Titre : Statistiques comme sujet

Statistiques comme sujet : Questions médicales fréquentes

Termes MeSH sélectionnés :

Diffusion Tensor Imaging
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"Questions et réponses médicales fréquentes sur Statistiques comme sujet", "description": "Une compilation de questions et réponses structurées, validées par des experts médicaux.", "datePublished": "2025-05-07", "inLanguage": "fr", "hasPart": [ { "@type": "MedicalWebPage", "name": "Diagnostic", "headline": "Diagnostic sur Statistiques comme sujet", "description": "Comment les statistiques aident-elles au diagnostic médical ?\nQuel rôle jouent les statistiques dans les tests de dépistage ?\nComment les statistiques influencent-elles les critères de diagnostic ?\nLes statistiques peuvent-elles prédire des maladies ?\nQuel est l'impact des statistiques sur le diagnostic précoce ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=4#section-diagnostic" }, { "@type": "MedicalWebPage", "name": "Symptômes", "headline": "Symptômes sur Statistiques comme sujet", "description": "Comment les statistiques aident-elles à identifier des symptômes ?\nLes statistiques peuvent-elles quantifier la gravité des symptômes ?\nQuel est le lien entre statistiques et symptômes rares ?\nComment les statistiques influencent-elles la reconnaissance des symptômes ?\nLes statistiques peuvent-elles aider à l'évaluation des symptômes chroniques ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=4#section-symptômes" }, { "@type": "MedicalWebPage", "name": "Prévention", "headline": "Prévention sur Statistiques comme sujet", "description": "Comment les statistiques contribuent-elles à la prévention des maladies ?\nQuel rôle jouent les statistiques dans les campagnes de vaccination ?\nLes statistiques peuvent-elles aider à cibler les populations à risque ?\nComment les statistiques évaluent-elles les programmes de dépistage ?\nQuel est l'impact des statistiques sur les recommandations de santé publique ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=4#section-prévention" }, { "@type": "MedicalWebPage", "name": "Traitements", "headline": "Traitements sur Statistiques comme sujet", "description": "Comment les statistiques évaluent-elles l'efficacité des traitements ?\nQuel rôle jouent les statistiques dans les essais cliniques ?\nLes statistiques peuvent-elles prédire la réponse au traitement ?\nComment les statistiques aident-elles à personnaliser les traitements ?\nQuel est l'impact des statistiques sur le suivi des traitements ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=4#section-traitements" }, { "@type": "MedicalWebPage", "name": "Complications", "headline": "Complications sur Statistiques comme sujet", "description": "Comment les statistiques aident-elles à identifier les complications ?\nQuel rôle jouent les statistiques dans la gestion des complications ?\nLes statistiques peuvent-elles prédire les complications ?\nComment les statistiques influencent-elles la recherche sur les complications ?\nQuel est l'impact des statistiques sur le suivi des complications ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=4#section-complications" }, { "@type": "MedicalWebPage", "name": "Facteurs de risque", "headline": "Facteurs de risque sur Statistiques comme sujet", "description": "Comment les statistiques identifient-elles les facteurs de risque ?\nQuel rôle jouent les statistiques dans l'évaluation des facteurs de risque ?\nLes statistiques peuvent-elles aider à réduire les facteurs de risque ?\nComment les statistiques influencent-elles les recommandations sur les facteurs de risque ?\nQuel est l'impact des statistiques sur la sensibilisation aux facteurs de risque ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=4#section-facteurs de risque" } ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Comment les statistiques aident-elles au diagnostic médical ?", "position": 1, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'analyser des données cliniques pour identifier des tendances et des anomalies." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans les tests de dépistage ?", "position": 2, "acceptedAnswer": { "@type": "Answer", "text": "Elles évaluent la sensibilité et la spécificité des tests pour déterminer leur efficacité." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles les critères de diagnostic ?", "position": 3, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à établir des seuils basés sur des données probantes pour des maladies spécifiques." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles prédire des maladies ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles utilisent des modèles prédictifs basés sur des données épidémiologiques." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur le diagnostic précoce ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'identifier des biomarqueurs et d'améliorer le dépistage précoce." } }, { "@type": "Question", "name": "Comment les statistiques aident-elles à identifier des symptômes ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent la fréquence et la distribution des symptômes dans des populations." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles quantifier la gravité des symptômes ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles mesurent l'intensité des symptômes à l'aide d'échelles validées." } }, { "@type": "Question", "name": "Quel est le lien entre statistiques et symptômes rares ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à caractériser et à comprendre la prévalence des symptômes rares." