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Completed Research Projects
Project 1: Prof. Dr. Burak Güçlü
| Title | Functional stimulation system for rehabilitation of gait and driving neural plasticity after spinal cord injury using graphene-based nerve electrodes |
| Supporting Agency | EU FLAG-ERA JTC 2021 (TÜBİTAK 1071) |
| PI | Prof. Dr. Burak Güçlü |
| Duration | 36 months (August 1st, 2022 - August 1st, 2025) |
| Open Positions | 1 M.S. student - |
| Description | Our part (WP5) in the consortium is to study plasticity in the rat somatosensory cortex after spinal cord injury (https://rescue-graph.com/). An applicant interested in joining the project towards an M.S. degree can email his/her CV and transcripts with a short statement of motivation. |
Project 2: Assoc. Prof. Dr. Daniela Schulz
|
Title |
Experience-driven (ed)-DBS to improve motor symptoms in the hemiparkinson rat model |
| Supporting Agency | Boğaziçi University, BAP Starting grant (SUP) |
| PI | Assoc. Prof. Dr. Daniela Schulz |
| Duration | 3 years (ended) |
| Open Positions | Volunteers |
| Description | Deep brain stimulation (DBS) is considered state-of-the-art for the treatment of motor symptoms in advanced Parkinson’s disease (PD). Symptoms, such as reduced motor speed, increased rigidity, and impaired balance are caused by a deficient dopamine (DA) system. However, standard DBS is administered chronically, in a way that ignores the normal functions of DA, for example, as a teaching signal. This might explain why treatment efficacy is only roughly 40%. Thus, our goal is to improve treatment efficacy by establishing a new method of DBS, which administers the stimulation acutely and in an experience-dependent fashion (ed-DBS). Data processing is in progress. |
| Infrastructure | Behavioral Biology Laboratory; https://bbl.bogazici.edu.tr/ |
| Collaboration | Tactile Research Laboratory |
Project 3: Prof. Dr. Esin Öztürk Işık
| Title |
Kan Beyin Bariyer Bozulumu Için Atardamar Fırıl Etiketleme (BBB-Asl) Tekniğinin Beyin Tümörlerinde Değerlendirilmesi (Jpnd`Debbie' Project - Work Package 5) (click here for more information) |
| Supporting Agency | JPND COFUND TÜBİTAK 1071 Grants |
| PI | Prof. Dr. Alp Dinçer |
| Duration | 48 months (2021 - 2025) |
| Description |
This study aimed to evaluate the BBB differences in different histopathological tumor grades in patients with glioma, a type of brain tumor, using the BBB-ASL technique. Moreover, the parameters of BBB integrity differences in histopathological tumor grades will be classified by machine learning algorithms. Additionally, the disruption in BBB integrity and water exchange differences between AD and glioma patients will be assessed. |
| Collaboration | Esin Öztürk Işık, Matthias Günther, Eric Achten, Henk Mutsaerts, Udunna Anazodo, Tormod Fladby, Catherine Morgan, David Thomas, Jennifer Linn, Saima Hilal |
Project 4: Prof. Dr. Esin Öztürk Işık
| Title | Advanced Magnetic Resonance Imaging and Machine Learning Based Product Development for Noninvasive Detection of Genetic Subgroups of Brain Membrane Tumors (click here for more information) |
| Supporting Agency | TÜBİTAK 1001 |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 48 months (2021 - 2025) |
| Description | The aim of this study is to develop a machine learning based product, for detection of a radiological signature specific to the NF2 molecular subgroup of meningiomas, using advanced MR imaging and radiomic features. Preoperative diagnosis of NF2 molecular subtype of meningiomas by machine learning methods based on non-invasive MRI techniques will contribute to appropriate treatment plann |
| Collaboration | Alp Dinçer, Koray Özduman, Alpay Özcan, Ayça Ersen Danyeli, Murat Şakir Ekşi, Özge Can |
Project 5: Prof. Dr. Esin Öztürk Işık
| Title | Development of a Diagnostic Tool for Identifying Genetic, Metabolic and Histopathologic Properties of Glial Brain Tumors (click here for more information) |
| Supporting Agency | TÜBİTAK 1003 |
| PI | Prof. Dr. Alp Dinçer |
| Duration | 48 months (2017 - 2020) |
| Description | The aim of this project is to combine multimodal MR information for applying machine learning methodologies with the aim of differentiating glioblastoma types. For this purpose, the MR, genetic and metabolomic data obtained from a prospective cohort will be analyzed and machine learning methods will be developed for predicting survival rate by only using the MR data. The genetic and metabolomic data obtained from biopsy will be included in the ground truth information. |
| Collaboration | Esin Öztürk Işık, Koray Özduman, Alpay Özcan, Özge Can |
Project 6: Prof. Dr. Esin Öztürk Işık
| Title | Assessment of Radiotherapy Planning Efficacy Based on Brain Tumor Shape Analysis (click here for more information) |
