Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analy...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921010211 |
| _version_ | 1819229453302104064 |
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| author | C.S. Parker T. Veale M. Bocchetta C.F. Slattery I.B. Malone D.L. Thomas J.M. Schott D.M. Cash H. Zhang |
| author_facet | C.S. Parker T. Veale M. Bocchetta C.F. Slattery I.B. Malone D.L. Thomas J.M. Schott D.M. Cash H. Zhang |
| author_sort | C.S. Parker |
| collection | DOAJ |
| description | Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease. |
| first_indexed | 2024-12-23T11:13:25Z |
| format | Article |
| id | doaj.art-96b87f50aff549fb9a6097058077ec26 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-23T11:13:25Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-96b87f50aff549fb9a6097058077ec262022-12-21T17:49:17ZengElsevierNeuroImage1095-95722021-12-01245118749Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted meansC.S. Parker0T. Veale1M. Bocchetta2C.F. Slattery3I.B. Malone4D.L. Thomas5J.M. Schott6D.M. Cash7H. Zhang8Centre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, Floor 1, London WC1V6LJ, United Kingdom; Corresponding author.The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom; UK Dementia Research Institute at UCL, UCL, London, United KingdomThe Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United KingdomThe Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United KingdomThe Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United KingdomThe Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom; Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, UCL, London, United KingdomThe Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United KingdomCentre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, Floor 1, London WC1V6LJ, United Kingdom; The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom; UK Dementia Research Institute at UCL, UCL, London, United KingdomCentre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, Floor 1, London WC1V6LJ, United KingdomNeurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.http://www.sciencedirect.com/science/article/pii/S1053811921010211Diffusion MRIMicrostructure imagingRegion-of-interestArithmetic meanTissue-weighted mean |
| spellingShingle | C.S. Parker T. Veale M. Bocchetta C.F. Slattery I.B. Malone D.L. Thomas J.M. Schott D.M. Cash H. Zhang Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means NeuroImage Diffusion MRI Microstructure imaging Region-of-interest Arithmetic mean Tissue-weighted mean |
| title | Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means |
| title_full | Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means |
| title_fullStr | Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means |
| title_full_unstemmed | Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means |
| title_short | Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means |
| title_sort | not all voxels are created equal reducing estimation bias in regional noddi metrics using tissue weighted means |
| topic | Diffusion MRI Microstructure imaging Region-of-interest Arithmetic mean Tissue-weighted mean |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921010211 |
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