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles la reconnaissance des symptômes ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données sur les symptômes les plus courants associés à des maladies." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles aider à l'évaluation des symptômes chroniques ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles permettent d'analyser l'impact des symptômes chroniques sur la qualité de vie." } }, { "@type": "Question", "name": "Comment les statistiques contribuent-elles à la prévention des maladies ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "Elles identifient les facteurs de risque et évaluent l'efficacité des interventions préventives." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans les campagnes de vaccination ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Elles évaluent l'impact des campagnes sur l'incidence des maladies évitables." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles aider à cibler les populations à risque ?", "position": 13, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles analysent les données démographiques pour identifier les groupes vulnérables." } }, { "@type": "Question", "name": "Comment les statistiques évaluent-elles les programmes de dépistage ?", "position": 14, "acceptedAnswer": { "@type": "Answer", "text": "Elles mesurent l'impact des programmes sur la détection précoce et la mortalité." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur les recommandations de santé publique ?", "position": 15, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données probantes pour orienter les politiques de santé publique." } }, { "@type": "Question", "name": "Comment les statistiques évaluent-elles l'efficacité des traitements ?", "position": 16, "acceptedAnswer": { "@type": "Answer", "text": "Elles comparent les résultats cliniques entre groupes traités et non traités." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans les essais cliniques ?", "position": 17, "acceptedAnswer": { "@type": "Answer", "text": "Elles déterminent la taille de l'échantillon et analysent les résultats pour valider les traitements." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles prédire la réponse au traitement ?", "position": 18, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles utilisent des modèles pour identifier les facteurs prédictifs de réponse." } }, { "@type": "Question", "name": "Comment les statistiques aident-elles à personnaliser les traitements ?", "position": 19, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent les données génétiques et cliniques pour adapter les traitements aux patients." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur le suivi des traitements ?", "position": 20, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'évaluer l'adhérence et les effets secondaires des traitements au fil du temps." } }, { "@type": "Question", "name": "Comment les statistiques aident-elles à identifier les complications ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent la fréquence des complications dans les études cliniques et épidémiologiques." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans la gestion des complications ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Elles évaluent les résultats des traitements pour minimiser les complications post-opératoires." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles prédire les complications ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles utilisent des modèles pour identifier les facteurs de risque de complications." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles la recherche sur les complications ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données pour orienter les recherches sur la prévention des complications." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur le suivi des complications ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'évaluer l'évolution des complications et d'ajuster les traitements." } }, { "@type": "Question", "name": "Comment les statistiques identifient-elles les facteurs de risque ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent les données épidémiologiques pour établir des associations entre facteurs et maladies." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans l'évaluation des facteurs de risque ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Elles quantifient l'impact des facteurs de risque sur l'incidence des maladies." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles aider à réduire les facteurs de risque ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles évaluent l'efficacité des interventions pour réduire les comportements à risque." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles les recommandations sur les facteurs de risque ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données probantes pour orienter les conseils de santé publique." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur la sensibilisation aux facteurs de risque ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à communiquer les risques aux populations pour promouvoir des comportements sains." } } ] } ] }

