were compared (Siemens 30, GE 48, and Siemens 54 directions). DTI data were fit using nine fitting algorithms (four linear least squares (LLS), two weighted LLS (WLLS), and three non-linear LLS (NLLS) from four software tools (FSL, DSI-Studio, CAMINO, and AFNI). Different cluster volumes and effect-sizes were observed across acquisitions and fits, but higher consistency was observed as the number of diffusion directions increased. Significant differences were observed between HC and AD groups for all acquisitions, while significant differences between HC and MCI groups were only observed for GE48 and SI54. Using the intraclass correlation coefficient, AFNI-LLS and CAMINO-RESTORE were the least consistent with the other algorithms. By combining data across all three acquisitions and nine fits, differences between AD and HC/MCI groups were observed in the fornix and corpus callosum, indicating FA differences in these regions may be robust DTI-based biomarkers. This study demonstrates that comparisons of FA across aging populations could be confounded by variability in acquisitions and fit methodologies and that identifying the most robust DTI methodology is critical to provide more reliable DTI-based neuroimaging biomarkers for assessing microstructural changes in AD.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (M.B.); (E.G.K.)
| | - Elizabeth G. Keeling
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (M.B.); (E.G.K.)
- School of Life Sciences, Arizona State University, Tempe, AZ 85013, USA
| | - Ryan R. Walsh
- Muhammad Ali Parkinson Center, Barrow Neurological Institute, Phoenix, AZ 85013, USA;
| | - Ashley M. Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ 85013, USA; (M.B.); (E.G.K.)
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5
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Beck D, de Lange AMG, Maximov II, Richard G, Andreassen OA, Nordvik JE, Westlye LT. White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. Neuroimage 2020; 224:117441. [PMID: 33039618 DOI: 10.1016/j.neuroimage.2020.117441] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/22/2022] Open
Abstract
The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the 'FA fine' metric of the RSI model and 'orientation dispersion' (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.
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Affiliation(s)
- Dani Beck
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Oslo, Norway.
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Ivan I Maximov
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | | | - Lars T Westlye
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway.
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6
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Lu Y, Li X, Geng D, Mei N, Wu PY, Huang CC, Jia T, Zhao Y, Wang D, Xiao A, Yin B. Cerebral Micro-Structural Changes in COVID-19 Patients - An MRI-based 3-month Follow-up Study. EClinicalMedicine 2020; 25:100484. [PMID: 32838240 PMCID: PMC7396952 DOI: 10.1016/j.eclinm.2020.100484] [Citation(s) in RCA: 356] [Impact Index Per Article: 89.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Increasing evidence supported the possible neuro-invasion potential of SARS-CoV-2. However, no studies were conducted to explore the existence of the micro-structural changes in the central nervous system after infection. We aimed to identify the existence of potential brain micro-structural changes related to SARS-CoV-2. METHODS In this prospective study, diffusion tensor imaging (DTI) and 3D high-resolution T1WI sequences were acquired in 60 recovered COVID-19 patients (56.67% male; age: 44.10 ± 16.00) and 39 age- and sex-matched non-COVID-19 controls (56.41% male; age: 45.88 ± 13.90). Registered fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were quantified for DTI, and an index score system was introduced. Regional volumes derived from Voxel-based Morphometry (VBM) and DTI metrics were compared using analysis of covariance (ANCOVA). Two sample t-test and Spearman correlation were conducted to assess the relationships among imaging indices, index scores and clinical information. FINDINGS In this follow-up stage, neurological symptoms were presented in 55% COVID-19 patients. COVID-19 patients had statistically significantly higher bilateral gray matter volumes (GMV) in olfactory cortices, hippocampi, insulas, left Rolandic operculum, left Heschl's gyrus and right cingulate gyrus and a general decline of MD, AD, RD accompanied with an increase of FA in white matter, especially AD in the right CR, EC and SFF, and MD in SFF compared with non-COVID-19 volunteers (corrected p value <0.05). Global GMV, GMVs in left Rolandic operculum, right cingulate, bilateral hippocampi, left Heschl's gyrus, and Global MD of WM were found to correlate with memory loss (p value <0.05). GMVs in the right cingulate gyrus and left hippocampus were related to smell loss (p value <0.05). MD-GM score, global GMV, and GMV in right cingulate gyrus were correlated with LDH level (p value <0.05). INTERPRETATION Study findings revealed possible disruption to micro-structural and functional brain integrity in the recovery stages of COVID-19, suggesting the long-term consequences of SARS-CoV-2. FUNDING Shanghai Natural Science Foundation, Youth Program of National Natural Science Foundation of China, Shanghai Sailing Program, Shanghai Science and Technology Development, Shanghai Municipal Science and Technology Major Project and ZJ Lab.
