Background: In clinical practice, there is a very common discrepancy between the clinical findings of patients with lumboischialgia and the radiological findings. Objective: This research aimed to determine the degree of correlation between the ODI index and the VAS scale with degenerative changes in the lumbar spine found using MRI. Methods: The study included 100 patients, who were referred for an MRI of the lumbar spine and who had a clear clinical picture of lumboischialgia. Patients underwent MRI. Degenerative changes in the lumbar spine and discs were analysed. Patients were asked to answer the questions in the questionnaire about the subjective feeling of pain and functional status, and ODI and VAS scores were calculated. Results: There has been a statistically significant correlation found between the answers to the survey questions and the VAS score (p < 0.001). There was a significant correlation obtained between the level of degeneration and the disability index (p = 0.022), while the correlation with the VAS score has not been found to be significant (p = 0.325). Conclusion: This study has demonstrated a significant correlation between the VAS pain score and the ODI, as well as a significant correlation between the level of degeneration on MRI scans and the disability index; however, the correlation of MRI scan results with VAS score has not been found to be significant.
{"title":"Correlation of MRI Findings with ODI and VAS Score in Patients with Lower Back Pain","authors":"Suada Hasanović Vučković, Sandra Vegar-Zubović, Lejla Milišić, Spomenka Kristić, Adnan Beganović, Lejla Dervišević, Zurifa Ajanović, Ilvana Hasanbegović, Aida Sarač Hadžihalilović","doi":"10.2174/18743129-v16-230911-2022-4","DOIUrl":"https://doi.org/10.2174/18743129-v16-230911-2022-4","url":null,"abstract":"Background: In clinical practice, there is a very common discrepancy between the clinical findings of patients with lumboischialgia and the radiological findings. Objective: This research aimed to determine the degree of correlation between the ODI index and the VAS scale with degenerative changes in the lumbar spine found using MRI. Methods: The study included 100 patients, who were referred for an MRI of the lumbar spine and who had a clear clinical picture of lumboischialgia. Patients underwent MRI. Degenerative changes in the lumbar spine and discs were analysed. Patients were asked to answer the questions in the questionnaire about the subjective feeling of pain and functional status, and ODI and VAS scores were calculated. Results: There has been a statistically significant correlation found between the answers to the survey questions and the VAS score (p < 0.001). There was a significant correlation obtained between the level of degeneration and the disability index (p = 0.022), while the correlation with the VAS score has not been found to be significant (p = 0.325). Conclusion: This study has demonstrated a significant correlation between the VAS pain score and the ODI, as well as a significant correlation between the level of degeneration on MRI scans and the disability index; however, the correlation of MRI scan results with VAS score has not been found to be significant.","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135258363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.2174/18744400-v15-e2206270
M. Palmucci, E. Tagliazucchi
Spontaneous human neural activity is organized into resting state networks, complex patterns of synchronized activity that account for the major part of brain metabolism. The correspondence between these patterns and those elicited by the performance of cognitive tasks would suggest that spontaneous brain activity originates from the stream of ongoing cognitive processing. To investigate a large number of meta-analytic activation maps obtained from Neurosynth (www.neurosynth.org), establishing the extent of task-rest similarity in large-scale human brain activity. We applied a hierarchical module detection algorithm to the Neurosynth activation map similarity network, and then compared the average activation maps for each module with a set of resting state networks by means of spatial correlations. We found that the correspondence between resting state networks and task-evoked activity tended to hold only for the largest spatial scales. We also established that this correspondence could be biased by the inclusion of maps related to neuroanatomical terms in the database (e.g. “parietal”, “occipital”, “cingulate”, etc.). Our results establish divergences between brain activity patterns related to spontaneous cognition and the spatial configuration of RSN, suggesting that anatomically-constrained homeostatic processes could play an important role in the inception and shaping of human resting state activity fluctuations.