Sous-catégories

64 au total
└─

Analyse actuarielle

Actuarial Analysis D000206 - N06.850.520.830.100
└─

Analyse de variance

Analysis of Variance D000704 - N06.850.520.830.150
└─

Aire sous la courbe

Area Under Curve D019540 - N06.850.520.830.200
└─

Analyse de regroupements

Cluster Analysis D016000 - N06.850.520.830.250
└─

Intervalles de confiance

Confidence Intervals D016001 - N06.850.520.830.275
└─

Interprétation statistique de données

Data Interpretation, Statistical D003627 - N06.850.520.830.300
└─

Analyse discriminante

Discriminant Analysis D016002 - N06.850.520.830.350
└─

Utilisation des installations et des services

Facilities and Services Utilization D000078336 - N06.850.520.830.375
└─

Analyse statistique factorielle

Factor Analysis, Statistical D005163 - N06.850.520.830.400
└─

Analyse appariée

Matched-Pair Analysis D016555 - N06.850.520.830.475
└─

Modèles statistiques

Models, Statistical D015233 - N06.850.520.830.500
└─

Méthode de Monte Carlo

Monte Carlo Method D009010 - N06.850.520.830.525
└─

Analyse multiniveaux

Multilevel Analysis D055361 - N06.850.520.830.562
└─

Probabilité

Probability D011336 - N06.850.520.830.600
└─

Utilisation des procédures et des techniques

Procedures and Techniques Utilization D000078337 - N06.850.520.830.675
└─

Analyse de régression

Regression Analysis D012044 - N06.850.520.830.750
└─

Sensibilité et spécificité

Sensitivity and Specificity D012680 - N06.850.520.830.872
└─

Analyse spatiale

Spatial Analysis D062206 - N06.850.520.830.933
└─

Lois statistiques

Statistical Distributions D016008 - N06.850.520.830.994
└─

Statistique non paramétrique

Statistics, Nonparametric D018709 - N06.850.520.830.995
└─

Processus stochastiques

Stochastic Processes D013269 - N06.850.520.830.996
└─

Analyse de survie

Survival Analysis D016019 - N06.850.520.830.998
└─└─

Analyse multifactorielle

Multivariate Analysis D015999 - N06.850.520.830.150.500
└─└─

Analyse de structure latente

Latent Class Analysis D000077272 - N06.850.520.830.250.338
└─└─

Analyse de positionnement multidimensionnel

Multidimensional Scaling Analysis D000084142 - N06.850.520.830.250.507
└─└─

Analyse en petite superficie

Small-Area Analysis D017062 - N06.850.520.830.250.675
└─└─

Analyses secondaires des données

Secondary Data Analysis D000094422 - N06.850.520.830.300.500
└─└─

Analyse de médiation

Mediation Analysis D000081983 - N06.850.520.830.400.500
└─└─

Modèles économiques

Models, Economic D018803 - N06.850.520.830.500.600
└─└─

Nomogrammes

Nomograms D049451 - N06.850.520.830.500.625
└─└─

Théorème de Bayes

Bayes Theorem D001499 - N06.850.520.830.600.200
└─└─

Fonctions de vraisemblance

Likelihood Functions D016013 - N06.850.520.830.600.400
└─└─

Odds ratio

Odds Ratio D016017 - N06.850.520.830.600.600
└─└─

Score de propension

Propensity Score D057216 - N06.850.520.830.600.650
└─└─

Risque

Risk D012306 - N06.850.520.830.600.800
└─└─

Incertitude

Uncertainty D035501 - N06.850.520.830.600.900
└─└─

Méthode des moindres carrés

Least-Squares Analysis D016018 - N06.850.520.830.750.400
└─└─

Modèles linéaires

Linear Models D016014 - N06.850.520.830.750.425
└─└─

Modèles logistiques

Logistic Models D016015 - N06.850.520.830.750.450
└─└─

Limite de détection

Limit of Detection D057230 - N06.850.520.830.872.500
└─└─

Rapport signal-bruit

Signal-To-Noise Ratio D059629 - N06.850.520.830.872.750
└─└─

Cartographie géographique

Geographic Mapping D062305 - N06.850.520.830.933.249
└─└─

Régression spatiale

Spatial Regression D000068916 - N06.850.520.830.933.375
└─└─

Analyse spatio-temporelle

Spatio-Temporal Analysis D062211 - N06.850.520.830.933.500
└─└─

Loi binomiale

Binomial Distribution D016010 - N06.850.520.830.994.250
└─└─

Loi du khi-deux

Chi-Square Distribution D016009 - N06.850.520.830.994.300
└─└─

Loi normale

Normal Distribution D016011 - N06.850.520.830.994.500
└─└─

Loi de Poisson

Poisson Distribution D016012 - N06.850.520.830.994.750
└─└─

Chaines de Markov

Markov Chains D008390 - N06.850.520.830.996.500
└─└─

Survie sans rechute

Disease-Free Survival D018572 - N06.850.520.830.998.300
└─└─

Estimation de Kaplan-Meier

Kaplan-Meier Estimate D053208 - N06.850.520.830.998.650
└─└─

Survie sans progression

Progression-Free Survival D000077982 - N06.850.520.830.998.825
└─└─

Modèles des risques proportionnels

Proportional Hazards Models D016016 - N06.850.520.830.998.912
└─└─└─

Analyse canonique des corrélations

Canonical Correlation Analysis D000089342 - N06.850.520.830.150.500.250
└─└─└─

Modèles économétriques

Models, Econometric D017059 - N06.850.520.830.500.600.500
└─└─└─

Facteurs de protection

Protective Factors D065840 - N06.850.520.830.600.800.582
└─└─└─

Appréciation des risques

Risk Assessment D018570 - N06.850.520.830.600.800.715
└─└─└─

Facteurs de risque

Risk Factors D012307 - N06.850.520.830.600.800.725
└─└─└─

Secteur de recensement

Census Tract D000091368 - N06.850.520.830.933.249.500
└─└─└─

Agrégat spatio-temporel

Space-Time Clustering D013029 - N06.850.520.830.933.500.500
└─└─└─└─

Analyse des modes de défaillance et de leurs effets en soins de santé

Healthcare Failure Mode and Effect Analysis D000067495 - N06.850.520.830.600.800.715.500
└─└─└─└─