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Key Words
- 3D-T1WI, 3 Dimensional T1-weighted Images
- AAL-3, Automated Anatomical Labelling Atlas-3
- ACE-2, Angiotensin Converting Enzyme-2
- AD, Axial Diffusivity
- CNS, Central Nervous System
- COVID-19
- COVID-19, Coronavirus Disease
- CR, Corona Radiata
- CSF, Cerebral Spinal Fluid
- Central Nervous System Diseases
- DICOM, Digital Imaging and Communications in Medicine
- DTI, Diffusion Tensor Imaging
- Diffusion Tensor Imaging
- EC, External Capsule
- FA, Fractional Anisotropy
- FOV, Field of View
- GM, Gray Matter
- GMV, Gray Matter Volume
- HIV, Human Immunodeficiency Virus
- HSV, Herpes Simplex Virus
- JEV, Japanese Encephalitis Virus
- LDH, Lactate Dehydrogenase
- MD, Mean Diffusivity
- MPRAGE, Magnetization Prepared Rapid Gradient Echo
- Neuroimaging
- OB, Olfactory Bulb
- PCR, Polymerase Chain Reaction
- Prospective studies
- RD, Radial Diffusivity
- SARS-CoV, Severe Acute Respiratory Syndrome Coronavirus
- SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus-2
- SFF, Superior Frontal-occipital Fasciculus
- TBSS, Track-based Spatial Statistics
- TE, Echo Time
- TR, Repetition Time
- UF, Uncinate Fasciculus
- URTI, Upper Respiratory Tract Infection
- VBM, Voxel-based Morphometry
- WBC, White Blood Cell
- WHO, World Health Organization
- WM, White Matter
- WMV, White Matter Volume
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Affiliation(s)
- Yiping Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Xuanxuan Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Nan Mei
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Pu-Yeh Wu
- GE Healthcare, MR Research China, Beijing, China (P Wu)
| | - Chu-Chung Huang
- Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China (C Huang)
| | - Tianye Jia
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, England (T Jia)
| | - Yajing Zhao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Dongdong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
| | - Anling Xiao
- Department of Radiology, Fu Yang No.2 Hospital, Anhui, China (A Xiao)
| | - Bo Yin
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China (Y Lu, X Li, D Geng, N Mei, Y Zhao, D Wang, B Yin)
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7
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Bergamino M, Keeling EG, Mishra VR, Stokes AM, Walsh RR. Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review. Front Neurol 2020; 11:314. [PMID: 32477235 PMCID: PMC7240075 DOI: 10.3389/fneur.2020.00314] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 03/31/2020] [Indexed: 12/15/2022] Open
Abstract
Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be impacted early in Parkinson disease (PD). Diffusion magnetic resonance imaging (dMRI) has been used extensively to understand such pathological WM changes, and the focus of this systematic review is to understand both the methods utilized and their corresponding results in the context of early-stage PD. Diffusion tensor imaging (DTI) is the most commonly utilized method to probe WM pathological changes. Previous studies have suggested that DTI metrics are sensitive in capturing early disease-associated WM changes in preclinical symptomatic regions such as olfactory regions and the substantia nigra, which is considered to be a hallmark of PD pathology and progression. Postprocessing analytic approaches include region of interest-based analysis, voxel-based analysis, skeletonized approaches, and connectome analysis, each with unique advantages and challenges. While DTI has been used extensively to study WM disorganization in early-stage PD, it has several limitations, including an inability to resolve multiple fiber orientations within each voxel and sensitivity to partial volume effects. Given the subtle changes associated with early-stage PD, these limitations result in inaccuracies that severely impact the reliability of DTI-based metrics as potential biomarkers. To overcome these limitations, advanced dMRI acquisition and analysis methods have been employed, including diffusion kurtosis imaging and q-space diffeomorphic reconstruction. The combination of improved acquisition and analysis in DTI may yield novel and accurate information related to WM-associated changes in early-stage PD. In the current article, we present a systematic and critical review of dMRI studies in early-stage PD, with a focus on recent advances in DTI methodology. Yielding novel metrics, these advanced methods have been shown to detect diffuse WM changes in early-stage PD. These findings support the notion of early axonal damage in PD and suggest that WM pathology may go unrecognized until symptoms appear. Finally, the advantages and disadvantages of different dMRI techniques, analysis methods, and software employed are discussed in the context of PD-related pathology.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Elizabeth G. Keeling
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Virendra R. Mishra
- Imaging Research, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Ryan R. Walsh
- Muhammad Ali Parkinson Center, Barrow Neurological Institute, Phoenix, AZ, United States
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8
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Maximov II, Alnæs D, Westlye LT. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum Brain Mapp 2019; 40:4146-4162. [PMID: 31173439 PMCID: PMC6865652 DOI: 10.1002/hbm.24691] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 05/14/2019] [Accepted: 05/27/2019] [Indexed: 12/30/2022] Open
Abstract
Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data.