{"title":"Divergences Between Resting State Networks and Meta-Analytic Maps Of Task-Evoked Brain Activity","authors":"M. Palmucci, E. Tagliazucchi","doi":"10.2174/18744400-v15-e2206270","DOIUrl":"https://doi.org/10.2174/18744400-v15-e2206270","url":null,"abstract":"\u0000 \u0000 Spontaneous human neural activity is organized into resting state networks, complex patterns of synchronized activity that account for the major part of brain metabolism. The correspondence between these patterns and those elicited by the performance of cognitive tasks would suggest that spontaneous brain activity originates from the stream of ongoing cognitive processing.\u0000 \u0000 \u0000 \u0000 To investigate a large number of meta-analytic activation maps obtained from Neurosynth (www.neurosynth.org), establishing the extent of task-rest similarity in large-scale human brain activity.\u0000 \u0000 \u0000 \u0000 We applied a hierarchical module detection algorithm to the Neurosynth activation map similarity network, and then compared the average activation maps for each module with a set of resting state networks by means of spatial correlations.\u0000 \u0000 \u0000 \u0000 We found that the correspondence between resting state networks and task-evoked activity tended to hold only for the largest spatial scales. We also established that this correspondence could be biased by the inclusion of maps related to neuroanatomical terms in the database (e.g. “parietal”, “occipital”, “cingulate”, etc.).\u0000 \u0000 \u0000 \u0000 Our results establish divergences between brain activity patterns related to spontaneous cognition and the spatial configuration of RSN, suggesting that anatomically-constrained homeostatic processes could play an important role in the inception and shaping of human resting state activity fluctuations.\u0000","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44104816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-22DOI: 10.2174/18744400-v15-e2206290
Pooja Kataria, Ayush Dogra, T. Sharma, Bhawna Goyal
Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival. Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth. This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification. In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.
{"title":"Trends in DNN Model Based Classification and Segmentation of Brain Tumor Detection","authors":"Pooja Kataria, Ayush Dogra, T. Sharma, Bhawna Goyal","doi":"10.2174/18744400-v15-e2206290","DOIUrl":"https://doi.org/10.2174/18744400-v15-e2206290","url":null,"abstract":"\u0000 \u0000 Due to the complexities of scrutinizing and diagnosing brain tumors from MR images, brain tumor analysis has become one of the most indispensable concerns. Characterization of a brain tumor before any treatment, such as radiotherapy, requires decisive treatment planning and accurate implementation. As a result, early detection of brain tumors is imperative for better clinical outcomes and subsequent patient survival.\u0000 \u0000 \u0000 \u0000 Brain tumor segmentation is a crucial task in medical image analysis. Because of tumor heterogeneity and varied intensity patterns, manual segmentation takes a long time, limiting the use of accurate quantitative interventions in clinical practice. Automated computer-based brain tumor image processing has become more valuable with technological advancement. With various imaging and statistical analysis tools, deep learning algorithms offer a viable option to enable health care practitioners to rule out the disease and estimate the growth.\u0000 \u0000 \u0000 \u0000 This article presents a comprehensive evaluation of conventional machine learning models as well as evolving deep learning techniques for brain tumor segmentation and classification.\u0000 \u0000 \u0000 \u0000 In this manuscript, a hierarchical review has been presented for brain tumor segmentation and detection. It is found that the segmentation methods hold a wide margin of improvement in the context of the implementation of adaptive thresholding and segmentation methods, the feature training and mapping requires redundancy correction, the input data training needs to be more exhaustive and the detection algorithms are required to be robust in terms of handling online input data analysis/tumor detection.\u0000","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42490913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-08DOI: 10.2174/18744400-v15-e2202040
Mitsuki Rikitake, J. Hata, Mayu Iida, Fumiko Seki, Rina Ito, Yuji Komaki, Chihoko Yamada, D. Yoshimaru, H. Okano, T. Shirakawa
Dystrophin strengthens muscle cells; however, in muscular dystrophy, dystrophin is deficient due to an abnormal sugar chain. This abnormality occurs in skeletal muscle and in brain tissue. This study aimed to non-invasively analyze the neural organization of the brain in muscular dystrophy. We used a mouse model of muscular dystrophy to study whether changes in brain structure and neurodegeneration following dystrophin deficiency can be assessed by 7T magnetic resonance imaging. C57BL/10-mdx (X chromosome-linked muscular dystrophy) mice were used as the dystrophic mouse model and healthy mice were used as controls. Ventricular enlargement is one of the most common brain malformations in dystrophin-deficient patients. Therefore, we examined whether ventricular enlargement was observed in C57BL/10-mdx using transverse-relaxation weighted images. Brain parenchyma analysis was performed using diffusion MRI with diffusion tensor images and neurite orientation dispersion and density imaging. Parenchymal degeneration was assessed in terms of directional diffusion, nerve fiber diffusion, and dendritic scattering density. For the volume of brain ventricles analyzed by T2WI, the average size was 1.5 times larger in mdx mice compared to control mice. In the brain parenchyma, a significant difference (p < 0.05) was observed in parameters indicating disturbances in the direction of nerve fibers and dendritic scattering density in the white matter region. Our results show that changes in brain structure due to dystrophin deficiency can be assessed in detail without tissue destruction by combining diffusion tensor images and neurite orientation dispersion and density imaging analyses.