Facteurs de risque de maladie cardiaque

Heart Disease Risk Factors D000082742 - N06.850.520.830.600.800.725.500
└─└─└─└─└─

Facteurs de risque cardiométabolique

Cardiometabolic Risk Factors D000083202 - N06.850.520.830.600.800.725.500.500
└─└─└─└─└─

Paradoxe de l'obésité

Obesity Paradox D000094408 - N06.850.520.830.600.800.725.500.750

Sources (10000 au total)

Advanced muscle imaging in adolescent elite rowers utilizing diffusion tensor imaging: Association of imaging findings with stroke typology.

Muscular overuse injuries are a common health issue in elite athletes. Changes in the muscular microenvironment can be depicted by Diffusion Tensor Imaging (DTI). We hypothesize that the biomechanics ... Twenty-two male elite rowers (12 sweep, 10 scull rowers) with a mean age of 15.8 ± 1.2 years underwent 3-Tesla MRI of the lumbar spine 6 hours after cessation of training. Apparent diffusion coefficie... ADC values in the erector spinae and multifidus muscle were significantly higher (p = 0.039) and FA values significantly lower (p < 0.001) in sweep rowers compared to scull rowers. There was no signif... Our DTI results show that lumbar spine muscle diffusivity is higher in sweep rowers than in scull rowers. Altered muscle diffusivity is suggestive of microscopic tissue disruption and might be attribu...

Evaluating the renal mild tubulointerstitial damage and renal function in IgAN patients: a comparative study based on diffusion kurtosis imaging and diffusion tensor imaging.

To compare the performance of 3.0 T magnetic resonance diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in evaluation of the degree of tubulointerstitial damage and renal function i... Both DKI and DTI were performed in 40 IgAN patients and 17 healthy volunteers. IgAN patients were divided into two groups according to tubulointerstitial lesion score: Mild injury group, n = 24; Moder... Cortical MK, Kr, Da and parenchymal Ka significantly differed among three groups (P < 0.05). Cortical MK, Kr, Ka were negatively correlated with estimated glomerular filtration rate (eGFR) (MK: r = - ... Compared with DTI, DKI was more sensitive and accurate to probe the renal function and the tubulointerstitial damage of IgAN, especially the mild tubulointerstitial damage....

Role of diffusion tensor imaging in neurological prognostication in spinal tuberculosis - A prospective pilot study.

The current study aims to explore the correlation between the Diffusion Tensor Imaging (DTI) indices and neurological status of individuals with TB spine with neurological deficit (TBSND). Further, fa... The current study included 51 individuals with TBSND undergoing posterior instrumentation and posterolateral decompression. All individuals underwent DTI scanning at the site of compression and at a n... The FA values at the compressed segment were significantly lower while the ADC values were significantly higher when compared with the non-compressed levels. Pre-operative motor and total neurological... Higher FA indices along with lower age constitute independent predictors for faster neurological recovery following decompression. FA values correlate with the neurological status of individuals with ...

Diffusion Tensor Imaging in Boys With Adrenoleukodystrophy: Identification of Cerebral Disease and Association With Neurocognitive Outcomes.

Childhood cerebral adrenoleukodystrophy (C-ALD) is a severe inflammatory demyelinating disease that must be treated at an early stage to prevent permanent brain injury and neurocognitive decline. In s... In this retrospective cohort study, we analyzed high-resolution anatomical MRI, DTI, and neurocognitive assessments from boys with C-ALD undergoing HCT at the University of Minnesota between 2011 and ... Among patients with NL-ALD (n = 14), stable or increasing FA, stable AD, and stable or decreasing RD and MD were generally observed during the 1-year study period across all ROIs. In comparison, patie... DTI was sensitive to lesion presence and severity as well as clinical neurocognitive effects of C-ALD. DTI metrics quantify C-ALD even at an early stage....

A systematic review of repeatability and reproducibility studies of diffusion tensor imaging of cervical spinal cord.