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Affiliation(s)
- Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dag Alnæs
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Department of Mental Health and AddictionNorwegian Centre for Mental Disorders Research spiepr132 (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
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9
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Gray matter nuclei damage in acute carbon monoxide intoxication assessed in vivo using diffusion tensor MR imaging. Radiol Med 2019; 125:80-86. [PMID: 31529401 DOI: 10.1007/s11547-019-01078-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 09/04/2019] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To observe the structural changes of gray matter nuclei in patients with acute carbon monoxide intoxication by diffusion tensor imaging (DTI), quantify the degree of deep gray matter damage in the brain by adopting imaging technology and research the characteristics of the damage and its pertinence with memory and cognitive impairment. METHODS Twenty-five patients with acute carbon monoxide intoxication and 25 healthy volunteers matched in sex and age were examined by routine head MRI and diffusion tensor imaging (DTI). Bilateral hippocampus, dater nucleus, thalamus, amygdala, globus pallidus and putamen were taken as regions of interest. The mean diffusion coefficient (MD), anisotropic fraction (FA) and appearance of deep gray matter nucleus in patients with acute carbon monoxide intoxication were analyzed. It found that the change of diffusion coefficient (ADC) and its clinical correlation with cognitive impairment were generated by carbon monoxide intoxication. RESULTS Compared with the healthy control group, the FA values of bilateral globus pallidus, hippocampus, dater nucleus and putamen decreased, while the FA values of amygdala and thalamus had no statistical significance; the MD values and ADC values of hippocampus, globus pallidus and putamen increased, while the MD and ADC values of dater nucleus, thalamus and amygdala had no statistical significance, either. CONCLUSION DTI is capable of sensitively reflecting the damage of gray matter nuclei caused by acute carbon monoxide intoxication and quantifying the degree of hypoxic brain damage in a certain extent, and may be related to cognitive impairment.
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Kremneva EI, Legostaeva LA, Morozova SN, Sergeev DV, Sinitsyn DO, Iazeva EG, Suslin AS, Suponeva NA, Krotenkova MV, Piradov MA, Maximov II. Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness. Brain Sci 2019; 9:brainsci9050123. [PMID: 31137909 PMCID: PMC6562474 DOI: 10.3390/brainsci9050123] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 05/23/2019] [Accepted: 05/23/2019] [Indexed: 01/06/2023] Open
Abstract
Diagnostic accuracy of different chronic disorders of consciousness (DOC) can be affected by the false negative errors in up to 40% cases. In the present study, we aimed to investigate the feasibility of a non-Gaussian diffusion approach in chronic DOC and to estimate a sensitivity of diffusion kurtosis imaging (DKI) metrics for the differentiation of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) from a healthy brain state. We acquired diffusion MRI data from 18 patients in chronic DOC (11 VS/UWS, 7 MCS) and 14 healthy controls. A quantitative comparison of the diffusion metrics for grey (GM) and white (WM) matter between the controls and patient group showed a significant (p < 0.05) difference in supratentorial WM and GM for all evaluated diffusion metrics, as well as for brainstem, corpus callosum, and thalamus. An intra-subject VS/UWS and MCS group comparison showed only kurtosis metrics and fractional anisotropy differences using tract-based spatial statistics, owing mainly to macrostructural differences on most severely lesioned hemispheres. As a result, we demonstrated an ability of DKI metrics to localise and detect changes in both WM and GM and showed their capability in order to distinguish patients with a different level of consciousness.
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Affiliation(s)
- Elena I Kremneva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | | | - Sofya N Morozova
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Dmitry V Sergeev
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Dmitry O Sinitsyn
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Elizaveta G Iazeva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Aleksandr S Suslin
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Natalia A Suponeva
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Marina V Krotenkova
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Michael A Piradov
- Research Center of Neurology, 80 Volokolamskoe shosse, 125367 Moscow, Russia.
| | - Ivan I Maximov
- Department of Psychology, University of Oslo, Forskningsveien 3A, 0373 Oslo, Norway.