{"title":"Analysis of Brain Structure and Neural Organization in Dystrophin-Deficient Model Mice with Magnetic Resonance Imaging at 7 T","authors":"Mitsuki Rikitake, J. Hata, Mayu Iida, Fumiko Seki, Rina Ito, Yuji Komaki, Chihoko Yamada, D. Yoshimaru, H. Okano, T. Shirakawa","doi":"10.2174/18744400-v15-e2202040","DOIUrl":"https://doi.org/10.2174/18744400-v15-e2202040","url":null,"abstract":"\u0000 \u0000 Dystrophin strengthens muscle cells; however, in muscular dystrophy, dystrophin is deficient due to an abnormal sugar chain. This abnormality occurs in skeletal muscle and in brain tissue.\u0000 \u0000 \u0000 \u0000 This study aimed to non-invasively analyze the neural organization of the brain in muscular dystrophy. We used a mouse model of muscular dystrophy to study whether changes in brain structure and neurodegeneration following dystrophin deficiency can be assessed by 7T magnetic resonance imaging.\u0000 \u0000 \u0000 \u0000 C57BL/10-mdx (X chromosome-linked muscular dystrophy) mice were used as the dystrophic mouse model and healthy mice were used as controls. Ventricular enlargement is one of the most common brain malformations in dystrophin-deficient patients. Therefore, we examined whether ventricular enlargement was observed in C57BL/10-mdx using transverse-relaxation weighted images. Brain parenchyma analysis was performed using diffusion MRI with diffusion tensor images and neurite orientation dispersion and density imaging. Parenchymal degeneration was assessed in terms of directional diffusion, nerve fiber diffusion, and dendritic scattering density.\u0000 \u0000 \u0000 \u0000 For the volume of brain ventricles analyzed by T2WI, the average size was 1.5 times larger in mdx mice compared to control mice. In the brain parenchyma, a significant difference (p < 0.05) was observed in parameters indicating disturbances in the direction of nerve fibers and dendritic scattering density in the white matter region.\u0000 \u0000 \u0000 \u0000 Our results show that changes in brain structure due to dystrophin deficiency can be assessed in detail without tissue destruction by combining diffusion tensor images and neurite orientation dispersion and density imaging analyses.\u0000","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44078070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-20DOI: 10.2174/1874440002114010028
Keerthiraj Bele, S. Ullal, A. Mahale, Sriti Rani
The mycotic aneurysm is a rare intracranial pathology seen with pre-existing infective endocarditis. It has a high mortality rate due to its risk of rupture and needs early diagnosis and treatment. A 23-year male patient who presented with infective endocarditis subsequently developed a left parietal-temporal intracranial haemorrhage with suspicion of aneurysm after the course of antibiotic treatment as seen on Computed Tomography (CT) scan. Digital Subtraction Angiography (DSA) revealed a ruptured fusosaccular aneurysm in the distal parietal branches of the left Middle Cerebral Artery (MCA), for which glue embolization of the distal parent artery and aneurysm was done. The interventional endovascular procedure was done with complete obliteration of the distal parent artery, mycotic aneurysm, and normal filling of the left internal cerebral artery (ICA) branches. Mycotic intracranial aneurysms (MIA) are a rare form of cerebrovascular pathology which needs early diagnosis with endovascular intervention when rupture occurs.
