Estimated intracranial volume from FreeSurfer is biased by total brain volume
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ABSTRACT:
SUBMITTER: Klasson N
PROVIDER: S-EPMC6143491 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
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Project description:Voxel-based morphometry (VBM) studies of Parkinson's disease (PD), have yielded mixed results, possibly due to several studies not accounting for common nuisance variables (age, sex, and total intracranial volume [TICV]). TICV is particularly important because there is evidence for larger TICV in PD. We explored the influence of these covariates on VBM by 1) comparing PD patients and controls before adding covariates, after adding age and sex, and after adding age, sex and TICV, and 2) by comparing controls split into large and small TICV before and after controlling for TICV, with age and sex accounted for in both analyses. Experiment 1 consisted of 40 PD participants and 40 controls. Experiment 2 consisted of 88 controls median split by TICV. All participants completed an MRI on a 3 T scanner. TICV was calculated as gray + white + CSF from Freesurfer. VBM was performed on T1 images using an optimized VBM protocol. Volume differences were assessed using a voxel-wise GLM analysis. Clusters were considered significant at >10 voxels and p < .05 corrected for familywise error. Before controlling for covariates, PD showed reduced GM in temporal, occipital, and cerebellar regions. Controlling for age and sex did not affect the pattern of significance. Controlling for TICV reduced the size of the significant region although it still contained portions of bilateral temporal lobes, occipital lobes and cerebellum. The large TICV group showed reduced volume in temporal, parietal, and cerebellar areas. None of these differences survived controlling for TICV. This demonstrates that TICV influences VBM results independently from other factors. Controlling for TICV in VBM studies is recommended.
| S-EPMC5502201 | biostudies-literature
Project description:Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of brain tissues. None of the three provide explicit skull/CSF boundary (SCB) since it is challenging to distinguish these dark structures in a T1-weighted image. However, explicit SCB not only leads to a natural way of obtaining TICV (i.e., counting voxels inside the skull) but also allows sub-definition of TICV, for example, the posterior fossa volume (PFV). In this article, they proposed to use multi-atlas label fusion to obtain TICV and PFV simultaneously. The main contributions are: (1) TICV and PFV are simultaneously obtained with explicit SCB from a single T1-weighted image. (2) TICV and PFV labels are added to the widely used BrainCOLOR atlases. (3) Detailed mathematical derivation of non-local spatial STAPLE (NLSS) label fusion is presented. As the skull is clearly distinguished in CT images, we use a semi-manual procedure to obtain atlases with TICV and PFV labels using 20 subjects who both have a MR and CT scan. The proposed method provides simultaneous TICV and PFV estimation while achieving more accurate TICV estimation compared with FreeSurfer, FSL, SPM12, and the previously proposed STAPLE based approach. The newly developed TICV and PFV labels for the OASIS BrainCOLOR atlases provide acceptable performance, which enables simultaneous TICV and PFV estimation during whole brain segmentation. The NLSS method and the new atlases have been made freely available. Hum Brain Mapp 38:599-616, 2017. © 2016 Wiley Periodicals, Inc.
| S-EPMC5225112 | biostudies-literature
Project description:BackgroundThis paper summarizes the findings of a long term study addressing the question of how several brain volume measure are related to three major mental illnesses in a Colorado subject group. It reports results obtained from a large N, collected and analyzed by the same laboratory over a multiyear period, with visually guided MRI segmentation being the primary initial analytic tool.MethodsIntracerebral volume (ICV), total brain volume (TBV), ventricular volume (VV), ventricular/brain ratio (VBR), and TBV/ICV ratios were calculated from a total of 224 subject MRIs collected over a period of 13 years. Subject groups included controls (C, N = 89), and patients with schizophrenia (SZ, N = 58), bipolar disorder (BD, N = 51), and schizoaffective disorder (SAD, N = 26).ResultsICV, TBV, and VV measures compared favorably with values obtained by other research groups, but in this study did not differ significantly between groups. TBV/ICV ratios were significantly decreased, and VBR increased, in the SZ and BD groups compared to the C group. The SAD group did not differ from C on any measure.ConclusionsIn this study TBV/ICV and VBR ratios separated SZ and BD patients from controls. Of interest however, SAD patients did not differ from controls on these measures. The findings suggest that the gross measure of TBV may not reliably differ in the major mental illnesses to a degree useful in diagnosis, likely due to the intrinsic variability of the measures in question; the differences in VBR appear more robust across studies. Differences in some of these findings compared to earlier reports from several laboratories finding significant differences between groups in VV and TBV may relate to phenomenological drift, differences in analytic techniques, and possibly the "file drawer problem".
