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Zhang Y, Banihashemi L, Versace A, Samolyk A, Taylor M, English G, Schmithorst VJ, Lee VK, Stiffler R, Aslam H, Panigrahy A, Hipwell AE, Phillips ML. Early Infant Prefrontal Cortical Microstructure Predicts Present and Future Emotionality. Biol Psychiatry 2024:S0006-3223(24)01220-4. [PMID: 38604525 DOI: 10.1016/j.biopsych.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/05/2024] [Accepted: 04/01/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND High levels of infant negative emotionality (NE) and low positive emotionality (PE) predict future emotional and behavioral problems. The prefrontal cortex (PFC) supports emotional regulation, with each PFC subregion specializing in specific emotional processes. Neurite Orientation Dispersion and Density Imaging (NODDI) estimates microstructural integrity and myelination via the neurite density index (NDI) and dispersion via the orientation dispersion index (ODI), with potential to more accurately evaluate microstructural alterations in the developing brain. Yet, no study has used these indices to examine associations between PFC microstructure and concurrent or developing infant emotionality. METHODS We modeled PFC subregional NDI and ODI at 3 months with caregiver-reported infant NE and PE at 3 months (n=61) and at 9 months (n=50), using multivariable and subsequent bivariate regression models. RESULTS The most robust statistically-significant findings were positive associations among 3-month rACC ODI and cACC NDI and concurrent NE, and 3-month lOFC ODI and prospective NE; and a negative association between 3-month dlPFC ODI and concurrent PE. Multivariate models also revealed that other PFC subregional microstructure measures, and infant and caregiver sociodemographic and clinical factors, predicted infant 3- and 9-month NE and PE. CONCLUSIONS Greater NDI and ODI, reflecting greater microstructural complexity, in PFC regions supporting salience perception (rACC), decision-making (lOFC), action selection (cACC), and attentional processes (dlPFC) might result in greater integration of these subregions with other neural networks, greater attention to salient negative external cues, thus higher NE and/or lower PE. These findings provide potential infant cortical markers of future psychopathology risk.
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Affiliation(s)
- Yicheng Zhang
- University of Pittsburgh Swanson School of Engineering, Department of Bioengineering, Pittsburgh, PA.
| | - Layla Banihashemi
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Amelia Versace
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Alyssa Samolyk
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Megan Taylor
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Gabrielle English
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Vanessa J Schmithorst
- UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA
| | - Vincent K Lee
- University of Pittsburgh Swanson School of Engineering, Department of Bioengineering, Pittsburgh, PA; UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA
| | - Richelle Stiffler
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Haris Aslam
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Ashok Panigrahy
- UPMC Children's Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA
| | - Alison E Hipwell
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Mary L Phillips
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
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Phillips JS, Adluru N, Chung MK, Radhakrishnan H, Olm CA, Cook PA, Gee JC, Cousins KAQ, Arezoumandan S, Wolk DA, McMillan CT, Grossman M, Irwin DJ. Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease. Front Neurosci 2024; 18:1353306. [PMID: 38567286 PMCID: PMC10986184 DOI: 10.3389/fnins.2024.1353306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.
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Affiliation(s)
- Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hamsanandini Radhakrishnan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip A. Cook
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - James C. Gee
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Katheryn A. Q. Cousins
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Memory Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Faiyaz A, Doyley MM, Schifitto G, Uddin MN. Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview. Front Neurol 2023; 14:1168833. [PMID: 37153663 PMCID: PMC10160660 DOI: 10.3389/fneur.2023.1168833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 03/27/2023] [Indexed: 05/10/2023] Open
Abstract
Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.