{"title":"Unusual Intracranial Manifestation of Infective Endocarditis","authors":"Keerthiraj Bele, S. Ullal, A. Mahale, Sriti Rani","doi":"10.2174/1874440002114010028","DOIUrl":"https://doi.org/10.2174/1874440002114010028","url":null,"abstract":"\u0000 \u0000 The mycotic aneurysm is a rare intracranial pathology seen with pre-existing infective endocarditis. It has a high mortality rate due to its risk of rupture and needs early diagnosis and treatment.\u0000 \u0000 \u0000 \u0000 A 23-year male patient who presented with infective endocarditis subsequently developed a left parietal-temporal intracranial haemorrhage with suspicion of aneurysm after the course of antibiotic treatment as seen on Computed Tomography (CT) scan. Digital Subtraction Angiography (DSA) revealed a ruptured fusosaccular aneurysm in the distal parietal branches of the left Middle Cerebral Artery (MCA), for which glue embolization of the distal parent artery and aneurysm was done.\u0000 \u0000 \u0000 \u0000 The interventional endovascular procedure was done with complete obliteration of the distal parent artery, mycotic aneurysm, and normal filling of the left internal cerebral artery (ICA) branches.\u0000 \u0000 \u0000 \u0000 Mycotic intracranial aneurysms (MIA) are a rare form of cerebrovascular pathology which needs early diagnosis with endovascular intervention when rupture occurs.\u0000","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41621864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-24DOI: 10.2174/1874440002114010001
O. Kheiralla, Aymen Abdalkariem, A. AlGhamdi, A. Tajaldeen, Naif Hamid
The Stria Medullaris (SM) is a white-matter tract that contains afferent fibres that connect the cognitive-emotional areas in the forebrain to the Habenula (Hb). The Hb plays an important role in behavioral responses to reward, stress, anxiety, pain, and sleep through its action on neuromodulator systems. The Fasciculus Retroflexus (FR) forms the primary output of the Hb to the midbrain. The SM, Hb, and FR are part of a special pathway between the forebrain and the midbrain known as the Dorsal Diencephalic Conduction system (DDC). Hb dysfunction is accompanied by different types of neuropsychiatric disorders, such as schizophrenia, depression, and Treatment-Resistant Depression (TRD). Due to difficulties in the imaging assessment of the SM and HB in vivo, they had not been a focus of clinical studies until the invention of Diffusion Tensor Imaging (DTI), which has revolutionized the imaging and investigation of the SM and Hb. DTI has facilitated the imaging of the SM and Hb and has provided insights into their properties through the investigation of their monoamine dysregulation. DTI is a well-established technique for mapping brain microstructure and white matter tracts; it provides indirect information about the microstructural architecture and integrity of white matter in vivo, based on water diffusion properties in the intraand extracellular space, such as Axial Diffusivity (AD), Radial Diffusivity (RD), mean diffusivity, and Fractional Anisotropy (FA). Neurosurgeons have recognized the potential value of DTI in the direct anatomical targeting of the SM and Hb prior to Deep Brain Stimulation (DBS) surgery for the treatment of certain neuropsychiatric conditions, such as TRD. DTI is the only non-invasive method that offers the possibility of visualization in vivo of the white-matter tracts and nuclei in the human brain. This review study summarizes the use of DTI as a promising new imaging method for accurate identification of the SM and Hb, with special emphasis on direct anatomical targeting of the SM and Hb prior to DBS surgery.