| S-EPMC2958994 | biostudies-literature
Project description:IntroductionTotal kidney volume (TKV) is a qualified biomarker for disease progression in autosomal dominant polycystic kidney disease (ADPKD). Recent studies suggest that TKV estimated using ellipsoid formula correlates well with TKV measured by manual planimetry (gold standard). We investigated whether the ellipsoid formula could replace manual planimetry for follow-up of ADPKD patients.MethodsAbdominal magnetic resonance images of patients with ADPKD performed between January 1, 2013, and June 31, 2019, in Saint-Luc Hospital, Brussels, were used. Two radiologists independently performed manual TKV (mTKV) measures and kidney axial measures necessary for estimating TKV (eTKV) using ellipsoid equation. Repeatability and reproducibility of axial measures, mTKV and eTKV, and agreement between mTKV and eTKV were assessed (Bland-Altman). Intraclass correlation coefficient (ICC) was used to assess agreement on Mayo Clinic Imaging Classification (MCIC) scores.Results140 patients were included with mean age 45±13 years, estimated glomerular filtration rate (eGFR) 71±31 ml/min per 1.73 m2, and mTKV 1697±1538 ml. Repeatability and reproducibility were superior for mTKV versus eTKV (repeatability coefficient 2.4% vs. 14% in senior reader, and reproducibility coefficient 6.7% vs. 15%). Intertechnique reproducibility coefficient (95% confidence interval [CI]) was 19% (17%, 21%) in senior reader. Intertechnique agreement on derived MCIC scores was very good (ICC = 0.924 [0.884, 0.949]).ConclusionTKV estimated using ellipsoid equation demonstrates poor repeatability and reproducibility compared with that of mTKV. Intertechnique agreement is also limited, even when measurements are performed by an experienced radiologist. Estimated TKV, however, accurately determines MCIC score.
| S-EPMC8589695 | biostudies-literature
Project description:ObjectivesTotal intracranial volume (TICV) is an important control variable in brain-behavior research, yet its calculation has challenges. Manual TICV (Manual) is labor intensive, and automatic methods vary in reliability. To identify an accurate automatic approach we assessed the reliability of two FreeSurfer TICV metrics (eTIV and Brainmask) relative to manual TICV. We then assessed how these metrics alter associations between left entorhinal cortex (ERC) volume and story retention.MethodsForty individuals with Parkinson's disease (PD) and 40 non-PD peers completed a brain MRI and memory testing. Manual metrics were compared to FreeSurfer's Brainmask (a skull strip mask with total volume of gray, white, and most cerebrospinal fluid) and eTIV (calculated using the transformation matrix into Talairach space). Volumes were compared with two-way interclass correlations and dice similarity indices. Associations between ERC volume and Wechsler Memory Scale-Third Edition Logical Memory retention were examined with and without correction using each TICV method.ResultsBrainmask volumes were larger and eTIV volumes smaller than Manual. Both automated metrics correlated highly with Manual. All TICV metrics explained additional variance in the ERC-Memory relationship, although none were significant. Brainmask explained slightly more variance than other methods.ConclusionsOur findings suggest Brainmask is more reliable than eTIV for TICV correction in brain-behavioral research. (JINS, 2018, 24, 206-211).
| S-EPMC7111586 | biostudies-literature
Project description:Total intracranial volume (TIV/ICV) is an important covariate for volumetric analyses of the brain and brain regions, especially in the study of neurodegenerative diseases, where it can provide a proxy of maximum pre-morbid brain volume. The gold-standard method is manual delineation of brain scans, but this requires careful work by trained operators. We evaluated Statistical Parametric Mapping 12 (SPM12) automated segmentation for TIV measurement in place of manual segmentation and also compared it with SPM8 and FreeSurfer 5.3.0. For T1-weighted MRI acquired from 288 participants in a multi-centre clinical trial in Alzheimer's disease we find a high correlation between SPM12 TIV and manual TIV (R(2)=0.940, 95% Confidence Interval (0.924, 0.953)), with a small mean difference (SPM12 40.4±35.4ml lower than manual, amounting to 2.8% of the overall mean TIV in the study). The correlation with manual measurements (the key aspect when using TIV as a covariate) for SPM12 was significantly higher (p<0.001) than for either SPM8 (R(2)=0.577 CI (0.500, 0.644)) or FreeSurfer (R(2)=0.801 CI (0.744, 0.843)). These results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology. We also briefly discuss some aspects of the statistical modelling approaches to adjust for TIV.