- Norwegian Centre for Mental Disorders Research (NORMENT), Norway and Institute of Clinical Medicine, University of Oslo, Oslo Universitetssykehus Bygg 48 Ullevål, 0317 Oslo, Norway.
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11
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Isotropically weighted intravoxel incoherent motion brain imaging at 7T. Magn Reson Imaging 2018; 57:124-132. [PMID: 30472300 DOI: 10.1016/j.mri.2018.11.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 10/30/2018] [Accepted: 11/17/2018] [Indexed: 12/13/2022]
Abstract
Perfusion magnetic resonance imaging (MRI) is a promising non-invasive technique providing insights regarding the brain's microvascular architecture in vivo. The scalar perfusion metrics can be used for quantitative diagnostics of various brain abnormalities, in particular, in the stroke cases and tumours. However, conventional MRI-based perfusion approaches such as dynamic contrast-enhanced perfusion imaging or arterial spin labelling have a few weaknesses, for instance, contrast agent deposition, low signal-to-noise ratio, limited temporal and spatial resolution, and specific absorption rate constraints. As an alternative, the intravoxel incoherent motion (IVIM) approach exploits an extension of diffusion MRI in order to estimate perfusion parameters in the human brain. Application of IVIM imaging at ultra-high field MRI might employ the advantage of a higher signal-to-noise ratio, and thereby the use of higher spatial and temporal resolutions. In the present work, we demonstrate an application of recently developed isotropic diffusion weighted sequences to the evaluation of IVIM parameters at an ultra-high 7T field. The used sequence exhibits high immunity to image degrading factors and allows one to acquire the data in a fast and efficient way. Utilising the bi-exponential fitting model of the signal attenuation, we performed an extensive analysis of the IVIM scalar metrics obtained by a isotropic diffusion weighted sequence in vivo and compared results with a conventional pulsed gradient sequence at 7T. In order to evaluate a possible metric bias originating from blood flows, we additionally used a truncated b-value protocol (b-values from 100 to 200 s/mm2 with the step 20 s/mm2) accompanied to the full range (b-values from 0 to 200 s/mm2). The IVIM scalar metrics have been assessed and analysed together with a large and middle vessel density atlas of the human brain. We found that the diffusion coefficients and perfusion fractions of the voxels consisting of large and middle vessels have higher values in contrast to other tissues. Additionally, we did not find a strong dependence of the IVIM metrics on the density values of the vessel atlas. Perspectives and limitations of the developed isotropic diffusion weighted perfusion are presented and discussed.
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Pfuhl G, King JA, Geisler D, Roschinski B, Ritschel F, Seidel M, Bernardoni F, Müller DK, White T, Roessner V, Ehrlich S. Preserved white matter microstructure in young patients with anorexia nervosa? Hum Brain Mapp 2018; 37:4069-4083. [PMID: 27400772 DOI: 10.1002/hbm.23296] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Revised: 06/13/2016] [Accepted: 06/15/2016] [Indexed: 02/06/2023] Open
Abstract
A massive but reversible reduction of cortical thickness and subcortical gray matter (GM) volumes in Anorexia Nervosa (AN) has been recently reported. However, the literature on alterations in white matter (WM) volume and microstructure changes in both acutely underweight AN (acAN) and after recovery (recAN) is sparse and results are inconclusive. Here, T1-weighted and diffusion-weighted MRI data in a sizable sample of young and medication-free acAN (n = 35), recAN (n = 32), and age-matched female healthy controls (HC, n = 62) were obtained. For analysis, a well-validated global probabilistic tractography reconstruction algorithm including rigorous motion correction implemented in FreeSurfer: TRACULA (TRActs Constrained by UnderLying Anatomy) were used. Additionally, a clustering algorithm and a multivariate pattern classification technique to WM metrics to predict group membership were applied. No group differences in either WM volume or WM microstructure were detected with standard analysis procedures either in acAN or recAN relative to HC after controlling for the number of performed statistical tests. These findings were not affected by age, IQ, or psychiatric symptoms. While cluster analysis was unsuccessful at discriminating between groups, multivariate pattern classification showed some ability to separate acAN from HC (but not recAN from HC). However, these results were not compatible with a straightforward hypothesis of impaired WM microstructure. The current findings suggest that WM integrity is largely preserved in non-chronic AN. This finding stands in contrast to findings in GM, but may help to explain the relatively intact cognitive performance of young patients with AN and provide the basis for the fast recovery of GM structures. Hum Brain Mapp 37:4069-4083, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Gerit Pfuhl
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.,Department of Psychology, UiT the Arctic University of Norway & Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | - Joseph A King
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.,Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Daniel Geisler
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.,Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Benjamin Roschinski
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Franziska Ritschel
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.,Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Maria Seidel
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.,Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Fabio Bernardoni
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany.,Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Dirk K Müller
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Tonya White
- Department of Child and Adolescent Psychiatry & Department of Radiology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Veit Roessner
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
| | - Stefan Ehrlich
- Eating Disorders Research and Treatment Center at the Dept. of Child and Adolescent Psychiatry, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany. .,Division of Psychological and Social Medicine and Developmental Neurosciences, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany. .,MGH/MIT/HMS Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts. .,Harvard Medical School, Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts.