{"title":"Diffusion Tensor Imaging: A Promising New Technique for Accurate Identification of the Stria Medullaris and Habenula","authors":"O. Kheiralla, Aymen Abdalkariem, A. AlGhamdi, A. Tajaldeen, Naif Hamid","doi":"10.2174/1874440002114010001","DOIUrl":"https://doi.org/10.2174/1874440002114010001","url":null,"abstract":"The Stria Medullaris (SM) is a white-matter tract that contains afferent fibres that connect the cognitive-emotional areas in the forebrain to the Habenula (Hb). The Hb plays an important role in behavioral responses to reward, stress, anxiety, pain, and sleep through its action on neuromodulator systems. The Fasciculus Retroflexus (FR) forms the primary output of the Hb to the midbrain. The SM, Hb, and FR are part of a special pathway between the forebrain and the midbrain known as the Dorsal Diencephalic Conduction system (DDC). Hb dysfunction is accompanied by different types of neuropsychiatric disorders, such as schizophrenia, depression, and Treatment-Resistant Depression (TRD). Due to difficulties in the imaging assessment of the SM and HB in vivo, they had not been a focus of clinical studies until the invention of Diffusion Tensor Imaging (DTI), which has revolutionized the imaging and investigation of the SM and Hb. DTI has facilitated the imaging of the SM and Hb and has provided insights into their properties through the investigation of their monoamine dysregulation. DTI is a well-established technique for mapping brain microstructure and white matter tracts; it provides indirect information about the microstructural architecture and integrity of white matter in vivo, based on water diffusion properties in the intraand extracellular space, such as Axial Diffusivity (AD), Radial Diffusivity (RD), mean diffusivity, and Fractional Anisotropy (FA). Neurosurgeons have recognized the potential value of DTI in the direct anatomical targeting of the SM and Hb prior to Deep Brain Stimulation (DBS) surgery for the treatment of certain neuropsychiatric conditions, such as TRD. DTI is the only non-invasive method that offers the possibility of visualization in vivo of the white-matter tracts and nuclei in the human brain. This review study summarizes the use of DTI as a promising new imaging method for accurate identification of the SM and Hb, with special emphasis on direct anatomical targeting of the SM and Hb prior to DBS surgery.","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":"14 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48606942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.2174/1874440002114010016
P. Manava, P. Hastreiter, R. Schmieder, Susanne Jung, R. Fahlbusch, A. Dörfler, M. Lell, M. Buchfelder, R. Naraghi
In this study, we attempted to identify clinical parameters predicting the absence or presence of Neurovascular Compression (NVC) at the Ventrolateral Medulla (VLM) in arterial hypertension (HTN) in MRI findings. Cardiovascular and pulmonary afferences are transmitted through the left vagus and glossopharyngeal nerve to the brain stem and vasoactive centers. Evidence supports the association between HTN and NVC at the left VLM. Several independent studies indicate a reduction of HTN after Microvascular Decompression (MVD) of the left. Several independent studies indicate a reduction of HTN after Microvascular Decompression (MVD) of the left VLM. Image processing of MRI provides comprehensible detection of NVC. HTN affects hemodynamic parameters and organs. This study analyzes and correlates clinical data and MRI findings in patients with and without NVC at the VLM in treatment resistant HTN to obtain possible selection criteria for neurogenic hypertension. In 44 patients with treatment resistant HTN, we compared MRI findings of neurovascular imaging to demographic, clinical and lifestyle data, office and 24-hour ambulatory Blood Pressure (BP), and cardiovascular imaging and parameters. Twenty-nine (66%) patients had evidence of NVC at the VLM in MRI. Sixteen patients (36%) had unilateral NVC on the left side, 7 (16%) unilateral right and 6 (14%) bilateral NVC. Fifteen (34%) had no evidence of NVC at the VLM. Patients with left sided NVC were significantly younger, than those without NVC (p=0.034). They showed a statistically significant variance in daytime (p=0.020) and nighttime diastolic BP (p<0.001) as the mean arterial pressure (p=0.020). Other measured parameters did not show significant differences between the two groups. We suggest to examine young adults with treatment resistant HTN for the presence of NVC at VLM, before signs of permanent organ damage appear. Clinical and hemodynamic parameters did not emerge as selection criteria to predict NVC. MVD as a surgical treatment of NVC in HTN is not routine yet as a surgical treatment of NVC in HTN is not routine yet. Detection of NVC by imaging and image processing remains the only criteria to suggest MVD, which should be indicated on an individual decision.