Diffusion tensor imaging (DTI) techniques are being studied as a possible diagnostic and predictive tool for the evaluation of cervical spinal cord disease. This systematic review aims to evaluate the... A search in the PubMed, Scopus, Web of Science and Ovid electronic databases was conducted for articles published between January 1990 and February 2022 that related to the repeatability and reproduci... A total of 11 studies were included and assessed for different characteristics, including sample size,(3-34) re-test time interval (<1 h to >3 months), test-retest reproducibility scores and acquisiti... This study revealed that the application of DTI and its related measures in a clinical setting in the assessment of cervical spinal cord changes is feasible and reproducible. However, cervical spinal ... DTI with its parametric maps provide broad evaluation of the tissue structure of axonal white matter and are being studied as a possible diagnostic and predictive tool for the assessment of cervical s...

Reproducibility of automated calculation technique for diffusion tensor image analysis along the perivascular space.

The method of diffusion tensor image analysis along the perivascular space (DTI-ALPS) was gathering attention to evaluate the brain's glymphatic function or interstitial fluid dynamics. However, to th... This study included 23 participants, including 2 with Alzheimer's disease, 15 with mild cognitive impairment, and 6 with cognitive normals. Scan and rescan data of diffusion magnetic resonance images ... The absolute difference in ALPS-index values between scan and rescan was larger in the ALPS index than in the vALPS index by approximately 0.6% as the relative difference. Cohen's d for the left and r... The vALPS index based on DTI-ALPS maintaining tensor vector orientation information has higher reproducibility than the ALPS index. This result encourages a multisite study on the ALPS index with a la...

The comparison of diffusion tensor imaging in human hearts between 1.5 T and 3.0 T.

The aim was to compare the diffusion tensor imaging (DTI) indices derived from human hearts between 1.5 T and 3.0 T scanners. Additionally, the reproducibility of DTI indices was assessed between 1.5 ... A total of 18 ex-vivo hearts were derived from patients who underwent heart transplantation. The DTI schemes were performed at 1.5 T and 3.0 T, respectively. Then, the same slices from each ex-vivo he... No significant differences (all P > 0.05) were found in the DTI indices between 1.5 T and 3.0 T scanners. Interestingly, the ICC of all DTI indices was relatively lower with a low b-value. The reprodu... The DTI indices of ex-vivo human hearts between 1.5 T and 3.0 T scanners had no significant differences. The consistency of DTI indices needed caution using a low b-value with different field strength...

A diffusion tensor imaging study in schizophrenia patients with clozapine induced obsessive compulsive symptoms.

The aim of this study was to evaluate brain connectivity by diffusion tensor imaging (DTI) in schizophrenia patients with clozapine-induced obsessive compulsive symptoms (OCS).... Eighteen schizophrenia patients, nine of which had clozapine-induced OCS (Clz-OCS (+)), 9 without OCS (Clz-OCS (-)) and 9 healthy controls were included. Psychopathology was evaluated with Positive an... Tract-Based Spatial Statistics based comparison of DTI revealed lower fractional anisotropy in the genu of corpus callosum (CC), right cingulum, left frontal white matter (WM) in the Clz-OCS (+) group... WM integrity in several pathways such as cortico-striato-thalamo-cortical circuitry and orbito-frontal tracts seems to be affected differently in patients with Clz-OCS (+). Different neuroplastic effe...

Diffusion tensor imaging studies on subjects with suicidal thoughts and behaviors: A descriptive literature review.

Globally, suicide represents the second leading cause of death in young people aged 15-29 years for both sexes, after traffic accidents. Suicide occurs not only in high-income countries, in high-incom... This review was conducted on studies focused on white matter alterations in people who have attempted or thought about suicide. We searched PubMed and Scopus databases and screened references of inclu... White matter alterations in suicidal behaviors were found in the prefrontal cortex, orbitofrontal cortex, internal capsule, corpus callosum, and default mode networks, which are critical cerebral area... White matter alteration in cerebral areas involving high cognitive process and emotional regulation to confer a heightened vulnerability for suicidal behavior. Suicide is a complex process ranging fro...

Utility of diffusion tensor imaging in differentiating benign from malignant hepatic focal lesions.

To assess the diagnostic accuracy of diffusion tensor imaging (DTI) in the characterization of hepatic focal lesions (HFLs) and compare it to diffusion-weighted imaging (DWI).... Prospective analysis was done for 49 patients (23 male and 26 female) with 74 HFLs who underwent dynamic MRI, DWI, and DTI. Apparent diffusion coefficient (ADC) values from DWI, fractional anisotropy ... The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10... MD and FA of DTI are non-invasive, very good, and excellent discriminators superior to ADC measured by DWI for the differentiation of HFLs.... • The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10...