Protocol for a machine learning algorithm predicting depressive disorders using the T1w/T2w ratio.
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ABSTRACT:
SUBMITTER: Baranger DAA
PROVIDER: S-EPMC8720909 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:Newer magnetic resonance imaging (MRI) techniques have shown promise in capturing early Parkinson disease (PD)-related changes in the substantia nigra pars compacta (SNc), the key pathological loci. Their translational value, however, is hindered by technical complexity and inconsistent results. METHODS:A novel yet simple MRI contrast, the T1w/T2w ratio, was used to study 76 PD patients and 70 controls. The T1w/T2w ratio maps were analyzed using both voxel-based and region-of-interest approaches in normalized space. The sensitivity and specificity of the SNc T1w/T2w ratio in discriminating between PD and controls also were assessed. In addition, its diagnostic performance was tested in a subgroup of PD patients with disease duration??2 years (PDE). A second independent cohort of 73 PD patients and 49 controls was used for validation. RESULTS:Compared to controls, PD patients showed a higher T1w/T2w ratio in both the right (cluster size?=?164mm3 , p?<?0.0001) and left (cluster size?=?213mm3 , p?<?0.0001) midbrain that was located ventrolateral to the red nucleus and corresponded to the SNc. The region-of-interest approach confirmed the group difference in the SNc T1w/T2w ratio between PD and controls (p?<?0.0001). The SNc T1w/T2w ratio had high sensitivity (0.908) and specificity (0.80) to separate PD and controls (area under the curve [AUC]?=?0.926), even for PDE patients (AUC?=?0.901, sensitivity?=?0.857, specificity?=?0.857). These results were validated in the second cohort. INTERPRETATION:The T1w/T2w ratio can detect PD-related changes in the SNc and may be used as a novel, parsimonious in vivo biomarker for the disease, particularly for early stage patients, with high translational value for clinical practice and research. ANN NEUROL 2019;85:96-104.
| S-EPMC6342624 | biostudies-literature
Project description:Diagnosis of multiple system atrophy (MSA) may be improved by using multimodal imaging approaches. We investigated the use of T1-weighted/T2-weighted (T1w/T2w) images ratio combined with voxel-based morphometry to evaluate brain tissue integrity in MSA compared to Parkinson's disease (PD) and healthy controls (HC). Twenty-six patients with MSA, 43 patients with PD and 56 HC were enrolled. Whole brain voxel-based and local regional analyses were performed to evaluate gray and white matter (GM and WM) tissue integrity and mean regional values were used for patients classification using logistic regression. Increased mean regional values of T1w/T2w in bilateral putamen were detected in MSA-P compared to PD and HC. The combined use of regional GM and T1w/T2w values in the right and left putamen showed the highest accuracy in discriminating MSA-P from PD and good accuracy in discriminating MSA from PD and HC. A good accuracy was also found in discriminating MSA from PD and HC by either combining regional GM and T1w/T2w values in the cerebellum or regional WM and T1w/T2w in the cerebellum and brainstem. The T1w/T2w image ratio alone or combined with validated MRI parameters can be further considered as a potential candidate biomarker for differential diagnosis of MSA.
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Project description:One of the challenges in glioblastoma (GBM) imaging is to visualize non-enhancing tumor (NET) lesions. The ratio of T1- and T2-weighted images (rT1/T2) is reported as a helpful imaging surrogate of microstructures of the brain. This research study investigated the possibility of using rT1/T2 as a surrogate for the T1- and T2-relaxation time of GBM to visualize NET effectively. The data of thirty-four histologically confirmed GBM patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were collected for analysis. Two of them also underwent MR relaxometry with rT1/T2 reconstructed for all cases. Met-PET was used as ground truth with T2-FLAIR hyperintense lesion, with >1.5 in tumor-to-normal tissue ratio being NET. rT1/T2 values were compared with MR relaxometry and Met-PET. rT1/T2 values significantly correlated with both T1- and T2-relaxation times in a logarithmic manner (p < 0.05 for both cases). The distributions of rT1/T2 from Met-PET high and low T2-FLAIR hyperintense lesions were different and a novel metric named Likeliness of Methionine PET high (LMPH) deriving from rT1/T2 was statistically significant for detecting Met-PET high T2-FLAIR hyperintense lesions (mean AUC = 0.556 ± 0.117; p = 0.01). In conclusion, this research study supported the hypothesis that rT1/T2 could be a promising imaging marker for NET identification.
