scholarly journals Chronic subdural hematomas: challenges and solutions. Part I. Clinical variants and diagnosis

2021 ◽  
Vol 2 (2) ◽  
pp. 21-27
Author(s):  
Leonid B. Likhterman ◽  
◽  
Aleksandr D. Kravchuk ◽  
Vladimir A. Okhlopkov ◽  
◽  
...  
Keyword(s):  
Brain Imaging ◽  
Intracranial Hemorrhage ◽  
Subdural Hematoma ◽  
Clinical Course ◽  
Chronic Subdural Hematoma ◽  
Imaging Data ◽  
Clinical Variants ◽  
Brain Compression ◽  
Brain Imaging Data ◽  
Ct And Mri

Chronic subdural hematoma (cSDH) is a multifactorial extensive intracranial hemorrhage, causing the local and/or general brain compression. Hematoma has a delimiting capsule, which defines all pathophysiological features, clinical course and treatment tactics. The paper reports contemporary views on ethiology and clinical course of cSDH. Emphasis is placed on the diagnosis. Based on the analysis of 558 verified cSDH observations, the phasal course and brain imaging data are reported. CT and MRI signs of cSDH are defined.

Association of Inter-individual Differences in Imaging Markers with Schizophrenia Phenotypes

2017 ◽  
Vol 41 (S1) ◽  
pp. S43-S44
Author(s):  
G. Pergola ◽  
T. Quarto ◽  
M. Papalino ◽  
P. Di Carlo ◽  
P. Selvaggi ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Clinical Course ◽  
Clinical Information ◽  
Roc Curves ◽  
Individual Variability ◽  
Clinical Population ◽  
Imaging Data ◽  
Mri Scans ◽  
Brain Imaging Data ◽  
Genetically Determined

IntroductionNeuroimaging studies have identified several candidate biomarkers of schizophrenia. However, it is unclear whether the considerable variability in these neurobiological correlates between patients can be translated into the clinical setting.ObjectivesWe aimed to identify neuroimaging predictors of clinical course in patients with schizophrenia. Combined with the identification of genetically determined markers of schizophrenia risk, our studies aimed to elucidate the biological basis and the clinical relevance of inter-individual variability between patients.MethodsWe included over 150 patients with schizophrenia and 279 healthy volunteers across five neuroimaging centers in the framework of the IMAGEMEND project [1]. We performed multiple studies on MRI scans using random forests and ROC curves to predict clinical course. Data from healthy controls served to normalize the data from the clinical population and to provide a benchmark for the findings.ResultsWe identified ensembles of neuroimaging markers and of genetic variants predictive of clinical course. Results highlight that (i) brain imaging carries significant clinical information, (ii) clinical information at baseline can considerably increase prediction accuracy.ConclusionThe methodological challenges and the results will be discussed in the context of recent findings from other multi-site studies. We conclude that brain imaging data on their own right are relevant to stratify patients in terms of clinical course; however, complementing these data with other modalities such as genetics and clinical information is necessary to further develop the field towards clinical application of the predictions.Disclosure of interestGiulio Pergola is the academic supervisor of a Hoffmann-La Roche Collaboration grant that partially funds his salary.

scholarly journals The Open Brain Consent : Informing research participants and obtaining consent to share brain imaging data

2021 ◽  
Author(s):  
Elise Bannier ◽  
Gareth Barker ◽  
Valentina Borghesani ◽  
Nils Broeckx ◽  
Patricia Clement ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Research Participants ◽  
Brain Imaging Data

Learning from Enhanced Contextual Similarity in Brain Imaging Data for Classification of Schizophrenia

2017 ◽  
pp. 265-275 ◽  
Author(s):  
Tewodros Mulugeta Dagnew ◽  
Letizia Squarcina ◽  
Massimo W. Rivolta ◽  
Paolo Brambilla ◽  
Roberto Sassi
Keyword(s):  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data

Recurrent Chronic Subdural Hematoma: Clinical and Imaging Risk Factors

2018 ◽  
Vol 15 (01) ◽  
pp. 008-015 ◽  
Author(s):  
Benaissa Abdennebi ◽  
Maher Al Shamiri
Keyword(s):  
Risk Factors ◽  
Subdural Hematoma ◽  
Heart Diseases ◽  
Chronic Subdural Hematoma ◽  
Antiplatelet Agents ◽  
The Elderly ◽  
Imaging Data ◽  
Anticoagulation Agents ◽  
The Right ◽  
Recurrence Group

Abstract Background Chronic subdural hematoma (CSDH) is a major cause of neurosurgical emergencies in the elderly. Despite the use of routine surgical practices, recurrence of this condition is expected. This study was conducted to identify the risk factors (RF) for recurrent CSDH. Methods Between January 2016 and July 2017, 103 consecutive patients suffering from CSDH were admitted to our department. The no-recurrence group (NRG) consisted of 91 patients, and the recurrence group (RG) consisted of 12 patients. To identify the RF involved in recurrent CSDH, we analyzed multiple factors, including patient comorbidities and imaging data. Results Between the two groups, there were no statistical differences (p > 0.05) for head trauma, diabetes mellitus (DM), high blood pressure, heart diseases, anticoagulation agents, or seizures; however, DM was associated with one of the above-mentioned factors. In contrast, there were significant differences for antiplatelet agents (APA) (p < 10–6) and the right side of the hematoma location (p = 0.03). Conclusion Although the literature highlights the controversy regarding RF for CSDH, we detected APA and the right side as RF, whereas DM alone or associated with another comorbidity does not affect the CSDH outcome.

scholarly journals A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinlong Hu ◽  
Yuezhen Kuang ◽  
Bin Liao ◽  
Lijie Cao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Motor Task ◽  
Data Classification ◽  
Fmri Data ◽  
Imaging Data ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Brain Imaging Data ◽  
3D Cnn

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

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