Browsing by Author "Daković, Marko (23491743200)"

Purpose: To evaluate the diagnostic value of diffusion-weighted magnetic resonance imaging (DWMRI) and transient elastography (TE) in quantification of liver fibrosis in patients with chronic cholestatic liver diseases. Materials and methods: Forty-five patients underwent DWMRI, TE, and liver biopsy for staging of liver fibrosis. Apparent diffusion coefficient (ADC) was calculated for six locations in the liver for combination of five diffusion sensitivity values b = 0, 50, 200, 400 and 800 s/mm2. A receiver operating characteristic (ROC) analysis was performed to determine the diagnostic performance of DWMRI and TE. Segmental ADC variations were evaluated by means of coefficient of variation. Results: The mean ADCs (×10-3 mm2/s; b = 0-800 s/mm2) were significantly different at stage F1 versus F ≥ 2 (p < 0.05) and F2 versus F4. However, no significant difference was found between F2 and F3. For prediction of F ≥ 2 and F ≥ 3 areas under the ROC curves were 0.868 and 0.906 for DWMRI, and 0.966 and 0.960 for TE, respectively. The sensitivity and specificity were 90.9% and 89.3% for F ≥ 2 (ADC ≤ 1.65), and 92.3% and 92.1% for F ≥ 3 (ADC ≤ 1.63). Segmental ADC variation was lowest for F4 (CV = 9.54 ± 6.3%). Conclusion: DWMRI and TE could be used for assessment of liver fibrosis with TE having higher diagnostic accuracy and DWMRI providing insight into liver fibrosis distribution. © 2011 Elsevier Ireland Ltd.

Background: Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology. Methods: The data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network. Results: SVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity. Conclusions: The SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment. © 2023, The Author(s).

We aimed to explore whether a migraine with aura (MA) is associated with structural changes in tracts of a white matter and to compare parameters of diffusivity between subgroups in migraineurs. Forty-three MA and 20 healthy subjects (HS), balanced by sex and age, were selected for this study. Analysis of diffusion tensor parameters was used to identify differences between MA patients and HS, and then between MA subgroups. A diffusion tensor probabilistic tractography analysis showed that there is no difference between MA patients and HS. However, using more-liberal uncorrected statistical threshold, we noted a trend in MA patients toward lower diffusivity indices of selected white matter tracts located in the forceps minor and right anterior thalamic radiation (ATR), superior longitudinal fasciculus (temporal part) (SLFT), cingulum-cingulate tract, and left uncinate fasciculus. Migraineurs who experienced somatosensory and dysphasic aura, besides visual symptoms, had tendency toward lower diffusivity indices, relative to migraineurs who experienced only visual symptoms, in the right inferior longitudinal fasciculus, forceps minor, and right superior longitudinal fasciculus (parietal part), SLFT, and cingulum-angular bundle. Aura frequency were negatively correlated with axial diffusivity and mean diffusivity of the right ATR (partial correlation = − 0.474; p = 0.002; partial correlation = − 0.460; p = 0.002), respectively. There were no significant differences between MA patients and HS, neither between MA subgroups. Migraineurs with abundant symptoms during the aura possibly have more myelinated fibers relative to those who experience only visual symptoms. Lower diffusivity indices of the right ATR are linked to more frequent migraine with aura attacks. © 2018, Belgian Neurological Society.

Introduction: Migraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world’s population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients. Methods: This research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training. Results: The results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification. Discussion: This method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA. Copyright © 2023 Mitrović, Petrušić, Radojičić, Daković and Savić.

Introduction: Migraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world’s population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients. Methods: This research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training. Results: The results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification. Discussion: This method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA. Copyright © 2023 Mitrović, Petrušić, Radojičić, Daković and Savić.

Background: This study investigated for a possible contributing role of hippocampus in the different clinical phenotypic manifestations of migraine aura. Methods: Herein, patients were categorized as those with pure visual aura (MwAv), those who reported additional somatosensory and dysphasic symptoms (MwAvsd), and healthy controls (HCs). Neuroimaging data obtained using FreeSurfer-based segmentation of hippocampal subfields were compared between HCs and patients with migraine with aura, as well as between HCs and those with MwAv and MwAvsd. The average migraine aura complexity score (MACS) was calculated for each patient to investigate the correlation between hippocampal subfield volume and migraine aura complexity. Results: Herein, 46 patients with migraine with aura (28 MwAvsd and 18 MwAv) and 31 HCs were included. There were no significant differences in the hippocampal subfields between HCs and patients with migraine with aura. The average MACS negatively correlated with the volumes of the left and right hippocampi, Cornu Ammonis (CA) 1, CA3, CA4, molecular layer, left granule cell layer of the dentate gyrus, hippocampal fissure, and hippocampus-amygdala transition area. The MwAvsd subgroup had significantly smaller whole hippocampal volumes in both hemispheres, as well as in both subicula, compared with the MwAv subgroup and HCs. In addition, the left molecular layer, right CA1, and hippocampal fissures were significantly smaller in the MwAvsd group than in the MwAv subgroup and HCs. Conclusions: Smaller left and right hippocampal volumes, particularly of the subiculum/CA1 area, may play an important role in the pathophysiology of somatosensory and dysphasic symptoms in migraine with aura. © The Author(s) 2024.