| S-EPMC4265726 | biostudies-literature
Project description:BackgroundTwo intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV).ObjectiveTo determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components-LITV-with CITV.MethodsWe compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients.ResultsIn both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model.ConclusionAfter accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients.
| S-EPMC5745302 | biostudies-literature
Project description:ObjectivesMR planimetry of brainstem structures can be helpful for the discrimination of Parkinsonian syndromes. It has been suggested that ageing might influence brainstem MR measurements assessed by MR planimetry, while effects of gender and total intracranial volume (TIV) have not been assessed so far. The aim of this study was to evaluate age, gender and TIV effects on brainstem MR planimetric measures.MethodsBrainstem MR planimetric measures of diameters (midbrain, pons, middle and superior cerebellar peduncle) and areas (pons and midbrain), the derived ratios, and the magnetic resonance Parkinsonism index (MRPI) were assessed on 1.5-T MR images in a large cohort of 97 healthy controls and analysed for the influence of age, gender and TIV with univariate and multivariate linear models.ResultsNeither gender nor age effects on planimetric measurements were observed in the population relevant for the differential diagnosis of neurodegenerative Parkinsonism, aged 50 to 80 years, except for single area-derived measurements, with gender effects on pontine area (p = 0.013) and age effects on midbrain area (p = 0.037). Results were similar upon inclusion of the TIV in the analyses.ConclusionsThere is no need to correct for age, gender or TIV when using brainstem-derived MR planimetric measurements in the differential diagnosis of neurodegenerative Parkinsonism.Key points• There were no gender effects on single or combined imaging measurements of the brainstem in the population aged 50 to 80 years, the age range relevant for the differential diagnosis of neurodegenerative Parkinsonism (except for pontine area). • There were no age effects on single or combined imaging measurements of the brainstem in the population aged 50 to 80 years, the age range relevant for the differential diagnosis of neurodegenerative Parkinsonism (except for midbrain area). • There is no need for age- or gender-specific cut-offs for the relevant age group.
| S-EPMC7160097 | biostudies-literature
Project description:BackgroundComputed tomography perfusion (CTP)-estimated core volume is associated with functional outcomes in acute ischemic stroke. This relationship might differ among patients, depending on brain volume.Materials and methodsWe retrospectively included patients from the MR CLEAN Registry. Cerebrospinal fluid (CSF) and intracranial volume (ICV) were automatically segmented on NCCT. We defined the proportion of the ICV and total brain volume (TBV) affected by the ischemic core as ICVcore and TBVcore. Associations between the core volume, ICVcore, TBVcore, and functional outcome are reported per interquartile range (IQR). We calculated the area under the curve (AUC) to assess diagnostic accuracy.ResultsIn 200 patients, the median core volume was 13 (5-41) mL. Median ICV and TBV were 1377 (1283-1456) mL and 1108 (1020-1197) mL. Median ICVcore and TBVcore were 0.9 (0.4-2.8)% and 1.7 (0.5-3.6)%. Core volume (acOR per IQR 0.48 [95%CI 0.33-0.69]), ICVcore (acOR per IQR 0.50 [95%CI 0.35-0.69]), and TBVcore (acOR per IQR 0.41 95%CI 0.33-0.67]) showed a lower likelihood of achieving improved functional outcomes after 90 days. The AUC was 0.80 for the prediction of functional independence at 90 days for the CTP-estimated core volume, the ICVcore, and the TBVcore.ConclusionCorrecting the CTP-estimated core volume for the intracranial or total brain volume did not improve the association with functional outcomes in patients who underwent EVT.
| S-EPMC10971384 | biostudies-literature
Project description:We present the package freesurfer, a set of R functions that interface with Freesurfer, a commonly-used open-source software package for processing and analyzing structural neuroimaging data, specifically T1-weighted images. The freesurfer package performs operations on nifti image objects in R using command-line functions from Freesurfer, and returns R objects back to the user. freesurfer allows users to process neuroanatomical images and provides functionality to convert and read the output of the Freesurfer pipelines more easily, including brain images, brain surfaces, and Freesurfer output tables.
| S-EPMC6051196 | biostudies-other