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Affiliation(s)
- Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
| | - Marvin M. Doyley
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Md Nasir Uddin
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
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De Luca A, Schilling KG, Ianus A. Editorial: Is two better than one? Exploring tissue microstructure with multi-modal imaging: Quantitative MRI and beyond. Front Neurosci 2023; 17:1139400. [PMID: 36845436 PMCID: PMC9946448 DOI: 10.3389/fnins.2023.1139400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Affiliation(s)
- Alberto De Luca
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands,Department of Neurology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States,Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal,*Correspondence: Andrada Ianus ✉
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Murdy TJ, Dunn AR, Singh S, Telpoukhovskaia MA, Zhang S, White JK, Kahn I, Febo M, Kaczorowski CC. Leveraging genetic diversity in mice to inform individual differences in brain microstructure and memory. Front Behav Neurosci 2023; 16:1033975. [PMID: 36703722 PMCID: PMC9871587 DOI: 10.3389/fnbeh.2022.1033975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023] Open
Abstract
In human Alzheimer's disease (AD) patients and AD mouse models, both differential pre-disease brain features and differential disease-associated memory decline are observed, suggesting that certain neurological features may protect against AD-related cognitive decline. The combination of these features is known as brain reserve, and understanding the genetic underpinnings of brain reserve may advance AD treatment in genetically diverse human populations. One potential source of brain reserve is brain microstructure, which is genetically influenced and can be measured with diffusion MRI (dMRI). To investigate variation of dMRI metrics in pre-disease-onset, genetically diverse AD mouse models, we utilized a population of genetically distinct AD mice produced by crossing the 5XFAD transgenic mouse model of AD to 3 inbred strains (C57BL/6J, DBA/2J, FVB/NJ) and two wild-derived strains (CAST/EiJ, WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI) to probe neural microanatomy in 83 regions of interest (ROIs). At 5 months of age, these mice underwent contextual fear conditioning (CFC). Strain had a significant effect on dMRI measures in most ROIs tested, while far fewer effects of sex, sex*strain interactions, or strain*sex*5XFAD genotype interactions were observed. A main effect of 5XFAD genotype was observed in only 1 ROI, suggesting that the 5XFAD transgene does not strongly disrupt neural development or microstructure of mice in early adulthood. Strain also explained the most variance in mouse baseline motor activity and long-term fear memory. Additionally, significant effects of sex and strain*sex interaction were observed on baseline motor activity, and significant strain*sex and sex*5XFAD genotype interactions were observed on long-term memory. We are the first to study the genetic influences of brain microanatomy in genetically diverse AD mice. Thus, we demonstrated that strain is the primary factor influencing brain microstructure in young adult AD mice and that neural development and early adult microstructure are not strongly altered by the 5XFAD transgene. We also demonstrated that strain, sex, and 5XFAD genotype interact to influence memory in genetically diverse adult mice. Our results support the usefulness of the 5XFAD mouse model and convey strong relationships between natural genetic variation, brain microstructure, and memory.
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Affiliation(s)
| | - Amy R. Dunn
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - Surjeet Singh
- The Jackson Laboratory, Bar Harbor, ME, United States
| | | | | | | | - Itamar Kahn
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Marcelo Febo
- Department of Neuroscience, University of Florida College of Medicine, Gainesville, FL, United States
| | - Catherine C. Kaczorowski
- The Jackson Laboratory, Bar Harbor, ME, United States,*Correspondence: Catherine C. Kaczorowski,
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Neumane S, Gondova A, Leprince Y, Hertz-Pannier L, Arichi T, Dubois J. Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome. Front Neurosci 2022; 16:932386. [PMID: 36507362 PMCID: PMC9732267 DOI: 10.3389/fnins.2022.932386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.
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Affiliation(s)
- Sara Neumane
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Andrea Gondova
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Yann Leprince
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Lucie Hertz-Pannier
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Tomoki Arichi
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Jessica Dubois
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
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Finkelstein A, Faiyaz A, Weber MT, Qiu X, Uddin MN, Zhong J, Schifitto G. Fixel-Based Analysis and Free Water Corrected DTI Evaluation of HIV-Associated Neurocognitive Disorders. Front Neurol 2021; 12:725059. [PMID: 34803875 PMCID: PMC8600320 DOI: 10.3389/fneur.2021.725059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/04/2021] [Indexed: 12/12/2022] Open
Abstract
Background: White matter (WM) damage is a consistent finding in HIV-infected (HIV+) individuals. Previous studies have evaluated WM fiber tract-specific brain regions in HIV-associated neurocognitive disorders (HAND) using diffusion tensor imaging (DTI). However, DTI might lack an accurate biological interpretation, and the technique suffers from several limitations. Fixel-based analysis (FBA) and free water corrected DTI (fwcDTI) have recently emerged as useful techniques to quantify abnormalities in WM. Here, we sought to evaluate FBA and fwcDTI metrics between HIV+ and healthy controls (HIV−) individuals. Using machine learning classifiers, we compared the specificity of both FBA and fwcDTI metrics in their ability to distinguish between individuals with and without cognitive impairment in HIV+ individuals. Methods: Forty-two HIV+ and 52 HIV– participants underwent MRI exam, clinical, and neuropsychological assessments. FBA metrics included fiber density (FD), fiber bundle cross section (FC), and fiber density and cross section (FDC). We also obtained fwcDTI metrics such as fractional anisotropy (FAT) and mean diffusivity (MDT). Tract-based spatial statistics (TBSS) was performed on FAT and MDT. We evaluated the correlations between MRI metrics with cognitive performance and blood markers, such as neurofilament light chain (NfL), and Tau protein. Four different binary classifiers were used to show the specificity of the MRI metrics for classifying cognitive impairment in HIV+ individuals. Results: Whole-brain FBA showed significant reductions (up to 15%) in various fiber bundles, specifically the cerebral peduncle, posterior limb of internal capsule, middle cerebellar peduncle, and superior corona radiata. TBSS of fwcDTI metrics revealed decreased FAT in HIV+ individuals compared to HIV– individuals in areas consistent with those observed in FBA, but these were not significant. Machine learning classifiers were consistently better able to distinguish between cognitively normal patients and those with cognitive impairment when using fixel-based metrics as input features as compared to fwcDTI metrics. Conclusion: Our findings lend support that FBA may serve as a potential in vivo biomarker for evaluating and monitoring axonal degeneration in HIV+ patients at risk for neurocognitive impairment.