| Supporting Agency | Boğaziçi University BAP Grants |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 12 months (2022 - 2023) |
| Description | The primary goal of Gamma Knife (GK) dose planning is to cover any shape irregularities of target that strongly affect dose distribution inside and outside the target. However, there is currently no clinically practical tool for estimating the effect of shape irregularity of target on dose plan efficiency. In this study, the main aims are to measure tumor shape irregularity and analyze its effect on GK plan efficiency and treatment outcomes. In the present study, GK treatment plans created by Perfection/ICON platform for vestibular schwannoma. Tumor shape irregularity will be measured using radiomic shape features from segmented magnetic resonance (MR) images. Dose planning efficiency will be measured using the selectivity index (SI), gradient index (GI), Paddick conformality index (PCI), and efficacy index (EI). Correlation and linear regression analyses will be applied between the shape irregularity features and the dose plan indices. Then, machine learning algorithms will be used to identify the best-performing shape feature to predict dose plan efficiency. Finally, treatment outcomes at year 2 will be investigated, including tumor growth control and functional hearing preservation using Cox regression analyses to determine whether tumor shape irregularity has any effect on GK plan efficiency and treatment outcomes. Among the various shape irregularity metrics, the one with the highest predictive performance will be selected. It is expected that the irregularity of tumor shape will provide useful information to the clinician with the initial estimation of treatment efficacy and the comparisons of tumors. |
Project 7: Prof. Dr. Esin Öztürk Işık
| Title | The Effect of Super Resolution Deep Learning on Radiomic Shape Features Acquired from Diffusion MRI of Stroke Patients |
| Supporting Agency | Boğaziçi University BAP Grants |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 12 months (2022 - 2023) |
| Description | In this project, we aimed to assess the robustness of the radiomics shape features obtained from low resolution DWI MRI images, and super resolved images using deep learning that have 2 or 4 fold higher resolution. As a result, robustness of widely used radiomic shape features will be assessed upon application of super resolution and unstable shape features will be determined. |
Project 8: Prof. Dr. Esin Öztürk Işık
| Title | Automatic Assessment of Gait Impairments in Stroke using Artificial Intelligence, Wearable Technology and Neuroimaging |
| Supporting Agency | Aberystwyth University CIDRA Grants |
| PI | Prof. Dr. Okar Akanyeti |
| Duration | 12 months (2020- 2021) |
| Description | The interdisciplinary project aims for delivering wearable technology to improve the lives of stroke patients in Turkey. Recently developed by Aberystwyth University (in collaboration with NHS Wales), the wearable tech provides high resolution movement data to quantify walking disability and evaluate the efficacy of stroke treatment. By providing objective measurements, it will promote consistency of service across Turkey and empower patients to be more independent. Collecting data over long periods in more natural settings will reveal new insights into stroke prevalence and outcomes. It will be easier to identify patients with the greatest health needs and offer personalized rehabilitation programs. |
| Collaboration | Esin Öztürk Işık, Hale Saybaşılı, Can Yücesoy, Alp Dinçer, Dilaver Kaya, Nazire Afşar, Federico Villagra Povina |
Project 9: Prof. Dr. Esin Öztürk Işık
| Title | Determination of Metabolic and Perfusion Magnetic Resonance Imaging Based Biomarkers in Parkinson's Disease Dementia and Parkin Gene Mutations |
| Supporting Agency | Boğaziçi University BAP Grants |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 24 months (2019 - 2020) |
| Description | The aim of this study is to determine biomarkers indicating metabolic and perfusion changes in the brain of PARK2 mutation carriers and patients with Parkinson's disease dementia by using ASL-MRI and 1H-MRSI techniques at 3T. |
Project 10: Prof. Dr. Esin Öztürk Işık
| Title | 1H Bilateral, Flexible Breast RF Coil Design for Magnetic Resonance Imaging Systems |
| Supporting Agency | TÜBİTAK 1001 |
| PI | Prof. Dr. Korkut Yeğin |
| Duration | 24 months (2017 - 2019) |
| Description | The main aim of this study is to design 1H bilateral, flexible breast RF coil for magnetic resonance imaging systems and to develop a software for analysis of breast MR images obtained with this coil. |
| Collaboration | Esin Öztürk Işık |
Project 11: Prof. Dr. Esin Öztürk Işık
| Title | In-vivo Examination of Brain Metabolism with Accelerated J-Resolved PRESS MR Spectroscopic Imaging using Compressed Sensing |