{"title":"Neurovascular Compression in Arterial Hypertension: Correlation of Clinical Data to 3D-Visualizations of MRI-Findings","authors":"P. Manava, P. Hastreiter, R. Schmieder, Susanne Jung, R. Fahlbusch, A. Dörfler, M. Lell, M. Buchfelder, R. Naraghi","doi":"10.2174/1874440002114010016","DOIUrl":"https://doi.org/10.2174/1874440002114010016","url":null,"abstract":"In this study, we attempted to identify clinical parameters predicting the absence or presence of Neurovascular Compression (NVC) at the Ventrolateral Medulla (VLM) in arterial hypertension (HTN) in MRI findings. Cardiovascular and pulmonary afferences are transmitted through the left vagus and glossopharyngeal nerve to the brain stem and vasoactive centers. Evidence supports the association between HTN and NVC at the left VLM. Several independent studies indicate a reduction of HTN after Microvascular Decompression (MVD) of the left. Several independent studies indicate a reduction of HTN after Microvascular Decompression (MVD) of the left VLM. Image processing of MRI provides comprehensible detection of NVC. HTN affects hemodynamic parameters and organs. This study analyzes and correlates clinical data and MRI findings in patients with and without NVC at the VLM in treatment resistant HTN to obtain possible selection criteria for neurogenic hypertension. In 44 patients with treatment resistant HTN, we compared MRI findings of neurovascular imaging to demographic, clinical and lifestyle data, office and 24-hour ambulatory Blood Pressure (BP), and cardiovascular imaging and parameters. Twenty-nine (66%) patients had evidence of NVC at the VLM in MRI. Sixteen patients (36%) had unilateral NVC on the left side, 7 (16%) unilateral right and 6 (14%) bilateral NVC. Fifteen (34%) had no evidence of NVC at the VLM. Patients with left sided NVC were significantly younger, than those without NVC (p=0.034). They showed a statistically significant variance in daytime (p=0.020) and nighttime diastolic BP (p<0.001) as the mean arterial pressure (p=0.020). Other measured parameters did not show significant differences between the two groups. We suggest to examine young adults with treatment resistant HTN for the presence of NVC at VLM, before signs of permanent organ damage appear. Clinical and hemodynamic parameters did not emerge as selection criteria to predict NVC. MVD as a surgical treatment of NVC in HTN is not routine yet as a surgical treatment of NVC in HTN is not routine yet. Detection of NVC by imaging and image processing remains the only criteria to suggest MVD, which should be indicated on an individual decision.","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68074104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.2174/1874440002013010037
Ruban Nersisson, Arjun Sengupta, Swapnil Sarkar, Sushant Agrawal, Pushpreet Singh, A. N. Josephraj, Palani Thanaraj, V. Rajinikanth
Tinnitus is a hearing disorder that causes ringing, buzzing or hissing sensation to the patient’s auditory senses. It has become a very common complaint over the years affecting around 7-8% of the human population all over the world. The disorder causes the patients to feel irritable, annoyed, depressed, and distressed. As a result, it obstructs their sense of relaxation, enjoyment, and even their sleep - thus forcing them to avoid any social gatherings. There has been a substantial amount of work that has been carried out pertinent to this disorder. This paper reviews existing research and work done regarding Tinnitus effects, causes, and diagnosis. The numerous ways in which Tinnitus could affect an individual have been depicted. From the plethora of probable causes of this disorder, the most conceivable ones are highlighted. Moreover, this paper documents and reviews the attempts at treating Tinnitus, relevant engineering breakthroughs, and the various ways in which Tinnitus noise is suppressed – such as Tinnitus Retraining Therapy, Neuromodulation, and Signal processing approach. The manuscripts highlight the pros and cons of these methods. Over 45 research articles and other reliable internet medical sources were reviewed and these pieces of work were contrasted. These findings should help in understanding both – the disorder, as well as the situation of the patients suffering from it. Through this manuscript, an attempt was made to spread awareness about the mysterious disorder.