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Project description:BackgroundCross-sectional studies suggest normal appearing white matter (NAWM) integrity loss may lead to cortical atrophy in late-stage relapsing-remitting multiple sclerosis (MS).ObjectiveTo investigate the relationship between NAWM integrity and cortical thickness from first clinical presentation longitudinally.MethodsNAWM integrity and cortical thickness were assessed with 3T magnetic resonance imaging (MRI) in 102 patients with clinically isolated syndrome or early MS (33.2 (20.1-60.1) years old, 68% female) from first clinical presentation over 2.8 ± 1.6 years. Fifty healthy controls (HCs) matched for age and sex were included. NAWM integrity was evaluated using the standardized T1w/T2w ratio (sT1w/T2w). The association between sT1w/T2w and cortical thickness was assessed using linear mixed models. The effect of disease activity was investigated using the No Evidence of Disease Activity (NEDA-3) criteria.ResultsAt baseline, sT1w/T2w (p = 0.152) and cortical thickness (p = 0.489) did not differ from HCs. Longitudinally, decreasing sT1w/T2w was associated with cortical thickness and increasing lesion burden (marginal R2 = 0.061). The association was modulated by failing NEDA-3 (marginal R2 = 0.097).ConclusionsT1w/T2w may be a useful MRI biomarker for early MS, detecting relevant NAWM damage over time using conventional MRI scans, although with less sensitivity compared to quantitative measures.
| S-EPMC8597181 | biostudies-literature
Project description:The restructuring and optimization of the cerebral cortex from early childhood and through adolescence is an essential feature of human brain development, underlying immense cognitive improvements. Beyond established morphometric cortical assessments, the T1w/T2w ratio quantifies partly separate biological processes, and might inform models of typical neurocognitive development and developmental psychopathology. In the present study, we computed vertex-wise T1w/T2w ratio across the cortical surface in 621 youths (3-21 years) sampled from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and tested for associations with individual differences in age, sex, and both general and specific cognitive abilities. The results showed a near global linear age-related increase in T1w/T2w ratio across the brain surface, with a general posterior to anterior increasing gradient in association strength. Moreover, results indicated that boys in late adolescence had regionally higher T1w/T2w ratio as compared to girls. Across individuals, T1w/T2w ratio was negatively associated with general and several specific cognitive abilities mainly within anterior cortical regions. Our study indicates age-related differences in T1w/T2w ratio throughout childhood, adolescence, and young adulthood, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment.
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Project description:Several MRI techniques have become available to support the early diagnosis of multiple system atrophy (MSA), but few longitudinal studies on both MSA variants have been performed, and there are no established MRI markers of disease progression. We aimed to characterize longitudinal brain changes in 26 patients with MSA (14 MSA-P and 12 MSA-C) over a 1-year follow-up period in terms of local tissue density and T1w/T2w ratio in a-priori regions, namely, bilateral putamen, cerebellar gray matter (GM), white matter (WM), and substantia nigra (SN). A significant GM density decrease was found in cerebellum and left putamen in the entire group (10.7 and 33.1% variation, respectively) and both MSA subtypes (MSA-C: 15.4 and 33.0% variation; MSA-P: 7.7 and 33.2%) and in right putamen in the entire group (19.8% variation) and patients with MSA-C (20.9% variation). A WM density decrease was found in the entire group (9.3% variation) and both subtypes in cerebellum-brainstem (MSA-C: 18.0% variation; MSA-P: 5% variation). The T1w/T2w ratio increase was found in the cerebellar and left putamen GM (6.6 and 24.9% variation), while a significant T1w/T2w ratio decrease was detected in SN in the entire MSA group (31% variation). We found a more progressive atrophy of the cerebellum in MSA-C with a similar progression of putaminal atrophy in the two variants. T1w/T2w ratio can be further studied as a potential marker of disease progression, possibly reflecting decreased neuronal density or iron accumulation.
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Project description:Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness.
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Project description:Various MRI techniques, including myelin water imaging, T1w/T2w ratio mapping and diffusion-based imaging can be used to characterize tissue microstructure. However, surprisingly few studies have examined the degree to which these MRI measures are related within and between various brain regions. Therefore, whole-brain MRI scans were acquired from 31 neurologically-healthy participants to empirically measure and compare myelin water fraction (MWF), T1w/T2w ratio, fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) in 25 bilateral (10 grey matter; 15 white matter) regions-of-interest (ROIs). Except for RD vs. T1w/T2w, MD vs. T1w/T2w, moderately significant to highly significant correlations (p < 0.001) were found between each of the other measures across all 25 brain structures [T1w/T2w vs. MWF (Pearson r = 0.33, Spearman ρ = 0.31), FA vs. MWF (r = 0.73, ρ = 0.75), FA vs. T1w/T2w (r = 0.25, ρ = 0.22), MD vs. AD (r = 0.57, ρ = 0.58), MD vs. RD (r = 0.64, ρ = 0.61), AD vs. MWF (r = 0.43, ρ = 0.36), RD vs. MWF (r = -0.49, ρ = -0.62), MD vs. MWF (r = -0.22, ρ = -0.29), RD vs. FA (r = -0.62, ρ = -0.75) and MD vs. FA (r = -0.22, ρ = -0.18)]. However, while all six MRI measures were correlated with each other across all structures, there were large intra-ROI and inter-ROI differences (i.e., with no one measure consistently producing the highest or lowest values). This suggests that each quantitative MRI measure provides unique, and potentially complimentary, information about underlying brain tissues - with each metric offering unique sensitivity/specificity tradeoffs to different microstructural properties (e.g., myelin content, tissue density, etc.).
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