| Supporting Agency | Boğaziçi University BAP Grants |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 12 months (2017 - 2018) |
| Description | The main aim of this study is to design 1H bilateral, flexible breast RF coil for magnetic resonance imaging systems and to develop a software for analysis of breast MR images obtained with this coil. |
Project 12: Prof. Dr. Esin Öztürk Işık
| Title | Determination of Multimodality Magnetic Resonance Imaging Based Biomarkers for Mild Cognitive Impairment in Parkinson Disease |
| Supporting Agency | TÜBİTAK 1001 |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 36 months (2015 - 2018) |
| Description | The main aim of this study to develop biomarkers that would indicate the presence of mild cognitive imparment (MCI) in Parkinson disease (PD) and the probability of its’ evolution into dementia by evaluating the findings from multimodality structural, metabolic, and functional MR imaging of patients diagnosed with PD-MCI or cognitively intact Parkinson’s disease, and healthy controls. |
| Collaborations | Tamer Demiralp, Hakan Gürvit, Başar Bilgiç, Haşmet Hanağası, Aziz Uluğ, Erdem Tüzün |
Project 13: Prof. Dr. Esin Öztürk Işık
| Title | Investigation of the Human Brain Metabolism in-vivo in Chronic Liver Failure Using Magnetic Resonance Spectroscopic Editing Techniques |
| Supporting Agency | Boğaziçi University BAP Grants |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 36 months (2015 - 2018) |
| Description | The main objective of this study is to understand the metabolic changes in the brain that occur due to minimal hepatic encephalopathy, and define metabolic biomarkers that can help in the diagnosis of this disorder. |
| Collaborations | Bahattin Hakyemez, Emre Ökeer, Tuba Erürker Öztürk, Aylin Bican Demir |
Project 14: Prof. Dr. Esin Öztürk Işık
| Title | Feasibilty Study of Obtaining High-Resolution Spectroscopy Images Using Data Fusion Techniques (click here for more information) |
| Supporting Agency | Royal Society Newton Mobility Grant |
| PI | Prof. Dr. Esin Öztürk Işık/Maria Valdes-Hernandez |
| Duration | 12 months (2016 - 2017) |
| Description | The main objective of this study is to develop a pipeline to increase the resolution of magnetic resonance spectroscopy images to provide the international scientific and research community a technique to study tissue microstructure and metabolic changes in neurological diseases, and increase research output by optimising tissue characterisation complementing the information from multimodal and multispectral magnetic resonance imaging with the application of this technique. |
Project 15: Prof. Dr. Esin Öztürk Işık
| Title | Accelerated Phosphorus MR Spectroscopic Imaging of Brain Tumors at 3T using Compressed Sensing (click here for more information) |
| Supporting Agency | TÜBİTAK 3501 Career Development Grants |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 24 months (2012 - 2014) |
| Description | Phosphorus magnetic resonance spectroscopic imaging (31P-MRSI) is a non-invasive MR spectroscopic imaging technique that detects the phosphorus containing metabolites of the brain. 31P MRSI provides in-vivo quantitative information about the energy metabolism, oxygen state and pH within a given region of interest. Although, phosphorus magnetic resonance spectroscopic imaging provides vast information, it has not been widely used in the clinical settings yet. One of the major reasons of this problem is the low MR signal of phosphorus, because phosphorus is 15 times less abundant in the body than proton, and its gyromagnetic ratio is less than half of that of proton’s (1H=42.58 MHz/T, 31P=17.2 MHz/T). It is possible to average out multiple phosphorus signal acquisitions to get a higher signal to noise ratio (SNR), but this would result in longer scan times. Faster phosphorus MR spectroscopic imaging techniques should be devised to enable a wider use of 31P-MRSI. In this study, we aimed to implement compressed sensing technique for fast phosphorus magnetic resonance spectroscopic imaging. |
| Collaborations | Bahattin Hakyemez |
Project 16: Prof. Dr. Esin Öztürk Işık
| Title | Phosphorus MR Spectroscopic Imaging of Brain Tumors at 3T (31P_SPECTRA_3T) (click here for more information) |
| Supporting Agency |
FP7 Marie Curie International Reintegration Grants (IRG) |
| PI | Prof. Dr. Esin Öztürk Işık |
| Duration | 48 months (2010 - 2014) |
| Description |
The goal of this project is to apply phosphorus magnetic resonance spectroscopic imaging accurately at high field 3T scanners to add new information regarding the characteristics of brain tumors and produce a new metric that estimates the aggressiveness of a brain tumor using 31P MRSI peak intensities. |