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Bergamino M, Farmer M, Yeh HW, Paul E, Hamilton JP. Statistical differences in the white matter tracts in subjects with depression by using different skeletonized voxel-wise analysis approaches and DTI fitting procedures. Brain Res 2017. [DOI: 10.1016/j.brainres.2017.06.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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14
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Maximov II, Tonoyan AS, Pronin IN. Differentiation of glioma malignancy grade using diffusion MRI. Phys Med 2017; 40:24-32. [PMID: 28712716 DOI: 10.1016/j.ejmp.2017.07.002] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 06/26/2017] [Accepted: 07/04/2017] [Indexed: 12/31/2022] Open
Abstract
Modern diffusion MR protocols allow one to acquire the multi-shell diffusion data with high diffusion weightings in a clinically feasible time. In the present work we assessed three diffusion approaches based on diffusion and kurtosis tensor imaging (DTI, DKI), and neurite orientation dispersion and density imaging (NODDI) as possible biomarkers for human brain glioma grade differentiation based on the one diffusion protocol. We used three diffusion weightings (so called b-values) equal to 0, 1000, and 2500s/mm2 with 60 non-coplanar diffusion directions in the case of non-zero b-values. The patient groups of the glioma grades II, III, and IV consist of 8 subjects per group. We found that DKI, and NODDI scalar metrics can be effectively used as glioma grade biomarkers with a significant difference (p<0.05) for grading between low- and high-grade gliomas, in particular, for glioma II versus glioma III grades, and glioma III versus glioma IV grades. The use of mean/axial kurtosis and intra-axonal fraction/orientation dispersion index metrics allowed us to obtain the most feasible and reliable differentiation criteria. For example, in the case of glioma grades II, III, and IV the mean kurtosis is equal to 0.31, 0.51, and 0.90, and the orientation dispersion index is equal to 0.14, 0.30, and 0.59, respectively. The limitations and perspectives of the biophysical diffusion models based on intra-/extra-axonal compartmentalisation for glioma differentiation are discussed.
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Affiliation(s)
- Ivan I Maximov
- Experimental Physics III, TU Dortmund University, 44221, Germany.
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Vellmer S, Tonoyan AS, Suter D, Pronin IN, Maximov II. Validation of DWI pre-processing procedures for reliable differentiation between human brain gliomas. Z Med Phys 2017; 28:14-24. [PMID: 28532604 DOI: 10.1016/j.zemedi.2017.04.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 02/21/2017] [Accepted: 04/20/2017] [Indexed: 01/06/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a powerful tool in clinical applications, in particular, in oncology screening. dMRI demonstrated its benefit and efficiency in the localisation and detection of different types of human brain tumours. Clinical dMRI data suffer from multiple artefacts such as motion and eddy-current distortions, contamination by noise, outliers etc. In order to increase the image quality of the derived diffusion scalar metrics and the accuracy of the subsequent data analysis, various pre-processing approaches are actively developed and used. In the present work we assess the effect of different pre-processing procedures such as a noise correction, different smoothing algorithms and spatial interpolation of raw diffusion data, with respect to the accuracy of brain glioma differentiation. As a set of sensitive biomarkers of the glioma malignancy grades we chose the derived scalar metrics from diffusion and kurtosis tensor imaging as well as the neurite orientation dispersion and density imaging (NODDI) biophysical model. Our results show that the application of noise correction, anisotropic diffusion filtering, and cubic-order spline interpolation resulted in the highest sensitivity and specificity for glioma malignancy grading. Thus, these pre-processing steps are recommended for the statistical analysis in brain tumour studies.
