ArticleHuman brain mapping2014
Methods for identifying subject-specific abnormalities in neuroimaging data.
Article in Human brain mapping, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced biomarkers of pediatric mild traumatic brain injury: Progress and perils.Neuroscience and biobehavioral reviews · 2018Pooled it
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- Improved Injury Detection Through Harmonizing Multi-Site Neuroimaging Data after Experimental TBI: A Translational Outcomes Project in NeuroTrauma (TOP-NT) Consortium Study.bioRxiv : the preprint server for biology · 2025Article
- Changes in cerebrovascular reactivity within functional networks in older adults with long-COVID.Frontiers in neurology · 2025Article
- Improved injury detection through harmonizing multi-site neuroimaging data after experimental TBI: a Translational Outcomes Project in Neurotrauma consortium study.Frontiers in neurology · 2025Article
- Longitudinal changes in diffusion tensor imaging in hemodialysis patients.Hemodialysis international. International Symposium on Home Hemodialysis · 2024Article
- Comparison of automated and manual quantification methods for neuromelanin-sensitive MRI in Parkinson's disease.Human brain mapping · 2024Article
- Sex- and Age-Related Differences in Post-Concussive Symptom Reporting Among Children and Their Parents.Journal of neurotrauma · 2024Article
- Non-Linear Device Head Coupling and Temporal Delays in Large Animal Acceleration Models of Traumatic Brain Injury.Annals of biomedical engineering · 2022Article
- Comparison of Methods for Classifying Persistent Post-Concussive Symptoms in Children.Journal of neurotrauma · 2020Article
- Resting-State fMRI Metrics in Acute Sport-Related Concussion and Their Association with Clinical Recovery: A Study from the NCAA-DOD CARE Consortium.Journal of neurotrauma · 2020Article
- PAPPI: Personalized analysis of plantar pressure images using statistical modelling and parametric mapping.PloS one · 2020Article
- White matter alterations in early-stage Alzheimer's disease: A tract-specific study.Alzheimer's & dementia (Amsterdam, Netherlands) · 2019Article
- Spatial distribution bias in subject-specific abnormalities analyses.Brain imaging and behavior · 2018Article
- Regional Cerebral Blood Flow in Children and Young Adults with Chronic Kidney Disease.Radiology · 2018Article
- White matter abnormalities in mild traumatic brain injury with and without post-traumatic stress disorder: a subject-specific diffusion tensor imaging study.Brain imaging and behavior · 2018Article
- An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data.Brain imaging and behavior · 2018Article
- Neuropsychological Recovery Trajectories in Moderate to Severe Traumatic Brain Injury: Influence of Patient Characteristics and Diffuse Axonal Injury.Journal of the International Neuropsychological Society : JINS · 2018Article
- Inter-Subject Variability of Axonal Injury in Diffuse Traumatic Brain Injury.Journal of neurotrauma · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Algorithms that are capable of capturing subject-specific abnormalities (SSA) in neuroimaging data have long been an area of focus for diverse neuropsychiatric conditions such as multiple sclerosis, schizophrenia, and traumatic brain injury. Several algorithms have been proposed that define SSA in patients (i.e., comparison group) relative to image intensity levels derived from healthy controls (HC) (i.e., reference group) based on extreme values. However, the assumptions underlying these approaches have not always been fully validated, and may be dependent on the statistical distributions of the transformed data. The current study evaluated variations of two commonly used techniques ("pothole" method and standardization with an independent reference group) for identifying SSA using simulated data (derived from normal, t and chi-square distributions) and fractional anisotropy maps derived from 50 HC. Results indicated substantial group-wise bias in the estimation of extreme data points using the pothole method, with the degree of bias being inversely related to sample size. Statistical theory was utilized to develop a distribution-corrected z-score (DisCo-Z) threshold, with additional simulations demonstrating elimination of the bias and a more consistent estimation of extremes based on expected distributional properties. Data from previously published studies examining SSA in mild traumatic brain injury were then re-analyzed using the DisCo-Z method, with results confirming the evidence of group-wise bias. We conclude that the benefits of identifying SSA in neuropsychiatric research are substantial, but that proposed SSA approaches require careful implementation under the different distributional properties that characterize neuroimaging data.
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