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Affiliation(s)
- Alan Finkelstein
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
| | - Miriam T Weber
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States
| | - Md Nasir Uddin
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Jianhui Zhong
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States.,Department of Physics and Astronomy, University of Rochester, Rochester, NY, United States.,Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States.,Department of Neurology, University of Rochester, Rochester, NY, United States.,Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
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Jordan K, Morin O, Wahl M, Amirbekian B, Chapman C, Owen J, Mukherjee P, Braunstein S, Henry R. An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography. Front Oncol 2019; 9:810. [PMID: 31544062 PMCID: PMC6730482 DOI: 10.3389/fonc.2019.00810] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 08/08/2019] [Indexed: 12/04/2022] Open
Abstract
There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning.
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Affiliation(s)
- Kesshi Jordan
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Michael Wahl
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States.,Department of Radiation Oncology, Samaritan Pastega Regional Cancer Center, Corvallis, OR, United States
| | - Bagrat Amirbekian
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States
| | - Christopher Chapman
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Julia Owen
- Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology, University of Washington, Seattle, WA, United States
| | - Pratik Mukherjee
- Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.,Center for Imaging of Neurodegenerative Disease, San Francisco VA Medical Center, San Francisco, CA, United States
| | - Steve Braunstein
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, United States
| | - Roland Henry
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States.,Joint Graduate Group in Bioengineering, University of California San Francisco and University of California Berkeley, San Francisco/Berkeley, CA, United States.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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9
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Moura LM, Luccas R, de Paiva JPQ, Amaro E, Leemans A, Leite CDC, Otaduy MCG, Conforto AB. Diffusion Tensor Imaging Biomarkers to Predict Motor Outcomes in Stroke: A Narrative Review. Front Neurol 2019; 10:445. [PMID: 31156529 PMCID: PMC6530391 DOI: 10.3389/fneur.2019.00445] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 04/12/2019] [Indexed: 12/14/2022] Open
Abstract
Stroke is a leading cause of disability worldwide. Motor impairments occur in most of the patients with stroke in the acute phase and contribute substantially to disability. Diffusion tensor imaging (DTI) biomarkers such as fractional anisotropy (FA) measured at an early phase after stroke have emerged as potential predictors of motor recovery. In this narrative review, we: (1) review key concepts of diffusion MRI (dMRI); (2) present an overview of state-of-art methodological aspects of data collection, analysis and reporting; and (3) critically review challenges of DTI in stroke as well as results of studies that investigated the correlation between DTI metrics within the corticospinal tract and motor outcomes at different stages after stroke. We reviewed studies published between January, 2008 and December, 2018, that reported correlations between DTI metrics collected within the first 24 h (hyperacute), 2-7 days (acute), and >7-90 days (early subacute) after stroke. Nineteen studies were included. Our review shows that there is no consensus about gold standards for DTI data collection or processing. We found great methodological differences across studies that evaluated DTI metrics within the corticospinal tract. Despite heterogeneity in stroke lesions and analysis approaches, the majority of studies reported significant correlations between DTI biomarkers and motor impairments. It remains to be determined whether DTI results could enhance the predictive value of motor disability models based on clinical and neurophysiological variables.