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Affiliation(s)
- Sebastian Vellmer
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
| | | | - Dieter Suter
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | | | - Ivan I Maximov
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
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16
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Vellmer S, Stirnberg R, Edelhoff D, Suter D, Stöcker T, Maximov II. Comparative analysis of isotropic diffusion weighted imaging sequences. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2017; 275:137-147. [PMID: 28073068 DOI: 10.1016/j.jmr.2016.12.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 12/21/2016] [Accepted: 12/22/2016] [Indexed: 06/06/2023]
Abstract
Visualisation of living tissue structure and function is a challenging problem of modern imaging techniques. Diffusion MRI allows one to probe in vivo structures on a micrometer scale. However, conventional diffusion measurements are time-consuming procedures, because they require several measurements with different gradient directions. Considerable time savings are therefore possible by measurement schemes that generate an isotropic diffusion weighting in a single shot. Multiple approaches for generating isotropic diffusion weighting are known and have become very popular as useful tools in clinical research. Thus, there is a strong need for a comprehensive comparison of different isotropic weighting approaches. In the present work we introduce two new sequences based on simple (co)sine modulations and compare their performance to established q-space magic-angle spinning sequences and conventional DTI, using a diffusion phantom assembled from microcapillaries and in vivo experiments at 7T. The advantages and disadvantages of all compared schemes are demonstrated and discussed.
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Affiliation(s)
- Sebastian Vellmer
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
| | | | - Daniel Edelhoff
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | - Dieter Suter
- Experimental Physics III, TU Dortmund University, Dortmund, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Physics and Astronomy, University of Bonn, Bonn, Germany
| | - Ivan I Maximov
- Experimental Physics III, TU Dortmund University, Dortmund, Germany.
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17
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Grinberg F, Maximov II, Farrher E, Neuner I, Amort L, Thönneßen H, Oberwelland E, Konrad K, Shah NJ. Diffusion kurtosis metrics as biomarkers of microstructural development: A comparative study of a group of children and a group of adults. Neuroimage 2017; 144:12-22. [DOI: 10.1016/j.neuroimage.2016.08.033] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Revised: 07/21/2016] [Accepted: 08/17/2016] [Indexed: 01/08/2023] Open
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18
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Warbrick T, Fegers-Stollenwerk V, Maximov II, Grinberg F, Shah NJ. Using Structural and Functional Brain Imaging to Investigate Responses to Acute Thermal Pain. THE JOURNAL OF PAIN 2016; 17:836-44. [PMID: 27102895 DOI: 10.1016/j.jpain.2016.03.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 02/21/2016] [Accepted: 03/05/2016] [Indexed: 02/04/2023]
Abstract
UNLABELLED Despite a fundamental interest in the relationship between structure and function, the relationships between measures of white matter microstructural coherence and functional brain responses to pain are poorly understood. We investigated whether fractional anisotropy (FA) in 2 white matter regions in pathways associated with pain is related to the functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) response to thermal stimulation. BOLD fMRI was measured from 16 healthy male subjects during painful thermal stimulation of the right arm. Diffusion-weighted images were acquired for each subject and FA estimates were extracted from the posterior internal capsule and the cingulum (cingulate gyrus). These values were then included as covariates in the fMRI data analysis. We found BOLD response in the midcingulate cortex (MCC) to be positively related to FA in the posterior internal capsule and negatively related to FA in the cingulum. Our results suggest that the MCC's involvement in processing pain can be further delineated by considering how the magnitude of the BOLD response is related to white matter microstructural coherence and to subjective perception of pain. Considering relationships to white matter microstructural coherence in tracts involved in transmitting information to different parts of the pain network can help interpretation of MCC BOLD activation. PERSPECTIVE Relationships between functional brain responses, white matter microstructural coherence, and subjective ratings are crucial for understanding the role of the MCC in pain. These findings provide a basis for investigating the effect of the reduced white matter microstructural coherence observed in some pain disorders on the functional responses to pain.
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Affiliation(s)
- Tracy Warbrick
- Institute of Neuroscience and Medicine, Jülich, Germany.
| | | | | | - Farida Grinberg
- Institute of Neuroscience and Medicine, Jülich, Germany; Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine, Jülich, Germany; Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen, Germany; Jülich Aachen Research Alliance (JARA) - Translational Brain Medicine, Aachen and Jülich, Germany
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