{"title":"Tinnitus: A Tingling Mystery to be Decrypted","authors":"Ruban Nersisson, Arjun Sengupta, Swapnil Sarkar, Sushant Agrawal, Pushpreet Singh, A. N. Josephraj, Palani Thanaraj, V. Rajinikanth","doi":"10.2174/1874440002013010037","DOIUrl":"https://doi.org/10.2174/1874440002013010037","url":null,"abstract":"Tinnitus is a hearing disorder that causes ringing, buzzing or hissing sensation to the patient’s auditory senses. It has become a very common complaint over the years affecting around 7-8% of the human population all over the world. The disorder causes the patients to feel irritable, annoyed, depressed, and distressed. As a result, it obstructs their sense of relaxation, enjoyment, and even their sleep - thus forcing them to avoid any social gatherings. There has been a substantial amount of work that has been carried out pertinent to this disorder. This paper reviews existing research and work done regarding Tinnitus effects, causes, and diagnosis. The numerous ways in which Tinnitus could affect an individual have been depicted. From the plethora of probable causes of this disorder, the most conceivable ones are highlighted. Moreover, this paper documents and reviews the attempts at treating Tinnitus, relevant engineering breakthroughs, and the various ways in which Tinnitus noise is suppressed – such as Tinnitus Retraining Therapy, Neuromodulation, and Signal processing approach. The manuscripts highlight the pros and cons of these methods. Over 45 research articles and other reliable internet medical sources were reviewed and these pieces of work were contrasted. These findings should help in understanding both – the disorder, as well as the situation of the patients suffering from it. Through this manuscript, an attempt was made to spread awareness about the mysterious disorder.","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44655873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-31DOI: 10.2174/1874440002013010051
D. C. Lepcha, Bhawna Goyal, Ayush Dogra
Image Fusion is the method which conglomerates complimentary information from the source images to a single fused image . There are numerous applications of image fusion in the current scenario such as in remote sensing, medical diagnosis, machine vision system, astronomy, robotics, military units, biometrics, and surveillance. In this case multi-sensor or multi-focus devices capture images of the particular scene which are complementary in the context of information content to each other. The details from complementary images are combined through the process of fusion into a single image by applying the algorithmic formulas. The main goal of image fusion is to fetch more and proper information from the primary or source images to the fused image by minimizing the loss of details of the images and by doing so to decrease the artifacts in the final image. In this paper, we proposed a new method to fuse the images by applying a cross bilateral filter for gray level similarities and geometric closeness of the neighboring pixels without smoothing edges. Then, the detailed images obtained by subtracting the cross bilateral filter image output from original images are being filtered through the rolling guidance filter for scale aware operation. In particular, it removes the small-scale structures while preserving the other contents of the image and successfully recovers the edges of the detailed images. Finally, the images have been fused using a weighted computed algorithm and weight normalization. The results have been validated and compared with various existing state-of-the-art methods both subjectively and quantitatively. It was observed that the proposed method outperforms the existing methods of image fusion.
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Pub Date : 2020-11-25DOI: 10.2174/1874440002013010030
S. Kihira, C. Koo, K. Nael, P. Belani
This was a retrospective single-center study. Between March 2017 to May 2018, adult patients who underwent brain MRI with the inclusion of ASL perfusion and who had bilateral reductions of CBF in the parieto-occipital regions were included. ASL was performed using a pseudocontinuous arterial spin labeling (pCASL) technique on 1.5T MR system. Age and gender-matched patients with no perfusion defect were concurrently collected. Comorbidity data was collected from EMR, including major depressive disorder, Alzheimer’s disease, Parkinson’s disease, Schizophrenia, anxiety disorder, hypertension, diabetes mellitus type II, coronary artery disease, and chronic kidney disease. A Pearson’s ChiSquare test was performed to assess for comorbidities associated with hypoperfusion of the parieto-occipital lobes.
{"title":"Regional Parieto-occipital Hypoperfusion on Arterial Spin Labeling Associates with Major Depressive Disorder","authors":"S. Kihira, C. Koo, K. Nael, P. Belani","doi":"10.2174/1874440002013010030","DOIUrl":"https://doi.org/10.2174/1874440002013010030","url":null,"abstract":"This was a retrospective single-center study. Between March 2017 to May 2018, adult patients who underwent brain MRI with the inclusion of ASL perfusion and who had bilateral reductions of CBF in the parieto-occipital regions were included. ASL was performed using a pseudocontinuous arterial spin labeling (pCASL) technique on 1.5T MR system. Age and gender-matched patients with no perfusion defect were concurrently collected. Comorbidity data was collected from EMR, including major depressive disorder, Alzheimer’s disease, Parkinson’s disease, Schizophrenia, anxiety disorder, hypertension, diabetes mellitus type II, coronary artery disease, and chronic kidney disease. A Pearson’s ChiSquare test was performed to assess for comorbidities associated with hypoperfusion of the parieto-occipital lobes.","PeriodicalId":37431,"journal":{"name":"Open Neuroimaging Journal","volume":"13 1","pages":"30-36"},"PeriodicalIF":0.0,"publicationDate":"2020-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45187037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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