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Affiliation(s)
- Luciana M. Moura
- Neurostimulation Laboratory, Neurology Department, Hospital das Clínicas/São Paulo University, São Paulo, Brazil
| | - Rafael Luccas
- Neurostimulation Laboratory, Neurology Department, Hospital das Clínicas/São Paulo University, São Paulo, Brazil
| | | | - Edson Amaro
- Hospital Israelita Albert Einstein, São Paulo, Brazil
- Lim 44, Department of Radiology and Oncology, Faculdade de Medicina, Hospital das Clínicas/São Paulo University, São Paulo, Brazil
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, UMC Utrecht, Utrecht, Netherlands
| | - Claudia da C. Leite
- Lim 44, Department of Radiology and Oncology, Faculdade de Medicina, Hospital das Clínicas/São Paulo University, São Paulo, Brazil
| | - Maria C. G. Otaduy
- Lim 44, Department of Radiology and Oncology, Faculdade de Medicina, Hospital das Clínicas/São Paulo University, São Paulo, Brazil
| | - Adriana B. Conforto
- Neurostimulation Laboratory, Neurology Department, Hospital das Clínicas/São Paulo University, São Paulo, Brazil
- Hospital Israelita Albert Einstein, São Paulo, Brazil
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Shapey J, Vos SB, Vercauteren T, Bradford R, Saeed SR, Bisdas S, Ourselin S. Clinical Applications for Diffusion MRI and Tractography of Cranial Nerves Within the Posterior Fossa: A Systematic Review. Front Neurosci 2019; 13:23. [PMID: 30809109 PMCID: PMC6380197 DOI: 10.3389/fnins.2019.00023] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 01/11/2019] [Indexed: 12/21/2022] Open
Abstract
Objective: This paper presents a systematic review of diffusion MRI (dMRI) and tractography of cranial nerves within the posterior fossa. We assess the effectiveness of the diffusion imaging methods used and examine their clinical applications. Methods: The Pubmed, Web of Science and EMBASE databases were searched from January 1st 1997 to December 11th 2017 to identify relevant publications. Any study reporting the use of diffusion imaging and/or tractography in patients with confirmed cranial nerve pathology was eligible for selection. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) tool. Results: We included 41 studies comprising 16 studies of patients with trigeminal neuralgia (TN), 22 studies of patients with a posterior fossa tumor and three studies of patients with other pathologies. Most acquisition protocols used single-shot echo planar imaging (88%) with a single b-value of 1,000 s/mm2 (78%) but there was significant variation in the number of gradient directions, in-plane resolution, and slice thickness between studies. dMRI of the trigeminal nerve generated interpretable data in all cases. Analysis of diffusivity measurements found significantly lower fractional anisotropy (FA) values within the root entry zone of nerves affected by TN and FA values were significantly lower in patients with multiple sclerosis. Diffusivity values within the trigeminal nerve correlate with the effectiveness of surgical treatment and there is some evidence that pre-operative measurements may be predictive of treatment outcome. Fiber tractography was performed in 30 studies (73%). Most studies evaluating fiber tractography involved patients with a vestibular schwannoma (82%) and focused on generating tractography of the facial nerve to assist with surgical planning. Deterministic tractography using diffusion tensor imaging was performed in 93% of cases but the reported success rate and accuracy of generating fiber tracts from the acquired diffusion data varied considerably. Conclusions: dMRI has the potential to inform our understanding of the microstructural changes that occur within the cranial nerves in various pathologies. Cranial nerve tractography is a promising technique but new avenues of using dMRI should be explored to optimize and improve its reliability.
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Affiliation(s)
- Jonathan Shapey
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.,Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sjoerd B Vos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.,Translational Imaging Group-Centre for Medical Image Computing, University College London, London, United Kingdom.,Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,The Ear Institute, University College London, London, United Kingdom.,The Royal National Throat, Nose and Ear Hospital, London, United Kingdom
| | | | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Abstract
Magnetic resonance imaging (MRI) is capable of generating high-resolution brain images with fine anatomic detail and unique tissue contrasts that reveal structures that are not visible to the eye. Sharply defined gray- and white-matter interfaces allow for quantitative anatomic analysis that can be accurately performed with largely automated segmentation methods. In an analogous fashion, diffusion MRI in the brain provides structural information based on contrasts derived from the diffusivity of water in brain tissue, which can highlight the orientation of neuronal axons. Also using largely automated methods, diffusion MRI can be used to generate models of white-matter tracts throughout the brain, a method known as tractography, as well as characterize the microstructural integrity of neuronal axons. Tractographic analysis has helped to define connectivity in the brain that powerfully informs understanding of brain function, and, together with other diffusion metrics, is useful in evaluation of the normal and diseased brain. The quantitative methods of brain segmentation, tractography, and diffusion MRI extend MRI into a realm beyond visual inspection and provide otherwise unachievable sensitivity and specificity in the analysis of brain structure and function.
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Affiliation(s)
- Mykol Larvie
- Divisions of Neuroradiology and Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Boston, MA, USA.
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
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